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Multiple Intelligences: What Does the Research Say?

Proposed by Howard Gardner in 1983, the theory of multiple intelligences has revolutionized how we understand intelligence. Learn more about the research behind his theory.

Many educators have had the experience of not being able to reach some students until presenting the information in a completely different way or providing new options for student expression. Perhaps it was a student who struggled with writing until the teacher provided the option to create a graphic story, which blossomed into a beautiful and complex narrative. Or maybe it was a student who just couldn't seem to grasp fractions, until he created them by separating oranges into slices.

Because of these kinds of experiences, the theory of multiple intelligences resonates with many educators. It supports what we all know to be true: A one-size-fits-all approach to education will invariably leave some students behind. However, the theory is also often misunderstood, which can lead to it being used interchangeably with learning styles  or applying it in ways that can limit student potential. While the theory of multiple intelligences is a powerful way to think about learning, it’s also important to understand the research that supports it.

Howard Gardner's Eight Intelligences

The theory of multiple intelligences challenges the idea of a single IQ, where human beings have one central "computer" where intelligence is housed. Howard Gardner, the Harvard professor who originally proposed the theory, says that there are multiple types of human intelligence, each representing different ways of processing information:

• Verbal-linguistic intelligence refers to an individual's ability to analyze information and produce work that involves oral and written language, such as speeches, books, and emails.
• Logical-mathematical intelligence describes the ability to develop equations and proofs, make calculations, and solve abstract problems.
• Visual-spatial intelligence allows people to comprehend maps and other types of graphical information.
• Musical intelligence enables individuals to produce and make meaning of different types of sound.
• Naturalistic intelligence refers to the ability to identify and distinguish among different types of plants, animals, and weather formations found in the natural world.
• Bodily-kinesthetic intelligence entails using one's own body to create products or solve problems.
• Interpersonal intelligence reflects an ability to recognize and understand other people's moods, desires, motivations, and intentions.
• Intrapersonal intelligence refers to people's ability to recognize and assess those same characteristics within themselves.

The Difference Between Multiple Intelligences and Learning Styles

One common misconception about multiple intelligences is that it means the same thing as learning styles. Instead, multiple intelligences represents different intellectual abilities. Learning styles, according to Howard Gardner, are the ways in which an individual approaches a range of tasks. They have been categorized in a number of different ways -- visual, auditory, and kinesthetic, impulsive and reflective, right brain and left brain, etc. Gardner argues that the idea of learning styles does not contain clear criteria for how one would define a learning style, where the style comes, and how it can be recognized and assessed. He phrases the idea of learning styles as "a hypothesis of how an individual approaches a range of materials."

Everyone has all eight types of the intelligences listed above at varying levels of aptitude -- perhaps even more that are still undiscovered -- and all learning experiences do not have to relate to a person's strongest area of intelligence. For example, if someone is skilled at learning new languages, it doesn’t necessarily mean that they prefer to learn through lectures. Someone with high visual-spatial intelligence, such as a skilled painter, may still benefit from using rhymes to remember information. Learning is fluid and complex, and it’s important to avoid labeling students as one type of learner. As Gardner states, "When one has a thorough understanding of a topic, one can typically think of it in several ways."

What Multiple Intelligences Theory Can Teach Us

While additional research is still needed to determine the best measures for assessing and supporting a range of intelligences in schools, the theory has provided opportunities to broaden definitions of intelligence. As an educator, it is useful to think about the different ways that information can be presented. However, it is critical to not classify students as being specific types of learners nor as having an innate or fixed type of intelligence.

Practices Supported by Research

Having an understanding of different teaching approaches from which we all can learn, as well as a toolbox with a variety of ways to present content to students, is valuable for increasing the accessibility of learning experiences for all students. To develop this toolbox, it is especially important to gather ongoing information about student strengths and challenges as well as their developing interests and activities they dislike. Providing different contexts for students and engaging a variety of their senses -- for example, learning about fractions through musical notes, flower petals, and poetic meter -- is supported by research. Specifically:

• Providing students with multiple ways to access content improves learning (Hattie, 2011).
• Providing students with multiple ways to demonstrate knowledge and skills increases engagement and learning, and provides teachers with more accurate understanding of students' knowledge and skills (Darling-Hammond, 2010).
• Instruction should be informed as much as possible by detailed knowledge about students' specific strengths, needs, and areas for growth (Tomlinson, 2014).

As our insatiable curiosity about the learning process persists and studies continue to evolve, scientific research may emerge that further elaborates on multiple intelligences, learning styles, or perhaps another theory. To learn more about the scientific research on student learning, visit our Brain-Based Learning topic page .

Darling-Hammond, L. (2010). Performance Counts: Assessment Systems that Support High-Quality Learning . Washington, DC: Council of Chief State School Officers.

Hattie, J. (2011). Visible Learning for Teachers: Maximizing Impact on Learning . New York, NY: Routledge.

Tomlinson, C. A. (2014). The Differentiated Classroom: Responding to the Needs of All Learners . Alexandria, VA: ASCD.

Resources From Edutopia

• Are Learning Styles Real - and Useful? , by Todd Finley (2015)
• Assistive Technology: Resource Roundup , by Edutopia Staff (2014)
• How Learning Profiles Can Strengthen Your Teaching , by John McCarthy (2014)
• An Interview with the Father of Multiple Intelligences , by Owen Edwards (2009)

Additional Resources on the Web

• Howard Gardner’s website
• Howard Gardner: ‘Multiple intelligences’ are not ‘learning styles’ (The Washington Post, 2013)
• Books published by Howard Gardner
• Multiple Intelligences Resources (ASCD)
• Project Zero (Harvard Graduate School of Education)
• Multiple Intelligences Research Study (MIRS)
• Multiple Intelligences Lesson Plan (Discovery Education)
• Multiple Intelligences Resources (New Horizons for Learning [NHFL], John Hopkins University)

• Center for Innovative Teaching and Learning
• Instructional Guide

Howard Gardner's Theory of Multiple Intelligences

Many of us are familiar with three broad categories in which people learn: visual learning, auditory learning, and kinesthetic learning. Beyond these three categories, many theories of and approaches toward human learning potential have been established. Among them is the theory of multiple intelligences developed by Howard Gardner, Ph.D., John H. and Elisabeth A. Hobbs Research Professor of Cognition and Education at the Harvard Graduate School of Education at Harvard University. Gardner’s early work in psychology and later in human cognition and human potential led to his development of the initial six intelligences. Today there are nine intelligences, and the possibility of others may eventually expand the list.

Gardner’s Multiple Intelligences Summarized

• Verbal-linguistic intelligence (well-developed verbal skills and sensitivity to the sounds, meanings and rhythms of words)
• Logical-mathematical intelligence (ability to think conceptually and abstractly, and capacity to discern logical and numerical patterns)
• Spatial-visual intelligence (capacity to think in images and pictures, to visualize accurately and abstractly)
• Bodily-kinesthetic intelligence (ability to control one’s body movements and to handle objects skillfully)
• Musical intelligences (ability to produce and appreciate rhythm, pitch and timber)
• Interpersonal intelligence (capacity to detect and respond appropriately to the moods, motivations and desires of others)
• Intrapersonal (capacity to be self-aware and in tune with inner feelings, values, beliefs and thinking processes)
• Naturalist intelligence (ability to recognize and categorize plants, animals and other objects in nature)
• Existential intelligence (sensitivity and capacity to tackle deep questions about human existence such as, “What is the meaning of life? Why do we die? How did we get here?”

(“Tapping into Multiple Intelligences,” 2004)

Gardner (2013) asserts that regardless of which subject you teach—“the arts, the sciences, history, or math”—you should present learning materials in multiple ways. Gardner goes on to point out that anything you are deeply familiar with “you can describe and convey … in several ways. We teachers discover that sometimes our own mastery of a topic is tenuous, when a student asks us to convey the knowledge in another way and we are stumped.” Thus, conveying information in multiple ways not only helps students learn the material, it also helps educators increase and reinforce our mastery of the content. 

… regardless of which subject you teach—“the arts, the sciences, history, or math”—you should present learning materials in multiple ways.

Gardner’s multiple intelligences theory can be used for curriculum development, planning instruction, selection of course activities, and related assessment strategies. Gardner points out that everyone has strengths and weaknesses in various intelligences, which is why educators should decide how best to present course material given the subject-matter and individual class of students. Indeed, instruction designed to help students learn material in multiple ways can trigger their confidence to develop areas in which they are not as strong. In the end, students’ learning is enhanced when instruction includes a range of meaningful and appropriate methods, activities, and assessments.

Multiple Intelligences are Not Learning Styles

While Gardner’s MI have been conflated with “learning styles,” Gardner himself denies that they are one in the same. The problem Gardner has expressed with the idea of “learning styles” is that the concept is ill defined and there “is not persuasive evidence that the learning style analysis produces more effective outcomes than a ‘one size fits all approach’” (as cited in Strauss, 2013). As former Assistant Director of Vanderbilt University’s Center for Teaching Nancy Chick (n.d.) pointed out, “Despite the popularity of learning styles and inventories such as the VARK, it’s important to know that there is no evidence to support the idea that matching activities to one’s learning style improves learning.” One tip Gardner offers educators is to “pluralize your teaching,” in other words to teach in multiple ways to help students learn, to “convey what it means to understand something well,” and to demonstrate your own understanding. He also recommends we “drop the term ‘styles.’ It will confuse others and it won’t help either you or your students” (as cited in Strauss, 2013).

… “pluralize your teaching,” in other words to teach in multiple ways to help students learn, to “convey what it means to understand something well,” and to demonstrate your own understanding.

Gardner himself asserts that educators should not follow one specific theory or educational innovation when designing instruction but instead employ customized goals and values appropriate to teaching, subject-matter, and student learning needs. Addressing the multiple intelligences can help instructors pluralize their instruction and methods of assessment and enrich student learning.

