AI Unplugged: Embodied Interactions for AI Literacy in Higher Education

TL;DR

This paper introduces a novel pedagogical approach using embodied, unplugged activities to enhance AI literacy in higher education, bridging conceptual understanding and technical skills.

Contribution

It presents four unplugged AI activities, demonstrating their effectiveness in fostering intuition and facilitating transition to formal AI concepts and coding.

Findings

Unplugged activities improve students' understanding of AI concepts.

Students can better transition from intuition to formal AI models.

The approach bridges conceptual reasoning and technical skills in AI education.

Abstract

As artificial intelligence (AI) becomes increasingly integrated into daily life, higher education must move beyond code-centric instruction to foster holistic AI literacy. We present a novel pedagogical approach that integrates embodied, unplugged activities into a university-level Introduction to AI course. Inspired by the effectiveness of CS Unplugged in K-12 education, our physical, collaborative activities gave students a first-person perspective on AI decision-making. Through interactive games modeling Search Algorithms, Markov Decision Processes, Q-learning, and Hidden Markov Models, students built an intuition for complex AI concepts and more easily transitioned to mathematical formalizations and code implementations. We present four unplugged AI activities, describe how to bridge from unplugged activities to plugged coding tasks, reflect on implementation challenges, and propose refinements. We suggest that unplugged activities can effectively bridge conceptual reasoning and technical skill-building in university-level AI education.