#AI research alert: The Influence of #AI on #CriticalThinking and #Creativity in #L2 Learning Contexts: A Social Cognitive Perspective

Quick FYI email blog post.  Aside from the studies main findings, I found the use an AI self-efficacy scale intriguing…a form of self-efficacy that likely will be included in more and more studies…and should be monitored in the increasing volume of AI intervention studies.  My interest comes from the inclusion of self-efficacy under self-beliefs, along with motivational achievement orientations and self-regulated learning strategies, in my recent article describing the cognitive-affective-motivation model of learning (CAMML: McGrew, 2022; click here to view/download).

 

Good news…and open access article available at link below.👍 

 

The Influence of AI on Critical Thinking and Creativity in L2 Learning Contexts: A Social Cognitive Perspective 
https://www.mdpi.com/2079-3200/14/5/78

Click on image to enlarge for easy viewing

    Abstract

The expanding role of artificial intelligence (AI) in education raises important questions about how AI-supported learning may foster higher-order thinking and creative talent development. Guided by social cognitive theory, the current research examined how AI self-efficacy predicts creativity among second language (L2) learners through the mediating roles of AI literacy and critical thinking disposition. Two substudies were conducted. Study 1 (N = 72) tested a simple mediation model and demonstrated that AI self-efficacy positively predicted creativity both directly and indirectly through AI literacy. Study 2 (N = 135) extended these findings by incorporating critical thinking disposition and by using another measure of creativity. Results showed that AI self-efficacy positively predicted creativity, and this relationship was mediated independently by AI literacy and critical thinking disposition, as well as sequentially through both factors. The current study provides empirical evidence for pathways linking AI self-efficacy, AI literacy, critical thinking disposition, and creativity in AI-supported L2 learning. It highlights the importance of reflective and critical use of AI tools in language education.

 

Keywords: AI self-efficacy;  AI literacy;  critical thinking;  creativity;  second language learning

 

 

AI Brief: The trilogy-of-the-mind individual difference construct (cognitive, conative, affective) “band is getting back together” as CAMML

I am currently working to expand my skill set by incorporating AI tools. Although adapting to new technologies can be challenging, leveraging these resources offers significant benefits for professional growth.

This AI Brief was produced by requesting Google NotebookLM—recommended by Dr. Adam Lockwood—to generate a narrative summary of my 2022 PDF article describing the Cognitive-Affective-Motivation-Model of Learning (CAMML). While I found the first results promising, I made more edits to enhance its accuracy and informativeness. My next goal is to use AI to summarize multiple articles, find similarities and differences, and potentially create comparative tables (again following guidance graciously provided by Dr. Lockwood).

These incremental steps mark my transition toward utilizing AI to support one of my primary professional interests: producing informative blog and social media posts aimed at professionals such as school psychologists and special education teachers working with students who often are marginalized in educational contexts. The goal is to help bridge the gap between theory, technology, research, and practical application.

 

Feedback is encouraged and may be directed to iqmcgrew@gmail.com or via the social media platform (LinkedIn, Twitter/X, BlueSky) comment feature where this blog post was discovered. I’m hoping to add AI Briefs as a regular feature of IQs Corner Blog and associated social media platforms.

 

 

AI Brief: The trilogy-of-the-mind individual difference construct (cognitive, conative, affective) “band is getting back together” as CAMML

 

Dr. Kevin McGrew with assist from Google NotebookLM

 

The Cognitive-Affective-Motivation Model of Learning (CAMML; McGrew, 2002)[1] is a proposed theoretical framework designed to integrate contemporary motivational, affective, and cognitive constructs into a unified model for the practice of school psychology. The central thesis of the framework is that school psychologists must move beyond a narrow focus on intelligence (general intelligence or psychometric g in particular) to embrace an updated "trilogy-of-the-mind" model, which views intellectual functioning as the inseparable interaction of cognition, conation (motivation/volition), and affect.

