TrainYourSnakeAI: A Novel Tool to Teach Reinforcement Learning to Middle School Students
Artificial Intelligence (AI) is growing rapidly in our society and is now apparent in our day-to-day lives. With the recent burst of interest in AI, many individuals and children may view AI as something mystic and magical. It is important to demystify and introduce to them how AI is made and works. To address this need, we developed a software application that allows children to specify the parameters used by a Reinforcement Learning (RL) algorithm. Then students experience how RL is used to train an AI model to play the game "Snake." This software tool was tested with 71 middle school-age students. Here, we describe the design of the TrainYourSnakeAI application, the approach we used to introduce the associated ideas to middle school children, and how we assessed student learning. Qualitative data collected from students are presented and discussed. We surveyed their knowledge of AI before and after using the application. In this work, our research questions were: (RQ1) How can we create an engaging tool to teach reinforcement learning? and (RQ2) Does using our application foster a stronger understanding of reinforcement learning in children? Our findings indicate that students were able to understand the functionality of reward functions and how agents can learn from the environment using the concept of RL. We found that out of the 51 students who were not previously familiar with RL, 40 were able to provide adequate descriptions of RL after using TrainYourSnakeAI.
Authors
- Fred Martin (ORCID: https://orcid.org/0000-0003-2723-5611)
- C. Hinojosa
- Priyanka Kumar (ORCID: https://orcid.org/0000-0002-8598-7849)
- Pragathi Durga Rajarajan
Institutions
- The University of Texas of the Permian Basin (US)
- The University of Texas at San Antonio (US)
Publication Details
- Published
- 2025-02-12
- DOI
- https://doi.org/10.1145/3641554.3701907
- Citations
- 8
- Primary Topic
- Behavioral and Psychological Studies
- Type
- article
- Field-Weighted Citation Impact
- 27.56