Transforming education with generative AI (GAI): key insights and future prospects
Abstract
Generative artificial intelligence (GAI) has demonstrated remarkable potential in both educational practice and research, particularly in areas, such as personalized learning, adaptive assessment, innovative teaching methods, and cross-cultural communication. However, it faces several significant challenges, including the comprehension of complex domain knowledge, technological accessibility, and the delineation of AI's role in education. Addressing these challenges necessitates collaborative efforts from educators and researchers. This article summarizes the state-of-the-art large language models (LLMs) developed by various technology companies, exploring their diverse applications and unique contributions to primary, higher, and vocational education. Furthermore, it reviews recent research from the past three years, focusing on the challenges and solutions associated with GAI in educational practice and research. The aim of the review is to provide novel insights for enhancing human–computer interaction in educational settings through the utilization of GAI. Statistical analysis reveals that the current application of LLMs in the education sector is predominantly centered on the ChatGPT series. A key focus for future research lies in effectively integrating a broader range of LLMs into educational tasks, with particular emphasis on the interaction between multimodal LLMs and educational scenarios.
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GenAI-supported MOOCs learning is a complex learning environment.
“GenAI-supported MOOCs learning is a complex learning environment (Gutiérrez-Páez et al., 2023; Lang et al., 2025).”
From Exploring the factors influencing college students’ learning satisfaction in generative AI-supported MOOCs learning environment: a learning experience framework perspective (2025) · cites this paper as Lang et al., 2025
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Lv, P., Wang, X., Nie, Y., Wu, R., & Wang, L. (2025) · Frontiers in Psychology · doi:10.3389/fpsyg.2025.1633686 · Cited by 5 · see citing works
“…on the performance of different students, and providing feedback on tailored resources and suggestions after assessment, GenAI is an efficient learning sup-port tool, although it falls short at the graduate level, but excels in knowledge retrieval (Lang et al., 2025; Simon Frieder et al., 2023).”
- Authors
- Lang, Q., Wang, M., Yin, M., Liang, S., & Song, W.
- Affiliations
- Northeast Normal University (CN)
- Education University of Hong Kong (HK)
- Venue
- IEEE Transactions on Learning Technologies
- Published
- 2025
- Volume
- 18
- Pages
- 230-242
- ISSN
- 1939-1382 (ISSN-L)
- 2372-0050
- OpenAlex
- W4407098046
- Cited by
- 76
- References
- 18
- Citations by year
- 2026: 54 · 2025: 22
- FWCI
- 78.33
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- IEEE Transactions on Learning Technologies (publishedVersion)
- Funders
- Scientific Research Project of Education Department of Jilin Province (JJKH20231320KJ)
- National Natural Science Foundation of China (62307006)
- Foundation of KeyLaboratory of Education Informatization for Nationalities
- Ministry of Education (EIN2024C001)
- Topics
- Physical Sciences › Computer Science › Information Systems › Artificial Intelligence in Education
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Physical Sciences › Computer Science › Artificial Intelligence › Intelligent Tutoring Systems and Adaptive Learning
- Keywords
- generative AI in education, large language models, ChatGPT, personalized learning, adaptive testing, teaching methods, intercultural communication, human-computer interaction in education, multimodal large language models, primary education, higher education, vocational education, technology accessibility
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