Let LLM Tutors Ask First: Proactive LLM-Based Tutoring at Scale in a 1,500-Student Online Classroom

Jonghoon Lee, Geonjae Youn, Seongmin Lee, Chaemoon Im, Joongheon Kim, Chuck Yoo

Keywords: Large Language Models and its applications, Coding Education

TL;DR: Deployment and evaluation of our AI tutor framework SCALA in a 1500-online classroom.

Abstract: Large-scale introductory CS courses, often enrolling thousands of students, struggle to provide personalized support and encourage active participation. While recent large language models (LLMs) have enabled AI teaching assistants at scale, most existing systems remain reactive, responding only after students explicitly initiate queries. We present SCALA, a student-centered AI learning assistant designed to provide proactive support for students. SCALA introduces predictive query management, a mechanism that generates likely student questions and answers ahead of lectures. Students may choose to view these pre-generated question–answer pairs or engage in interactive conversations with our tutoring model via the same interface. We evaluate SCALA through a semester-long deployment in an undergraduate Python course with over 1,500 students, and find that predictive queries are frequently selected in practice and substantially overlap with real student questions. Based on student feedback, learners preferred SCALA’s responses to their real queries over alternatives such as GPT-4o. These results suggest proactive support as a promising direction for future development of AI-powered teaching assistants. We will release our codebase and interactive demo upon acceptance.

Submission Type: Deployed

Submission Number: 377