2025/10/31 by Izsak, Noa
Computer Science · Social Sciences · #Artificial Intelligence (cs.AI) #Computers and Society (cs.CY) #Educational Assessment and Pedagogy #F.4.0 #FOS: Computer and information sciences #Formal Languages and Automata Theory (cs.FL) #I.2.0 #K.3.1 #Reflective Practices in Education #Teaching and Learning Programming
paper · doi:10.48550/arxiv.2511.01906
openalex publication_date 2025/10/31 · openalex created_date 2025/11/06 · openalex updated_date 2026/07/28
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.