A systematic literature review of game-based learning in Artificial Intelligence education

Abstract

ABSTRACT In recent years, Game-Based Learning (GBL) has been widely adopted in various educational settings. This paper aims to review empirical studies that adopt GBL in the field of AI education and explore its future research perspectives. After a systematic keyword search in the online database and a snowballing approach, a total of 125 empirical papers with 133 studies were targeted as samples. Results indicated that the games in AI education are mainly fell into five categories: Puzzle games are the most used in the curriculum (27.07%), followed by Reasoning strategy games (23.31%), Robot games (18.05%), Role-playing games (9.02%) and Simulation games (6.77%). Among them, 22.39% of games were with real characters, 11.94% were with virtual characters and 64.18% were with no characters. Besides, games were used in three main forms in AI education: games as teaching tools (78.95%), games as student works (12.03%), and games as a competing mechanism (9.02%). Researchers mainly paid attention to the effect of GBL on students’ Opinions and Attitude (52.96%) and Learning achievement (24.04%), while the other three categories such as Skills and ability, Interaction, and Cognition were not extensively measured. The cross-sectional analysis, research gaps, and potential directions for future research were also discussed.