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

Recent advances in generative artificial intelligence (AI), including large language models (LLMs), have opened new possibilities in education and in particular on scaling up personalization. One form of personalization that generative AI powers is interactive learning via tutoring dialogues between AI-powered tutors and students. These interactions have the potential to tailor instruction to each student's needs and progress, while offering personalized feedback, all in real time, in a scalable way. Given the widespread success of human tutors for improving student outcomes [29], many recent works have developed LLM-based tutors, showing promise across various educational domains [15, 25, 30, 32, 33, 39, 42, 50]. Many LLM-based tutors are even deployed in practice, such as Khan Academy's Khanmigo [21] and Carnegie Learning's LiveHint [4]. Several preliminary studies have shown that interacting with LLMs can increase student learning [52], although some have shown that students can develop an over-reliance on LLMs which negatively impacts their learning [23]. Many prior works have focused on improving LLMs' ability to follow effective tutoring principles, adapting them for the tutoring task that they are not pre-trained for. One approach, explored in [46], analyzes the decision-making process underlying human tutor utterances, showing that integrating expert decisions enhances LLM-based tutoring. Another study, [28], examines tutor moves in interactions with an LLM-powered simulated student agent, demonstrating that move annotation data contributes to better tutoring performance.