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I feel as though I have a good understanding of the basic concepts of the study design, however I would like to work on finding more culturally responsive ways of teaching the content. When I was in high school, we didn't learn how some cultures could respond differently to experiments or experience some psychological concepts differently, or even how disability might affect this. I would like to work on emphasizing individual differences in all topics and to foster critical thinking around this. While this issue may be the hardest to teach given my own unique identity, I hope to learn to find ways to be respectful and ethical while also allowing for all identities to be seen. This may also be difficult to incorporate in such a content filled curriculum so I will have to plan to interweave these instances of questioning.

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I feel like teaching psychology, in comparison to other curriculum areas, might expose teachers and students to sensitive content areas, like mental ill-health. As is mentioned in the study design, teachers are not counsellors but I imagine this could be tricky for students to understand.

I feel pretty confident that, after a bit of a refresh, the content across most of the units will be easy to teach for me. It's easy because I find psychology really interesting, particularly the biological/neuroscience aspects like areas of the brain and how neurotransmitters work. I'm not the best at statistics but I feel like I understand it enough to teach it at a year 11/12 level, but I guess we'll find out! I think I might also struggle with Unit 3, particularly Area of Study 2 which focuses on memory formation. I wasn't super engaged in this part of psychology in my own studies and so consequentially (and ironically) have not committed a lot of it to memory.

In terms of engaging students, I think implementing applied learning as much as possible will be really beneficial. The things I remember from my own studies were the "applied" activites/assessments. For instance, I created a health promotion initiative based on social psychology theory, I drew the parts of a brain on a swimming cap (I think I still have it!), and I created a wellness app based in mindfulness/positive psychology - to name a few.

I'm curious to see how I juggle curriculum with these kinds of activities and am keen to learn from others, including from experienced teachers on placement.

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Reflection on “The trouble with Bloom’s taxonomy in an age of AI?"

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Reflection on “The trouble with Bloom’s taxonomy in an age of AI?"

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This article offers a powerful point for reflection — human cognition is not strictly hierarchical. Different cognitive processes are interconnected and often work in collaboration. Depending on the type of task and the learner involved, the sequence of cognitive skills can shift and adapt.

This also raises an important question for the age of AI: how can teachers guide students to use AI effectively as a learning tool? For example, students might begin by using AI to generate a "creative" output, and then be guided through a process of comparison, evaluation, understanding, and final definition. This encourages a more active and inquiry-based learning experience.

Sean

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Educators have always faced the challenge of how to determine if their students are thinking. Since the 1950s, Benjamin Bloom's taxonomy of educational objectives has been one of the most influential pieces of educational psychology (see Figure 1). However, in an age where teachers may be using AI as tools to assist them in task creation, we need to reconsider how we design teaching for thinking.

Large language models (LLMs) and AI tools such as ChatGPT and Gemini use Bloom's taxonomy as one of the most significant influences on how critical thinking is defined. In this article, we explore the challenge of integrating AI into teaching, learning and assessment while Bloom's taxonomy is a significant part of its training model.

What is Bloom's taxonomy?

Bloom's taxonomy, a six-category framework of educational goals, has been highly influential for educators for nearly 70 years because it creates a clear structure for learning, is highly versatile across contexts, and facilitates the creation of critical-thinking activities.

It's versatile but is Bloom's accurate?

Criticisms of Bloom's taxonomy have also been around for more than half a century, and many are well known. One of the major issues is that the taxonomy does not capture the complexity of cognitive skills. It can be seen as inaccurate as cognitive processes interrelate with each other rather than building on each other.

To illustrate, let's look at an "understanding" task (see Figure 2): "Define ."

This is a deceptively complex task. Not only would it require a standard understanding of respiration, response to stimuli and reproduction, but it also merits discussion of more spiritual or existential issues. Ecosystems and stars may fit a definition of life. Any definition will be incomplete and debatable. A student would demonstrate high levels of critical thinking and insight if discussing the foundations of our understanding rather than merely explaining our definitions.

The taxonomy has seen revisions over time ( notably by David R. Krahwohl in 2002 ), but these still don't fully capture the interrelated and interdependent nature of thinking processes. Alternatives such as cognitive web models (as proposed by Peter Ellerton and co-authors from the University of Queensland Critical Thinking Project in 2022) argue that cognitive skills often require a host of related skills and tasks (see Figure 3), and this may be more useful in designing for learning objectives.

Prompting AI for assistance with teaching thinking

To illustrate, we prompted ChatGPT to create questions that demand increasing amounts of critical thinking on the following task: "Describe the structure of a cell." It responded with:

1. Basic understanding: Describe the basic structure of a cell, including its main components.

2. Comparing cell types: Compare and contrast the structures of prokaryotic and eukaryotic cells, highlighting their key differences and similarities.