Theoretical Foundations and the Rebirth of Conation

 

The CAMML framework is heavily rooted in the seminal work of Richard Snow, specifically his research on aptitude trait complexes. McGrew argues that the field of school psychology has historically neglected Snow’s broader definition of aptitude—which includes personality and motivational differences alongside cognitive abilities—and instead, has favored a restricted view of aptitude as synonymous with IQ or psychometric g.

 

CAMML seeks to resurrect conation (the proactive part of motivation connecting cognition and affect to behavior) as a core pillar of intellectual functioning. By "standing on the shoulders of giants" like Snow, Spearman, and Wechsler, the model asserts that cognitive processes cannot be understood in isolation from the "nonintellectual" (conative) factors that drive and direct them. For example, David Wechsler defined intelligence as "the aggregate or global capacity of the individual to act purposefully, to think rationally, and to deal effectively with his environment." While this is the core of Wechsler’s definition, he also strongly emphasized this capacity is influenced by non-intellective (conative) variables such as drive, persistence, interest, emotional states, and personality traits.

 

Structural Components of the CAMML Framework

 

The model organizes individual differences characteristics into three functional categories, which could be called the 3-D model.

 

●      Affective “Dispositions”: These are distal-to-learning traits, primarily represented by the Big 5 personality traits (specifically Openness and Conscientiousness) and their associated social-emotional facets (e.g, curiosity, creativity, persistence, focus, determination). These personality traits act as dispositions that indirectly influence learning through more proximal mechanisms.

 

●      Motivational "Drivers": Motivation is conceptualized as the initiation of behavior, formed by achievement orientations (e.g., goals, interests) and self-beliefs (e.g., self-efficacy, self-concept). These constructs are typically domain-specific (e.g., math) and work in synergistic "complexes" to energize a student's readiness to act.[2]

 

●      Volitional "Directors": Volition, or self-regulated learning (SRL), represents the post-decisional phase of action. These are the mechanisms that direct, control, and regulate behavior toward goals once a commitment to learn has been made.

 

A more detailed list of the 3-D CAMML domain constructs and definitions is available here. See figure below for a visual representation of the major affective and conative MACM constructs (click on image to enlarge for easy viewing and reading).

 

The "Crossing the Rubicon" Investment Model

 

The functional heart of CAMML is the "Crossing the Rubicon" model, which illustrates the pathway from initial desire to engaged motivated learning. In this model:

 

1. Pre-decisional Phase: Achievement orientations and self-beliefs drive or prepare the learner to start a wish—>want—> intention sequence, that eventually eventuates in motivated action.

2. Commitment: When the learner "crosses the Rubicon," they are making a firm commitment to motivated action through cognitive engagement.

3. Action Phase: Volitional (SRL) strategies steer the cyclical process via action results feedback while the learner invests cognitive abilities (such as those defined by CHC theory) to acquire knowledge.

4. Outcomes: This personal investment of fluid cognitive processes (Cattell's general gf that subsumes broad Gf, Gv, Ga, Gwm, Gl, Gr, and Gs abilities) during learning results in the development of crystallized knowledge systems (Cattell’s general gc that subsumes broad Gc, Grw, Gq, and Gkn abilities).

 

See figure below for visual representation of “the CAMML crossing the Rubicon model of motivated learning” (click on image to enlarge for easy viewing and reading).

 

Implications for School Psychology Practice

 

CAMML advocates for a paradigm shift in assessment and intervention. It suggests that school psychologists should transition from routine, comprehensive cognitive testing toward more time-efficient selective, referral-focused cognitive assessments combined with the assessment of key conative (non-cognitive) characteristics that contribute to learning aptitude complexes. This approach prioritizes identifying manipulable instructional levers, such as a student's motivational orientation (e.g., intrinsic motivation, interests, goal orientation), self-beliefs (e.g., locus of control, self-efficacy, growth or competence mindset), rather than relying exclusively on cognitive ability scores (especially full-scale IQ or g) that have proven hard to modify.

