Impact of AI-driven scaffolded feedback on student cognitive load and problem-solving efficiency

Abstract

The rapid integration of artificial intelligence (AI) into education has introduced new possibilities for personalized, real-time instructional support, particularly through AI-driven scaffolded feedback. This study examines the impact of such feedback on students’ cognitive load and problem-solving efficiency, addressing a notable gap in existing literature, which has largely focused on academic achievement and technology acceptance in higher education settings rather than on cognitive mechanisms among younger learners. Grounded in Cognitive Load Theory, Vygotsky’s Sociocultural Theory of scaffolding, and Self-Regulated Learning Theory, the study explores how AI-generated feedback that is timely, individualized, and responsive to student performance can reduce extraneous cognitive load while promoting deeper engagement, self-regulation, and more efficient problem-solving. Employing a data-mining approach, the research draws on quantitative data collected through surveys, assessments, classroom observations, and records generated by AI-assisted learning platforms to identify patterns linking scaffolded feedback, cognitive performance, and problem-solving outcomes. Findings indicate that well-structured AI feedback systems help organize instructional content, minimize unnecessary cognitive strain, and support faster, more effective problem-solving, though challenges remain regarding technology access, teacher readiness, data privacy, and the need for continued oversight of AI-based instruction. The study concludes that while AI-driven scaffolded feedback holds significant promise for enhancing learning in K-8 classrooms, further research is needed to explore its long-term effects and to develop evidence-based strategies for effective implementation, particularly at the elementary school level.