Bernstein, Denny, Leinonen, Patel et al. (2026) — cs.HC (SIGCSE Virtual 2026) 📄 Full text (arXiv)
Bernstein, Denny, Leinonen et al. (2026) investigate whether providing students with multiple, diverse LLM-generated explanations of code (rather than a single 'best' explanation) improves comprehension in introductory programming. Their findings show that exposure to diverse explanations significantly outperforms single-explanation conditions on measures of conceptual understanding and code comprehension. This challenges the common design assumption that AI-generated educational content should converge on a single 'correct' explanation, instead suggesting that llm-generated feedback-loop diversity supports deeper learning by exposing students to multiple perspectives. The study connects to scaffolding theory, where multiple representations support the gradual transfer of responsibility from tool to learner. It also informs active-learning pedagogy by providing a concrete implementation strategy for AI-assisted instruction. The work has implications for how student-experience of programming education can be enhanced through deliberately varied AI-generated content, relevant to stem-education course design.