Beyond Automation: AI as a Pedagogical Mediator in Collaborative Learning

Synthesis: Niari (2026) advances a conceptual framework that reconceptualizes AI as a pedagogical mediator in collaborative learning — not a tool, tutor, peer, or automated orchestrator. Drawing on socio-cultural theory, distributed cognition, connectivist perspectives, and sociomaterial/posthuman theory, the paper argues that AIED's dominant instrumental and individualistic framings are theoretically misaligned with collaboration understood as a socially mediated, co-regulated process. The framework distinguishes three overlapping layers of AI mediation — interactional, epistemic, and regulatory — and contends that agency, authority, and responsibility are dynamically redistributed across human and non-human actors without displacing learner or teacher agency.

Key Findings

From automation to mediation. The paper critiques the instrumental orientation of AIED (personalization, automation, instructional efficiency) that privileges individual outcomes over social and relational processes, arguing that generative and interactive AI participates directly in educational discourse and therefore cannot be treated as a neutral tool.
The pedagogical mediator construct. Mediation is defined as an agent (human or artificial) that actively shapes learning by structuring interaction, scaffolding sense-making, and supporting regulation; it is distinct from orchestration (coordination/management) and automation.
Three layers of AI mediation. (1) Interactional mediation shapes the organization and flow of collaborative discourse (prompting participation, foregrounding underrepresented contributions, highlighting convergence/divergence); (2) epistemic mediation influences the construction, evaluation, and circulation of knowledge — generating explanations, modeling reasoning, summarizing ideas — without adjudicating correctness; (3) regulatory mediation supports planning, monitoring, and evaluation of joint activity, contributing to socially shared regulation without centralizing control.
Redistribution, not displacement, of agency. AI mediation entails a dynamic renegotiation of agency and epistemic authority across sociotechnical systems rather than a zero-sum transfer; AI participates asymmetrically and does not claim epistemic parity with learners.
Boundary work against slippage. Unlike tutors (individualized instruction), peers/teammates (epistemic parity), coaches (individual optimization), or orchestrators (centralized control), mediators emphasize participation, relationality, and shared responsibility, keeping AI subordinate to teacher professional judgment.
Ethics and governance as constitutive. Equity/epistemic inclusion, transparency and pedagogical interpretability, teacher professional agency vs. technocratic governance, and responsible data stewardship are framed as integral to design — calling for "pedagogical governance" rather than regulatory compliance alone.

Conceptual Contribution

The paper positions AI as an active participant in the orchestration of interaction, epistemic sense-making, and shared regulation of collaborative activity, informed by socio-cultural theory, distributed cognition, connectivism, and sociomaterial/posthuman accounts of distributed agency. It explicitly critiques teammate/peer framings (e.g., AI speakers in Jigsaw groups, LLM writing partners) for obscuring asymmetries of responsibility, accountability, and ethical agency. Within a mediation framing, AI structures the conditions of collaboration rather than substituting for human interaction, preserving the dialogical and co-regulated nature of collaborative learning theory and the epistemic sovereignty of learners and teachers.

Ethical, Professional, and Design Implications

The framework treats equity, transparency, teacher professional judgment, and data governance as central pedagogical concerns. It argues that AI-mediated collaboration risks algorithmic authority and technocratic governance when shared regulation is reduced to dashboards, alerts, and nudges. Design should scaffold equitable participation, support co-regulation and metacognitive awareness, and remain interpretable, adjustable, and subordinate to teacher judgment. This aligns AI mediation with the pedagogical commitments of Constructivist and socio-cultural accounts rather than with a tool-centric pedagogical agent logic.

Connected Concepts

Connected Articles

AI Cognitive Partner Co Regulation Learning — AI as a cognitive partner in human-AI co-regulation of learning
AI Agents Constructive Conflict Design Education 2026 — AI agents in collaborative design education
Human AI Collaboration Prerequisite Functions — Prerequisite functions for effective human-AI collaboration
AI Collaborative Learning Systematic Review — Systematic review of AI-supported collaborative learning

Citation

Niari, M. (2026). Beyond Automation: AI as a Pedagogical Mediator in Collaborative Learning. Umanistica Digitale, (24).