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https://hdl.handle.net/2183/46704 Personalized Learning of Programming Fundamentals Through Robotic Simulations
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Leis, A., Mallo, A., Guerreiro-Santalla, S., Paz-López, A., Bellas, F. (2025). Personalized Learning of Programming Fundamentals Through Robotic Simulations. In: Bellas, F., Fontenla-Romero, O. (eds) AI in Education and Educational Research. AIEER 2024. Communications in Computer and Information Science, vol 2519. Springer, Cham. https://doi.org/10.1007/978-3-031-93409-4_13
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[Abstract]: Personalized learning is one of the main targets of the Artificial Intelligence in Education (AIEd) community. The potentiality of Artificial Intelligence (AI) techniques in student profiling could be very beneficial to support teachers when using technology in their classes. This way, each student could follow a learning path adapted to his/her progress, allowing the teacher to focus on those who require special attention. The current work is focused on this scope, and it presents the initial version of ProgTutor, an intelligent tutoring system designed to teach fundamental programming principles to high school and university students in a personalized manner. ProgTutor employs an interactive educational approach focused on solving realistic challenges with the assistance of a robot. The system uses a 3D simulator developed in Unity to take full advantage of the motivational and customization capabilities that this interface offers students. The current paper focuses on the conceptual and functional design of ProgTutor, presenting initial application results with high school students, and some interesting conclusions obtained from the teachers.
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This is an ACCEPTED MANUSCRIPT after peer review of the following published document: Leis, A., Mallo, A., Guerreiro-Santalla, S., Paz-López, A., Bellas, F. (2025). Personalized Learning of Programming Fundamentals Through Robotic Simulations. In: Bellas, F., Fontenla-Romero, O. (eds) AI in Education and Educational Research. AIEER 2024. Communications in Computer and Information Science, vol 2519. Springer, Cham. https://doi.org/10.1007/978-3-031-93409-4_13 © 2025. Link to published version: https://doi.org/10.1007/978-3-031-93409-4_13 General rights: This version of the conference paper has been accepted for publication, after peer review and is subject to Springer Nature’s AM terms of use, but is not the Version of Record and does not reflect post-acceptance improvements, or any corrections. The Version of Record is available online at: https://doi.org/10.1007/978-3-031-93409-4_13