3. Specialised cell structures: Explain how the structure of a plant cell differs from that of an animal cell, emphasising the unique organelles found in each type.

4. Cellular functions: How do the various organelles within a eukaryotic cell contribute to its overall function and survival?

5. Cellular adaptations: Analyse how the structure of cells in different organisms reflects their specialised functions and adaptations to their environments. Provide examples.

6. Theoretical cell modifications: If you were to design a hypothetical cell with a specific function, how would you modify its structure to optimise its performance? Explain your reasoning.

When asked it increased the critical thinking, ChatGPT responded that it incorporated elements of Bloom's taxonomy in designing to promote higher-order thinking, then demonstrated how each question aligned to the taxonomy. Generative AI still "believes" that cognitive skills are hierarchical.

This is a problem for educators in two significant ways.

First, the questions are wildly inconsistent in their level of difficulty. The sixth question (creating) is so challenging that it's likely only a biology expert would be able to answer it. This might be addressed through using better prompting practices, but this leads to the second problem.

When asking AI for greater levels of critical thinking, it struggles to discern among levels. "Comparing and contrasting" is an "analysis" skill, yet ChatGPT has treated this as an "understanding" skill. We'd likely use both, too, further demonstrating that these skills are interrelated rather than hierarchical.

Create AI prompts that widen perspective or engage with ambiguity

The obvious conclusion is that educators need to be careful with their prompting when it comes to using AI as a design tool. More than that, we need to recognise that the AI may create hierarchies (which are potentially inaccurate to boot) whenever prompted for suggestions or during brainstorming.

Educators will need to consider the specific cognitive skill they want to teach or assess (when analysing, do they really mean classifying?), along with the qualities of that thinking (do they care about having coherent classifications or broad classifications, for example).

Consider prompting AI for activities that require broader perspectives or engage with the ambiguities of the issue. "Create an activity that explores the many ways life can be defined" is going to produce greater variety and complexity than asking it to "add critical thinking or difficulty to this question". Given that contemporary critical-thinking pedagogy recognises the interrelatedness of thinking skills as well as the different qualities of thinking, it's essential that we exercise care when designing critical-thinking tasks.

Luke Zaphir and Dale Hansen are learning designers in the Institute of Teaching and Learning Innovation at the University of Queensland.
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The trouble with Bloom's taxonomy in an age of AI

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When using large language models to create learning tasks, educators should be careful with their prompts if the LLM relies on Bloom's taxonomy as a supporting dataset. Luke Zaphir and Dale Hansen break down the issues

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Reflection on "Why multiple intelligences theory is a neuromyth"

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Reflection on "Why multiple intelligences theory is a neuromyth"

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This is a thought-provoking article. While Multiple Intelligences (MI) Theory has helped promote differentiated instruction and strength-based learning, it lacks scientific evidence for its core claim — that intelligences are modular. The brain works in a highly interconnected way, with many cognitive abilities overlapping and interacting.

Take, for example, a successful singer. They don’t rely solely on musical intelligence. Telling a story involves linguistic intelligence, and performance calls on bodily-kinaesthetic skills. From my view, MI Theory may reflect more of a socially constructed way of categorising human abilities (like vocational labels), rather than a biologically accurate model of intelligence. So I am thinking if the popularity of MI Theory could unintentionally hold back educational progress.

Second, schools are not only for talents and should not just focus on nurturing individual strengths. We also need to help students develop in areas they struggle with — to ensure they gain the well-rounded skills for an unpredictable future.

Sean

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The College of New Jersey, Ewing Township, NJ, United States

A neuromyth is a commonly accepted but unscientific claim about brain function. Many researchers have claimed Howard Gardner's multiple intelligences (MI) theory is a neuromyth because they have seen no evidence supporting his proposal for independent brain-based intelligences for different types of cognitive abilities. Although Gardner has made claims that there are dedicated neural networks or modules for each of the intelligences, nonetheless Gardner has stated his theory could not be a neuromyth because he never claimed it was a neurological theory. This paper explains the lack of evidence to support MI theory. Most important, no researcher has directly looked for a brain basis for the intelligences. Moreover, factor studies have not shown the intelligences to be independent, and studies of MI teaching effects have not explored alternate causes for positive effects and have not been conducted by standard scientific methods. Gardner's MI theory was not a neuromyth initially because it was based on theories of the 1980s of brain modularity for cognition, and few researchers then were concerned by the lack of validating brain studies. However, in the past 40 years neuroscience research has shown that the brain is not organized in separate modules dedicated to specific forms of cognition. Despite the lack of empirical support for Gardner's theory, MI teaching strategies are widely used in classrooms all over the world. Crucially, belief in MI and use of MI in the classroom limit the effort to find evidence-based teaching methods. Studies of possible interventions to try to change student and teacher belief in neuromyths are currently being undertaken. Intervention results are variable: One research group found that teachers who knew more about the brain still believed education neuromyths. Teachers need to learn to detect and reject neuromyths. Widespread belief in a neuromyth does not make a theory legitimate. Theories must be based on sound empirical evidence. It is now time for MI theory to be rejected, once and for all, and for educators to turn to evidence-based teaching strategies.

