AI and Education Research Projects

This project investigates the responsible integration of artificial intelligence and large language models in computer science education. We develop evidence-based strategies for AI-integrated teaching, design assessments, and study the impact of generative AI on student learning outcomes.

Publications, Workshops and Presentations

• Workshop Presenter: Sehrish Basir Nizamani — Teaching and Learning in the Age of Generative AI, University of Mirpurkhas (Nov 2024)
• Round Table Discussion Presenter: Sehrish Basir Nizamani — Cross-Disciplinary Strategies for Teaching with AI, Conference on Higher Education Pedagogy (CHEP), Feb 2025
• Presenters: Margaret Ellis & Naren Ramakrishnan — Rethinking Education in the Age of AI, Physics Colloquium
• Workshop Presenters: Margaret Ellis & Naren Ramakrishnan — Rethinking Education in the Age of AI, Foundational Workshop of AI Across the University: Teaching Modules for Critical Engagement
• Poster Accepted at SIGCSE 2026: Demystify, Use, Reflect: Preparing students to be informed LLM-users — ND Chandrashekar, SB Nizamani, M Ellis, N Ramakrishnan (Access from: https://arxiv.org/abs/2511.11764)

Course materials

Access course materials: coursematerials2026.html

This Faculty Teaching Group aims to help faculty across disciplines design and integrate small learning modules that introduce students to the inner workings of Large Language Models (LLMs). The modules emphasize critical understanding of AI logic, data, limitations, and applications—empowering students to engage with AI confidently and responsibly in their fields.

Goals & Outcomes

• Develop discipline-specific AI/LLM learning modules accessible to non-CS students.
• Demystify AI through reflective, hands-on content.
• Build a network of faculty collaborators to foster interdisciplinary AI education.

Plan & Activities

• Foundational Workshop (Sep 2025): Introduce core AI/LLM concepts and visualization tools.
• Monthly Meetings (Oct–Dec 2025): Faculty co-design mini-modules tailored to their disciplines.
• Module Testing (Spring 2026): Pilot modules in real courses and gather student feedback.
• Peer Review (Feb–Mar 2026): Refine modules based on classroom data.
• Showcase (Apr 2026): Present final modules in a campus-wide event.

Collaborators

• Aparna Shah – School of Neuroscience, College of Science
• Traci Gardner – Department of English, CLAHS
• Saad Nizamani – Department of Computer Science, College of Engineering
• Hulya Dogan – Engineering Education, College of Engineering
• Can Dogan – Economics, Davis College of Business and Economics, Radford University
• Sana Illahe – Sociology, CLAHS
• Arianna Schuler Scott – Integrated Security Education and Research Center, Pamplin College of Business