Theoretical and Natural Science
- The Open Access Proceedings Series for Conferences
Vol. 34, 29 April 2024
* Author to whom correspondence should be addressed.
This paper delves into the application of generative artificial intelligence and large language models in the field of education, with a particular focus on the rising trend of large language models. Large language models play a crucial role in intelligent tutoring systems by accurately understanding student queries through deep learning and providing personalized responses. Case studies showcase the exemplary utilization of natural language processing techniques and reasoning engines, albeit facing challenges related to real-time processing and privacy concerns. The latter part of the paper concentrates on the application of generative artificial intelligence and large language models in curiosity-driven learning and the integration of multimodal educational systems, emphasizing the technical frameworks and challenges associated with multimodal integration. Finally, the paper provides insights into future developments, highlighting research on the potential benefits in the field of education, while emphasizing concerns related to ethics and privacy.
Generative Artificial Intelligence, Large Language Models, Educational Technology, Intelligent Tutoring Systems, Curiosity-Driven Learning, Problem Solving
1. Kasneci, E., Seßler, K., Küchemann, S., Bannert, M., Dementieva, D., Fischer, F., … Kasneci, G. (2023, January 30). ChatGPT for Good? On Opportunities and Challenges of Large Language Models for Education. https://doi.org/10.35542/osf.io/5er8f
2. FLOWERS - 2022 - Annual activity report. (n.d.). https://radar.inria.fr/report/2022/flowers/index. html
3. Ayanwale, M. A., Sanusi, I. T., Adelana, O. P., Aruleba, K., & Oyelere, S. S. (2022). Teachers’ readiness and intention to teach artificial intelligence in schools. Computers & Education: Artificial Intelligence, 3, 100099.
4. https://doi.org/10.1016/j.caeai.2022.100099
5. Katsarou, E. (2021). The Effects of Computer Anxiety and Self-Efficacy on L2 Learners’ Self-Perceived Digital Competence and Satisfaction in Higher Education. Journal of Education and E-learning Research, 8(2), 158–172. https://doi.org/10.20448/journal.509.2021.82.158.172
6. Oyelere, S. S., Sanusi, I. T., Agbo, F. J., Oyelere, A. S., Omidiora, J. O., Adewumi, A. E., & Ogbebor, C. (2022). Artificial intelligence in African schools: Towards a contextualized approach. 2022 IEEE Global Engineering Education Conference (EDUCON). https://doi.org/10.1109/educon52537.2022.9766550
7. Kasneci, E., Sessler, K., Küchemann, S., Bannert, M., Dementieva, D., Fischer, F., Gasser, U., Groh, G., Günnemann, S., Hüllermeier, E., Krusche, S., Kutyniok, G., Michaeli, T., Nerdel, C., Pfeffer, J., Poquet, O., Sailer, M., Schmidt, A., Seidel, T., . . . Kasneci, G. (2023). ChatGPT for good? On opportunities and challenges of large language models for education. Learning and Individual Differences, 103, 102274. https://doi.org/10.1016/j.lindif.2023.102274
8. Karch, T. (2022, October 3). Contrastive multimodal learning for emergence of Graphical Sensory-Motor Communication. arXiv.org. https://arxiv.org/abs/2210.06468
9. Abdelghani, R., Wang, Y. H., Yuan, X., Wang, T., Lucas, P., Sauzéon, H., & Oudeyer, P. Y. (2023). GPT-3 Driven Pedagogical Agent for Cultivating Curiosity in Children’s Questioning Skills. International Journal of Educational Artificial Intelligence, 1-36.
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. Authors who publish this series agree to the following terms:
1. Authors retain copyright and grant the series right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this series.
2. Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the series's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this series.
3. Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See Open Access Instruction).