 

The framework provides a "whole-child" perspective to better address the nuances of individual differences, particularly as students move from the traditional “industrial” model of education (i.e., regularly scheduled, structured, in-class teacher-directed learning) to more of an “information-age” paradigm of education—a paradigm that requires a fuller expression of independent motivated self-regulated learning (SRL).

PS - other CAMML related posts on this blog can be found by clicking here.

[1] All relevant references can be found in McGrew (2022).

[2] The motivation constructs included in the CAMML framework are drawn from earlier efforts to develop the McGrew Model of Achievement Competence Motivation (MACM). A detailed explanation of the evolution and development of the MACM model is available elsewhere (McGrew et al., 2004). A series of recent MACM PowerPoint® modules is available here.

What does #ElonMusks #Grok (#xAI) think of Dr. Kevin #McGrew ? - My Grok-based professional bio

I’ve finally started to play around with different AI programs.  I’ve been taking topics where I know the extant research well (e.g., CHC theory of intelligence, the WJ series of tests) and asking AI agents to summarize the information.  Today I had the idea of asking Grok to write me a professional bio.  After all, I know more about me than I do any other topic.  Below is the result.  I’ve made edits (strikethroughs) and added text (red font) to make it more accurate. 

I can now see how this technology can help save time with certain tasks. The bio is relatively accurate for a rough draft, but it did need some revisions and notable additions (here-and-there) to be more accurate and thorough.  I’ve included a link to my formally posted bio for comparison purposes—yes, it is long as it is my “long” form bio I submit along with my CV when engaged in court/legal work.  

The Grok bio does not include URL links to material.  Perhaps there is a way to ask Grok to include links, but I don’t know if that is possible at this point.  My guess is that it probably can.  If you want to see more information on any topic, just go the theMindHub.com and navigate accordingly.

I did notice that during the 2+ minutes of Grok work, it spent a significant amount of time on my professional web page.  I guess this means that if you want an AI tool to write your professional bio, you must have some decent content up on the web…it can’t make up material from thin air.  Also, it would appear good at providing rough drafts that still requires a content expert to revise accordingly.

All in all, an interesting excercise.

Dr. Kevin S. McGrew bio written by AI program Grok (xAI) on 09-29-25

(strikethrough and edits in red font)

Can be compared to Dr. McGrew’s formal bio posted at theMindHub.com

 

Dr. Kevin S. McGrew is a prominent educational psychologist and psychometrician whose career spans over five decades, beginning as a practicing school psychologist for 12 years (1975–1989) after earning his MS in School Psychology from Minnesota State University Moorhead. He later obtained a PhD in Educational Psychology (Special Education) from the University of Minnesota in 1989, focusing on research methods and applied psychometrics. McGrew has held academic roles such as Professor of Applied Psychology at St. Cloud State University (1990–2000) and Visiting Lecturer and Adjunct Research Professor at the University of Minnesota (2000–2024). He also served as Research Director for the Woodcock-Muñoz Foundation (2005-2014), Associate Director for Measurement Learning Consultants (2008-2014), and Director of the Institute for Applied Psychometrics (1998-current). His work has profoundly shaped the fields of human intelligence theory and testing, emphasizing empirical rigor, practical application in education and law, and integration of cognitive and non-cognitive factors.

Contributions to Intelligence Theory

McGrew is widely recognized as a leading scholar in the Cattell-Horn-Carroll (CHC) theory of cognitive abilities, a comprehensive psychometric framework that integrates fluid and crystallized intelligence (Cattell-Horn) with Carroll’s three-stratum model. Alongside Dr. Joel Schneider, he has served as an unofficial “gatekeeper” of CHC theory, authoring seminal updates and cross-battery interpretations that have made it the dominant model in contemporary intelligence research, test development, and and interpretation of intellectual assessment results.  His efforts have advanced CHC from a theoretical construct model to a practical tool for diagnosing learning disabilities, intellectual giftedness, and intellectual disabilities, influencing guidelines in the American Association on Intellectual and Developmental Disabilities (AAIDD) manual (2021). 