Introduction

Howard Gardner's theory ( Gardner, 1983, 1993, 2011) of multiple intelligences is widely regarded as a neuromyth ( Geake, 2008; Dekker et al., 2012; Howard-Jones, 2014; Ruhaak and Cook, 2018; Blanchette Sarrasin et al., 2019; Rogers and Cheung, 2020; Craig et al., 2021; Menz et al., 2021). A neuromyth is a commonly accepted but unscientific claim about brain function that may be based on a misunderstanding of brain research findings ( Dekker et al., 2012). The goal of this paper is to explain why Howard Gardner's Multiple Intelligences (MI) theory is a neuromyth.

Specifically, critics have claimed that Gardner's neuromyth is his proposal that humans have independent brain-based intelligences for different types of cognitive abilities: linguistic; logical-mathematical; visual-spatial; bodily-kinesthetic; musical; interpersonal; intrapersonal; and naturalistic.

Although critics have labeled MI theory as a neuromyth, little evidence has been published to support this claim. Geake (2008) noted that one neuroscience study found that the frontal lobe governed different forms of cognition. From this Geake argued the intelligences could not have separate brain networks. Howard-Jones (2014) asserted that the "general processing complexity of the brain makes it unlikely that anything resembling Multiple Intelligences theory can ever be used to describe it" (p. 818), but he did not provide evidence of the brain's complexity. Craig et al. (2021), Dekker et al. (2012), Ruhaak and Cook (2018), and Blanchette Sarrasin et al. (2019) simply labeled MI theory as a neuromyth.

Rousseau (2021b) argued that MI theory itself was not a neuromyth. He stated that it was a "legitimate scientific theory of intelligence" (p. 4) that could be validated by finding the brain bases for the intelligences. However, Rousseau (2021b) claimed that it was a myth that matching students' multiple intelligences to teaching methods could enhance learning.

The current criticisms of Gardner's theory are not new ( Shaler, 2006; Waterhouse, 2006), and over time Gardner has taken four distinct unsatisfactory positions to defend against criticisms of his theory. One position was to ignore criticisms. Gardner asserted "For over a decade, I was content to let MI theory take on a life of its own. I had issued an ensemble of ideas (or "memes") to the world, and I was inclined to let those memes fend for themselves" ( Gardner, 2011, p. 79). A second position was to argue that empirical support for his theory was provided by his personal reading of research: "Gardner cast a wide net that included neuroscience, cognitive science, anthropology, and evolutionary sciences. This broader view allowed Gardner to reconceptualize intelligences" ( Gardner and Moran, 2006, p. 229). But personal reading of research is how theories are created, not how they are validated. Empirical support for a theory requires testing the theory. Gardner's third position was to tell researchers to read his previous writings to find an argument for the validity of his theory ( Gardner, 2020b). He stated "there is an entire 400-page book Howard Gardner Under Fire in which I respond to these and other critiques ( Shaler, 2006). I would ask that both researchers and educators review these writings and exchanges before connecting the theory that I developed with the provocative, and contentious phrase "neuromyth" ( Gardner, 2020b, p. 3).

A fourth position has been Gardner's continuing assertion ( Gardner, 2011) that MI theory could not be a neuromyth because he had never claimed there were brain bases for the intelligences. He stated "while brain evidence was cited in my original work, I have never claimed that "MI" is a neurological theory" ( Gardner, 2011, p. 3). However, Gardner did make claims for the neural basis of the intelligences. Gardner argued "that the mind/brain consists of many modules/organs/intelligences, each of which operates according to its own rules in relative autonomy from the others" ( Gardner, 2011, p. xxiii). Gardner also argued that patients with brain damage demonstrated that "various abilities can be destroyed, or spared, in isolation from other abilities" ( Gardner, 1993, p. 7). Gardner defined each intelligence as a separate pattern of thinking, "where each operates according to its own procedures and has its own biological bases" ( Gardner, 2011, p. 72). Importantly, Gardner asserted that each "intelligence ...clearly involves processes that are carried out by dedicated neural networks. No doubt each of the intelligences has its characteristic neural processes" ( Gardner, 2020a, p. 94). Gardner specifically argued that "MI theory demands that linguistic processing, for example, occur via a different set of neural mechanisms than does spatial or interpersonal processing" ( Gardner, 2020a, p. 99), and Gardner and his colleagues ( Davis et al., 2011) proposed that there should be "an atlas of the neural correlates of each of the intelligences" (p. 495). Davis et al. also predicted that "The biological basis of the theory-its neural and genetic correlates-should be clarified in the coming years" ( Davis et al., 2011, p. 498).

