Designing for Self-Regulation: A Systematic Literature Review of Metacognitive Prompts in Technology-enhanced Learning

• In Event: AERA Poster Session Wednesday 3:45pm
  In Poster Session: Systematic Review and Meta-Analysis: Methods, Contexts, and Impacts

Abstract

This systematic review synthesizes 19 empirical studies on metacognitive prompts in technology-enhanced learning, guided by Flavell’s and Zimmerman’s frameworks. Using a theory-informed coding scheme, we analyze how predictive, monitoring, and reflective prompts align with phases of self-regulated learning (SRL). Results indicate that reflective prompts are most commonly used and often effective, while predictive prompts remain underexplored. Over-reliance on AI-generated prompts may reduce learner autonomy, suggesting the need for adaptive strategies. The review contributes a three-phase typology, design principles for timing and delivery, and highlights research gaps—particularly in adult learning and human–AI collaboration. This study offers actionable insights for researchers and designers aiming to support SRL through evidence-based prompt design in digital learning environments.

Authors

• Shiyang Zhang, University of Pennsylvania

• Xianghui Meng, Columbia University