McGrew has also pioneered integrative models that extend beyond pure cognition. He developed the Model of Academic Competence and Motivation (MACM) in the early 2000s, which posits that academic success arises from the interplay of cognitive abilities, conative (motivational) factors self-efficacy, achievement orientations, self-beliefs, self-regulated learning, and affective elements such as personality and social-emotional skills, interest and anxiety.  This evolved into the broader Cognitive-Affective-Motivation Model of Learning (CAMML), emphasizing how these dimensions interact to predict school achievement and inform interventions.  His research on psychometric network analysis has further refined CHC by modeling complex interrelationships among CHC abilities “beyond g” (general intelligence), as highlighted in his 2023 co-authored paper, named the Journal of Intelligence’s “Best Paper” of the year.  McGrew has explored the Flynn effect (rising IQ scores over time) and its implications for the interpretation of intelligence test scores in Atkins intellectual disability death penalty cases. theory, as well as CHC’s links to adaptive behavior and neurotechnology applications for cognitive enhancement.

Contributions to Intelligence Testing

McGrew’s practical impact is most evident in intelligence test development and interpretation, where he championed “intelligent testing”—an art-and-science approach inspired by Alan Kaufman that prioritizes the interpretation of broad CHC composite scores profile analysis over single or global IQ scores.  As primary measurement consultant for the Woodcock-Johnson Psychoeducational Battery—Revised (WJ-R, 1991), he authored its technical manual and conducted statistical analyses for restandardization.  The WJ-R battery was the first major battery of individually administered cognitive and achievement tests based on the first integration of the psychometric intelligence research of Raymond Cattell and John Horn (aka, the Cattell-Horn Gf-Gc model of intelligence) and John Carroll’s seminal (1993) three-stratum model of intelligence.  He co-authored the Woodcock-Johnson III (WJ III, 2001) and Woodcock-Johnson IV (WJ IV, 2014), which were the first major batteries explicitly grounded in CHC theory, introducing subtests for underrepresented abilities like auditory processing and long-term retrieval.  As senior co-author, he led the development of the digitally administered Woodcock-Johnson V (WJ V, 2025), incorporating recent advances in CHC theory and psychometric network analysis and conative measures. 

Internationally, McGrew consulted on the Indonesian AJT Cognitive Assessment (2014–2017), creating the world’s first CHC-based individually administered intelligence test in that country.  He and advised the Ayrton Senna Institute on large-scale cognitive assessments in Brazil (2016–2025) and contributed to ASI research focused on integrating constructs from McGrew’s CAMML model with the ASI Big-5 personality based social-emotional skill model.  He has provided expert psychometric testimony (through written declarations and/or court testimony) in over 50 Atkins v. Virginia death penalty cases since 2009 and has contributed to refining intellectual disability criteria through CHC lenses. 

Publications and Knowledge Dissemination

McGrew has authored or co-authored over 100 peer-reviewed journal articles, book chapters, and eight norm-referenced test batteries, alongside four books on intelligence test interpretation, including Clinical Interpretation of the Woodcock-Johnson Tests of Cognitive Ability (1997, revised 2005).  His prolific output includes contributions to the APA Handbook of Intellectual and Developmental Disabilities (2021) and critiques of intellectual disability diagnostics.  He maintains influential blogs like IQ’s Corner (www.iqscorner.com), which has synthesized CHC and intelligence theory and related assessment research for practitioners since 2004, and engages on platforms like X (@iqmobile), LinkedIn, and BlueSky (@iqmcgrew.bsky.social) to democratize complex psychometric concepts and share research and insights based on his multiple areas of expertise. 