But, to date, no neural correlates of the intelligences have been found ( Waterhouse, 2006; Geake, 2008; Dekker et al., 2012; Howard-Jones, 2014; Ruhaak and Cook, 2018; Blanchette Sarrasin et al., 2019; Craig et al., 2021; Rousseau, 2021b). Consequently, until researchers find evidence that "each of the intelligences has its characteristic neural processes" ( Gardner, 2020b, p. 94), MI theory is and will remain a neuromyth. And until each intelligence is shown to have its own unique brain processing, there is no basis for the superiority of MI teaching strategies. Despite the lack of evidence for multiple intelligences, Ruhaak and Cook (2018) reported that 90% of teacher trainees surveyed in the United States planned to use MI teaching strategies, and Blanchette Sarrasin et al. (2019) reported that 94% of teachers surveyed in Quebec stated they used MI theory in the classroom. Others, including Abenti (2020), Hanafin (2014), Shearer (2020a), and Yavich and Rotnitsky (2020) all have argued that use of MI theory in the classroom was effective and important. Shearer noted that many educators "continue to alter lesson plans, re-envision curriculum and design whole schools inspired by the multiple intelligences" ( Shearer, 2020a, p. 49). But because MI theory has not been validated, significant efforts should be made to dispel the widespread belief in MI theory and the belief in the effectiveness of MI-inspired teaching strategies.

Three lines of evidence undermine MI theory

Three lines of evidence presented here explain why MI theory is a neuromyth. Each line of evidence counters a specific belief about multiple intelligences. One, although there is a pervasive public belief that there are distinct multiple intelligences, studies have shown that the intelligences do not function separately from one another. Two, although many educators believe in the effectiveness of MI teaching strategies, these strategies have not been studied appropriately and cannot prove the intelligences exist. Three, although many educators and researchers believe there is a brain basis for each intelligence, this is improbable because neuroscience research has shown that the brain is organized in complex multifunction networks, and multifunction networks, as understood now, preclude the possibility of separate neural networks for the individual intelligences.

The paper begins with a brief review of the lack of standard measures for the intelligences, and then discusses each of the three lines of evidence that makes MI theory a neuromyth. Following this, the paper explains the brain's multifunction networks. The paper concludes with a discussion of possible ways to dispel the belief that MI theory is a good basis for teaching.

There are no standard measures of the intelligences

Teele (1992) published the Teele Inventory of Multiple Intelligences (TIMI). This psychometric test of the intelligences was used in 1,000 schools. However, McMahon et al. (2004) evaluated the TIMI and discovered that "Reliability analyses for each of the subscales of the TIMI suggested that the instrument does not provide consistent measurement" (p. 48). They concluded that the poor reliability of the TIMI meant that it should not be used by educators.

At present there are no standard measures of the intelligences, thus individual researchers have to create their own measures for the intelligences. Unfortunately, without standard measures, MI study findings cannot be compared to one another. Also, the lack of standard measures means that no synthesis of MI research findings can be built.

Evidence for the independence of the intelligences is lacking

Gardner (1983, 1993, 2011, 2017, 2020a) claimed that the intelligences were independent abilities, and Gardner stated that evidence for "the independence of an intelligence comes from studies that identify individuals who either excel at, or lack, a certain capacity, as well as "dedicated" neural regions that appear to subserve these capacities" ( Gardner, 2011, p. 50). Gardner and Walters stated that the independence of the intelligences was a crucial component of MI theory, and they worried that if there were "a significant correlation among these faculties, as measured by appropriate assessments, the supposed independence of the faculties would be invalidated" ( Gardner and Walters, 1993, p. 38).

Gardner (2006) claimed that Visser et al. (2006a) only found intercorrelations for the intelligences because they used measures that relied on two of the multiple intelligences-linguistic and logical-mathematical. Visser et al. (2006b), however, responded that intercorrelations of the intelligences depended on the association of individual differences in task performance, and not on whether a test item used language or reasoning. Visser et al. (2006b), also noted that Gardner had not established measures for his eight intelligences, and the researchers requested that Gardner should do so. However, as noted above, no standard measures of the intelligences have yet been developed.

