Highly recommended AI paper regarding human & AI capabilities. HCQM: A dual-use human capability assessment and prescriptive target for engineering synthetic cognitive architectures (AI)

I recently exchanged emails and zoomed with Kameron Green who works in the AI space regarding his proposed Human Capability Quotient Map (HCQM).  I was thoroughly impressed with his proposed dual-use meta-taxonomy of human competence taxonomies (e.g., CHC; executive function; self-regulation; motivation; etc) into a grand overarching ability and capacity taxonomy.  As stated in the paper “HCQM is positioned to support broad human capability assessment while also providing a partial prescriptive target, at the capacity layer, for engineering synthetic cognitive architectures.”  

IMHO his grand model is one of the most comprehensive proposed high-level integrations of human individual difference constructs and emerging AI competencies I have read.  Although long, I urge those interested in human individual difference domains and AI evaluation frameworks to read his working paper. The paper is relevant to those studying individual differences in humans and those working on models by which to evaluate AI.  

The paper can be found (and downloaded) at the link provided by Kameron (click here).  He has also shared access to his paper at his LinkedIN profile (which is the URL associated with the hyper-link with his name in the first sentence of this post).

I’m sharing this link/paper with his permission.

 

Abstract

The field of intelligence research has long been characterized by fragmentation, with distinct traditions examining general cognitive abilities (Carroll, 1993; McGrew, 2009), emotional and social competencies (Salovey & Mayer, 1990; Goleman, 1995), creativity (Guilford, 1950), metacognition (Flavell, 1979), grit and adaptability (Duckworth et al., 2007), and more recent constructs such as digital intelligence (Park, 2019) and systems thinking (Senge, 1990; Meadows, 2008). While each line of inquiry has yielded valuable insights and measurement tools, the absence of an integrated taxonomy limits holistic assessment of human potential and the principled design of synthetic cognitive architectures. This paper proposes the Human Capability Quotient Map (HCQM) as a hierarchical synthesis that organizes eight top-level domains (General Cognitive Intelligence, Executive/Self-Regulatory Intelligence, Emotional & Social Intelligence, Creative & Innovation Intelligence, Motivational & Adaptive Intelligence, Learning & Knowledge Intelligence, Digital & Technological Intelligence, and Systems & Strategic Intelligence) into a coherent framework. Each domain includes subcomponents and observable indicators drawn from established psychometric, psychological, and cognitive-science literature.

HCQM makes three contributions: the integration itself, a coverage-asymmetry observation, and an explicitly dual-use framing; the two durable differentiators are the integration and the dual-use framing. HCQM is positioned to support broad human capability assessment while also providing a partial prescriptive target, at the capacity layer, for engineering synthetic cognitive architectures, with the explicit limitation that full architectural prescriptiveness requires a companion specification document (§6.6). These are applied to a coverage asymmetry: the human-capability tradition treats motivational, affective, cultural, and adversity capability as first-class (contemporary CHC, for example, now includes emotional intelligence as a broad ability), whereas contemporary AI architecture and evaluation frameworks largely do not. Unlike purely evaluative taxonomies such as DeepMind's 2026 Measuring Progress Toward AGI: A Cognitive Framework (Burnell et al., 2026), which identifies 10 cognitive faculties for benchmarking AGI progress, HCQM brings these dimensions to bear as a specification target. Unlike engineering-oriented architectural frameworks such as CoALA (Sumers et al., 2024), which specifies memory modules, action spaces, and decision loops for language agents, HCQM specifies the capacity surface those structural slots are expected to implement. The dimensions the AI frameworks omit are not a miscellaneous remainder: they concentrate in the motivational, adaptive, and metacognitive capacities (persistence under failure, strategy revision, self-monitoring, calibration) that govern whether a long-horizon autonomous agent is reliable, as distinct from whether it is capable. HCQM's distinctive AI-facing contribution is to organize these as a first-class reliability-and-autonomy layer and specify it at the capacity level (§5.2); a worked example maps a documented long-horizon agent failure pattern to the layer it omits. HCQM consolidates and ports existing constructs rather than claiming to discover them.

