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HAIML: A Human-Centered AI Metacognitive Learning Model — A Framework for Human Agency and Reflective Learning in the Age of Artificial Intelligence

◐ medium · 2026-08-12metacognition, self-regulated-learning, human-in-the-loop, ai-literacy, cognitive-offloading, over-reliance, ethics, student-ai-interaction

Synthesis: HAIML is a human-centered framework for learning in AI-supported environments that preserves human agency, metacognitive awareness, ethical reasoning, and personal responsibility. Grounded in self-efficacy, self-regulated learning, experiential learning, metacognition, and automation-bias research, the model spans three interconnected layers — Experiential AI Use, Metacognitive Reflection, and Ethical Decision-Making — guiding learners from direct engagement with AI to reflective and responsible use.

Key Findings

1. A gap in existing frameworks. AI-literacy, technical-proficiency, responsible-use, and academic-integrity frameworks give little attention to how AI influences the learner's thinking and decision-making processes.

2. Three-layer model. HAIML organizes learning around Experiential AI Use, Metacognitive Reflection, and Ethical Decision-Making, moving learners from direct engagement with AI to reflection on how AI shapes their thinking, and finally to informed, responsible decision-making.

3. Agency-preserving design. The framework integrates concepts from human agency, self-regulated learning, metacognition, and automation-bias research to keep learners in control of AI-mediated cognition.

Implications

For Metacognition and Self Regulated Learning, HAIML offers a structured way to make AI interactions objects of reflection, addressing Cognitive Offloading and Over Reliance risks by keeping learners aware of how AI influences their thinking. It complements AI Literacy frameworks by adding a metacognitive and ethical layer focused on internal decision-making rather than tool proficiency alone.

The model's human-centered orientation connects to Human AI Collaboration and to Human AI Collaboration design, and to Ethics in education. As a framework paper, it would benefit from empirical validation of whether the three-layer progression measurably improves agency and reflection in AI-supported learning.

Connected Concepts

  • Metacognition
  • Self Regulated Learning
  • Human AI Collaboration
  • AI Literacy
  • Cognitive Offloading
  • Over Reliance
  • Student Modeling
  • Ethics

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    • Citation

    Reardon, C. (2026). HAIML: A human-centered AI metacognitive learning model — A framework for human agency and reflective learning in the age of artificial intelligence. EdArXiv preprint.