Chick, N. (n.d.). Learning styles . Retrieved from https://cft.vanderbilt.edu/guides-sub-pages/learning-styles-preferences/

Gardner, H. (2013). Frequently asked questions—Multiple intelligences and related educational topics. Retrieved from https://howardgardner01.files.wordpress.com/2012/06/faq_march2013.pdf

Strauss, V. (2013, Oct. 16). Howard Gardner: “Multiple intelligences” are not “learning styles.” The Washington Post . Retrieved from https://www.washingtonpost.com/news/answer-sheet/wp/2013/10/16/howard-gardner-multiple-intelligences-are-not-learning-styles/

Tapping into multiple intelligences . (2004). Retrieved from https://www.thirteen.org/edonline/concept2class/mi/index.html

Selected Resources

MI OASIS: The Official Authoritative Site of Multiple Intelligences. Access at https://www.multipleintelligencesoasis.org/

Suggested citation

Northern Illinois University Center for Innovative Teaching and Learning. (2020). Howard Gardner’s theory of multiple intelligences. In Instructional guide for university faculty and teaching assistants. Retrieved from https://www.niu.edu/citl/resources/guides/instructional-guide

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BRIEF RESEARCH REPORT article

Discussion of teaching with multiple intelligences to corporate employees' learning achievement and learning motivation.

• 1 Fuzhou University of International Studies and Trade, Fuzhou, China
• 2 College of Business and Management, Xiamen Huaxia University, Fuzhou, China
• 3 School of Business, Fuzhou Institute of Technology, Fuzhou, China
• 4 Department of Business Administration, Social Enterprise Research Center, Fu Jen Catholic University, New Taipei City, Taiwan
• 5 Master Program in Entrepreneurial Management, National Yunlin University of Science and Technology, Yulin, Taiwan

The development of multiple intelligences used to focus on kindergartens and elementary schools as educational experts and officials considered that the development of students' multiple intelligences should be cultivated from childhood and slowly promoted to other levels. Nevertheless, the framework of multiple intelligences should not be simply promoted in kindergartens and elementary schools, but was also suitable in high schools, universities, and even graduate schools or in-service training. Taking employees in Southern Taiwan Science Park as the research subjects, total 314 employees in high-tech industry are preceded the 16-week (3 h per week for total 48 h) experimental teaching research. The research results show that (1) teaching with multiple intelligences would affect learning motivation, (2) teaching with multiple intelligences would affect learning achievement, and (3) learning motivation reveals remarkably positive effects on learning achievement. According to the results to proposed discussions, it is expected to help high-tech industry, when developing human resource potential, effectively well-utilize people's gifted uniqueness

Introduction

Domestic education system, for a long time, paid attention to intellectual education. In the reflection before education reform, it was discovered that over-emphasizing intellectual education resulted in many students being sacrificed under the education system. Under the education reform in past years, the situation is gradually improved. Everyone possesses distinct intelligences and various combination and application methods that multi-methods should be used for the evaluation. Such methods provide special children with the growth model to develop the potential. Teachers' teaching with multiple intelligences allows such students fully developing the potential. Multiple intelligences particularly emphasize the application of intelligence in real life situations that the integration of teaching with multiple intelligences could help teachers assist in students' learning with multiple instruction and students expand abilities beyond subjects emphasized in traditional education. It would help current teaching styles.

Everyone presents the unique operation method that, with proper encouragement and guidance, the intelligence could achieve certain standards. For this reason, multiple intelligences allow each student finding out the sky and reaching the goal of adaptive development. The emergence of knowledge-based economy in past years reveals the importance of human capital of a nation. In face of increasing employment population domestically, understanding the ability for the right job in the right place is an extremely important issue for individuals or enterprises. The development of multiple intelligences used to focus on kindergartens and elementary schools as educational experts and officials considered that the development of students' multiple intelligences should be cultivated from childhood and slowly promoted to other levels. For high and elementary school students, multiple intelligences could help teachers better understand students from the intelligence distribution of students. For instance, multiple intelligences could be utilized for digging out gifted students and further providing them with suitable development opportunities to make the growth. Besides, multiple intelligences could be used for supporting students with problems and adopting more suitable methods for their learning. Regarding research on multiple intelligences, Ronald et al. (2001) covered the research objects of kindergarten pupils, higher graders of elementary schools, and high school students as well as the research fields of foreign language vocabulary memory, motivation to learn, mathematical problem solving, and reading comprehension of English and mathematics. Such research findings showed that multiple intelligences applied teaching activities could significantly enhance students' learning achievement, promote the motivation to learn, enhance reading the comprehension, and even enhance the ability of cooperative learning with peers. Broadly speaking, the framework of multiple intelligences cannot be promoted simply in kindergartens and elementary schools, but are suitable for high schools, universities, and even graduate schools or in-service training. A lot of international MBA courses are added creative thinking to strengthen the development of adaptability and creativity in the new era. For this reason, teaching with multiple intelligences to corporate employees' learning achievement and learning motivation is discussed in this study, expecting to help high-tech industry effectively well-utilize people's gifted uniqueness in the challenge of developing human resource potential.

Literature Review

Simoncini et al. (2018) stated that teaching with multiple intelligences stressed on the provision of democratic, respectful, and multiple learning environment for each student being able to present the ability, self-affirm personal performance, and further induce strong learning interests to surpass the originally dominant intelligence field in learning outcome. Inan and Erkus (2017) indicated that using multiple intelligences for curriculum design could provide various intellectual learning activities and create the environment with which students were comfortable. Learning was the preparation for challenge; learners would develop by accepting challenges exceeding the current abilities. Encouraging students deeply and meaningfully to engage in the learned topics was the solid and durable learning basis for learning new affairs. The application of multiple intelligences and the creation of diverse classrooms to develop students' specialty allowed students maintaining learning motivation with active participation, building self-confidence, and developing self-motivation. Minnier et al. (2019) mentioned that the application of multiple intelligences to teaching was different from traditional teaching; teaching with multiple intelligences adopted multiple instruction strategies and activities. Many studies indicated that the application of multiple intelligences to teaching enhanced students' learning motivation and interests. The following hypothesis is therefore proposed in this study.

H1 : Teaching with multiple intelligences would affect learning motivation.

Moncada and Mire (2017) indicated that teachers had to know each student's strengths and traits and appreciate individual advantages to give guidance and inspiration in order to strengthen the learning confidence. Multiple intelligences reminded teachers to comprehend and apply diverse teaching methods, transform existing curricula, or units into multiple learning opportunities, as well as carefully consider the taught concepts and confirm the most appropriate intelligence for communicating the content before planning curricula in order to ensure the achievement of proper teaching goals and promote students' learning achievement. Awang et al. (2017) proposed that teaching with multiple intelligences could positively enhance students' academic performance to make progress on English listening, speaking, reading, and writing. After applying multiple intelligences to English teaching, students enhanced learning achievement, learning interests, and learning motivation. Several researchers proposed that students appeared positive change on the learning achievement. Khong et al. (2017) indicated in the research results that higher-grader students in elementary schools being taught science based on multiple intelligences outperformed those receiving traditional teaching. According, the following hypothesis is proposed in this study.

H2 : Teaching with multiple intelligences would affect learning achievement.

Russell et al. (2017) considered that the achievement of meaningful and effective learning to skillfully grasp the concept relied on students' intrinsic motivation, when students expected to acquire certain knowledge with e-learning. Khow and Visvanathan (2017) considered the value of e-learning that students could enhance learning achievement by acquiring good performance and presenting intrinsic motivation to contact broad professional knowledge/competence. Hunter and Hunter (2018) stated that students with high learning motivation presented more definite goals and strong desire to well-learn the learning content and showed higher expectation and better self-efficacy. It was also discovered that students with high learning motivation appear better performance, and students with intrinsic motivation outperformed those with extrinsic motivation. Consequently, the following hypothesis is proposed in this study.

H3 : Learning motivation presents significantly positive effects on learning achievement.

Methodology

Measurement of research variable, (1) teaching with multiple intelligences.

Referring to Minnier et al. (2019) , the following dimensions for the curriculum design of teaching with multiple intelligences, according to student needs, are proposed in this study.

1. Intrapersonal intelligence: Intrapersonal intelligence is defined as the intrapersonal ability according to individual self-knowing ability and self-perception to keenly and precisely perceive personal inner emotion, motivation, ability, intention, and desire.

2. Interpersonal intelligence: Intrapersonal intelligence is defined as being able to effectively perceive and discriminate others' emotion, affection, intention, feeling, motivation, and expectation as well as make proper responses to interpersonal relationship to further get along with people harmoniously.

3. Content-based curriculum: Content-based curriculum integrates knowledge and life, provides students with opportunities to apply knowledge, well-utilize community resources, and integrate community professional manpower for students learning with multiple intelligences and increasing learning channels.

4. Situated learning: Learning situations are co-constructed and maintained by teachers and students, are free, open, and cooperative, pay attention to overall conceptual knowledge orientation, and match students' sensory learning with teaching resources for learning in the real-life situation and respecting the difference in learners' learning outcome.

(2) Learning Motivation

According to the research of Cheng et al. (2018) , students' learning motivation is divided into intrinsic learning motivation orientation and extrinsic learning motivation orientation in this study, as below.

1. Intrinsic orientation: containing favor of challenging courses, regarding learning as interest and hobby, considering that learning could expand vision, being able to actively learn new courses, learning for developing self-potential and realizing ideas.

2. Extrinsic orientation: covering learning for receiving others' affirmation, acquiring better performance, passing examinations or evaluation, showing off to others, competing with classmates, obtaining appreciation and attention from elders or the opposite sex, preventing from punishment and scold, avoiding the shame of failure, and entering ideal schools in the future.

(3) Learning Achievement

Referring to Zebari et al. (2018) , the following dimensions for learning achievement are proposed in this study.

