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Viability of Large Language Models as CS Theory Tutors
0
Zitationen
3
Autoren
2026
Jahr
Abstract
Large Language Models (LLMs) promise explanations at a scale that traditional office-hours or even intelligent tutoring systems struggle to match. However, their suitability for Computer Science subjects such as Theory of Computing (ToC) remains unanswered due to how LLMs can frequently hallucinate information; the goal of ToC courses is proving precise statements rigorously. In this poster we evaluate OpenAI's GPT-4 model across 18 ToC sub-topics involving regular languages, context-free languages, Turing machines, and (un)decidability. We generated realistic ''average-student'' questions and follow-up ones and then scored each answer with a five-criterion rubric: accuracy, completeness, clarity, pedagogical scaffolding, and quality of follow-up questions. Our overall results show that GPT-4 is marginal at performing as a ToC tutor, and our analysis identifies strengths in conceptual explanation and weak spots for proof-oriented questions, e.g., reductions.
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