An LED Assisted Chessboard for Use as an Educational Tool; The Impact of Artificial Intelligence in Education and its Effect on Student Learning
Weeden, James, School of Engineering and Applied Science, University of Virginia
Forelle, MC, EN-Engineering and Society, University of Virginia
The rise of artificial intelligence (AI) has brought a wide array of unique opportunities to the field of education in both the classroom and in personal entertainment. Technologies implementing AI have the ability to adapt to the needs of their user to achieve tasks such as personal tutoring, gameplay advice, student assessment, and test scoring. Many advantages can be seen from AI, but the perils of security risks, changing employment requirements, and increased socio global disparities stand as obstacles in the design of effective technologies. My technical research project builds off the advances in educational AI with an assistive LED chess board that illuminates tiles on a physical board to indicate the best potential piece movements to learning players. Delving into the known educational advantages of gameplay, this project aims to serve as a physical model of the potential of AI integrated with physical hardware in parallel to the success seen in current educational AI research.
The AI powered chess board is an interactive board game intended to bridge the assistance of machine learning based algorithms to players using a physical board. Belonging to the Internet of Things (IoT), the chess board will help those who are uninitiated in chess to learn how the game works and to further the progression of players who want to enhance their gameplay. It can track the location of each unique chess piece using sensors on the board, and relay that information to a chess engine on a personal computer (PC). With the piece locations fed into a PC, the interactive chess board illuminates chess tiles showing the user recommendations from a chess engine. Users can configure these recommendations including engine strength, frequency, and the number of recommendations in the graphical user interface. Each chess piece has a magnet at its base, and a network of 64 hall-effect sensors determines the position of the chess pieces. Using a known chess position, either the starting position or a mid-game position, the sensor network is scanned repeatedly by the Raspberry Pi Pico, tracking the movements of chess pieces. This method allows the identities of the pieces to be differentiated in software. Using the board position and the user’s recommendation settings, chess move recommendations are generated using Stockfish running on a personal computer (PC). The Raspberry Pi Pico interfaces with light-emitting diodes (LEDs) to illuminate the chess squares involved in the recommendations. This chess board integrates artificial intelligence and human-computer interaction, allowing chess players to study the strategies of a reputable chess engine while building their intuition and skills.
My STS research discusses the impact of new artificial intelligence technologies on social groups including students, educators, and institutions when applied to the field of education. With AI seeing a quick increase in use, this paper aims to study the potential benefits for each of these groups along with risk factors that can lead to significant changes in the structure of teaching. Using the Social Construction of Technology (SCOT) model, relevant research breakthroughs and critical analyses of AI education are presented to give a comprehensive understanding of modern perspectives. Covering the interconnections between each of these social groups is imperative in producing technologies and legislation that promote the ethical and wide reaching educational gains that AI can provide. With this study, it is my objective to give a clear starting point for creating an understanding of the current technological environment in AI and promote future developments in researching this field.
Being able to work on both a related capstone and STS project gave the unique opportunity to review theory and practice of educational AI simultaneously. With widespread implementations of AI in the classroom still in infancy and policy unstructured, having a physical model and directly observing chess player satisfaction and responses became invaluable in directing the scope of my research. Keeping in mind the goals of AI to improve learning on an individual level and prevent rising risks in privacy in my STS research additionally allowed for final iterations of the capstone project to take these factors into account to stay relevant in consumer desires and potential legislative changes were the capstone project to develop into a sellable product. Proactive action and interconnected analysis of the social groups in the educational spectrum gives a great opportunity to build sensible AI solutions, and taking these steps will ensure the technologies usefulness in the next generation of teaching to come.
Special thanks to my family and friends who have without end supported me through my time here during my undergraduate experience at UVA.
BS (Bachelor of Science)
Artificial Intelligence, Education, Pedagogy, Social Construction of Technology, Personalized Tutors, Students, Machine Learning, Automated Scoring
School of Engineering and Applied Science
Bachelor of Science in Electrical and Computer Engineering
Technical Advisor: Harry Powell
STS Advisor: MC Forelle
Technical Team Members: Iain Ramsey, Srikar Chittari, Ramie Katan
All rights reserved (no additional license for public reuse)