Almeida et al. (2010) conducted a confirmatory factor analysis on scores from a set of Gardner's multiple intelligence assessment tasks, including linguistic, logical, visual/spatial, bodily-kinesthetic, naturalistic and musical intelligences, and scores from the Battery of General and Differential Aptitudes (BADyG), including reasoning, memory, verbal aptitude, numerical aptitude and spatial aptitude, in a sample of 294 children. Almeida et al. (2010) found that there was no general common factor across the two test batteries. Instead, the researchers found that there were two factors, one for the BADyG measures, and another for the multiple intelligence assessment scores. The single multiple intelligence factor suggests that the individual intelligences are not independent of one another.

Castejon et al. (2010) explored the construct validity of the multiple intelligences through confirmatory factor analysis of measures of the intelligences that they had developed. They reported that no model exhibited a totally satisfactory fit to the empirical data, but the best fitting factor structure was a model that was based in the intercorrelation of the intelligences, along with some individuation. The researchers ( Castejon et al., 2010) concluded that their analyses demonstrated that the intelligences are not truly independent of one another. Consequently, to date, no clear division of cognition into separate intelligences has been proven.

Belief in MI teaching strategies is strong but is not supported by valid evidence

Even though Gardner and colleagues stated that "Individualized education does not depend on the existence of MI theory" ( Davis et al., 2011, p. 499), teacher training continues to promote MI theory as an effective basis for individualized lessons ( Sheahan et al., 2015; Armstrong, 2018; Abenti, 2020; Attwood, 2020). Myriad educators and researchers believe that "teachers should be encouraged to learn about student engagement strategies which MI theory provides" ( Attwood, 2020, p. 6), and they have argued that individualized and non-individualized MI teaching strategies are more effective than standard instruction. For example, Yavich and Rotnitsky (2020) asserted that MI teaching strategies can significantly contribute to "the development of logical, critical and creative thinking abilities, as well as the development of high levels of thinking" (p. 111), and Abenti (2020) reviewed studies of MI teaching strategies and concluded that MI teaching methods are the best way to "educate the most people in any education environment" (p. 32).

Problems for studies of MI teaching strategies

There are many published papers addressing the use of MI theory in the classroom. A search of databases on January 14, 2023 using the query 'multiple intelligences classroom' for the time period 1983 to 2022 found 407 papers on Education Full Text, and 92 papers on PubMed. Most of the published classroom studies found improved learning through use of MI teaching methods. Bas (2016), Batdi (2017), and Ferrero et al. (2021) conducted meta-analyses of studies of the effectiveness of using MI in the classroom, and all three meta-analyses reported many studies with positive results for MI teaching. Some MI teaching strategies have focused on teaching students the different intelligences. For example, Winarti et al. (2019) compared student improvement in the eight intelligences when taught by MI strategies with student improvement when not taught using MI strategies. Students taught by MI strategies showed significant improvement in measures of six intelligences-intrapersonal, interpersonal, kinesthetic, visual spatial, musical intelligence, and linguistic intelligence. By contrast, there was no improvement in the control group for any of the eight intelligences.

Most MI teaching strategies, however, follow Gardner's (2011) argument that the intelligences be used "for inculcating diverse subject matter" (p. 409). For example, Sheahan et al. (2015) conducted a study of first year nursing students who were learning clinical skills. The researchers reported that 46 first year nursing students taught by multiple intelligence strategies received significantly higher scores on three structured clinical examination tests than a control group of 44 first year nursing students taught by conventional methods.

However, the apparent success of matching a student's stronger intelligence to specific task can cannot prove the existence of multiple intelligences because there is no evidence that the intelligences are independent ( Visser et al., 2006a; Almeida et al., 2010; Castejon et al., 2010), and no evidence that there is a brain basis for each intelligence ( Waterhouse, 2006; Geake, 2008; Dekker et al., 2012; Howard-Jones, 2014; Ruhaak and Cook, 2018; Blanchette Sarrasin et al., 2019; Craig et al., 2021; Rousseau, 2021b). Davis et al. argued "because any educational intervention is multifaceted, it is not possible to attribute school success or failure strictly to MI interventions" ( Davis et al., 2011, p. 498). In fact, it is unlikely that any enhanced learning is due to the existence of multiple intelligences. Where MI intervention studies have reported evidence of enhanced learning, it is likely that learning is actually enhanced by other well-known methods that have been tested and found effective. These include, but are not limited to, the extended repetition of information, greater student attention to novel teaching strategies, increased personal attention from the instructor, and the greater enthusiasm of teachers using new teaching methods. Importantly, cognitive neuroscience research has established that repetition enhances learning ( Zhan et al., 2018; Adams and Delany, 2023), and that there is a novelty 'switch' in the brain that enhances the processing of something new in the environment ( Gómez-Ocádiz et al., 2022). Learning is also enhanced when there is individual attention to a student by a teacher ( Schacter, 2000), and when excitement occurs at the time the material is presented ( Perugini et al., 2012; Leventon et al., 2018).