1. Learning effect-including test performance, time for completing schedule, and term performance.

2. Learning gain-containing learning satisfaction, achievement, and preference.

Method and Model

Structural equation model is used as the research method in this study and Amos is utilized as the statistical tool. Structural equation model (SEM), also named covariance structure analysis, is used for analyzing causality model and precedes path analysis (PA), factor analysis, regression analysis, and analysis of variance. Structural equation model consists of two parts. The first part, measurement model, aims to construct the latent variable model with observed variables to understand the relationship between observed variables and latent variables; the constructed mathematical model is Confirmatory Factor Analysis (CFA). The second part, Structure Model, mainly discusses the causality among latent variables with path analysis, where observed variables are used; latent variables are used for Structure Model.

Research Subject and Sampling Data

Aiming at employees in Southern Taiwan Science Park as the research objects, total 314 employees in high-tech industry are preceded the 30-week (2 h per week for total 30 h) experimental research. The questionnaire survey is preceded after the end of the 30-week course, and statistical methods are applied to test various hypotheses. Among the distributed 314 copies of questionnaire, 297 copies are valid, with the valid retrieval rate 95%.

Reliability and Validity Test

Reliability and validity are important measurement standards. Merely the data results acquired from the questionnaire design with reliability and validity present the research value. AMOS is used for Confirmatory Factor Analysis (CFA) in this study, and SPSS 21 is applied to calculate the reliability and validity to test the questionnaire scale achieving the reliability and validity standard.

Empirical Result

Factor analysis and validity analysis.

Based on factor loadings, all items in this study are preceded confirmatory analysis. The factor loadings should be higher than 0.7; if not, the item does not show the representativeness and is removed. The Confirmatory Factor Analysis results show that all factor loadings of teaching with multiple intelligences, learning motivation, and learning achievement conform to the standard (>0.7), revealing high validity of the questionnaire scale.

Cronbach's α is used in this study for evaluating reliability; Cronbach's α higher than 0.7 achieves the reliability standard, and the ideal value should be higher than 0.9. Cronbach's α of teaching with multiple intelligences, learning motivation, and learning achievement in this study is higher than the suggested threshold and with the lowest value up to 0.8, revealing high reliability of the questionnaire scale.

Test of Model Fit

“Maximum Likelihood” (ML) is utilized in this study for the estimation; the obtained Amos analysis results achieve convergence. The indicators standing for the external quality of model show (1) χ 2 ratio = χ 2 = 1.627, smaller than 3, (2) goodness-of-fit index GFI = 0.97, higher than 0.9 and adjusted goodness-of-fit index AGFI = 0.82, higher than 0.8, (3) root mean square residual RMR = 0.029, smaller than 0.05, and (4) incremental fit index 0.94, higher than 0.9. Overall speaking, the actual number of 297 samples is higher than the requirement for the basic number of samples, and the overall model fit indicators pass the test, fully reflecting good internal quality of the structural equation model.

Regarding the test of internal quality of structure, the squared multiple correlation (SMC) of manifest variables is higher than 0.5, revealing good measurement indicators of latent variables. Furthermore, latent variables of teaching with multiple intelligences, learning motivation, and learning achievement show the component reliability higher than 0.6 and the average variance extracted of dimensions is higher than 0.5, apparently meeting the requirement for the internal quality of model.

Test of Path Relationship

Latent variables of intrapersonal intelligence, intrinsic orientation, and learning effect are regarded as the reference indicators with fixed 1. From the causality path in Table 1 and Figure 1 , the estimates between other dimensions and variables appear significance. Interpersonal intelligence = 0.99 shows less explanatory power than intrapersonal intelligence, and learning gain = 1.06 presents better explanatory power than learning effect.

Table 1 . Overall linear structure model analysis result.

Figure 1 . Path relationship.

Teaching with multiple intelligences could effectively enhance the learning motivation of employees in high-tech industry to promote and continue the learning achievement. The research results are consistent with most of past research results ( Ikiz and Cakar, 2010 ; Mahasneh, 2013 ). As Akkuzu and Akçay (2011) revealed, teaching with multiple intelligences was more effective than traditional teaching styles and such activities were interesting to facilitate students' interests in participation in course activities. In this case, the application of teaching with multiple intelligences allows employees in high-tech industry preceding learning activity with the advantageous intelligence to be more confident of learning challenges, rather than being inoculated to result in getting half the results with double efforts for learning with weaker intelligence, and further help promote the performance of organizational learning. The use of computers is inevitable for modern people; the use of ppt, films, or mv could properly attract the attention of employees in high-tech industry. Well-begun is half done; besides, computer-assisted teaching could largely assist employees in more difficult intelligence activity design, such as space, natural observer, and music intelligence. Teachers therefore should flexibly apply such resources. Moreover, teachers should take a long-term view, rather than focusing on immediate results. The cultivation of employees' active learning and high learning motivation would multiply and endure the learning validity.

The research results reveal that Consistent with most past research results, it reveals that teaching with multiple intelligences indeed could effectively promote learning achievement and motivation to learn ( Gardner and Hatch, 1989 ; Barrington, 2004 ; Akkuzu and Akçay, 2011 ). Employees in high-tech industry remarkably enhance learning achievement and learning motivation after the teaching with multiple intelligences. In this case, relevant academic competition could be held in organizations with proper rewards to effectively apply the employees' learning effectiveness and increase the learning motivation. Different from traditional teaching, teaching with multiple intelligences, with more personal practice and participation, allows employees in high-tech industry grasping the learning, rather than simply accepting knowledge. As a result, employees would enhance self-efficacy. For instance, employees in high-tech industry, under group learning, observation, and brainstorming, would make progress on reports, and learning comprehension as well as deepen and broaden learning motivation. Teachers, during the instruction, should praise and encourage for the progress of employees, create low-pressure, relaxing, and comfortable learning environment, and give more learning confidence to strength the learning motivation of employees in high-tech industry.

Data Availability Statement

The original contributions presented in the study are included in the article/supplementary materials, further inquiries can be directed to the corresponding authors.

Ethics Statement

The present study was conducted in accordance with the recommendations of the Ethics Committee of the Fuzhou Institute of Technology, with written informed consent being obtained from all the participants. All the participants were asked to read and approve the ethical consent form before participating in the present study. The participants were also asked to follow the guidelines in the form in the research. The research protocol was approved by the Ethical Committee of the Fuzhou Institute of Technology.

Author Contributions

D-YL performed the initial analyses and wrote the manuscript. J-HC, C-MC, K-PH, and CJ assisted in the data collection and data analysis. All authors revised and approved the submitted version of the manuscript.

Conflict of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher's Note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

Acknowledgments

The authors thank the reviewers for their valuable comments.

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Keywords: multiple intelligences, learning achievement, learning motivation, content-based curriculum, situated learning

Citation: Lei D-Y, Cheng J-H, Chen C-M, Huang K-P and James Chou C (2021) Discussion of Teaching With Multiple Intelligences to Corporate Employees' Learning Achievement and Learning Motivation. Front. Psychol. 12:770473. doi: 10.3389/fpsyg.2021.770473

Received: 03 September 2021; Accepted: 20 September 2021; Published: 18 October 2021.

Reviewed by:

Copyright © 2021 Lei, Cheng, Chen, Huang and James Chou. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Jui-Hsi Cheng, ray1806@gmail.com

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• > The Cambridge Handbook of Intelligence
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Book contents

• The Cambridge Handbook of Intelligence
• Copyright page
• Contributors
• Part I Intelligence and Its Measurement
• Part II Development of Intelligence
• Part III Intelligence and Group Differences
• Part IV Biology of Intelligence
• Part V Intelligence and Information Processing
• Part VI Kinds of Intelligence
• 27 The Theory of Multiple Intelligences
• 28 The Augmented Theory of Successful Intelligence
• 29 Emotional Intelligence
• 30 Practical Intelligence
• 31 Social Intelligence
• 32 Collective Intelligence
• 33 Leadership Intelligence
• 34 Cultural Intelligence
• 35 Mating Intelligence
• 36 Consumer and Marketer Intelligence
• Part VII Intelligence and Its Role in Society
• Part VIII Intelligence and Allied Constructs
• Part IX Folk Conceptions of Intelligence
• Part X Conclusion
• Author Index
• Subject Index

27 - The Theory of Multiple Intelligences

from Part VI - Kinds of Intelligence

Published online by Cambridge University Press:  13 December 2019

The theory of multiple intelligences (MI) was set forth in 1983 by Howard Gardner. The theory holds that all individuals have several, relatively autonomous intelligences that they deploy in varying combinations to solve problems or create products that are valued in one or more cultures. Together, the intelligences underlie the range of adult roles found across cultures. MI thus diverges from theories entailing general intelligence, or g, which hold that a single mental capacity is central to all human problem-solving and that this capacity can be ascertained through psychometric assessment. This chapter presents the evidence and criteria used to develop MI, clarifies misconceptions about the theory, and examines critiques of the theory. It considers Einstein’s typology of scientific theories through which it is possible to understand MI as a “constructive theory.” It then examines issues of assessment entailing MI and educational applications of the theory.

