ELEC631 – Human/AI Teaming in Education (Advanced Machine Learning – Fall 2025)

Generative Artificial intelligence (GenAI) methods have grown radically in power and utility over the past few years and have begun to impact education in a substantive way. In general, this course will study GenAI at the human/machine frontier. In particular, this course will study how GenAI systems can be used to enhance the (peer) tutoring experience, where students learn from each other, with one student (the tutor) providing academic support to others (the tutee(s)). This course will both expose students to new GenAI and human/AI-teaming topics and provide first-hand experience through a group project involving designing and prototyping an AI support system for peer tutoring in collaboration with the Rice OASUS tutoring center.

Concepts to be discussed will include: Human/AI teaming, principles of peer tutoring, learning science of peer tutoring, large language models (LLMs) for education, and user-centered design of AI systems

  • Location: Maxfield Hall 251
  • Time: Friday 2pm
  • Instructors: Richard Baraniuk (ECE, OpenStax), Lorenzo Luzi (D2K Lab) & Debshila Basu Mallick (SafeInsights/OpenStax)
  • TA/Grader: Tam Nguyen
  • Level: Open to both undergraduates and graduate students from all departments
  • Prerequisites: Required: Knowledge of machine learning and AI concepts and techniques plus familiarity with a programming language such as Python, R, or MATLAB. Desired: Knowledge of basics of genAI (e.g., prompt engineering).
  • Class format: This is a combination of a “reading course” and a "project course." For lectures each week, students will read and present papers from the technical literature to the rest of the class in a lively debate format. Students will also obtain valuable hands-on experience through a major group project that involves designing and prototyping an AI system to support peer instruction at the Rice OASUS tutoring center.