Demystify, Use, Reflect: Preparing Students to Be Informed LLM-Users
SIGCSE 2026 poster and course materials for helping students engage with large language models critically, confidently, and responsibly.
Paper: Demystify, Use, Reflect: Preparing Students to Be Informed LLM-Users (arXiv:2511.11764)
Team
Overview
This work presents a set of teaching strategies and course materials designed to help computer science students become informed and reflective users of large language models (LLMs). Rather than treating LLMs as black-box helpers, the materials invite students to understand how these models work, when they are helpful, where they fail, and how to integrate them thoughtfully into their problem-solving and learning practices.
The materials on this page are shared to support instructors who wish to use them in their own courses. They are licensed openly under CC BY-NC-ND 4.0 and may be reused with attribution for non-commercial purposes, but may not be altered or adapted.
Poster
You can download the SIGCSE 2026 poster here:
- Download poster (PDF) (coming soon)
Course Materials
These materials were developed for use in CS 2104: Problem Solving in Computer Science and focus on helping students:
- Understand the basic ideas behind LLMs
- Use LLMs to support (not replace) their own reasoning
- Engage in structured reflection about AI use in coursework
- Recognize limitations, bias, and hallucinations
Curriculum Design Team
- Margaret Ellis (Curriculum Designer, Instructor of Record, Slide/Content Developer)
- Naren Ramakrishnan (Curriculum Designer, Course Consultant)
- Sehrish Basir Nizamani (Curriculum Designer, Slide/Content Developer)
Teaching Materials by Week
Week 1
Focus: AI literacy · Prompting · Course navigation · Syllabus understanding
- W1-CW: Enhancing Course Understanding with AI Tools (Perplexity.ai) — Download W1-CW (PDF)
- W1-HW: LLM Articles — Download W1-HW (PDF)
Week 2
Focus: Transformers · Tokenization · Self-attention · LLM fundamentals
- W2-L: LLMs — How and Why They Work (Transformer Foundations) — Download W2-L (PDF)
- W2-CW: Transformer Explainer — Download W2-CW (PDF)
Week 3
Focus: Algorithmic thinking · Flowcharts · Python basics · LLM-supported debugging
- W3-L: Python & Algorithms — Download W3-L (PDF)
- W3-HW: Character AI — Download W3-HW (PDF)
Week 4
Focus: Strings · Regex · Substring algorithms · Problem-solving patterns
- W4-L: Strings, Regex, and Algorithmic Thinking — Download W4-L (PDF)
- W4-HW: Wannabe Palindrome Problem — Download W4-HW (PDF)
Week 5
Focus: Recursion · Backtracking · Dynamic programming · Algorithm correctness · LLM-assisted problem solving
- W5-L: Recursion, Backtracking & Algorithmic Efficiency — Download W5-L (PDF)
- W5-CW: Software Engineering Exit Slip #1 — Download W5-CW (PDF)
- W5-CWb: Software Engineering Exit Slip #2 — Download W5-CWb (PDF)
- W5-HW: Knapsack Problem Comprehension — Download W5-HW (PDF)
Week 6
Focus: Software engineering · Requirements · Design · LLM-assisted development
- W6-L: Introduction to Software Engineering — Download W6-L (PDF)
- W6-HW: Knapsack Problem Implementation — Download W6-HW (PDF)
Week 8
Focus: Version control · Git workflows · Collaboration skills
- W8-L: Command Line & Git Skills — Download W8-L (PDF)
Week 9
Focus: Networking · Internet architecture · Data centers · TCP/IP layers
- W9-L: Basics of the Internet — Download W9-L (PDF)
- W9-HW: HTML Table Design — Download W9-HW (PDF)
Week 10
Focus: Human-Computer Interaction . Usability Evaluation
- W10-CWa: Website Usability Evaluation Assignment — Download W10-CWa (PDF)
Week 13
Focus: Data Science · AI reliability · Ethics in coding workflows
- W13-HW: LLM & Coding Articles — Download W13-HW (PDF)
How to Use These Materials
These resources are designed to be modular. You can:
- Adopt the full set into a dedicated AI-and-problem-solving module
- Pull a single assignment or activity into an existing course unit
- Use the slides for a guest lecture or one-off session on LLMs
Unless otherwise noted, all materials on this page are shared under the Creative Commons Attribution–NonCommercial–NoDerivatives 4.0 International License (CC BY-NC-ND 4.0).
You are free to:
- Share – copy and redistribute the material in any medium or format
Recommended Citation
If you use or reference these teaching materials in your own courses, publications, or talks, please cite:
Ellis, M., Ramakrishnan, N., & Nizamani, S. B. (2025). Demystify, Use, Reflect: Teaching Materials for Supporting Informed LLM-Use in Computer Science Courses. Retrieved from https://people.cs.vt.edu/sehrishbasir/coursematerials2026.html
@misc{llm-materials-2025,
title = {Demystify, Use, Reflect: Teaching Materials for Supporting Informed LLM-Use in Computer Science Courses},
author = {Ellis, Margaret and Ramakrishnan, Naren and Nizamani, Sehrish Basir},
year = {2025},
howpublished = {Course materials},
note = {Available at https://people.cs.vt.edu/sehrishbasir/coursematerials2026.html}
}
Acknowledgements
This work was supported by the CETL Instructional High Impact Project Grant. We gratefully acknowledge this support, which made the development and dissemination of these teaching materials possible.
Contact
For questions about these materials or collaboration inquiries, please contact:
Sehrish Basir Nizamani
Department of Computer Science, Virginia Tech
Email: sehrishbasir@vt.edu