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• The Theory of Multiple Intelligences
• By Mindy L. Kornhaber
• Edited by Robert J. Sternberg , Cornell University, New York
• Book: The Cambridge Handbook of Intelligence
• Online publication: 13 December 2019
• Chapter DOI: https://doi.org/10.1017/9781108770422.028

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Gardner’s multiple intelligences in science learning: A literature review

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Fibriyana Safitri , Dadi Rusdiana , Wawan Setiawan; Gardner’s multiple intelligences in science learning: A literature review. AIP Conf. Proc. 28 April 2023; 2619 (1): 100014. https://doi.org/10.1063/5.0122560

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The purpose of this article is to review articles related to multiple intelligences in learning and figure out the multiple intelligences approach in learning activities and several bits of intelligence that play an important role in science learning. This paper is the result of a review of 40 articles related to learning activities that use multiple intelligence-based approaches, media, and learning models, published from 2011 to 2021. The multiple intelligences theory was put forward by Howard Gardner, an expert in education and psychology. There are nine types of intelligence based on Gardner’s theory, namely: verbal-linguistic intelligence, visual-spatial intelligence, musical intelligence, logical-mathematics intelligence, interpersonal intelligence, intrapersonal intelligence, bodily-kinesthetic intelligence, naturalist intelligence, and existential intelligence, which have different characteristics. The method used in this study is a systematic literature review with the following stages: determining research questions; determining criteria; generating a framework for articles; searching, filtering, and selecting; analyzing and interpreting the content of each reviewed article; article writing, and publishing. This study discusses Gardner’s multiple intelligence theory, the multiple intelligences approach in learning activities, and the most influential intelligence in science learning. The results show that several bits of intelligence play an important role in science learning.

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“Neuromyths” and Multiple Intelligences (MI) Theory: A Comment on Gardner, 2020

Introduction.

Neuromyths are misconceptions about the brain and learning. The most pervasive neuromyths contain a “kernel of truth” (Grospietsch and Mayer, 2018 ). For instance, consider the following popular neuromyth: People are either “left-brained” or “right-brained,” which helps to explain individual differences in learning . On the one hand, classical neuroscience findings did provide solid basic evidence that the human brain displays a certain degree of functional hemispheric lateralization (Gazzaniga et al., 1962 , 1963 ). However, on the other hand, the idea of a “dominant” cerebral hemisphere is not supported by neuroscience (Nielsen et al., 2013 ). Due to fatal mutations from kernels of truth, neuromyths are typically defined as distortions, oversimplifications, or abusive extrapolations of well-established neuroscientific facts (OECD, 2002 ; Pasquinelli, 2012 ; Howard-Jones, 2014 ).

In the past decade, numerous surveys have been conducted in more than 20 countries around the world to measure the prevalence of neuromyth beliefs among educators (Torrijos-Muelas et al., 2021 ). A large-scale survey conducted in Quebec, Canada, by Blanchette Sarrasin et al. ( 2019 ) revealed that 68% of teachers somewhat or strongly agreed (rating of 4 or 5 on a 5-point scale) with the following neuromyth statement:

Students have a predominant intelligence profile, for example logico-mathematical, musical, or interpersonal, which must be considered in teaching .

This is not an idiosyncratic case in the field (see Table 1 ). In another survey conducted in Spain, Ferrero et al. ( 2020 ) reported that teachers gave an average rating of 4.47 [on a 5-point scale, from 1 ( definitely false) to 5 (definitely true )] to a closely similar neuromyth statement:

Prevalence of beliefs, among educators, about the false claim that tailoring instruction to pupils' MI intelligence profiles promotes learning, in different countries around the world.

Adapting teaching methods to the “multiple intelligences” of students leads to better learning .

The opening survey statement from Blanchette Sarrasin et al. ( 2019 ) caught Howard Gardner's attention, because it clearly draws from his Multiple Intelligences (henceforth MI) theory (Gardner, 1983 ). In a recent paper, Gardner ( 2020 ) says he was disturbed by this so-called “neuromyth,” both because it says nothing about the brain, and because it is not an idea that he has put forth or defended. On that basis, Gardner ( 2020 ) argues that MI theory does not qualify as a neuromyth. According to the author of Frames of Mind , some years ago, there may have been merit in exposing neuromyths, but the practice has gone too far and has now become problematic rather than helpful.

In this opinion paper, I first challenge Gardner's ( 2020 ) view that MI theory contains no “neuro.” Then, I highlight the fact that Gardner and his research team spent an entire decade, through the Spectrum Project , contemplating the hypothesis—embedded into the opening survey statement—that matching modes of instruction to MI intelligence profiles promotes learning. When taken for granted, such an unproven research hypothesis is considered as a false belief—a neuromyth derived from MI theory. Then, I argue that research aimed at testing the MI–instruction “matching” hypothesis is still hampered by a lack of satisfactory measures of MI intelligence profiles. Finally, I expose how Gardner's ( 2020 ) position may, paradoxically, entertain the “problematic” neuromyth. To foster a more constructive dialog between scientists and educators, I follow Gardner's ( 2020 ) advice to properly qualify (i.e., to debunk) the survey statement, in terms of both robustness and caveats.

Biological Basis for Specialized Intelligences

Gardner ( 2020 ) states that “there is no mention of the brain” in his original work, insisting that “MI is a psychological theory, pure and simple” (p. 3). Because MI theory contains no “neuro,” he claims, there is no reason why it should be associated with the “provocative and contentious neuromyth” term. However, Gardner has typically called MI “a psychobiological theory: psychological because it is a theory of the mind, biological because it privileges information about the brain, the nervous system, and ultimately, [he] believe[s], the human genome” (Gardner, 2011b , p. 7). In the opening chapters of Frames of Mind , after disposing of traditional, IQ theories of intelligence, Gardner ( 1983 ) draws from brain science of the day to posit the basic premise of MI theory—that intelligences are distinct computational capacities that have emerged, over the course of evolution and across cultures, from the human cerebral cortex:

We find, from recent work in neurology, increasingly persuasive evidence for functional units in the nervous systems. There are units subserving microscopic abilities in the individual columns of the sensory or frontal areas; and there are much larger units, visible to inspection, which serve more complex and molar human functions, like linguistic or spatial processing. These suggest a biological basis for specialized intelligences (p. 57).

Such neurological evidence led Gardner ( 1983 ) to include potential isolation by brain damage as one of eight criteria—actually “the single most instructive line of evidence” (p. 63)—to define an intelligence. Critical insights for MI theory also came from Gardner's earlier neuropsychological research conducted in the 1970s on brain-damaged patients suffering from aphasia (Gardner, 2011b , 2016 ). Consistent with intelligences as biopsychological potentials to process information , Davis et al. ( 2011 ) noted that it would be “desirable to secure an atlas of the neural correlates of each of the intelligences” (p. 495) and current neuroscientific investigations of MI theory are undergoing in that direction. For instance, a brain lesion restricted to the left parietal lobe would selectively impair the capacity to discriminate living from non-living entities, i.e., naturalistic intelligence (Shearer and Karanian, 2017 ).

But even with no “neuro” at all, MI theory would still qualify as a potential source of neuromyths, as any scientific theory could—be it psychological, neurological, or a mix of both. Myths may have nothing to do with the brain, but are, nonetheless, myths. Over time, the term “neuromyth” has become a common umbrella to a wide range of unsubstantiated claims, especially in the education field. Some of those claims clearly evoke the brain (e.g., We only use 10% of our brain) , while others do not (e.g., Listening to Mozart's music makes children smarter ). Would it be more appropriate to drop the “neuro” prefix and collectively call them “edumyths”? Actually, it does not matter. They are myths.

Above all, the primary aim of MI theory was to expand the traditional, narrow IQ concept of intelligence to the whole spectrum of brain computational powers, not to provide brain-based educational recommendations. The basic idea of MI theory is that Homo sapiens is biologically endowed with a set of relatively autonomous mental tools (termed “intelligences”) that can be activated to solve problems or to fashion products that are of cultural value. MI theory posits that every individual has, at their disposal, a full intellectual profile of eight intelligences. From one individual to another, some intelligences exhibit low, some exhibit average, and some others exhibit strong biopsychological potentials, but the whole MI intelligence profile—a spectrum of brain computational powers working in synergy—is mobilized to adapt Homo sapiens to newly encountered, culture-bound situations.

The Elusive Quest for Optimal Matching

Unlike Gardner's ( 2020 ) allegation, the claim in the opening survey statement is not that MI theory is a neuromyth. There has been considerable progress in brain science over the past four decades, and neurological underpinnings of the original rendition of MI theory (Gardner, 1983 ) might need an update (Gardner, 2016 ), but MI theory is still a plausible, legitimate scientific theory of intelligence. The false claim in the opening survey statement is that tailoring instruction to pupils' MI intelligence profiles promotes learning. Gardner ( 2020 ) states that he has “gone to great pains to emphasize that even if the theory is plausible, no educational recommendations follow directly from it” (p. 3). However, since the inception of MI theory some 40 years ago, regarding applications of MI theory in education, Gardner oscillates between two views: the “Rorschach” view and the “matching” view.

According to the “Rorschach” view, defended by Gardner ( 2020 ), no direct educational implications derive from research findings. Cultural values always interface the leap from science to practice. In this view, MI theory is a catalyst for reflection on a pluralistic, rather than a unitary, view of intelligence (Gardner, 1995a ). To use Gardner's ( 2006 ) analogy, from the teachers' standpoint, MI theory is an educational Rorschach test, a backdrop “to support almost any pet educational idea that they had” (Gardner, 2011b , p. 5). MI theory implies only two non-prescriptive teaching practices: “individualizing” and “pluralizing.” By using multiple “entry points” (presenting the teaching materials in more than one way), teachers might activate all intelligences and foster optimal learning, “since some individuals learn better through stories, others through work of art, or hands-on activities” (Gardner, 2011b , p. 7).

According to the alternative, “matching” view, clearly embedded in the opening neuromyth statement, Gardner ( 2020 ) states that it is “not an idea that [he] has put forth or defended” (p. 2). However, in the closing chapter of Frames of Mind , from a purely speculative and prospective standpoint, Gardner ( 1983 ) is quite sympathetic to the idea of matching teaching materials and modes of instruction to MI intelligence profiles:

Educational scholars nonetheless cling to the vision of the optimal match between student and material. In my own view, this tenacity is legitimate: after all, the science of educational psychology is still young; and in the wake of superior conceptualizations and finer measures [emphasis mine], the practice of matching the individual learner's profile to the materials and modes of instruction may still be validated. Moreover, if one adopts M.I. theory, the options for such matches increase: as I have already noted, it is possible that the intelligences can function both as subject matters in themselves and as the preferred means for inculcating diverse subject matter (p. 390).

