Training LLM-Based Tutors to Improve Student Learning Outcomes in Dialogues

Alexander Scarlatos, Naiming Liu, Jaewook Lee, Richard G. Baraniuk, Andrew S. Lan

Abstract: Generative artificial intelligence (AI) has the potential to scale up personalized tutoring through large language models (LLMs), with recent works focusing on training or prompting LLMs to follow effective pedagogical principles. However, these models are not trained to maximize student learning throughout the course of a dialogue, so may engage with students in a suboptimal way. We address this limitation by introducing an approach to train LLMs to generate tutor utterances that maximize the likelihood of student correctness, while still encouraging the model to follow good pedagogical practice. Specifically, we generate a set of candidate tutor utterances and score them using (1) an LLM-based student model to predict the chance of correct student responses and (2) a pedagogical rubric evaluated by GPT-4o. We then use the resulting data to train an open-source LLM, Llama 3.1 8B, using direct preference optimization (DPO). We show that tutor utterances generated by our model lead to significantly higher chances of correct student responses while maintaining the pedagogical quality of GPT-4o. We also conduct qualitative analyses and a human evaluation to demonstrate that our model generates high quality tutor utterances. (This work is partially supported by Renaissance Philanthropy via the learning engineering virtual institute (LEVI) and NSF grants 2118706, 2237676, and 2341948.) (Our code is available at https://github.com/umass-ml4ed/tutorbot-dpo.)

External IDs:dblp:conf/aied/ScarlatosLLBL25