🧠 AI Ed Wiki

📄 Research Article

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

● high · 2026-07-02llm, higher-ed, student-experience, academic-integrity, stem-education, llm-in-education, ai-literacy, rag

Margaret Ellis, Nikitha Donekal Chandrashekar, Sehrish Basir Nizamani, Mohammed Farghally, Jake O'Brien, Naren Ramakrishnan — SIGCSE Virtual 2026, submitted 29 Jun 2026

Margaret Ellis, Nikitha Donekal Chandrashekar, Sehrish Basir Nizamani, Mohammed Farghally, Jake O'Brien, Naren Ramakrishnan — SIGCSE Virtual 2026, submitted 29 Jun 2026

DURA framework (Demystify-Use-Reflect-Assess) for structured LLM integration in CS2 courses shows students value instructional guidance alongside LLM access, with increased office hours attendance.

Key Contributions

  • DURA framework (Demystify-Use-Reflect-Assess) for structured LLM integration in CS2 courses shows students value instructional guidance alongside LLM access, with increased office hours attendance.

    • Connections to AI in Education

    This paper contributes to the growing body of research on AI applications in educational settings, specifically in the domains of AI Education, Intelligent Tutoring, and Equity. The findings have implications for how educators design learning experiences that leverage AI while maintaining appropriate pedagogical oversight.

    Connected Concepts

  • AI Education
  • Intelligent Tutoring
  • Equity

    • Connected Articles

  • Mixed Reality Engineering Learning — Visualizing Engineering Fundamentals: Design of Mixed Reality and Physical Toolkits for Effective Learning
  • Cogtax Cognitive Taxonomy — CogTax: A Four-Level Cognitive Taxonomy for Command-Line Computing Education
  • Less Deliberate Teams LLM — Less Deliberate in Teams: Student LLM Use Across Individual and Collaborative Work
  • Tactile Statistical Graphs Accessibility — Touching and Feeling the Data: A Reusable Software Pipeline for Tactile Statistical Graphs in Accessible Education
  • Commenting Copilot Student Code Specs — Commenting with Copilot: A Taxonomy and Multi-Year Analysis of Student Code-Generation Specifications
  • Adaptive Virtual Patient Psychotherapy Training — The Empirically Grounded Adaptive Virtual Patient for Psychotherapy Training

    • Citation

    Margaret Ellis, Nikitha Donekal Chandrashekar, Sehrish Basir Nizamani, Mohammed Farghally, Jake O'Brien, Naren Ramakrishnan (2026). Demystify, Use, Reflect, Assess (DURA): An Experience Report on LLM Integration in CS2. arXiv:2606.30908. SIGCSE Virtual 2026, submitted 29 Jun 2026