Artificial Intelligence and Communities of Inquiry: Reimagining Educational Experiences

Synthesis: This conceptual article by the creators of the Community of Inquiry (CoI) framework argues that generative AI adoption in education requires a coherent theoretical framework. Drawing on the CoI framework and its construct of shared metacognition, it shows how collaborative inquiry can integrate AI in ways that preserve human Agency and sustain deep, meaningful learning. It warns that without critical, reflective inquiry, learners risk passivity, diminished authenticity, and overdependence on AI outputs. Shared Metacognition — collective monitoring and management of thinking — offers a responsible pathway for engaging critically with AI-generated content.

Key Findings

A coherent theoretical framework is essential: The authors argue AI adoption must move beyond ad hoc implementation; the CoI framework, a collaborative-constructivist process model grounded in critical reflection and discourse, is well suited to guide responsible integration of generative AI (Stenbom & Garrison, 2026).
AI is a sociotechnical actor, not a neutral tool: Within a community of inquiry, AI intersects with Teaching, Social, and Cognitive Presence in different ways, and may assume multiple, coexisting roles depending on design and positioning.
Five roles for AI in a community of inquiry: (1) support for learning design, (2) independent resource for learners, (3) support function and guide for instructors via analytics, (4) AI agent as a member of the community, and (5) sustained dialogical relationship of inquiry between human and AI agent.
Risks of uncritical adoption: If reflective inquiry is bypassed, learners may become passive recipients of AI-generated information, accept outputs uncritically, and forfeit the cognitive engagement and authenticity essential to deep learning.
Shared Metacognition is the responsible pathway: Comprising self- and co-regulation (each with monitoring and management functions), shared metacognition enables learners to audit and verify AI results and maintain reflective responsibility, supported by the Shared Metacognition instrument (Garrison & Akyol, 2015a, 2015b).
Learning analytics and human oversight: AI-driven analytics can reveal learning processes and support monitoring of presences, but must be balanced against risks of bias and oversimplification; the human instructor remains in charge, with AI serving as adviser.
Meaning requires human purpose: Regardless of AI's apparent reasoning, meaning becomes educational only when humans interpret, question, and integrate outputs — requiring educators to slow inquiry for reflection, verification, and skeptical engagement.

Implications for AI in Education

Generative AI can meaningfully support collaborative inquiry when framed within a coherent conceptual perspective that emphasizes skeptical engagement, collaborative reflection, and the preservation of human purpose. Educators should treat AI as a catalyst for inquiry — structuring complexity, visualizing relationships, and prompting deeper questioning — rather than a shortcut that bypasses reasoning and negotiation of meaning. Teaching Presence becomes critical in designing, facilitating, and directing critical engagement with AI content, while learning analytics (e.g., AI-assisted coding of Cognitive Presence) can scale diagnostic insight. Crucially, instructional leadership must model critical use and resist surrendering academic direction, keeping human agency central to sense-making and shared knowledge construction.

Connected Concepts

Connected Articles

Citation

Stenbom, S., & Garrison, D.R. (2026). Artificial Intelligence and Communities of Inquiry: Reimagining Educational Experiences. International Review of Research in Open and Distributed Learning, 27(2), 114-131.