Using artificial intelligence to guide and personalize learner support in game-based learning environments.
Game-Based Learning Environments (GBLE) show promise in fostering Computational Thinking (CT) and programming skills in K-12 education, by letting students design and run algorithms to advance in games by means of simplified programming blocks.
However, such GBLEs can be challenging to some students due to their open-ended nature and minimal guidance, and research has shown that these difficulties can be in part alleviated by adaptive support.
In this project, we aim to create and evaluate AI-guided feedback, which would be tasked with predicting the level of support a learner needs—based on their current solution and behavior, as well as select or generate suitable feedback..
In the long-run the personalization aim at easing the adoption of GBLEs at school in CT courses.