@inproceedings{lee-etal-2026-llm,
title = "Let {LLM} Tutors Ask First: Proactive {LLM}-Based Tutoring at Scale in a 1,500-Student Online Classroom",
author = "Lee, Jonghoon and
Youn, Geonjae and
Lee, Seongmin and
Im, Chaemoon and
Kim, Joongheon and
Yoo, Chuck",
editor = "Li, Yunyao and
Rehm, Georg and
Tu, Mei",
booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics ({ACL} 2026)",
month = jul,
year = "2026",
address = "San Diego, California, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.acl-industry.107/",
pages = "1541--1554",
ISBN = "979-8-89176-394-4",
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."
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%0 Conference Proceedings
%T Let LLM Tutors Ask First: Proactive LLM-Based Tutoring at Scale in a 1,500-Student Online Classroom
%A Lee, Jonghoon
%A Youn, Geonjae
%A Lee, Seongmin
%A Im, Chaemoon
%A Kim, Joongheon
%A Yoo, Chuck
%Y Li, Yunyao
%Y Rehm, Georg
%Y Tu, Mei
%S Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (ACL 2026)
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, USA
%@ 979-8-89176-394-4
%F lee-etal-2026-llm
%X 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.
%U https://aclanthology.org/2026.acl-industry.107/
%P 1541-1554
Markdown (Informal)
[Let LLM Tutors Ask First: Proactive LLM-Based Tutoring at Scale in a 1,500-Student Online Classroom](https://aclanthology.org/2026.acl-industry.107/) (Lee et al., ACL 2026)
ACL
- Jonghoon Lee, Geonjae Youn, Seongmin Lee, Chaemoon Im, Joongheon Kim, and Chuck Yoo. 2026. Let LLM Tutors Ask First: Proactive LLM-Based Tutoring at Scale in a 1,500-Student Online Classroom. In Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (ACL 2026), pages 1541–1554, San Diego, California, USA. Association for Computational Linguistics.