Albeit speculative, and much to Gardner's surprise, these few lines have attracted tremendous interest in the education field. But testing the matching hypothesis required, in the first place, “finer measures” of MI intelligence profiles. Gardner ( 1992 ) proposed, as an alternative to IQ-like paper-and-pencil (standardized) intelligence tests, natural observations of Homo sapiens freely evolving in ecologically valid, culturally meaningful contexts. For instance, to measure spatial intelligence , “one should allow an individual to explore a terrain for a while and see whether she can find her way around it reliably” (Gardner, 1995b , p. 202). Gardner and his research team spent an entire decade, after the publication of Frames of Mind , exploring the plausibility of a MI theory-based “child-centered” learning program. Their most ambitious initiative was the Spectrum Project , aimed at creating a museum-like, rich environment for children to deploy their biopsychological potentials (intelligences). A set of 15 learning activities covering seven knowledge domains was created to provide a contextually valid assessment battery of MI intelligence profiles. For instance, to assess interpersonal intelligence , children manipulated figures in a scaled-down, 3D replica of their classroom (Chen and Gardner, 2012 ). The distribution of strengths and weaknesses across the range of intelligences was called the Spectrum profile . The ultimate goal was to develop individualized educational interventions adapted to MI intelligence profiles.

However, MI theory does not only posit the existence of eight neurologically plausible intelligences, it also posits that each individual actually combines several intelligences to tackle any given task, making it unlikely for a test to capture purely specific intelligence strengths and weaknesses (e.g., a test that would isolate bodily-kinesthetic from musical, spatial, and interpersonal intelligences, while observing an individual dancing the tango). Although the 15 assessment tasks from the Spectrum battery have been “shown to demonstrate reliability” (Davis et al., 2011 , p. 496), valid measures of single or multiple deployment of the eight intelligences are still unsettled:

Direct experimental tests of the [MI] theory are difficult to implement and so the status of the theory within academic psychology remains indeterminate. The biological basis of the theory—its neural and genetic correlates—should be clarified in the coming years. But in the absence of consensually agreed upon measures of the intelligences, either individually or in conjunction with one another, the psychological validity of the theory will continue to be elusive (Davis et al., 2011 , p. 498).

Reflecting back on assessment tools for the multiple intelligences, Gardner ( 2016 ) admitted that he has “not devoted significant effort to creating such tests” (p. 169). In light of the enormous investment of time and money, he did not want himself to be “in the assessment business” (Gardner, 2011a , p. xiii). Above all, measuring multiple intelligences is inconsistent with Gardner's critique of the traditional IQ theories of intelligence and, for that reason, he shows “reluctance to create a new kind a strait jacket (Johnny is musically smart but spatially dumb )” (Gardner, 2011b , p. 5).

Accordingly, the opening survey statement is considered as a neuromyth because of a lack of compelling evidence—mainly due to unsatisfactory measures of MI intelligence profiles—that matching modes of instruction to MI intelligence profiles promotes learning. This intuitively appealing hypothesis, contemplated by Gardner's research team at some point (the Spectrum Project ) but still open to scientific inquiry, has somehow been taken for granted by laypersons and, over time, embedded into popular culture. In other words, it became a neuromyth.

Entertaining the “Problematic” Neuromyth

Gardner ( 2020 ) blames survey designers for putting up statements “conflating science and practice” and for creating rather than exposing neuromyths. He warns that by “waving the provocative neuromyth flag” with the opening survey statement, the baby (MI theory) might be thrown out with the bathwater (unsubstantiated educational claims derived from it).

First, neither Blanchette Sarrasin et al. ( 2019 ) nor other researchers in the field deliberately put up, in their respective surveys, neuromyth statements. Neuromyths are creatures of their own, to be chased, not created. Twenty-five years ago, Gardner ( 1995b ) debunked seven common myths that have grown up from MI theory. Myth #3 (“Multiple intelligences are learning styles”) was so persistent that Gardner ( 2013 ) found it necessary to debunk it once again in the new millennium. Survey designers simply exposed yet another, very prevalent myth: Tailoring instruction to pupils' MI intelligence profiles promotes learning.

Second, any scientific theory is a potential source of neuromyths. As noted by Geake ( 2008 ), the most pervasive neuromyths are ingrained into valid science. Is Roger Sperry's Nobel Prize at stake just because abusive extrapolations of his findings on functional hemispheric lateralization have given rise to one of the most pervasive neuromyths (“left-brained”—“right-brained” people)? By exposing such a popular neuromyth, might the baby (Sperry's contributions to neuroscience) be thrown out with the bathwater? The scientific integrity of MI theory cannot be harmed by the “problematic” neuromyth. Legitimate scientific theories and discoveries are challenged by empirical scrutiny, not by false beliefs loosely inspired from them.

Gardner ( 2020 ) argues that the way claims are conveyed in neuromyth survey statements (in an all-or-none, true/false fashion) is deceptive. To foster a more constructive dialog between scientists and educators, he advocates that research findings with potential educational implications should be properly qualified , in terms of both robustness and caveats. Surprisingly, rather than qualifying the message (the false claim in the opening survey statement), Gardner ( 2020 ) shoots the messengers (survey designers). A “more constructive” approach would be (1) to underline the scientific robustness of MI theory—its neurological plausibility (Posner, 2004 ) and (2) to disclose caveats pertaining to direct application of MI theory in educational settings, most notably that research aimed at testing the MI–instruction “matching” hypothesis is still hampered by a lack of consensually agreed upon measures of MI intelligence profiles (Davis et al., 2011 ). By shooting the messengers rather than qualifying the message (debunking yet another common myth that has grown up from MI theory), Gardner ( 2020 ) refrains from pulling the bathtub plug and entertains unsubstantiated educational implications of a legitimate scientific theory of intelligence.

Author Contributions

The author confirms being the sole contributor of this work and has approved it for publication.

Conflict of Interest

The author declares that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher's Note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

Acknowledgments

I am thankful to Liliane Lalonde for her help with the English language.

Funding. This work was supported by the Canada Foundation for Innovation John R. Evans Leaders Fund grant 18356.

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The Lasting Impact of Multiple Intelligences

In 1983, in one of the most influential books in a peerlessly influential career, Howard Gardner upended popularly accepted notions of how children think and learn. He proposed, in Frames of Mind , that there was not just a single intelligence that could be measured by one IQ test, but multiple intelligences — many ways of learning and knowing.

With his best-known work, Howard Gardner shifted the paradigm and ushered in an era of personalized learning.

The notion of multiple intelligences — and Gardner’s follow-up ideas about teaching individual students in the ways they can best learn, and teaching important concepts in multiple ways, for many access points — shifted the paradigm, ushering in an era of personalized learning whose promise is still being explored.

Gardner never rested at multiple intelligences. In an award-winning career — which has included MacArthur and Guggenheim fellowships, the University of Louisville’s Grawemeyer Award in Education, and innumerable honorary degrees — he’s focused on ethical development , citizenship (including digital citizenship), professionalism, and the value of college and the liberal arts . He may have retired from teaching in 2019, but his work continues. – Video directed by Jill Anderson, produced by Elio Pajares

Learn More and Connect

Visit Project Zero's website .

Read more about Howard Gardner's intellectual and pedagogical legacy .

Learn about Gardner's latest research on the future of college .

The Theory of Multiple Intelligences

• January 2011
• In book: Cambridge Handbook of Intelligence (pp.485-503)
• Chapter: 24
• Publisher: Cambridge University Press
• Editors: R.J. Sternberg, S.B. Kaufman

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multiple intelligences

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• Verywell Mind - Gardner's Theory of Multiple Intelligences
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multiple intelligences , theory of human intelligence first proposed by the psychologist Howard Gardner in his book Frames of Mind (1983). At its core, it is the proposition that individuals have the potential to develop a combination of eight separate intelligences, or spheres of intelligence; that proposition is grounded on Gardner’s assertion that an individual’s cognitive capacity cannot be represented adequately in a single measurement, such as an IQ score . Rather, because each person manifests varying levels of separate intelligences, a unique cognitive profile would be a better representation of individual strengths and weaknesses, according to this theory. It is important to note that, within this theory, every person possesses all intelligences to some degree.

Gardner posited that in order for a cognitive capacity to qualify as an independent “intelligence” (rather than as a subskill or a combination of other kinds of intelligence), it must meet eight specific criteria . First, it must be possible to thoroughly symbolize that capacity by using a specific notation that conveys its essential meaning. Second, neurological evidence must exist that some area of the brain is specialized to control that particular capacity. Third, case studies must exist that show that some subgroups of people (such as child prodigies) exhibit an elevated mastery of a given intelligence. Fourth, the intelligence must have some evolutionary relevance through history and across cultures . Fifth, the capacity must have a unique developmental history for each individual, reflecting each person’s different level of mastery of it. Sixth, the intelligence must be measurable in psychometric studies that are reflective of differing levels of mastery across intelligences. Seventh, the intelligence must have some definite set of core operations that are indicative of its use. Last, the proposed intelligence must be already plausible on the basis of existing means of measuring intelligence.

Gardner’s original theoretical model included seven separate intelligences, with an eighth added in 1999:

• logical-mathematical
• bodily-kinesthetic
• interpersonal
• intrapersonal
• naturalistic

These eight intelligences can be grouped into the language-related, person-related, or object-related. The linguistic and musical intelligences are said to be language-related, since they engage both auditory and oral functions, which Gardner argued were central to the development of verbal and rhythmic skill. Linguistic (or verbal-linguistic) intelligence, manifested both orally and in writing, is the ability to use words and language effectively. Those who possess a high degree of verbal-linguistic intelligence have an ability to manipulate sentential syntax and structure, easily acquire foreign languages, and typically make use of a large vocabulary. Musical intelligence includes the ability to perceive and express variations in rhythm, pitch , and melody; the ability to compose and perform music; and the capacity to appreciate music and to distinguish subtleties in its form. It is similar to linguistic intelligence in its structure and origin, and it employs many of the same auditory and oral resources. Musical intelligence has ties to areas of the brain that control other intelligences as well, such as is found in the performer who has a keen bodily-kinesthetic intelligence or the composer who is adept at applying logical-mathematical intelligence toward the manipulation of ratios, patterns, and scales of music.

