Assistant Teaching Professor of Psychology
AI Strategist for the Psychology Digital Immersion Programs
Arizona State University
Advancing human-centered AI, metacognitive learning,
ethical decision-making, and cognitive resilience.
Creator of HAIML (Human-Centered AI Metacognitive Learning Model)
Explore HAIML Framework →AI systems may remain opaque. Human judgment cannot.
The Human-Centered AI Metacognitive Learning Model (HAIML), developed by Catheryn Reardon, PhD, is a framework for learning and decision-making in an age when artificial intelligence grows more powerful and less interpretable by the day.
As AI recedes behind complexity, HAIML turns the lens inward. Rather than asking only how the system works, it asks how humans are thinking alongside it. The model preserves human agency, deepens metacognitive awareness, and anchors ethical responsibility where it belongs: with the person making the decision.
In this framework, learners are not passive consumers of AI output. They are active, critical decision-makers who continuously evaluate when AI supports their thinking, when it may distort it, and how it shapes their reasoning, confidence, choices, and judgment over time.
HAIML reframes AI-supported learning as an opportunity to strengthen rather than outsource human thinking. By combining experiential AI use, metacognitive reflection, and ethical decision-making, the framework supports the development of cognitive resilience: the ability to think critically, adapt to uncertainty, evaluate AI-generated information, and maintain sound judgment while working alongside increasingly capable systems.
HAIML preserves human agency, structures reflective AI use, and supports the development of cognitive resilience.
As AI becomes more autonomous, human expertise becomes more important, not less.
Artificial intelligence is rapidly evolving from a tool that assists human work to systems capable of planning, generating, evaluating, and increasingly acting on behalf of humans. As AI becomes more capable, the central challenge for education is no longer simply teaching students how to use AI. The greater challenge is helping learners remain thoughtful, reflective, ethical, and accountable while working alongside increasingly autonomous systems.
Content experts, educators, and researchers continue to play a critical role in this future. While AI may generate information, humans remain responsible for judgment, interpretation, ethical reasoning, and deciding what is meaningful. The role of educators is shifting from delivering information to developing thinkers.
Figure 1. Human Expertise in the Age of Agentic AI. Conceptual framework and content developed by Catheryn Reardon, PhD (2026). Visual rendering created using generative AI under human direction, review, and editorial oversight. © 2026 Catheryn Reardon, PhD. All rights reserved.
This framework provides a structured, human-centered approach to AI use in coursework and is informed by HAIML. It supports responsible AI use while preserving critical thinking, human judgment, transparency, and learner accountability.
This work supports the integration of AI into education and professional practice with a focus on critical thinking, ethical awareness, metacognition, cognitive resilience, and human judgment, particularly when AI systems cannot be fully understood or explained.
My work examines how people learn, think, and make decisions while working alongside artificial intelligence. My research focuses on human-centered AI, metacognition, ethical decision-making, cognitive resilience, instructor presence, and the design of learning environments that preserve human agency and accountability.
Across this work, I explore how AI-supported experiences can strengthen critical thinking, reflective judgment, decision quality, and effective human-AI collaboration rather than encourage dependence on automated systems.
Catheryn Reardon, PhD is an Assistant Teaching Professor of Psychology at Arizona State University and AI Strategist for the Psychology Digital Immersion Programs. Her work focuses on human-centered AI, metacognition, decision-making, cognitive resilience, and the development of responsible human-AI learning environments.