AI-Facilitated Formative Feedback and Students’ Classroom Engagement: A Review of Pedagogical Integration, Learning Outcomes, and Teacher-Student Interaction Dynamics
TL;DR
A directional shift in teacher function from primary feedback provider to orchestrator of AI-generated analytics was the most consistent finding across studies and substantiate a conditional engagement model and emphasises that teacher orchestration is a non-negotiable precondition for the effectiveness of AI feedback in classroom ecologies.
Abstract
Artificial intelligence (AI) supported formative feedback has emerged as ostensibly transformative pedagogical tool, however, its influence within traditional (physical) classroom settings are still insufficiently synthesized. The present review, conducted in accordance with PRISMA 2020 guidelines, examined 21 empirical studies published from 2014 till 2026 to examine how AI-supported formative feedback effect student’s engagement and redefines teacher-student interaction patterns in traditional classrooms informed by a multi-perspectival conceptual framework incorporating formative feedback theory, the tripartite engagement model, and classroom orchestration theory, thematic synthesis indicated that AI feedback effects on behavioral, cognitive, and emotional engagement are conditional rather than universal, moderated by feedback elaboration level, temporal granularity, cognitive load, and learner prior knowledge. A directional shift in teacher function from primary feedback provider to orchestrator of AI-generated analytics was the most consistent finding across studies. Three structural tensions were identified: scalability versus relational quality, personalization versus cognitive overload, and AI reliability versus contextual responsiveness. These findings substantiate a conditional engagement model and emphasises that teacher orchestration is a non-negotiable precondition for the effectiveness of AI feedback in classroom ecologies.
