INSIGHT: An Explainable, Instructor-Guided AI Assistant for Active Learning in CS1

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Authors: Muntasir Hoq, Jessica L. Vandenberg, Seung Y. Lee, Bradford Mott, James Lester, Narges Norouzi, Shuyin Jiao, Bita Akram
Cited by: 1
References: 4

Abstract

Active learning in introductory programming depends on frequent, high-quality feedback, yet instructors often struggle to deliver consistent support at scale. In this poster, we introduce INSIGHT, an AI-driven classroom assistant designed to promote active learning in introductory programming (CS1) courses through scalable, personalized, and explainable feedback. The assistant combines the generative capabilities of large language models (LLMs) with instructor-in-the-loop authoring and an explainable code analysis engine to ensure pedagogically aligned support. Instructors can co-design problems with LLM assistance, provide exemplar solutions, define common student errors, and author targeted feedback. The AI engine analyzes student code submissions, identifies misconceptions, and maps them to instructor-verified feedback in real time. INSIGHT is designed to ensure that key educational concepts and common misconceptions are explicitly addressed by instructors, while also leveraging LLMs to provide reasonable feedback for novel or edge-case solutions that instructors may not have anticipated. By combining instructor expertise with the flexibility of generative AI, the assistant helps close feedback gaps and ensures more comprehensive coverage of student learning needs, especially in large or diverse classrooms with limited instructional support.

Fields of Study & Subjects

Teaching and Learning Programming Intelligent Tutoring Systems and Adaptive Learning Machine Learning and Algorithms

Publication details

Journal / source
Proceedings of the 57th ACM Technical Symposium on Computer Science Education V.2
Publisher
ACM
Pages
1363–1364
Publication date
13 February 2026