Optimizing guiding feedback through pedagogical agents and generative artificial intelligence

Abstract

Feedback is a central factor in supporting students’ learning processes. In digital learning environments, informative tutoring feedback (ITF) strategies can be employed to support students with formative feedback without revealing correct solutions. For mathematical tasks, the ITF-strategy guiding feedback was conceptualized. It aims to provide learners with error-specific hints and offers them the possibility to solve tasks step-by-step. Initial studies on the use of guiding feedback demonstrated positive cognitive, motivational, and metacognitive effects. However, the findings also suggested that many students did not engage sufficiently with the feedback. To address this issue, this article proposes an optimization of guiding feedback involving the integration of pedagogical agents to provide the feedback to students and the use of generative artificial intelligence (GenAI) to support the generation of error-specific information. This article introduces the concept of the optimized feedback strategy, explains its potential to enhance students’ engagement with feedback, and illustrates it through examples.