Awards and Legacy

McGrew’s influence is underscored by prestigious honors, including the University of Minnesota Distinguished Alumni Award (2016), Minnesota School Psychologists Association Lifetime Achievement Award (2015), Alan S. Kaufman Excellence in Assessment Award (2023), and the Dr. Richard W. Woodcock Award for Innovations in Ability Testing (2025).  His work has bridged theory and practice, empowering educators, clinicians, and policymakers to use intelligence assessments more equitably and effectively, while advocating for a holistic view of human potential that includes motivation and self-regulation alongside cognition.

 

Research Alert: (Lets hear it for conative abilities!!!!) Non-cognitive skills mediate education-related polygenic score associations with academic achievement across development

Important “in press” (and downloadable copy) article available here.  This is a quick email-generated FYI post.
 

I know at times my posting about non-cognitive conative abilities may seem repetitive.  It is—these skills (motivation, perseverance, mindset, learning strategies, self-regulatory strategies) are important and need to have more attention in school-based assessments for struggling learners!!!  See recent May 6 “AI Brief” about the need to get the triology-of-the-mind band back together post for more info.

Abstract 

The role of environmental, developmental, and psychological processes in translating genetic dispositions into observed academic achievement remains under-investigated. Here, we examine whether non-cognitive skills—including motivation, attitudes, and emotional and behavioural functioning—mediate the genetic prediction of academic achievement across development. We analyse data from 5,016 children enrolled in the Twins Early Development Study at ages 7, 9, 12, and 16, as well as their parents and  teachers. We find that non-cognitive skills mediated between less than 5 and up to 64% of the genetic prediction of academic achievement. Mediation effects are larger and more robust for motivation and attitudes (β ≈ 0.13) than for emotional and behavioural functioning (β ≈ 0.01–0.03). This pattern holds longitudinally and is replicated in within-family analyses, where non-cognitive skills accounted for up to 83% of the total mediation effects. These findings highlight the contribution of non-cognitive skills beyond shared familial factors, likely reflecting how children evoke and select experiences that align with their genetic propensity and lead to differences in academic development.

 

Select quotes from article

 

The term ‘non-cognitive skills' describes attitudes and characteristics  that impact life outcomes beyond what cognitive tests can measure and predict. These skills encompass motivation, perseverance, mindset, learning strategies, social skills, and self-regulatory strategies.  Non-cognitive skills are associated with educational outcomes beyond cognitive ability. Studies have found that self-efficacy and personality predict academic achievement beyond cognitive ability across compulsory education. Other studies have linked personality, self-regulation, and motivation to academic performance. More recently, our research highlighted that the association between non-cognitive skills and academic achievement increases substantially across compulsory education, from age 7 to 16. Previous research also showed that greater self-control and, to a lesser extent, interpersonal skills, partly mediated the genetic prediction of adult educational attainment.

…the mediating role of education-specific non-cognitive skills increased developmentally, pointing to the growing importance of students' perceived non-cognitive profiles and experiences in their academic journeys. This developmental increase is consistent with the possibility that, as they grow up, children become more aware of their aptitudes and appetites towards learning. As they gain greater self-awareness and autonomy, students might become increasingly more able to shape their environmental contexts in ways that allow them to cultivate these non-cognitive skills and, in turn, foster their academic performance

Lets hear it for the CAMML framework.

AI Brief: What does Grok say about Richard Snow’s aptitude trait complexes for learning?

 The following is a Grok AI generated summary when prompted (by Dr. Kevin McGrew) to explain Richard Snow’s aptitude complexes on March 7, 2026).

(Grok refers to a generative artificial intelligence developed by xAI, an Elon Musk company, as well as a foundational term in science fiction for deep understanding. Several editorial changes were made to insure accuracy, and select links added, by McGrew—to the Grok summary).

 

Richard E. Snow’s aptitude complexes (introduced in the 1980s–1990s, with key publications in 1987, 1992, and especially the 1996 chapter with Lyn Corno and Douglas Jackson) represent a major shift in how educational psychologists think about aptitude—moving far beyond the traditional narrow view of aptitude as “just cognitive ability” (e.g., IQ or a single test score).

 

Core Definition

Snow redefined aptitude as:

“the degree of readiness to learn and to perform well in a particular situation or in a fixed domain.”

