Thinking Like a Student: AI-Supported Reflective Planning in a Theory-Intensive Computer Science Course

TL;DR

This paper demonstrates how large language models can support reflective planning in a challenging computer science course, improving student understanding and confidence through structured, AI-assisted session design.

Contribution

It introduces a novel use of LLMs as reflective tools for instructors to identify conceptual challenges and improve pedagogical strategies in theory-intensive courses.

Findings

Positive student feedback on confidence and clarity

Reduced student anxiety in abstract topics

Enhanced pedagogical design using LLM insights

Abstract

In the aftermath of COVID-19, many universities implemented supplementary "reinforcement" roles to support students in demanding courses. Although the name for such roles may differ between institutions, the underlying idea of providing structured supplementary support is common. However, these roles were often poorly defined, lacking structured materials, pedagogical oversight, and integration with the core teaching team. This paper reports on the redesign of reinforcement sessions in a challenging undergraduate course on formal methods and computational models, using a large language model (LLM) as a reflective planning tool. The LLM was prompted to simulate the perspective of a second-year student, enabling the identification of conceptual bottlenecks, gaps in intuition, and likely reasoning breakdowns before classroom delivery. These insights informed a structured, repeatable session format combining targeted review, collaborative examples, independent student work, and guided walkthroughs. Conducted over a single semester, the intervention received positive student feedback, indicating increased confidence, reduced anxiety, and improved clarity, particularly in abstract topics such as the pumping lemma and formal language expressive power comparisons. The findings suggest that reflective, instructor-facing use of LLMs can enhance pedagogical design in theoretically dense domains and may be adaptable to other cognitively demanding computer science courses.