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

2026-06-03

AI Summary

This study addresses the tension between proactive artificial intelligence (AI) systems and established principles of effective instruction, arguing that such AI may undermine learner agency and cognitive engagement. Grounded in six core pedagogical principles, this work proposes a learner-centered design framework for educational AI that systematically reconciles these tensions. The framework integrates mechanisms such as intentional friction, dynamic scaffolding, human-in-the-loop oversight, and judicious automation. Emphasizing theory-driven interaction design and human-AI collaborative learning, it ensures that AI augments—rather than replaces—human learning processes. The resulting design guidelines offer both theoretical rigor and practical feasibility for developing educational AI systems that genuinely support meaningful 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.

Problem

• agentic AI
• learner agency
• cognitive effort
• automation
• pedagogical principles

Innovation

• agentic AI
• intentional friction
• dynamic scaffolding
• human-in-the-loop
• pedagogical principles