It is a predisposition or propensity—a latent quality that enables more advanced performance under specific conditions. Aptitudes are situational and domain-specific: what makes someone ready to succeed in math class may be different from what works in a history seminar or a hands-on lab.  

Aptitude Complexes (the key innovation)

Snow argued that single constructs (like “fluid reasoning” or “achievement motivation”) are insufficient. Instead, success in learning comes from aptitude complexes—dynamic constellations or critical combinations of variables that work together as a coordinated system.

These complexes draw from the classic “trilogy of the mind”:

•  Cognition — abilities and processes for analyzing, interpreting, and solving (e.g., reasoning, knowledge, strategies, cognitive style, CHC abilities).

•  Affect — emotions, anxiety, self-concept, emotion regulation, personality traits.

•  Conation — motivation, volition, goal-setting, effort, persistence, will (the “want to” and “stick with it” aspects).

An aptitude complex is not just a list of traits—it is how these elements assemble and coordinate in real time within a specific task and context. They are amalgams of cognitive, conative, and affective characteristics.

 

The Two Pathways That Build Aptitude Complexes

Snow (and later Corno et al., 2002) described aptitudes developing through two parallel, interacting pathways (sometimes called the commitment pathway and the performance/action pathway):

1.  Commitment Pathway (motivational/affective/volitional)

     •  Assembles motivational resources that energize effort.

     •  Affective and volitional processes modulate how the work proceeds (e.g., regulating anxiety, sustaining intention).

     •  Outcome: Propensity (how likely the person is to engage and persist).

2.  Performance (Action) Pathway (cognitive)

     •  Assembles and deploys cognitive resources (abilities, strategies, knowledge) to do the task.

     •  Outcome: Ability/accuracy in execution.

When the two pathways coordinate effectively in a given “problem space” (the task + the broader situation/context), successful performance occurs. Over repeated experiences, these coordinated processes coalesce into stable aptitude complexes. The learner’s perception of the situation’s constraints and affordances is crucial—aptitudes are dynamic and transactional (person ↔ environment). 

Aptitudes are both input (what the student brings to learning) and product (they develop and change through education).

Snow Academic Aptitude Model (SAAM)

Snow synthesized decades of research into a provisional taxonomy (called the Snow Academic Aptitude Model or SAAM in an unpublished working white paper [6-15-04; Increasing the Chance of No Child Being Left Behind: Beyond Cognitive and Achievement Abilities, by Kevin McGrew, David Johnson, Anna Casio, Jeffrey Evans]) which is now available for download here. It treats academic aptitudes as a “multivariate repertoire” of readiness for general and domain-specific learning. It explicitly includes affect (personality and social-emotional skills) and conative related skills and traits (e.g., achievement motivation, goal orientation, freedom from debilitating anxiety, positive self-concept, self-efficacy, interests) alongside cognitive abilities. The model emphasizes that “there is more to knowing about an individual’s propensity for academic learning than simply knowing their IQ scores!” 

 

Why This Mattered (and Still Matters)

Snow’s work grew out of his famous Aptitude-Treatment Interaction (ATI) research with Lee Cronbach. He showed that simple aptitude scores rarely predict learning outcomes well because real-world performance depends on these richer, situation-specific complexes.

This framework directly inspired later theories (including Kevin McGrew’s 2022 CAMML model, which updates Snow’s complexes with contemporary CHC cognitive theory, Big Five personality, and self-regulated learning mechanisms while retaining the commitment/action pathway structure).

In short, Snow’s aptitude complexes gave us a whole-person, dynamic, domain-specific way to understand why two students with the same IQ can have dramatically different learning outcomes: their aptitude complexes (the coordinated cognitive + affective + conative “package” or “amalgam”) fit the demands of the task and context differently.

Snow’s legacy is summarized in the 2002 book Remaking the Concept of Aptitude (Corno et al.), which remains the definitive extension of his ideas.