Abstract
Personalized language learning (PLL) is central to educational and industrial communities worldwide. In our ongoing work, we developed a new multimodal intelligent tutoring system (ITS) with dynamic adaptation for a task-based workplace English language learning. Our work focuses on helping users improve their speaking and behavioral communication skills in the context of a fictional workplace environment while interacting with character-avatars(e.g., project supervisors and fellow team members). The system captures the users’ multimodal interaction footprint, including clickstream, audio, and video data. Our learner model measures user skill mastery based on users’ response data and provides dynamic adaption on item difficulty.
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Chopade, P., Pu, S., LaMar, M., Kurzum, C. (2023). Task-Based Workplace English: An Adaptive Multimodal Tutoring System. In: Wang, N., Rebolledo-Mendez, G., Dimitrova, V., Matsuda, N., Santos, O.C. (eds) Artificial Intelligence in Education. Posters and Late Breaking Results, Workshops and Tutorials, Industry and Innovation Tracks, Practitioners, Doctoral Consortium and Blue Sky. AIED 2023. Communications in Computer and Information Science, vol 1831. Springer, Cham. https://doi.org/10.1007/978-3-031-36336-8_59
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DOI: https://doi.org/10.1007/978-3-031-36336-8_59
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