{"ID":3052358,"CreatedAt":"2026-06-04T04:41:36.695875263Z","UpdatedAt":"2026-06-06T07:20:10.253769126Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2606.04543","arxiv_id":"2606.04543","title":"Agentic AI and Pedagogical Best Practice: The Tension Between Automation and Learning","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.","short_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-prio...","url_abs":"https://arxiv.org/abs/2606.04543","url_pdf":"https://arxiv.org/pdf/2606.04543v1","authors":"[\"Steve Woollaston\",\"Brendan Flanagan\",\"Isanka Wijerathne\",\"Hiroaki Ogata\"]","published":"2026-06-03T07:26:23Z","proceeding":"cs.CY","tasks":"[\"cs.CY\"]","methods":"[]","has_code":false}