Journal: Region - Educational Research and Reviews DOI: 10.32629/rerr.v8i5.5479
Abstract
Generative AI can supply an explanation before a learner has decided what evidence would make it credible. In scientific inquiry, AIenabled assistance may improve an answer while removing responsibility for judging it. This paper proposes epistemic responsibility allocation as a guiding principle for educational multi-agent systems. A responsibility specifies what an actor must justify, what the actor is prohibited from doing, and when decisionmaking authority passes to a teacher. An Expert verifies approved sources, a Peer raises evidence-seeking challenges, and a Coach manages and withdraws instructional scaffolds. A ledger adapts Toulmin's structure to record claims, evidence, warrants, qualifiers, challenges, and student revisions. Four propositions are examined through a classroom comparison with a single-agent interface, providing an initial empirical test of the framework. Multiple agents are useful only when permissions create distinct, auditable responsibilities among them.
Keywords
generative artificial intelligence; epistemic responsibility; scientific argumentation; multi-agent systems; learning design; teacher orchestration
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