Teaching AI Interactively: A Case Study in Higher Education

2026-03-30

AI Summary

This study addresses the learning challenges in introductory university-level artificial intelligence courses—stemming from abstract concepts, mathematical complexity, and diverse student backgrounds—by proposing an interactive pedagogical model that integrates device-free embodied simulations, collaborative programming experiments, and structured reflection. Adapting the CS Unplugged approach for the first time in a higher education AI context, the model enables students to experience AI decision-making processes from a first-person perspective, enhancing engagement without compromising academic rigor or altering existing assessment structures. Quasi-experimental results demonstrate significant improvements in attendance, perceived assessment validity, and overall course evaluations. Qualitative feedback further indicates that the learning environment became more supportive, fostering greater participation and increased student confidence in grasping core AI concepts.

Abstract

Introductory artificial intelligence (AI) courses present significant learning challenges due to abstract concepts, mathematical complexity, and students' diverse technical backgrounds. While active and collaborative pedagogies are often recommended, implementation can be difficult at scale due to large class sizes and the intensive design effort required of instructors. This paper presents a quasi-experimental case study examining the redesign of in-class instructional time in a university-level Introduction to Artificial Intelligence course. Inspired by CS Unplugged approaches, we redesigned the summer offering, integrating embodied, unplugged simulations, collaborative programming labs, and structured reflection to provide students with a first-person perspective on AI decision-making. We maintained identical assignments, exams, and assessments as the traditional lecture-based offering. Using course evaluation data, final grade distributions, and post-course interviews, we examined differences in student engagement, experiences, and traditional learning outcomes. Quantitative results show that students in the redesigned course reported higher attendance, stronger agreement that assessments measured their understanding, and greater overall course effectiveness, despite no significant differences in final grades or self-reported learning. Qualitative findings indicate that unplugged simulations and collaboration fostered a safe, supportive learning environment that increased engagement and confidence with AI concepts. These results highlight the importance of in-class instructional design in improving students' learning experiences without compromising rigor.

Problem

• artificial intelligence education
• active learning
• collaborative learning
• CS Unplugged
• instructional design

Innovation

• CS Unplugged
• embodied simulation
• collaborative learning
• active pedagogy
• AI education