LLM-Based Course Comprehension Evaluator
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Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-63028-6_35
Reference22 articles.
1. Putnam, V., Conati, C.: Exploring the need for explainable artificial intelligence (XAI) in intelligent tutoring systems (ITS). Los Angel (2019)
2. Akyuz, Y.: Effects of intelligent tutoring systems (ITS) on personalized learning (PL). Creat. Educ. 11(06), 953–978 (2020). https://doi.org/10.4236/ce.2020.116069
3. Sychev, O., Anikin, A., Penskoy, N., Denisov, M., Prokudin, A.: CompPrehension - model-based intelligent tutoring system on comprehension level. In: Cristea, A.I., Troussas, C. (eds.) Intelligent Tutoring Systems: 17th International Conference, ITS 2021, Virtual Event, June 7–11, 2021, Proceedings, pp. 52–59. Springer International Publishing, Cham (2021). https://doi.org/10.1007/978-3-030-80421-3_6
4. Paladines, J., Ramirez, J.: A systematic literature review of intelligent tutoring systems with dialogue in natural language. IEEE Access 8, 164246–164267 (2020). https://doi.org/10.1109/ACCESS.2020.3021383
5. Xiao, C., Xu, S.X., Zhang, K., Wang, Y., Xia, L.: “Evaluating reading comprehension exercises generated by LLMs: a showcase of ChatGPT in education applications. In: Proceedings of the 18th Workshop on Innovative Use of NLP for Building Educational Applications (BEA 2023), Toronto, Canada: Association for Computational Linguistics, pp. 610–625 (2023). https://doi.org/10.18653/v1/2023.bea-1.52
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