Demystify, Use, Reflect, Assess (DURA): An Experience Report on LLM Integration in CS2

2026-06-29

AI Summary

This study addresses how to effectively integrate large language models (LLMs) into foundational computer science courses to enhance learning outcomes and develop workplace-relevant skills. It proposes the DURA instructional framework, which restructures a CS2 course through four phases: understanding LLM principles,规范使用与溯源 (regulated use with attribution), reflective strategy development, and adaptive assessment. Innovatively combining metacognitive reflection activities with proctored, retakeable assessments, the approach ensures authentic evaluation of programming proficiency. Findings indicate that students strategically leverage LLMs while continuing to engage traditional learning supports; office hour utilization slightly increased, and students reported significantly stronger perceptions of instructor care.

Abstract

Student access to Large Language Models (LLMs) is reshaping learning behaviors; at the same time students are entering the workforce where effective LLM use is becoming an expected skill. In this Experience Report we share our DURA framework (Demystify-Use-Reflect-Assess) and materials we used to restructure our CS2 course to allow the use of LLMs. We first demystified LLMs, then provided guidance on use with required attribution. We also added reflections related to LLM use at three points throughout the semester to encourage student meta-cognition around LLM use. We increased the value of proctored assessments in tandem with allowing retakes and including questions that explicitly assess skills from programming assignments. Students reported using LLMs for clarifying course concepts, debugging, understanding assignment guidelines, and determining test cases, but also still sought assistance via office hours and TAs, monitored Piazza, and reviewing course content. Students articulated thoughtful and strategic approaches to LLM use and also valued the instructional content and guidance from course staff. Student use of office hours increased slightly this semester and student perceptions that the instructor cares about them and their learning improved.

Problem

• Large Language Models
• CS2
• LLM integration
• student learning
• academic integrity

Innovation

• DURA framework
• Large Language Models (LLMs)
• CS2 education
• metacognition
• assessment design