Drawing on CHC theory (Carroll, 1993; Schneider & McGrew, 2018), multiple-intelligences and triarchic models (Gardner, 1983; Sternberg, 1985), cultural intelligence (Earley & Ang, 2003), computational thinking (Wing, 2006), and modern cognitive architectures for language agents (Sumers et al., 2024), HCQM offers a synthesis rather than a novel discovery. We outline design principles, detail the hierarchical structure, discuss applications in human development and AI engineering, and acknowledge limitations, including the need for empirical validation, operationalized assessment instruments, and an explicit evaluation protocol. This paper aims to stimulate cross-disciplinary dialogue and guide future instrument development and architectural design.

Keywords: human capabilities, intelligence taxonomy, cognitive architecture, AGI evaluation, capability-grounded architectures, autonomous agents, agent reliability, long-horizon agents, metacognition, grit, cultural intelligence, systems thinking, CHC theory, CoALA.

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IQ McGrew’s Recommended Reading: How Human Personality [and Intelligence] Will [May?] Change With the Use of Artificial Intelligence - #recrdg #personality #intelligence #AI #CHC #artificialintellignece #psychology #schoolpsychology #schoolpsychologists

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I seldom designate an article as a recommended reading. I typically make FYI posts about new research I finding interesting in my small corner of the larger sandbox of psychology…more as FYI alerts.  I break with my typical FYI research alert blogging behavior for this article by Dr. John D. Mayer.  I recommend reading Mayer’s thought provoking article—especially since it is open source and can be downloaded and read for free (click here to access).

Why?  Because it is a well-reasoned “thought piece” about the many unanswered questions regarding the potential positive and negative impact of AI on humans, in this case, human personality and cognition.  I’m relatively new to the fast-moving AI movement and, as an educational psychologist, I’m interested in how certain cognitive abilities (especially CHC cognitive abilities) may become “skilled” or “deskilled” with greater reliance on AI.  

Abstract

People change as they form new habits, encounter new situations, and mature. As people interact with artificial intelligence (AI), their personalities will change, including their emotional responses to AI, their cognition, and their self-understanding. The present theoretical integration draws together empirical studies of how personality changes in response to technological innovations, and to AI in particular. Research studies reviewed were selected according to their relevance and quality. Some key points include that (a) as AI becomes increasingly human-like, and humans represent themselves online, humans and bots become increasingly difficult to distinguish; (b) as people rely on AI as a coach to guide them in interpersonal interactions, they may become socially deskilled; and, (c) as they rely on AI for work tasks, they may become cognitively deskilled in key areas. These changes in personality will entail an overall shift in people’s self-concepts. Psychologists can track these changes by classifying people’s types of AI interactions and relating them to relevant personality attributes.

Effects of artificial #intelligence (#AI) on #educational functioning: A review and #metaanalysis — #EDPSY #schoolpsychologists #schoolpsychology #cognition

Link to journal

Abstract

 

Burgeoning integration of AI into educational settings could have profound implications for students’ performance. This systematic review and meta-analysis examined the effects of different types of AI and four levels of learning—cognition, knowledge utilization, meta-cognition, and psychological functioning—yielding 228 studies with 464 effect sizes that met criteria for inclusion. AI had large positive effects on cognition, r = 0.530, p < 0.001 [95%CI:0.447 to 0.613] and psychological functioning, r = 0.514, p < 0.001 [95%CI:0.246 to 0.720], moderate effects on knowledge utilization, r = 0.417, p < 0.001 [95%CI:0.305 to 0.747], and small effects on meta-cognition, r = 0.268, p = 0.21 [95%CI:-0.225 to 0.772]. Different types of AI had different effects on cognition, with generative AI demonstrating the largest effects, which were larger than other types of AI (e.g., intelligent tutoring, adaptive/personalized learning). However, different types of AI had comparable moderate effects in bolstering knowledge utilization and psychological functioning. AI had the largest effects on improving learning in arts and humanities. Analyses provided evidence for differential impact of AI on learning across countries with different economic advancement. The findings suggest that AI can be effective at improving learning under certain conditions and that the effectiveness varies with the type of AI.

IQs Corner pub alert: CHC theory of cognitive abilities used to define and evaluate AI - #AI #CHC #intelligence #schoolpsychology #schoolpsychologists #IQ #EDPSY

An exciting new paper from the Dan Hendryks et al. at the Center for AI Safety  The center is  a nonprofit with the mission “to reduce societal-scale risks from artificial intelligence.”  In this just released paper, they propose a modified CHC theory definition/framework for evaluating AI: 

• “AGI is an AI that can match or exceed the cognitive versatility and proficiency of a well-educated adult.”

