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Agentic AI and Pedagogical Best Practice: The Tension Between Automation and Learning

arXiv CS Thursday 04 June 2026, 04:00 UTC By Steve Woollaston, Brendan Flanagan, Isanka Wijerathne, Hiroaki Ogata 1 min read
Key Points

arXiv:2606.04543v1 Announce Type: new Abstract: Artificial intelligence in education is evolving from passive chatbots to proactive AI agents capable of initiation and goal-directed interactions. While offering opportunities for personalised learning, this shift risks undermining learner agency and cognitive effort. This paper reviews six pedagogical principles-prior knowledge activation, collaborative learning, problem-based learning, formative assessment, scaffolding, and...

arXiv:2606.04543v1 Announce Type: new Abstract: Artificial intelligence in education is evolving from passive chatbots to proactive AI agents capable of initiation and goal-directed interactions. While offering opportunities for personalised learning, this shift risks undermining learner agency and cognitive effort. This paper reviews six pedagogical principles-prior knowledge activation, collaborative learning, problem-based learning, formative assessment, scaffolding, and metacognition-through the lens of agentic AI. We discuss the tension between automation and learning, proposing design recommendations that prioritise intentional friction, dynamic scaffolding, human-in-the-loop oversight, and considered AI utilisation to ensure AI supports rather than supplants human learning.
Agentic AI (ORG) Pedagogical Best Practice (ORG) The Tension Between Automation and Learning arXiv:2606.04543v1 (ORG) AI (ORG)
Originally published by arXiv CS Read original →

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