Person-related intelligences include both interpersonal and intrapersonal cognitive capacities. Intrapersonal intelligence is identified with self-knowledge, self-understanding, and the ability to discern one’s strengths and weaknesses as a means of guiding one’s actions. Interpersonal intelligence is manifested in the ability to understand, perceive, and appreciate the feelings and moods of others. Those with high interpersonal intelligence are able to get along well with others, work cooperatively, communicate effectively, empathize with others, and motivate others.

The four object-related intelligences—logical-mathematical, bodily-kinesthetic, naturalistic, and spatial—are stimulated and engaged by the concrete objects one encounters and the experiences one has. Those objects include physical features of the environment such as plants and animals, concrete things, and abstractions or numbers that are used to organize the environment. Those who exhibit high degrees of logical-mathematical intelligence are able to easily perceive patterns, follow series of commands, solve mathematical calculations, generate categories and classifications, and apply those skills to everyday use. Bodily-kinesthetic intelligence is manifested in physical development, athletic ability, manual dexterity , and understanding of physical wellness. It includes the ability to perform certain valuable functions, such as those of the surgeon or mechanic, as well as the ability to express ideas and feelings as artisans and performers. Spatial intelligence, according to Gardner, is manifested in at least three ways: (1) the ability to perceive an object in the spatial realm accurately, (2) the ability to represent one’s ideas in a two- or three-dimensional form, and (3) the ability to maneuver an object through space by imagining it rotated or by seeing it from various perspectives. Though spatial intelligence may be highly visual, its visual component refers more directly to one’s ability to create mental representations of reality.

Naturalistic intelligence is a later addition to Gardner’s theoretical model and is not as widely accepted as the other seven. It includes the ability to recognize plants, animals, and other parts of the natural environment as well as to see patterns and organizational structures found in nature. Most notably, research remains inconclusive as to whether the naturalistic intelligence fulfills the criterion of being able to be isolated in neurophysiology. In 1999 Gardner also considered whether a ninth intelligence, existential , exists.

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The past, present and future of multiple intelligences

May 2012, Vol 43, No. 5

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Multiple intelligences: best ideas from research and practice, resource summary.

This work shows teachers and administrators how to successfully integrate Multiple Intelligences into their schools and classrooms. Based on a national investigation of more than 40 schools and on detailed case studies, this book illustrates how teachers in real-life situations in a range of different public schools were able to construct and implement curricula that enabled students to learn challenging disciplinary content through multiple intelligences. It also shows how the organizational practices within these teachers schools supported strong classroom work. Written in a clear, practical style, this book highlights how educators everywhere can both integrate MI theory and foster exceptional student work. This book will be an invaluable resource for soon-to-be as well as practicing teachers and administrators. ISBN: 978-0205342594

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Redefining intelligence and how we measure it.

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This summer, the world of college admissions is under the magnifying glass for what seems to be the nth time over the past few years. Some highlights from 2024 include this application cycle’s infamous FAFSA (Free Application for Federal Student Aid) application debacle , which delayed access to vital aid to millions of students for months. Throughout the year, elite universities have also been falling like dominoes in their reversal of the COVID era policy of test-optional admissions, requiring standardized test results again as part of applicants’ profile. Legacy admissions is also next in line to be under intense scrutiny, with California’s proposed state-wide ban leading the charge.

Amidst wave after wave of seismic changes affecting college admissions, what has remained constant is the achievement culture that underpins this entire industry. This phenomenon and its nefarious effects are discussed in depth in Never Enough , an investigative book by journalist Jennifer Wallace . In her recent webinar with Polygence , she peels back the many layers of the immense pressure placed on our students to achieve - something that is exacerbated by ever dwindling acceptance rates at elite colleges and generational changes to parenting. In fact, students are spending so many of their formative years playing this game of academic profile engineering that they barely give any attention to developing their authentic identities. What ends up happening is that they, with the support of an entourage of counselors and parents, are packaged into highly engineered applicant profiles that reflect hundreds of hours or time and thousands of dollars – all to receive 8 minutes of attention from an admissions officer on the other side of the desk. Those who are lucky to arrive at the doorstep of their dream college may realize that after all of that brand construction, they don’t actually know who they are or what they care about. They’ve perfected the art of the performative rat race but have lost touch with their authentic self. In a world where achievement engineering is the norm and not the exception, intellectual authenticity - something once taken for granted just a generation or two ago - has become the holy grail in admissions.

How can we empower students to become authentic thinkers instead of pressuring them to conform to a handful of sought-after profiles? The answer is to give them permission. Intelligence comes in so many different shapes and forms - we need to show our students that we live and breathe this conviction and that there is no hidden hierarchy ranking the relative value of each type of intelligence. Harvard psychologist Howard Gardner developed the theory of Multiple Intelligences in the late 1970’s and early 1980’s as a direct critique of the standard psychological view of intellect - that there is a single type of intelligence measured by IQ quizzes or other short answer tests. In this theory, Gardner formulates 8 types of intelligence - spatial, bodily-kinesthetic, musical, linguistic, logical-mathematical, interpersonal, intrapersonal, and naturalist, and argues that no one type is inherently superior to another. This challenges the widely held belief that the two types of intelligences that are measured by IQ tests - logical-mathematical and linguistic - are more critical than others.

At Polygence this way of thinking about human intelligence is so foundational to our mission that it inspired our name ( Poly - meaning “multiple”; and -gence - from “intelligence”). As the only research platform that supports humanistic, artistic, and creative projects in addition to traditional STEM projects, we celebrate and empower students to explore the world in all possible ways. It is a widely held misconception that research can only be done in labs by those in white lab coats and that the only acceptable way of showcasing the results of such inquiry is long-form academic papers peppered with citations. That is not only an overly restrictive view of research, but at times a harmful one that motivates students who are otherwise not intellectually passionate about STEM to force themselves into STEM research. Furthermore, research and its role in education remains relatively opaque in society - not many outside academia have a strong grasp of just how critical of an activity it is in advancing human knowledge and developing critical thought in our next generation. Knowing it ourselves is the first step in making this type of inquiry accessible to learners around us. Broadly speaking, research is any activity that broadens the horizon of human knowledge through one or more of the 8 intelligences. Composing a new song is as much a form of research as producing a podcast about dementia , just as animating a short film about environmental toxins is as worthy of a research topic as a paper about gene therapy as a treatment for cancer.

There are 2 major implications of Gardner’s theory in education: individuation and pluralization. Free form student-driven research as offered at Polygence is the best way of delivering on both of these promises. Individuation calls for the personalization of a project’s scope to the student’s specific interests and skill level. it takes into account the most effective ways that individual students learn and tailors the material and pedagogical approach accordingly. This tenet also harkens back to Benjamin Bloom’s famous 2 Sigma Problem , where he demonstrates that students tutored in one-on-one settings perform two standard deviations better than those in traditional classroom settings. Pluralization, on the other hand, calls for the presentation of the same concept in various formats that appeal to different forms of intelligence. This greatly expands the reach of any given topic, but also exposes students to diverse ways of thinking and learning.

This is also a fundamental reason why Polygence has recently cemented a partnership with Mastery Transcript Consortium. In order to fully take advantage of the permission to find themselves rather than to conform to narrowly defined molds, students need to be freed from the constant fear of being judged. Mastery based assessment is not about assigning a numerical value to a student’s achievement, nor is it about judging a student’s ability relative to his peers; rather, it’s about giving students the language to speak about the skills and competencies they developed through the experience of personalized research.

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This way of assessing students is based on an absolute scale of abilities whereas traditional grades only ever identify a student’s standing relative to peers. Rather than telling a college that a given applicant ranked 3rd in her class and is in the 98th percentile for verbal reasoning abilities, a mastery-based learning record brings to life that student’s abilities through qualitative descriptions, thereby giving colleges a more three-dimensional picture of its potential students. This will be a welcome change in the sea of cookie-cutter applications and identical test scores that flood admissions officers every year.

Example of a Mastery Learning Record

No matter where this series of changes to the college admissions landscape takes us, it remains our responsibility to ensure that the next generation arrives at college with a clarified rather than a muddied sense of their intellectual identity. The Latin etymology of the word “educate” breaks down into ducere , meaning “to lead”, and e(x) , meaning “out/out of”. Leading out of what? You may ask. I have always been inspired to interpret it as “to lead a learner out of darkness”. The journey of self discovery and enlightenment has sadly become so elusive in this hypercompetitive world of elite admissions, and we now find ourselves in a world where students are woefully unprepared to tackle the challenges of the workforce and of adulthood because they barely know what they are capable of. And they are capable of so much more than we give them credit for.

