Investigating How Generative AI Can Create Personalized Learning Materials Tailored to Individual Student Needs
- 1 Department of Computer Science, British University Dubai, Dubai, United Arab Emirates
- 2 Department of Computer Science, British University Dubai, Dubai, United Arab Emirates
- 3 Department of Cybersecurity, Dar Al-Hekma University, Jeddah, Saudi Arabia
- 4 Department of Engineering Science, Dubai, United Arab Emirates
- 5 College of Business, Abu Dhabi University, Dubai, United Arab Emirates
- 6 Department of Computer Information Science, Dubai, UAE
Abstract
This research focuses on the possibility of utilizing generative AI in developing learning content to suit each learner’s requirements. The study will evaluate the outcomes of the use of AI-created content in enhancing students’ interest, desire, and performance in contrast to conventional learning resources. A qualitative study involves interviewing educators and developers of AI to understand their experience and perception about the generative AI in education while the quantitative study involves the performance data of students to determine the effectiveness of the content generated by AI. The paper also explores the ethical and privacy issues that come with the integration of AI in learning and offers solutions to these issues. To compare the efficiency of the AI-generated learning material with the traditional one, the study designed a quantitative comparative study on the performance of the students in Object-Oriented Programming (OOP) course. The course was split into two equal independent assessments; the professors uploaded AI generated content such as the title of the lesson, the content that was taught, and the learning outcomes expected, for each class. LMS was integrated with the OpenAI API to write content that is in line with the learning objectives as defined earlier. Performance data of students as obtained from the two evaluations was used to determine the effect of using AI-generated contents on students’ learning. The results indicate that despite the students’ increased test scores and grades after applying AI-created study materials, some of them are not benefited from them. These are some of the effects that show that it is essential to consider aspects like students’ interest, their prior knowledge, and the quality of the AI model while adopting generative AI in education.
- Bahdanau, D., Cho, K., & Bengio, Y. (2014). Neural Machine Translation by Jointly Learning to Align and Translate. arXiv preprint arXiv:1409.0473.
- Branco, P., Torgo, L., & Ribeiro, R. P. (2017). A Survey of Predictive Modeling on Imbalanced Domains. ACM Computing Surveys, 49, 1-50. https://doi.org/10.1145/2907070
- Brown, P. F., & Yarowsky, D. (2000). Natural Language Processing with Small Feed-Forward Networks. In Proceedings of the 6th Conference on Natural Language Learning (pp. 1-4). Association for Computational Linguistics.
- Chen, T., & Guestrin, C. (2016). XGBoost: A Scalable Tree Boosting System. In Procee d ings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 785-794). Association for Computing Machinery. https://doi.org/10.1145/2939672.2939785
- Devlin, J., Chang, M. W., Lee, K., & Toutanova, K. (2018). Bert: Pre-Training of Deep Bidirectional Transformers for Language Understanding. In Proceedings of NAACL-HLT 2019 (pp. 4171-4186). Association for Computational Linguistics.
- Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep Learning (Vol. 1). MIT Press.
- He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep Residual Learning for Image Recognition. In 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (pp. 770-778). IEEE. https://doi.org/10.1109/cvpr.2016.90
- Hinton, G., Deng, L., Yu, D., Dahl, G., Mohamed, A., Jaitly, N. et al. (2012). Deep Neural Networks for Acoustic Modeling in Speech Recognition: The Shared Views of Four Research Groups. IEEE Signal Processing Magazine, 29, 82-97. https://doi.org/10.1109/msp.2012.2205597
- Kim, Y. (2014). Convolutional Neural Networks for Sentence Classification. In Procee d ings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP) (pp. 1746-1751). Association for Computational Linguistics. https://doi.org/10.3115/v1/d14-1181
- Kingma, D. P., & Ba, J. (2014). Adam: A Method for Stochastic Optimization. arXiv: 1412.6980.
- LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep Learning. Nature, 521, 436-444. https://doi.org/10.1038/nature14539
- Mikolov, T., Chen, K., Corrado, G., & Dean, J. (2013). Efficient Estimation of Word R e presentations in Vector Space. arXiv: 1301.3781.
- Pennington, J., Socher, R., & Manning, C. (2014). Glove: Global Vectors for Word Representation. In Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP) (pp. 1532-1543). Association for Computational Linguistics. https://doi.org/10.3115/v1/d14-1162