There are no empirical studies of the brain basis of MI theory

One, he claimed MI theory cannot be evaluated by research findings from cognitive psychology and neuroscience. Even though Gardner and Moran (2006) declared that MI theory "encompasses cognitive and developmental psychology, differential psychology, (and) neuroscience" (p. 227), nonetheless, Gardner (2020b) argued that MI theory could not be evaluated by research findings from cognitive psychology and neuroscience. He claimed this research used study samples that were too risky. He asserted that "Much neural evidence comes from studies of animals.... But even when the population that has been studied consists of Homo sapiens, generalizations are risky" ( Gardner, 2020b, p. 3). However, Gardner's concern for the riskiness of generalizations from these study samples is unfounded. Research using samples of animals and humans have been the source of most of our current valid knowledge of the brain basis of cognition ( Gazzaniga et al., 2018). Thus, MI theory can be evaluated by findings from cognitive psychology and cognitive neuroscience research. If his theory cannot be evaluated by scientific findings it remains a personal philosophy and an untested speculation.

Two, Gardner claimed that because MI theory is a scientific theory with educational implications, it cannot be tested for validity by standard research methods. Gardner (2020b) argued that a scientific theory with educational implications, such as MI theory, was different from a purely scientific theory. Gardner asserted that purely scientific theories could be supported or countered by research, but theories about the brain with educational implications could not be similarly tested because an "educational claim turns out to be either circular or vacuous. Indeed, once one moves from "science" to "education" one has indubitably entered the realm of cultural values" ( Gardner, 2020b, p. 2).

However, a scientific claim about the brain with educational implications can be supported or countered by research without entering the realm of cultural values. For example, one important scientific theory about the brain that has educational implications argues that student repetition of information enhances learning through brain activity changes. For example, a study by Zhan et al. (2018) found that repetition of information did improve long term learning, and repetition was correlated with significantly increasing activation in the hippocampus, as well as increased connectivity between the hippocampus and other brain regions. Another important scientific theory about brain activity with educational implications is that learning is enhanced by brain activity that occurs while we sleep-even during naps. Klinzing et al. (2019) reviewed a wide range of studies of the effect of sleep on learning, and concluded that what we learn while awake is repeated during slow wave sleep (SWS) and this neuronal replay while we sleep strengthens the information flow to brain networks, thus enhancing learning. Importantly, studies like those of Zhan et al. (2018) and Klinzing et al. (2019) can be supported or countered by future research.

Although future studies to explore the brain basis of the intelligences could be conducted, they will not stem from past or ongoing research, because, as noted above, there is no evidence that empirical brain validity studies have been done ( Waterhouse, 2006; Rousseau, 2021b). While it is possible there may be MI neural validity studies, none have been cited by Gardner (1993, 2011, 2020a, b). Moreover, evidence that validity studies have not been done can be seen in the lack of published studies. On January 10, 2023, using the query "validity Gardner multiple intelligences," for the time period 1983-2022, the database PubMed yielded only two published papers, neither of which were validity studies. And for the same query on the same date, the database Education Full Text yielded just nine papers published between 1983 and 2022, and among these nine papers, only one, Shearer (2020b), provided an assessment of MI neural validity. However, Shearer's paper ( Shearer, 2020b) does cite another Shearer study- Shearer and Karanian (2017).

Shearer and Karanian (2017) and Shearer (2020b) reported that characteristics of the intelligences were aligned with the functions of cognitive brain networks. However, because alignment studies do not measure brain processing while individuals are engaged in activities that characterize a particular intelligence, these studies are not true validity studies, and thus cannot prove that the intelligences have dedicated neural networks.

Shearer and Karanian (2017) reviewed 318 studies of brain regions and reported that brain region functions were aligned with the proposed functions of each of the eight intelligences. They concluded that their analysis of the 318 studies provided "robust evidence that each of the eight intelligences possesses its own unique neural architecture" ( Shearer and Karanian, 2017, p. 221). However, there are problems with Shearer and Karanian's methods and conclusions. First, the researchers did not adequately describe how they selected the 318 neuroscience studies. They simply stated "Several extensive meta-analysis and topic reviews served as guides for finding pertinent studies in the target area" ( Shearer and Karanian, 2017, p. 212). Second, the researchers claimed the unique neural architecture for linguistic intelligence included eight brain regions-temporal cortex, frontal cortex, parietal cortex, occipital cortex, the subcortical region, the cerebellum, the cingulate cortex, and the insular cortex. These eight regions comprise nearly the entire brain. Although language processing involves many links between multifunction networks across different regions of the brain ( Popham et al., 2021; Rolls et al., 2022; Wang et al., 2022), the entire brain is not a unique neural architecture dedicated to language processing.

