Publisher
Springer Nature Switzerland
Reference14 articles.
1. AlKhuzaey, S., Grasso, F., Payne, T.R., Tamma, V.: Text-based question difficulty prediction: a systematic review of automatic approaches. Int. J. Artif. Intell. Educ. 1–53 (2023)
2. Benedetto, L.: A quantitative study of NLP approaches to question difficulty estimation, pp. 428–434 (2023)
3. Benedetto, L., Cremonesi, P., Caines, A., Buttery, P., Cappelli, A., Giussani, A., Turrin, R.: A survey on recent approaches to question difficulty estimation from text. ACM Comput. Surv. (CSUR) (2022)
4. Bitew, S.K., Deleu, J., Develder, C., Demeester, T.: Distractor generation for multiple-choice questions with predictive prompting and large language models. arXiv preprint arXiv:2307.16338 (2023)
5. Caines, A., et al.: On the application of large language models for language teaching and assessment technology (2023)