From Cognitive Load to Co-Adaptive Scaffolding: A Design-Based Inquiry of an AI Learning Tool

• In Event: AER Roundtable Session Friday 1:45 pm - Gold 4
  In Roundtable Session: Designing for Self-Regulation: AI, Scaffolding, and Learner Agency

Abstract

For many higher education students, the act of preparing to learn has become a greater cognitive burden than learning itself. This design-based research project investigated the development and evaluation of SmartFlash, an AI-powered tool designed to mitigate this extraneous cognitive load and scaffold self-regulated learning (SRL) processes. Employing a theoretical framework that integrates Cognitive Load Theory, principles of SRL, and Human-Computer Interaction, our methodology translated user-identified needs into a high-fidelity prototype. Qualitative analysis (n=5) and formative evaluations guided iterative design refinements that reduced interface-induced cognitive load and enhanced metacognitive support, culminating in a co-adaptive system that balances automation with learner agency.

Authors

• Hongming Li, University of Florida

• Salah Esmaeiligoujar, University of Florida

• Hai Li, University of Florida

• Nazanin Adhami, University of Florida

• Rui Tammy Huang, University of Florida