Human-Centered AI, Cognitive Resilience, Metacognition, and Decision-Making
How do people continue to think well while working alongside increasingly capable artificial intelligence?
My research examines how people learn, think, and make decisions while collaborating with artificial intelligence. I focus on the human capabilities that remain essential as AI systems become more capable, less transparent, and increasingly embedded in education and professional practice.
Across this work, I investigate how human-centered AI design can preserve agency, strengthen metacognitive awareness, support ethical judgment, and develop cognitive resilience. I am especially interested in how learners and professionals evaluate AI-generated information, recognize cognitive and automation biases, adapt to uncertainty, and remain accountable for the decisions they make with AI support.
My work is guided by a central premise: the future of artificial intelligence should not be defined only by what increasingly capable systems can do, but also by the human capabilities that must be preserved and strengthened alongside them.
Rather than asking only how AI can make learning and decision-making faster or more efficient, my research asks how AI-enabled environments can cultivate reflection, judgment, ethical responsibility, and meaningful human agency. I study how educational experiences, decision-support systems, and human-AI workflows can help people remain thoughtful, adaptable, and accountable under conditions of uncertainty.
This perspective positions AI not as a replacement for human expertise, but as a context in which metacognition, cognitive resilience, and sound judgment become increasingly important.
Designing AI-supported systems and learning environments around human cognition, agency, judgment, and accountability rather than expecting people to adapt uncritically to technology.
Examining how learners and professionals maintain critical thinking, cognitive flexibility, ethical reasoning, and sound judgment while working with increasingly capable AI systems.
Studying how people monitor, evaluate, and regulate their thinking, confidence, choices, and learning processes in AI-supported environments.
Investigating decision-making under uncertainty, cognitive bias, automation bias, confidence calibration, and responsibility within human-AI decision processes.
Exploring how humans and AI can work together in ways that improve decision quality and learning while preserving meaningful human oversight and independent judgment.
Designing scalable learning environments, simulations, feedback systems, and guided AI experiences that support inquiry, reflection, ethical judgment, and learner agency.
My current research agenda brings together complementary areas of psychology, education, decision science, and human-centered technology design.
The Human-Centered AI Metacognitive Learning Model (HAIML) is a framework for learning and decision-making with AI while preserving human agency, reflection, and ethical responsibility.
HAIML integrates three interconnected components: experiential AI use, metacognitive reflection, and ethical decision-making. Rather than positioning AI as a replacement for human thinking, the framework guides learners to evaluate how AI affects their reasoning, confidence, choices, and understanding.
A central outcome of HAIML is the development of cognitive resilience. Through structured AI use and reflection, learners strengthen their capacity to think critically, adapt to uncertainty, evaluate AI-generated information, and maintain independent judgment and accountability.
A key area of my current research examines AI-supported grading and feedback systems operating within a human-in-the-loop model. This work investigates how students experience feedback when AI contributes to the process but instructors retain responsibility for design, oversight, interpretation, and final evaluation.
Research questions in this area address feedback clarity, usefulness, personalization, timeliness, trust, motivation, revision self-efficacy, and perceived instructor presence. I am also interested in whether students experience AI feedback as a shortcut, an automated evaluator, or a thought partner that supports deeper revision.
My work also explores the use of AI-guided questioning, synthetic populations, simulations, and specialized learning agents to support research literacy and decision-making.
These environments are designed to help learners formulate research questions, evaluate evidence, interpret data, examine methodological choices, and reflect on the ethical implications of research. This work includes the development of Spark and a broader human-centered AI psychology simulation environment.
My research investigates ethical questions surrounding AI in psychology, education, and the social and behavioral sciences. These questions include bias, fairness, authorship, transparency, digital identity, privacy, accountability, informed decision-making, and the psychological effects of interacting with AI systems.
I am especially interested in how learners and professionals can move beyond rule compliance toward reflective ethical judgment when AI systems produce plausible but uncertain, incomplete, or potentially biased outputs.
My earlier and ongoing research examines instructor social presence in online learning, with particular attention to how empathy, visibility, responsiveness, and communication influence student engagement and persistence.
This work continues to shape my approach to AI integration by keeping human connection, instructor judgment, and intentional communication central to digital learning design.
Development and study of a human-centered framework that integrates experiential AI use, metacognitive reflection, ethical decision-making, human agency, and cognitive resilience.
An AI-supported research learning experience designed to guide students through foundational research concepts using structured questioning, reflection, and human-centered instructional support.
Research examining students' experiences with human instructor feedback and AI-supported feedback, including trust, clarity, usefulness, revision confidence, motivation, and perceptions of AI as a thought partner.
A developing research and learning environment using simulations, synthetic populations, and specialized learning agents to support research literacy, evidence evaluation, decision-making, metacognition, and ethical reflection.
Ongoing scholarship examining how empathy, responsiveness, visibility, and intentional communication influence engagement, belonging, persistence, and learning in online environments.
My scholarship includes published and developing work on instructor social presence, online teaching, human-centered course design, AI-supported learning, metacognition, decision-making, and ethical human-AI collaboration.
Reardon, C. (2026). Introducing HAIML: A Human-Centered AI Metacognitive Learning Model: A Framework for Human Agency and Reflective Learning in the Age of Artificial Intelligence. EdArXiv.
Reardon, C. (Forthcoming book manuscript). Introducing HAIML: A Human-Centered AI Metacognitive Learning Model.
Emerging scholarship also examines cognitive resilience as a human capability for learning, judgment, and decision-making in increasingly AI-mediated environments.
My work is interdisciplinary and connects psychology, education, learning engineering, decision science, human factors, responsible AI, and workforce development.
I am particularly interested in collaborative projects that examine how people can remain reflective, adaptable, ethical, and accountable while working with AI. Potential areas of collaboration include AI-supported education, cognitive resilience, human-AI decision-making, research simulations, faculty development, responsible AI implementation, and the design of human-centered learning technologies.
Current and developing initiatives connect this research with university programs, learning engineering efforts, technology partners, educators, and interdisciplinary communities interested in the future of human learning and decision-making.