Teaching AI Interactively: A Case Study in Higher Education

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.

Figures (5)

• Figure 1: Quasi-experimental study design comparing the Spring 2025 (control) and Summer 2025 (intervention) offerings of the undergraduate artificial intelligence course. Both semesters used identical units, programming projects, comparable exams, and student surveys. The Summer offering replaced traditional lecture-based instruction with active, unplugged, and collaborative instruction, and interviews were conducted following the Summer semester.
• Figure 2: In the unplugged simulation, students collaboratively build the Q-table. In the left grid, students have no information about the utility of each action choice so they need to prioritize exploration. In the right grid, the actions for moving up or down have a high utility, so students want to exploit the existing information.
• Figure 3: Qualitative Data Analysis Process
• Figure 4: Stacked proportional bar charts showing student responses to survey items on assignment effectiveness, perceived learning, course effectiveness, preparedness, attendance, and homework completion comparing Spring and Summer semesters. Statistically significant responses are marked with an asterisk (*).
• Figure 5: Conceptual model of the instructional design. Complex AI topics are decomposed through active learning activities and supported by social interaction, enabling repeated experiences of progress that build student confidence.