The key problem with both Shearer and Karanian's study ( Shearer and Karanian, 2017) and Shearer's study ( Shearer, 2020b) is that the brain networks they have described are significantly oversimplified. Gardner's linguistic intelligence cannot be aligned with "the language network," because there is no isolated single "language network" in the brain ( Popham et al., 2021; Rolls et al., 2022; Wang et al., 2022). Networks for language and other forms of cognition are shared, and language processing occurs in the same networks that also process many other cognitive tasks such as mathematics, music, and logic, and varied perceptual and motor skills as well. For example, syntax, or word order, is generated by brain network activity in the inferior frontal gyrus region and the left orbitofrontal region. And syntax processing networks are driven by two word-level networks: the network that governs visual object meaning; and the network that governs visual motion, and auditory, and somatosensory semantic processes ( Rolls et al., 2022). Adding to the complexity of these processes, the brain networks governing syntax also serve as hubs for social perception and cognition, including the perception of faces and human motion, as well as the understanding others' actions, and mental states.

Another example of the complexity of language processing in the brain is that the network for understanding the meaning of verbs also provides control for our motor actions. Specifically, Wang et al. (2022) reported that the inferior parietal lobe supports verb generation and verb retrieval, and the inferior parietal lobe also processes abstract information of object-directed actions, and it governs action organization and understanding the intention of others' actions.

Because the varied neural networks that govern language processing also govern many other forms of cognition, a unitary dedicated language network cannot be isolated.

Multifunction brain networks

Thus, Gardner's MI theory was not a neuromyth in 1983 or 1993 because it was based on then current theories of brain modularity for cognition, and at that time few researchers were concerned by the absence of neural validity studies ( Waterhouse, 2006). However, since then evidence has demonstrated that the brain is not organized in cognitive modules uniquely dedicated to different forms of content ( Anderson, 2014; Sporns and Betzel, 2016; Zerilli, 2017; Elimari and Lafargue, 2020; Holyoak and Monti, 2021; Wang et al., 2021; Roy et al., 2022). Therefore, the lack of brain validity for the multiple intelligences has become a serious concern, and has led to the claims that MI theory is a neuromyth.

A likely cause for the functional diversity of many brain networks is the evolution of the brain through exaptation. Exaptation is the reuse of neurons in existing brain networks as the basis for new networks to support the additional processing activity needed for new skills and new knowledge ( Zerilli, 2017). Exaptation is adaptive because it reduces the amount of glucose energy needed to build new brain networks. It is likely that exaptation is the cause of the layering of varied perceptual, cognitive and motor functions in multi-use brain networks that govern many varied forms of thinking and action. And Elimari and Lafargue (2020) claimed that the most recently evolved cognitive skills, such as language and mathematics, rely on a "greater number and diversity of neural structures" (p. 11).

What will convince educators that MI theory is a neuromyth?

Even though Gardner has consistently argued that the intelligences are real ( Gardner, 1983, 1993, 2011), surprisingly Gardner (2011) also asserted the "intelligences are fictions-at most, useful fictions-for discussing processes and abilities that (like all of life) are continuous with one another" (p. 74).

As outlined here, studies have not confirmed that the intelligences are real. Factor studies have not shown the intelligences to be independent. Studies of MI teaching effects have not explored alternate causes for positive effects and have not been conducted by standard scientific methods. No research has directly looked for a brain basis for the intelligences. Most important, neuroscience research has shown that the brain is not organized in separate modules dedicated to specific forms of cognition. Consequently, MI theory is a neuromyth that should not be taught to teachers, and MI teaching strategies should not be used in the classroom.

Because so many educators use and value MI teaching strategies, it is likely that many teachers and researchers do not know the evidence that has made MI theory a neuromyth. Craig et al. (2021) reported that only 25% of teachers surveyed in Canada, the United States, and the United Kingdom believed MI theory was a neuromyth. Dekker et al. (2012) found that teachers who knew more about the brain still believed education neuromyths. The researchers also reported that "teachers who are enthusiastic about the possible application of neuroscience findings in the classroom, often find it challenging to distinguish pseudoscience from scientific facts" ( Dekker et al., 2012, p. 6). And Ruhaak and Cook (2018) found that teachers' "knowledge of neuromyths-not general knowledge of the brain-was associated with greater likelihood of implementing more effective instructional practices than ineffective, neuromyth-based approaches" (p. 160).

