TacpAgent: Enhancing Student Engagement in Classroom Exercises Through LLM-Generated Feedback - Zhang, Wanlu; Zhu, Jia; Yang, Xi; Shi, Weijie; Cui, Yue; Dai, Xinle; Sun, Jiewen (Proceedings of the AAAI Conference on Artificial Intelligence, 2026)
Classroom exercises are imperative for reinforcing learning. However, in conventional instruction, students frequently lack timely and personalized feedback. To address this, we present TacpAgent(Teaching Agent for Classroom Practice),a generative LLM-based agent that delivers detailed, individualized guided feedback to promote self-reflection through a prompt-template framework.In the context of this study, Classroom Practice is defined as the set of structured classroom exercises used for formative assessment.TacpAgent takes as input the teacher-prepared exercises and reference answers, together with students’ submitted responses, confidence levels, and brief explanations, then leverages the DeepSeek LLM with designed prompt templates to generate guided feedback and recommend relevant textbook sections for targeted review. We conducted a three-month quasi-experimental study with two high school classes (N=87). The study compared TacpAgent-supported exercises with traditional paper-based exercises. The experimental group showed significantly higher quiz scores (F=18.516, p＜0.001) and improved emotional (p＜0.001) and behavioral engagement (p=0.003). In contrast, the control group demonstrated no significant changes. The results suggest that TacpAgent may enable scalable, personalized formative assessment in classroom settings and provide practical guidance for integrating generative AI into everyday teaching.