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Factors influencing students’ acceptance and use generative artificial intelligence in elementary education: an expansion of the UTAUT model

• Published: 13 June 2024

Cite this article

• Lei Du   ORCID: orcid.org/0000-0002-2578-5452 1 &
• Beibei Lv   ORCID: orcid.org/0000-0002-9223-6250 1  

This research examines the influence of integrating generative artificial intelligence (GAI) in education, focusing on its acceptance and utilization among elementary education students. Grounded in the Task-Technology Fit (TTF) Theory and an expanded iteration of the Unified Theory of Acceptance and Use of Technology (UTAUT) model, the study analyzes key constructs—Performance Expectancy, Effort Expectancy, Social Influence, and Facilitating Conditions—on students’ behavioral intentions and usage behaviors concerning GAI. The UTAUT model, which integrates elements from multiple theories and is widely applied in educational contexts to understand technology adoption behaviors, provides a robust theoretical framework. Additionally, TTF theory, emphasizing the alignment of technology with specific instructional tasks, enhances our understanding of GAI acceptance. This study also investigates the moderating effects of TTF and gender within this framework. Data analysis, conducted through PLS-SEM, is based on responses from 279 elementary education students in China who completed an 8-week course incorporating GAI. Results indicate that Performance Expectancy, Social Influence, and Effort Expectancy significantly influence Behavioral Intention, while Facilitating Conditions have the strongest impact on actual Use Behavior, surpassing their influence on Behavioral Intention. Furthermore, Task-Technology Fit moderates both Performance Expectancy and Effort Expectancy in students’ consideration of GAI use. However, gender does not demonstrate a moderating effect in the overall model. These findings deepen our understanding of elementary school students’ acceptance of GAI technology and provide practical guidance for developers, educational policymakers, teachers, and researchers to effectively integrate GAI into elementary education while maintaining teaching quality.

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Acknowledgements

This research was funded by the Jiangsu Province Education Science “14th Five-Year Plan” Project (C/2023/01/64), and Interdisciplinary Research Foundation for the Doctoral Candidates of Beijing Normal University (Grant Number BNUXKJC2326).

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Du, L., Lv, B. Factors influencing students’ acceptance and use generative artificial intelligence in elementary education: an expansion of the UTAUT model. Educ Inf Technol (2024). https://doi.org/10.1007/s10639-024-12835-4

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What is AI (artificial intelligence)?

Humans and machines: a match made in productivity  heaven. Our species wouldn’t have gotten very far without our mechanized workhorses. From the wheel that revolutionized agriculture to the screw that held together increasingly complex construction projects to the robot-enabled assembly lines of today, machines have made life as we know it possible. And yet, despite their seemingly endless utility, humans have long feared machines—more specifically, the possibility that machines might someday acquire human intelligence  and strike out on their own.

Get to know and directly engage with senior McKinsey experts on AI

Sven Blumberg is a senior partner in McKinsey’s Düsseldorf office; Michael Chui is a partner at the McKinsey Global Institute and is based in the Bay Area office, where Lareina Yee is a senior partner; Kia Javanmardian is a senior partner in the Chicago office, where Alex Singla , the global leader of QuantumBlack, AI by McKinsey, is also a senior partner; Kate Smaje and Alex Sukharevsky are senior partners in the London office.

But we tend to view the possibility of sentient machines with fascination as well as fear. This curiosity has helped turn science fiction into actual science. Twentieth-century theoreticians, like computer scientist and mathematician Alan Turing, envisioned a future where machines could perform functions faster than humans. The work of Turing and others soon made this a reality. Personal calculators became widely available in the 1970s, and by 2016, the US census showed that 89 percent of American households had a computer. Machines— smart machines at that—are now just an ordinary part of our lives and culture.

Those smart machines are also getting faster and more complex. Some computers have now crossed the exascale threshold, meaning they can perform as many calculations in a single second as an individual could in 31,688,765,000 years . And beyond computation, which machines have long been faster at than we have, computers and other devices are now acquiring skills and perception that were once unique to humans and a few other species.

About QuantumBlack, AI by McKinsey

QuantumBlack, McKinsey’s AI arm, helps companies transform using the power of technology, technical expertise, and industry experts. With thousands of practitioners at QuantumBlack (data engineers, data scientists, product managers, designers, and software engineers) and McKinsey (industry and domain experts), we are working to solve the world’s most important AI challenges. QuantumBlack Labs is our center of technology development and client innovation, which has been driving cutting-edge advancements and developments in AI through locations across the globe.

AI is a machine’s ability to perform the cognitive functions we associate with human minds, such as perceiving, reasoning, learning, interacting with the environment, problem-solving, and even exercising creativity. You’ve probably interacted with AI even if you don’t realize it—voice assistants like Siri and Alexa are founded on AI technology, as are some customer service chatbots that pop up to help you navigate websites.

Applied AI —simply, artificial intelligence applied to real-world problems—has serious implications for the business world. By using artificial intelligence, companies have the potential to make business more efficient and profitable. But ultimately, the value of AI isn’t in the systems themselves. Rather, it’s in how companies use these systems to assist humans—and their ability to explain to shareholders and the public what these systems do—in a way that builds trust and confidence.

For more about AI, its history, its future, and how to apply it in business, read on.

Learn more about QuantumBlack, AI by McKinsey .

Introducing McKinsey Explainers : Direct answers to complex questions

What is machine learning.

Machine learning is a form of artificial intelligence that can adapt to a wide range of inputs, including large sets of historical data, synthesized data, or human inputs. (Some machine learning algorithms are specialized in training themselves to detect patterns; this is called deep learning. See Exhibit 1.) These algorithms can detect patterns and learn how to make predictions and recommendations by processing data, rather than by receiving explicit programming instruction. Some algorithms can also adapt in response to new data and experiences to improve over time.

The volume and complexity of data that is now being generated, too vast for humans to process and apply efficiently, has increased the potential of machine learning, as well as the need for it. In the years since its widespread deployment, which began in the 1970s, machine learning has had an impact on a number of industries, including achievements in medical-imaging analysis  and high-resolution weather forecasting.

The volume and complexity of data that is now being generated, too vast for humans to process and apply efficiently, has increased the potential of machine learning, as well as the need for it.

What is deep learning?

Deep learning is a more advanced version of machine learning that is particularly adept at processing a wider range of data resources (text as well as unstructured data including images), requires even less human intervention, and can often produce more accurate results than traditional machine learning. Deep learning uses neural networks—based on the ways neurons interact in the human brain —to ingest data and process it through multiple neuron layers that recognize increasingly complex features of the data. For example, an early layer might recognize something as being in a specific shape; building on this knowledge, a later layer might be able to identify the shape as a stop sign. Similar to machine learning, deep learning uses iteration to self-correct and improve its prediction capabilities. For example, once it “learns” what a stop sign looks like, it can recognize a stop sign in a new image.

What is generative AI?

Case study: vistra and the martin lake power plant.

Vistra is a large power producer in the United States, operating plants in 12 states with a capacity to power nearly 20 million homes. Vistra has committed to achieving net-zero emissions by 2050. In support of this goal, as well as to improve overall efficiency, QuantumBlack, AI by McKinsey worked with Vistra to build and deploy an AI-powered heat rate optimizer (HRO) at one of its plants.

“Heat rate” is a measure of the thermal efficiency of the plant; in other words, it’s the amount of fuel required to produce each unit of electricity. To reach the optimal heat rate, plant operators continuously monitor and tune hundreds of variables, such as steam temperatures, pressures, oxygen levels, and fan speeds.

Vistra and a McKinsey team, including data scientists and machine learning engineers, built a multilayered neural network model. The model combed through two years’ worth of data at the plant and learned which combination of factors would attain the most efficient heat rate at any point in time. When the models were accurate to 99 percent or higher and run through a rigorous set of real-world tests, the team converted them into an AI-powered engine that generates recommendations every 30 minutes for operators to improve the plant’s heat rate efficiency. One seasoned operations manager at the company’s plant in Odessa, Texas, said, “There are things that took me 20 years to learn about these power plants. This model learned them in an afternoon.”

Overall, the AI-powered HRO helped Vistra achieve the following:

• approximately 1.6 million metric tons of carbon abated annually
• 67 power generators optimized
• $60 million saved in about a year

Read more about the Vistra story here .

Generative AI (gen AI) is an AI model that generates content in response to a prompt. It’s clear that generative AI tools like ChatGPT and DALL-E (a tool for AI-generated art) have the potential to change how a range of jobs  are performed. Much is still unknown about gen AI’s potential, but there are some questions we can answer—like how gen AI models are built, what kinds of problems they are best suited to solve, and how they fit into the broader category of AI and machine learning.

For more on generative AI and how it stands to affect business and society, check out our Explainer “ What is generative AI? ”

What is the history of AI?

The term “artificial intelligence” was coined in 1956  by computer scientist John McCarthy for a workshop at Dartmouth. But he wasn’t the first to write about the concepts we now describe as AI. Alan Turing introduced the concept of the “ imitation game ” in a 1950 paper. That’s the test of a machine’s ability to exhibit intelligent behavior, now known as the “Turing test.” He believed researchers should focus on areas that don’t require too much sensing and action, things like games and language translation. Research communities dedicated to concepts like computer vision, natural language understanding, and neural networks are, in many cases, several decades old.

MIT physicist Rodney Brooks shared details on the four previous stages of AI:

Symbolic AI (1956). Symbolic AI is also known as classical AI, or even GOFAI (good old-fashioned AI). The key concept here is the use of symbols and logical reasoning to solve problems. For example, we know a German shepherd is a dog , which is a mammal; all mammals are warm-blooded; therefore, a German shepherd should be warm-blooded.

The main problem with symbolic AI is that humans still need to manually encode their knowledge of the world into the symbolic AI system, rather than allowing it to observe and encode relationships on its own. As a result, symbolic AI systems struggle with situations involving real-world complexity. They also lack the ability to learn from large amounts of data.

Symbolic AI was the dominant paradigm of AI research until the late 1980s.

Neural networks (1954, 1969, 1986, 2012). Neural networks are the technology behind the recent explosive growth of gen AI. Loosely modeling the ways neurons interact in the human brain , neural networks ingest data and process it through multiple iterations that learn increasingly complex features of the data. The neural network can then make determinations about the data, learn whether a determination is correct, and use what it has learned to make determinations about new data. For example, once it “learns” what an object looks like, it can recognize the object in a new image.