Many interventions have attempted to change teachers' belief in neuromyths. Rousseau (2021a) conducted a comprehensive review of interventions for fostering conceptual change in teachers that would improve their ability to recognize and refute neuromyths. Rousseau (2021a) reported that "The high prevalence of beliefs in neuromyths among educators did not decline over the past decade" (p. 9), and his review of intervention research led him to conclude that much more data was needed to understand what teaching processes would be most effective in changing teachers' minds. Rousseau's (2021a) review of interventions revealed that neither the direct refutation of neuromyths nor training in neuroscience had been shown to be consistently effective in reducing students' belief in neuromyths. Rousseau reported, however, that although an undergraduate course in neuroscience was "insufficient to reduce beliefs in neuromyths" ( Rousseau, 2021a, p. 8), "training in the context of teacher professional development (workshops, seminars) looks promising" ( Rousseau, 2021a, p. 8). Dubinsky et al. (2022) argued that teachers needed to learn current concepts of neuroscience, and posited that "where teachers are given the opportunity to discuss what the physiological neuroscience concepts might mean for their own classrooms, strong connections between neuroscience principles and effective pedagogical practices have been documented" (p. 272). Rousseau (2021a) stated that "Given that intuitive/anecdotal evidence might contribute to consolidate false beliefs through a variety of cognitive biases (availability, familiarity, confirmation biases), some intervention approaches focus upon making educators more aware of the propensity of the human mind to rely on intuitive thinking at the expense of rational thinking" (p. 6). Rousseau (2021a) argued that it is important to break through the intuitive beliefs many students have for neuromyths, and he recommended that teacher training programs address anecdotal evidence and focus on cognitive biases.

Craig et al. (2021), Dekker et al. (2012), Goswami (2006), Grospietsch and Mayer (2018), Howard-Jones (2014), Ruhaak and Cook (2018), and Blanchette Sarrasin et al. (2019) have also outlined various possible strategies to undo or reduce belief in neuromyths. Howard-Jones (2014) argued that that neuroscientists and educators should work together to establish a new field of educational neuroscience which could address neuromyths and "encourage scientific insight regarding the relationship of neural processes to the complex behaviors that are observed in the classroom" (p. 822). Dekker et al. (2012) recommended that research should discover what sources, such as books, colleagues, and commercial education companies, lead teachers to know and believe neuromyths. Dekker et al. (2012) also recommended that there be interdisciplinary connections between neuroscientists and educators. Goswami argued that learning current neuroscience findings was very important for educators ( Goswami, 2019), but had earlier cautioned that neuroscientists were not appropriate instructors for dispelling neuromyths because they provide "too much data" ( Goswami, 2006, p. 412), and "are not necessarily gifted at communicating with society at large" (p. 6). However, Goswami (2006) recommended that educators should establish a group of "ex-scientists with an interest in education, perhaps attached to universities or to national education departments. They could fulfill a dual role: interpreting neuroscience from the perspective of and in the language of educators, and feeding back research questions and ideas from educators to neuroscientists" (p. 413).

Goswami (2006) cautioned that "Most teachers prefer broad brush messages with a 'big picture,' and being 'told what works'" (p. 412). Blanchette Sarrasin et al. (2019) recommended that teachers should be trained in critical thinking, and that education textbooks should include current knowledge about the neuroscience of learning, and clear refutations of neuromyths. Similarly, Craig et al. (2021) argued that teacher training should include information that helps teachers be critical thinkers about educational neuroscience. And Craig et al. (2021) asserted that because school psychologists are trained in evaluating research and determining the evidence base for interventions, therefore "school psychologists are obligated to take action to de-implement harmful or wasteful practices in schools, drawing on their diverse and multi-faceted training across the domains of education, psychology, and neuroscience" (p. 136).

Dekker et al. (2012) proposed that it was important to develop and test interventions that explain neuromyths and teach educators evidence-based teaching methods, and this is happening with varying degrees of success ( Rousseau, 2021a). Educators need to learn to detect and reject neuromyths, and neuroscientists and educators should work together to establish a new field of educational neuroscience. Ideally, Gardner or an MI theory adherent should publish a paper or book in which the lack of evidence for multiple intelligences is accepted, reviewed and addressed. However, this is unlikely to happen. Nonetheless, the widespread belief in multiple intelligences inhibits the development of effective, evidence-based teaching strategies, and must be dispelled.

Conflict of interest

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References

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Frontiers | Why multiple intelligences theory is a neuromyth

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A neuromyth is a commonly accepted but unscientific claim about brain function. Many researchers have claimed Howard Gardner's multiple intelligences (MI)...