AI Unplugged: Embodied Interactions for AI Literacy in Higher Education

TL;DR

This work addresses the gap in university AI education where conceptual understanding and ethical reflection lag behind technical skill development. It introduces embodied, unplugged activities within a 12-week Summer 2025 Introduction to AI course to build first-person intuitions for algorithms such as BFS/DFS, MDPs, Q-learning, and HMMs, then bridges to formal modeling and coding. The contributions include four unplugged activities, a structured unplugged-to-plugged learning pipeline, and reflections on successes, challenges, and future refinements for scalable higher-ed adoption. The results suggest that unplugged AI education can boost engagement, clarify algorithmic reasoning, and support a holistic, practical AI literacy that extends beyond mere implementation.

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.

Figures (4)

• Figure 1: Sample cards that are given to each role with distributed information. Students use the cards to share information and trace through the search scenario.
• Figure 2: Sample Hit and Stand decks for Red and Black Jack. The "Hit" transitions for MDP that represents this game can be expressed in this grid format.
• Figure 3: A sample GridWorld environment for Q-Maze.
• Figure 4: Two sample maps with the HMM transitions and observations, from the perspective of the Deep Cover Spy that simulates the HMM.