Neural networks were first proposed in 1943 in an academic paper by neurophysiologist Warren McCulloch and logician Walter Pitts. Decades later, in 1969, two MIT researchers mathematically demonstrated that neural networks could perform only very basic tasks. In 1986, there was another reversal, when computer scientist and cognitive psychologist Geoffrey Hinton and colleagues solved the neural network problem presented by the MIT researchers. In the 1990s, computer scientist Yann LeCun made major advancements in neural networks’ use in computer vision, while Jürgen Schmidhuber advanced the application of recurrent neural networks as used in language processing.

In 2012, Hinton and two of his students highlighted the power of deep learning. They applied Hinton’s algorithm to neural networks with many more layers than was typical, sparking a new focus on deep neural networks. These have been the main AI approaches of recent years.

Traditional robotics (1968). During the first few decades of AI, researchers built robots to advance research. Some robots were mobile, moving around on wheels, while others were fixed, with articulated arms. Robots used the earliest attempts at computer vision to identify and navigate through their environments or to understand the geometry of objects and maneuver them. This could include moving around blocks of various shapes and colors. Most of these robots, just like the ones that have been used in factories for decades, rely on highly controlled environments with thoroughly scripted behaviors that they perform repeatedly. They have not contributed significantly to the advancement of AI itself.

But traditional robotics did have significant impact in one area, through a process called “simultaneous localization and mapping” (SLAM). SLAM algorithms helped contribute to self-driving cars and are used in consumer products like vacuum cleaning robots and quadcopter drones. Today, this work has evolved into behavior-based robotics, also referred to as haptic technology because it responds to human touch.

• Behavior-based robotics (1985). In the real world, there aren’t always clear instructions for navigation, decision making, or problem-solving. Insects, researchers observed, navigate very well (and are evolutionarily very successful) with few neurons. Behavior-based robotics researchers took inspiration from this, looking for ways robots could solve problems with partial knowledge and conflicting instructions. These behavior-based robots are embedded with neural networks.

Learn more about  QuantumBlack, AI by McKinsey .

What is artificial general intelligence?

The term “artificial general intelligence” (AGI) was coined to describe AI systems that possess capabilities comparable to those of a human . In theory, AGI could someday replicate human-like cognitive abilities including reasoning, problem-solving, perception, learning, and language comprehension. But let’s not get ahead of ourselves: the key word here is “someday.” Most researchers and academics believe we are decades away from realizing AGI; some even predict we won’t see AGI this century, or ever. Rodney Brooks, an MIT roboticist and cofounder of iRobot, doesn’t believe AGI will arrive until the year 2300 .

The timing of AGI’s emergence may be uncertain. But when it does emerge—and it likely will—it’s going to be a very big deal, in every aspect of our lives. Executives should begin working to understand the path to machines achieving human-level intelligence now and making the transition to a more automated world.

For more on AGI, including the four previous attempts at AGI, read our Explainer .

What is narrow AI?

Narrow AI is the application of AI techniques to a specific and well-defined problem, such as chatbots like ChatGPT, algorithms that spot fraud in credit card transactions, and natural-language-processing engines that quickly process thousands of legal documents. Most current AI applications fall into the category of narrow AI. AGI is, by contrast, AI that’s intelligent enough to perform a broad range of tasks.

How is the use of AI expanding?

AI is a big story for all kinds of businesses, but some companies are clearly moving ahead of the pack . Our state of AI in 2022 survey showed that adoption of AI models has more than doubled since 2017—and investment has increased apace. What’s more, the specific areas in which companies see value from AI have evolved, from manufacturing and risk to the following:

• marketing and sales
• product and service development
• strategy and corporate finance

One group of companies is pulling ahead of its competitors. Leaders of these organizations consistently make larger investments in AI, level up their practices to scale faster, and hire and upskill the best AI talent. More specifically, they link AI strategy to business outcomes and “ industrialize ” AI operations by designing modular data architecture that can quickly accommodate new applications.

What are the limitations of AI models? How can these potentially be overcome?

We have yet to see the longtail effect of gen AI models. This means there are some inherent risks involved in using them—both known and unknown.

The outputs gen AI models produce may often sound extremely convincing. This is by design. But sometimes the information they generate is just plain wrong. Worse, sometimes it’s biased (because it’s built on the gender, racial, and other biases of the internet and society more generally).

It can also be manipulated to enable unethical or criminal activity. Since gen AI models burst onto the scene, organizations have become aware of users trying to “jailbreak” the models—that means trying to get them to break their own rules and deliver biased, harmful, misleading, or even illegal content. Gen AI organizations are responding to this threat in two ways: for one thing, they’re collecting feedback from users on inappropriate content. They’re also combing through their databases, identifying prompts that led to inappropriate content, and training the model against these types of generations.

But awareness and even action don’t guarantee that harmful content won’t slip the dragnet. Organizations that rely on gen AI models should be aware of the reputational and legal risks involved in unintentionally publishing biased, offensive, or copyrighted content.

These risks can be mitigated, however, in a few ways. “Whenever you use a model,” says McKinsey partner Marie El Hoyek, “you need to be able to counter biases  and instruct it not to use inappropriate or flawed sources, or things you don’t trust.” How? For one thing, it’s crucial to carefully select the initial data used to train these models to avoid including toxic or biased content. Next, rather than employing an off-the-shelf gen AI model, organizations could consider using smaller, specialized models. Organizations with more resources could also customize a general model based on their own data to fit their needs and minimize biases.

It’s also important to keep a human in the loop (that is, to make sure a real human checks the output of a gen AI model before it is published or used) and avoid using gen AI models for critical decisions, such as those involving significant resources or human welfare.

It can’t be emphasized enough that this is a new field. The landscape of risks and opportunities is likely to continue to change rapidly in the coming years. As gen AI becomes increasingly incorporated into business, society, and our personal lives, we can also expect a new regulatory climate to take shape. As organizations experiment—and create value—with these tools, leaders will do well to keep a finger on the pulse of regulation and risk.

What is the AI Bill of Rights?

The Blueprint for an AI Bill of Rights, prepared by the US government in 2022, provides a framework for how government, technology companies, and citizens can collectively ensure more accountable AI. As AI has become more ubiquitous, concerns have surfaced  about a potential lack of transparency surrounding the functioning of gen AI systems, the data used to train them, issues of bias and fairness, potential intellectual property infringements, privacy violations, and more. The Blueprint comprises five principles that the White House says should “guide the design, use, and deployment of automated systems to protect [users] in the age of artificial intelligence.” They are as follows:

• The right to safe and effective systems. Systems should undergo predeployment testing, risk identification and mitigation, and ongoing monitoring to demonstrate that they are adhering to their intended use.
• Protections against discrimination by algorithms. Algorithmic discrimination is when automated systems contribute to unjustified different treatment of people based on their race, color, ethnicity, sex, religion, age, and more.
• Protections against abusive data practices, via built-in safeguards. Users should also have agency over how their data is used.
• The right to know that an automated system is being used, and a clear explanation of how and why it contributes to outcomes that affect the user.
• The right to opt out, and access to a human who can quickly consider and fix problems.

At present, more than 60 countries or blocs have national strategies governing the responsible use of AI (Exhibit 2). These include Brazil, China, the European Union, Singapore, South Korea, and the United States. The approaches taken vary from guidelines-based approaches, such as the Blueprint for an AI Bill of Rights in the United States, to comprehensive AI regulations that align with existing data protection and cybersecurity regulations, such as the EU’s AI Act, due in 2024.

There are also collaborative efforts between countries to set out standards for AI use. The US–EU Trade and Technology Council is working toward greater alignment between Europe and the United States. The Global Partnership on Artificial Intelligence, formed in 2020, has 29 members including Brazil, Canada, Japan, the United States, and several European countries.

Even though AI regulations are still being developed, organizations should act now to avoid legal, reputational, organizational, and financial risks. In an environment of public concern, a misstep could be costly. Here are four no-regrets, preemptive actions organizations can implement today:

• Transparency. Create an inventory of models, classifying them in accordance with regulation, and record all usage across the organization that is clear to those inside and outside the organization.
• Governance. Implement a governance structure for AI and gen AI that ensures sufficient oversight, authority, and accountability both within the organization and with third parties and regulators.
• Data management. Proper data management includes awareness of data sources, data classification, data quality and lineage, intellectual property, and privacy management.
• Model management. Organizations should establish principles and guardrails for AI development and use them to ensure all AI models uphold fairness and bias controls.
• Cybersecurity and technology management. Establish strong cybersecurity and technology to ensure a secure environment where unauthorized access or misuse is prevented.
• Individual rights. Make users aware when they are interacting with an AI system, and provide clear instructions for use.

How can organizations scale up their AI efforts from ad hoc projects to full integration?

Most organizations are dipping a toe into the AI pool—not cannonballing. Slow progress toward widespread adoption is likely due to cultural and organizational barriers. But leaders who effectively break down these barriers will be best placed to capture the opportunities of the AI era. And—crucially—companies that can’t take full advantage of AI are already being sidelined by those that can, in industries like auto manufacturing and financial services.

To scale up AI, organizations can make three major shifts :

• Move from siloed work to interdisciplinary collaboration. AI projects shouldn’t be limited to discrete pockets of organizations. Rather, AI has the biggest impact when it’s employed by cross-functional teams with a mix of skills and perspectives, enabling AI to address broad business priorities.
• Empower frontline data-based decision making . AI has the potential to enable faster, better decisions at all levels of an organization. But for this to work, people at all levels need to trust the algorithms’ suggestions and feel empowered to make decisions. (Equally, people should be able to override the algorithm or make suggestions for improvement when necessary.)
• Adopt and bolster an agile mindset. The agile test-and-learn mindset will help reframe mistakes as sources of discovery, allaying the fear of failure and speeding up development.

Learn more about QuantumBlack, AI by McKinsey , and check out AI-related job opportunities if you’re interested in working at McKinsey.

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This article was updated in April 2024; it was originally published in April 2023.

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