Given my extensive research and publications regarding the Cattell-Horn-Carroll (CHC) theory of cognitive abilities, I was pleasantly surprised when Dan reached out for my comments and suggested revisions to the paper.  

I was extremely impressed as Dan and his group had been involved in a deep dive in the CHC literature and had developed, without my involvement, an ingenuous internet-based CHC set of “test” items that can be submitted to different AI agents (GPT-4, GPT-5, Grok) to assess their CHC broad ability domain performance (to evaluate the extent to which AI agents demonstrate the “cognitive versatility and proficiency of a well-educated adult”).  I had zero involvement in the conceptualization or development of the AI modified/adapted CHC assessment framework and resulting CHC AI metrics. 

I want to express my appreciation to Dan for including me among the list of over 24 authors.  I’m very excited to monitor future developments by Dan and his group, as well as to see the impact of the CHC theory model on AI.

Links to secure copies of the paper (in various formats and social media platforms) are listed at the bottom of this post.  

Note.  Click on all images to enlarge for easy reading

Abstract

The lack of a concrete definition for Artificial General Intelligence (AGI) obscures the gap between today's specialized AI and human-level cognition. This paper introduces a quantifiable framework to address this, defining AGI as matching the cognitive versatility and proficiency of a well-educated adult. To operationalize this, we ground our methodology in Cattell-Horn-Carroll theory, the most em-pirically validated model of human cognition. The framework dissects general intelligence into ten core cognitive domains—including reasoning, memory, and perception—and adapts established human psychometric batteries to evaluate AI systems. Application of this framework reveals a highly “jagged” cognitive profile in contemporary models. While proficient in knowledge-intensive domains, current AI systems have critical deficits in foundational cognitive machinery, particularly long-term memory storage. The resulting AGI scores (e.g., GPT-4 at 27%, GPT-5 at 58%) concretely quantify both rapid progress and the substantial gap remaining before AGI.

Modified CHC model for evaluating AI agents

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As mentioned in the abstract, the paper reports on the CHC AGI capabilities of GPT-4 and GPT-5 in the following figure.  Click on images to enlarge.

I was pleased to see (on page 14 of the PDF paper) the following “intelligence as processor” figure which is based on work by myself and Joel Schneider. The model in Figure 3 (below) is based on Kevin S. McGrew and W. Joel Schneider. CHC theory revised: A visual-graphic summary of Schneider and McGrew's 2018 CHC update chapter. MindHub / IAPsych working paper, 2018.  http://www.iapsych.com/mindhubpub4.pdf.  

The Schneider & McGrew (2018) heuristic CHC information processing model is below the Figure 3 figure.  Click on images to enlarge.

Dan Hendrycks and the Center AI Safety provide brief overviews describing this work on LinkedIn as well as Twitter/X (both that can be monitored for comments).

A PDF copy of the paper can be downloaded here.  A clickable web-based version of the paper can be accessed here.

Exciting stuff !!

Book nook-chapter: Foundations of #AI in #Educational #Assessment

 

Abstract

This chapter explores the evolution and transformative potential of artificial intelligence (AI) in educational assessment, highlighting its ability to enhance the evaluation of student learning through adaptive, personalized, and dynamic approaches. AI technologies such as machine learning, natural language processing, and computer vision are revolutionizing assessment design by enabling the measurement of higher-order skills like critical thinking, problem-solving, and creativity. The chapter also addresses ethical and practical considerations, including algorithmic bias, data privacy, and equity in implementation, emphasizing the importance of responsible innovation. By examining historical assessment practices alongside contemporary AI applications, this chapter provides a comprehensive foundation for understanding how AI is reshaping education and establishing a roadmap for its equitable adoption.

#AI resource for #educators and #psychologists: Lockwood Educational and Psychological Consutling

I just connected (via LinkedIn) with Lockwood Educational and Psychological Consulting.  The group describes itself below.  Given the considerable interest in AI in education and psychology, I would suggest checking out their web page. I’ve not yet conducted a deep dive into the website, but it appears to be a solid AI-related resource.  I plan to take a closer look.

Is your district, organization, or practice considering implementing AI but concerned about the ethical and practical implications? You've come to the right place. With expertise in both AI, education, and psychology, I provide guidance to navigate these complex waters, ensuring ethical, effective, and confidence-inspiring AI integration in educational, and psychological practice settings.