INSIGHT: Bridging the Student-Teacher Gap in Times of Large Language Models
This addresses the problem of maintaining teacher-student engagement in higher education as AI tools become prevalent, though it is an incremental step focused on specific course integration.
The paper tackles the challenge of integrating AI into education without degrading student-teacher interactions by introducing INSIGHT, a modular proof-of-concept system that analyzes student questions to an LLM to dynamically build FAQs and provide insights for personalized support.
The rise of AI, especially Large Language Models, presents challenges and opportunities to integrate such technology into the classroom. AI has the potential to revolutionize education by helping teaching staff with various tasks, such as personalizing their teaching methods, but it also raises concerns, for example, about the degradation of student-teacher interactions and user privacy. Based on interviews with teaching staff, this paper introduces INSIGHT, a proof of concept to combine various AI tools to assist teaching staff and students in the process of solving exercises. INSIGHT has a modular design that allows it to be integrated into various higher education courses. We analyze students' questions to an LLM by extracting keywords, which we use to dynamically build an FAQ from students' questions and provide new insights for the teaching staff to use for more personalized face-to-face support. Future work could build upon INSIGHT by using the collected data to provide adaptive learning and adjust content based on student progress and learning styles to offer a more interactive and inclusive learning experience.