Abstract
This paper investigates personalization in the field of intelligent tutoring systems (ITS). We hypothesize that personalization in the way questions are asked improves student learning outcomes. Previous work on dialogue-based ITS personalization has yet to address question phrasing. We show that generating versions of the questions suitable for students at different levels of subject proficiency improves student learning gains, using variants written by a domain expert and an experimental A/B test. This insight demonstrates that the linguistic realization of questions in an ITS affects the learning outcomes for students.
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Acknowledgements
We’d like to thank Korbit for hosting our experiment on their platform, and Mitacs for their grant to support this project. We are grateful to the anonymous reviewers for their valuable feedback.
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Elkins, S., Kochmar, E., Belfer, R., Serban, I., Cheung, J.C.K. (2022). Question Personalization in an Intelligent Tutoring System. In: Rodrigo, M.M., Matsuda, N., Cristea, A.I., Dimitrova, V. (eds) Artificial Intelligence in Education. Posters and Late Breaking Results, Workshops and Tutorials, Industry and Innovation Tracks, Practitioners’ and Doctoral Consortium. AIED 2022. Lecture Notes in Computer Science, vol 13356. Springer, Cham. https://doi.org/10.1007/978-3-031-11647-6_121
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DOI: https://doi.org/10.1007/978-3-031-11647-6_121
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