ChatGPT for Good? On Opportunities and Challenges of Large Language Models for Education
Open accessAbstract
Large language models represent a significant advancement in the field of AI. The underlying technology is key to further innovations and, despite critical views and even bans within communities and regions, large language models are here to stay. This position paper presents the potential benefits and challenges of educational applications of large language models, from student and teacher perspectives. We briefly discuss the current state of large language models and their applications. We then highlight how these models can be used to create educational content, improve student engagement and interaction, and personalize learning experiences. With regard to challenges, we argue that large language models in education require teachers and learners to develop sets of competencies and literacies necessary to both understand the technology as well as their limitations and unexpected brittleness of such systems. In addition, a clear strategy within educational systems and a clear pedagogical approach with a strong focus on critical thinking and strategies for fact checking are required to integrate and take full advantage of large language models in learning settings and teaching curricula. Other challenges such as the potential bias in the output, the need for continuous human oversight, and the potential for misuse are not unique to the application of AI in education. But we believe that, if handled sensibly, these challenges can offer insights and opportunities in education scenarios to acquaint students early on with potential societal biases, criticalities, and risks of AI applications. We conclude with recommendations for how to address these challenges and ensure that such models are used in a responsible and ethical manner in education.
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Supervised fine-tuning is hampered by the scarcity of tutoring datasets, as their collection is costly and poses ethical concerns.
“However, supervised fine-tuning is hampered by the scarcity of tutoring datasets, as their collection is costly and poses ethical concerns (Kasneci et al., 2023).”
From Towards the Pedagogical Steering of Large Language Models for Tutoring: A Case Study with Modeling Productive Failure (2025) · cites this paper as Kasneci et al., 2023
These applications draw on GenAI's core strengths-natural language processing, adaptability, and scalability-while simultaneously addressing key institutional priorities such as improving efficiency, enhancing student engagement, and fostering skill development.
“They draw on GenAI's core strengths-natural language processing, adaptability, and scalability (Kasneci et al., 2023), while simultaneously addressing key institutional priorities such as improving efficiency, enhancing student engagement, and fostering skill development.”
From Pedagogical Applications of Generative AI in Higher Education: A Systematic Review of the Field (2025) · cites this paper as Kasneci et al., 2023
ChatGPT continues to dominate because of its ease of use, its effectiveness in text-based academic tasks, and its general suitability for higher education contexts.
“Despite the diversity of available tools, ChatGPT continues to dominate due to its ease of use (especially version 3.5), its effectiveness in text-based academic tasks (e.g., writing, critical thinking), and its general suitability for higher education contexts (Kasneci et al., 2023).”
From Pedagogical Applications of Generative AI in Higher Education: A Systematic Review of the Field (2025) · cites this paper as Kasneci et al., 2023
The current trend suggests a preference for GenAI tools that prioritize simplicity, accessibility, and alignment with core educational tasks, such as writing and critical thinking.
“Overall, the current trend suggests a preference for GenAI tools that prioritize simplicity, accessibility, and alignment with core educational tasks, such as writing and critical thinking (Kasneci et al., 2023).”
From Pedagogical Applications of Generative AI in Higher Education: A Systematic Review of the Field (2025) · cites this paper as Kasneci et al., 2023
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References 64 · Cited by 7,552
- Authors
- 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., Stadler, M., Weller, J., Kuhn, J., & Kasneci, G.
- Affiliations
- Technical University of Munich (DE)
- Ludwig-Maximilians-Universität München (DE)
- Corresponding author
- Kasneci, Enkelejda
- ORCID
- Kasneci, Enkelejda
- Seßler, Kathrin
- Küchemann, Stefan
- Bannert, Maria
- Dementieva, Daryna
- Fischer, Frank
- Gasser, Urs
- Groh, Georg
- Günnemann, Stephan
- Hüllermeier, Eyke
- Krusche, Stephan
- Kutyniok, Gitta
- Michaeli, Tilman
- Nerdel, Claudia
- Pfeffer, Jürgen
- Poquet, Oleksandra
- Sailer, Michael
- Schmidt, Albrecht
- Seidel, Tina
- Stadler, Matthias
- Weller, Jochen
- Kasneci, Gjergji
- Venue
- Learning and Individual Differences
- Publisher
- Center for Open Science
- Published
- 2023-01-30
- Volume
- 103
- Pages
- 102274-102274
- ISSN
- 1041-6080 (ISSN-L)
- OpenAlex
- W4323655724
- Cited by
- 7,552
- References
- 64
- Citations by year
- 2026: 3309 · 2025: 2019 · 2024: 1571 · 2023: 646 · 2022: 2 · 2017: 2 · 2012: 1
- FWCI
- 1771.16
- Type
- journal article
- Language
- en
- Open access
- green
- doi.org
- Locations
- Learning and Individual Differences (publishedVersion)
- Open access LMU (Ludwid Maxmilian's Universitat Munchen) (submittedVersion, cc-by)
- OPUS (Augsburg University) (submittedVersion, cc-by-nc-nd)
- mediaTUM (Technical University of Munich) (submittedVersion)
- mediaTUM – the media and publications repository of the Technical University Munich (Technical University Munich) (submittedVersion, cc-by-nc-nd)
- EdArXiv (OSF Preprints) (submittedVersion, cc-by)
- EdArXiv (OSF Preprints) (submittedVersion)
- 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
- large language models, artificial intelligence in education, ChatGPT, educational content development, student engagement, personalized learning, teacher competence, AI literacy, critical thinking, fact-checking, algorithmic bias, human oversight, responsible AI use, ethical use of AI, pedagogical integration
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