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Controllable text generation (CTG) by large language models has a huge potential to transform education for teachers and students alike.
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Pérez, E. V., Santos, L. M. R., Pérez, M. J. V., de Castro Fernández, J. P., & Martín, R. G. (2012). Automatic classification of question difficulty level: Teachers’ estimation vs. students’ perception. In 2012 Frontiers in Education Conference Proceedings (pp. 1-5). IEEE
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Wang, Z., Valdez, J., Basu Mallick, D., & Baraniuk, R. G. (2022). Towards Human-Like Educational Question Generation with Large Language Models. In Artificial Intelligence in Education: 23rd International Conference, AIED 2022, Durham, UK, 2022, Proceedings, Part I (pp. 153-166). Cham: Springer International Publishing
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Baidoo-Anu, D., & Owusu Ansah, L. (2023). Education in the Era of Generative Artificial Intelligence (AI): Understanding the Potential Benefits of ChatGPT in Promoting Teaching and Learning. Available at SSRN 4337484
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Liu, P., Yuan, W., Fu, J., Jiang, Z., Hayashi, H., & Neubig, G. (2023). Pre-train, prompt, and predict: A systematic survey of prompting methods in natural language processing. ACM Computing Surveys, 55(9), 1-35
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Mulla, N., & Gharpure, P. (2023). Automatic question generation: a review of methodologies, datasets, evaluation metrics, and applications. Progress in Artificial Intelligence, 1-32
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Terwiesch, C. (2023). Would Chat GPT Get a Wharton MBA? A Prediction Based on Its Performance in the Operations Management Course. Mack Institute for Innovation Management at the Wharton School, University of Pennsylvania
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Cited alongside, same era.