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- Publisher Website: 10.1145/3657604.3664718
- Scopus: eid_2-s2.0-85199889151
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Conference Paper: GPTutor: Great Personalized Tutor with Large Language Models for Personalized Learning Content Generation
Title | GPTutor: Great Personalized Tutor with Large Language Models for Personalized Learning Content Generation |
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Authors | |
Keywords | adaptive learning chatgpt large language models personalized learning prompt engineering |
Issue Date | 2024 |
Citation | L@S 2024 - Proceedings of the 11th ACM Conference on Learning @ Scale, 2024, p. 539-541 How to Cite? |
Abstract | We developed GPTutor, a pioneering web application designed to revolutionize personalized learning by leveraging the capabilities of Generative AI at scale. GPTutor adapts educational content and practice exercises to align with individual students' interests and career goals, enhancing their engagement and understanding of critical academic concepts. The system uses a serverless architecture to deliver personalized and scalable learning experiences. By integrating advanced Chain-of-Thoughts prompting methods, GPTutor provides a personalized educational journey that not only addresses the unique interests of each student but also prepares them for future professional success. This demo paper presents the design, functionality, and potential of GPTutor to foster a more engaging and effective educational environment. |
Persistent Identifier | http://hdl.handle.net/10722/354345 |
DC Field | Value | Language |
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dc.contributor.author | Chen, Eason | - |
dc.contributor.author | Lee, Jia En | - |
dc.contributor.author | Lin, Jionghao | - |
dc.contributor.author | Koedinger, Kenneth | - |
dc.date.accessioned | 2025-02-07T08:48:01Z | - |
dc.date.available | 2025-02-07T08:48:01Z | - |
dc.date.issued | 2024 | - |
dc.identifier.citation | L@S 2024 - Proceedings of the 11th ACM Conference on Learning @ Scale, 2024, p. 539-541 | - |
dc.identifier.uri | http://hdl.handle.net/10722/354345 | - |
dc.description.abstract | We developed GPTutor, a pioneering web application designed to revolutionize personalized learning by leveraging the capabilities of Generative AI at scale. GPTutor adapts educational content and practice exercises to align with individual students' interests and career goals, enhancing their engagement and understanding of critical academic concepts. The system uses a serverless architecture to deliver personalized and scalable learning experiences. By integrating advanced Chain-of-Thoughts prompting methods, GPTutor provides a personalized educational journey that not only addresses the unique interests of each student but also prepares them for future professional success. This demo paper presents the design, functionality, and potential of GPTutor to foster a more engaging and effective educational environment. | - |
dc.language | eng | - |
dc.relation.ispartof | L@S 2024 - Proceedings of the 11th ACM Conference on Learning @ Scale | - |
dc.subject | adaptive learning | - |
dc.subject | chatgpt | - |
dc.subject | large language models | - |
dc.subject | personalized learning | - |
dc.subject | prompt engineering | - |
dc.title | GPTutor: Great Personalized Tutor with Large Language Models for Personalized Learning Content Generation | - |
dc.type | Conference_Paper | - |
dc.description.nature | link_to_subscribed_fulltext | - |
dc.identifier.doi | 10.1145/3657604.3664718 | - |
dc.identifier.scopus | eid_2-s2.0-85199889151 | - |
dc.identifier.spage | 539 | - |
dc.identifier.epage | 541 | - |