Revolutionizing Education with Generative AI: Current Landscape and Future Outlook
Keywords:
Generative artificial intelligence, Education, Personalized learning, Adaptive learning environments, Educational analyticsAbstract
Generative artificial intelligence (AI) holds substantial promise for transforming education by enabling personalized learning experiences, automating content generation, and enhancing educational outcomes. This study reviews the current landscape and future outlook of generative AI in education, exploring its applications, benefits, challenges, ethical considerations, and future research directions. The review highlights diverse applications of generative AI, including personalized tutoring systems, adaptive learning environments, and data-driven educational analytics. These applications have shown potential in improving student engagement, learning efficacy, and instructional efficiency. However, the implementation of generative AI in education presents challenges such as algorithmic bias, transparency in decision-making, and ethical implications related to data privacy and technology dependence. Future research should focus on developing transparent and interpretable AI models, mitigating biases, ensuring data privacy, and assessing the long-term educational impacts of AI. Collaborative efforts across education, AI development, ethics, and policy-making are crucial to harnessing AI's potential responsibly and equitably in educational settings. This study advocates for informed decision-making and ethical considerations to maximize the benefits of generative AI while addressing its inherent challenges, ensuring a sustainable integration into educational practices.
References
Abdullah, M., Madain, A., & Jararweh, Y. (2022, November). ChatGPT: Fundamentals, applications and social impacts. In 2022 Ninth International Conference on Social Networks Analysis, Management and Security (SNAMS) (pp. 1-8). Ieee.
Andrin, G., & Kilag, O. K. (2023). Innovative Strategies for Research Enhancement: A Simulacrum Approach Among Master Teachers in the Division of Cebu City. Excellencia: International Multi-disciplinary Journal of Education (2994-9521), 1(5), 467-484.
Bibat, C. M., & Kilag, O. K. (2024). Heterogeneity in Research Capacities: Exploring Variations Among Philippine Higher Education Institutions. International Multidisciplinary Journal of Research for Innovation, Sustainability, and Excellence (IMJRISE), 1(5), 411-417.
Castelli, M., & Manzoni, L. (2022). Generative models in artificial intelligence and their applications. Applied Sciences, 12(9), 4127.
Dai, J., Wang, J., Huang, W., Shi, J., & Zhu, Z. (2020). Machinery health monitoring based on unsupervised feature learning via generative adversarial networks. IEEE/ASME Transactions on Mechatronics, 25(5), 2252-2263.
Furey, H., & Martin, F. (2019). AI education matters: a modular approach to AI ethics education. AI Matters, 4(4), 13-15.
Hughes, R. T., Zhu, L., & Bednarz, T. (2021). Generative adversarial networks–enabled human–artificial intelligence collaborative applications for creative and design industries: A systematic review of current approaches and trends. Frontiers in artificial intelligence, 4, 604234.
Jovanovic, M., & Campbell, M. (2022). Generative artificial intelligence: Trends and prospects. Computer, 55(10), 107-112.
Karras, T., Laine, S., & Aila, T. (2019). A style-based generator architecture for generative adversarial networks. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition (pp. 4401-4410).
Khosravi, H., Shum, S. B., Chen, G., Conati, C., Tsai, Y. S., Kay, J., ... & Gašević, D. (2022). Explainable artificial intelligence in education. Computers and Education: Artificial Intelligence, 3, 100074.
Kingma, D. P., & Welling, M. (2019). An introduction to variational autoencoders. Foundations and Trends® in Machine Learning, 12(4), 307-392.
Laupichler, M. C., Aster, A., Schirch, J., & Raupach, T. (2022). Artificial intelligence literacy in higher and adult education: A scoping literature review. Computers and Education: Artificial Intelligence, 3, 100101.
Lim, W. M., Gunasekara, A., Pallant, J. L., Pallant, J. I., & Pechenkina, E. (2023). Generative AI and the future of education: Ragnarök or reformation? A paradoxical perspective from management educators. The international journal of management education, 21(2), 100790.
Manire, E., Kilag, O. K., Cordova Jr, N., Tan, S. J., Poligrates, J., & Omaña, E. (2023). Artificial Intelligence and English Language Learning: A Systematic Review. Excellencia: International Multi-disciplinary Journal of Education (2994-9521), 1(5), 485-497.
Neller, T. W. (2017). AI education: Machine learning resources. Ai Matters, 3(2), 14-15.
Noy, S., & Zhang, W. (2023). Experimental evidence on the productivity effects of generative artificial intelligence. Science, 381(6654), 187-192.
Oermann, E. K., & Kondziolka, D. (2023). On chatbots and generative artificial intelligence. Neurosurgery, 92(4), 665-666.
Pavlik, J. V. (2023). Collaborating with ChatGPT: Considering the implications of generative artificial intelligence for journalism and media education. Journalism & mass communication educator, 78(1), 84-93.
Silver, D., Huang, A., Maddison, C. J., Guez, A., Sifre, L., Van Den Driessche, G., ... & Hassabis, D. (2016). Mastering the game of Go with deep neural networks and tree search. nature, 529(7587), 484-489.
Stokel-Walker, C. (2022). AI bot ChatGPT writes smart essays-should academics worry?. Nature.
Terwiesch, C. (2023). Would Chat GPT3 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, 45.
Uy, F. T., Sasan, J. M., & Kilag, O. K. (2023). School Principal Administrative-Supervisory Leadership During the Pandemic: A Phenomenological Qualitative Study. International Journal of Theory and Application in Elementary and Secondary School Education, 5(1), 44-62.
Yoo, J. (2019). A study on AI Education in Graduate School through IPA. Journal of The Korean Association of Information Education, 23(6), 675-687.
Zhai, X. (2022). ChatGPT user experience: Implications for education. Available at SSRN 4312418.
Zohny, H., McMillan, J., & King, M. (2023). Ethics of generative AI. Journal of medical ethics, 49(2), 79-80.
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- 2024-07-09 (2)
- 2024-07-04 (1)