Agentic AI and Pedagogical Best Practice: The Tension Between Automation and Learning
For educators and AI developers, this paper identifies a critical tension between automation and learning in AI-based education, but offers only conceptual recommendations without empirical validation.
This paper reviews six pedagogical principles in the context of agentic AI, highlighting the risk that proactive AI agents may undermine learner agency and cognitive effort, and proposes design recommendations to ensure AI supports rather than supplants human learning.
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.