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Supporting Self-Regulated Learning with Generative AI: A Case of Two Empirical Studies
Utrecht University, Heidelberglaan 8, 3584 CS Utrecht, the Netherlands, Heidelberglaan 8.
KTH, School of Electrical Engineering and Computer Science (EECS), Human Centered Technology, Media Technology and Interaction Design, MID.ORCID iD: 0000-0002-8543-3774
2024 (English)In: LAK-WS 2024 - Joint Proceedings of LAK 2024 Workshops, co-located with 14th International Conference on Learning Analytics and Knowledge, LAK 2024, CEUR-WS , 2024, p. 223-229Conference paper, Published paper (Refereed)
Abstract [en]

Self-regulated learning (SRL) plays an important role in academic success. However, many students struggle to effectively self-regulate their learning and they need support to improve their SRL as well as their learning outcomes. Research shows that SRL supports are generally effective but often do not benefit the students who need them the most. One reason is that the support is rarely personalized to their individual needs. With the advancement of technology and, more recently, the proliferation of generative AI-powered technologies (e.g., chatbots and large language models), there is a potential to better meet students’ needs, and at the same time, a greater call to examine ways to personalize SRL support using AI. In this workshop presentation, we introduce two work-in-progress empirical studies to explore the use of generative AI chatbots, specifically OpenAI’s ChatGPT, as a peer feedback tool and as a study tool to enhance SRL and learning performance in writing and reading, respectively, in the setting of higher education. Preliminary results of the empirical studies will be shared in the workshop. The presentation will contribute to the pressing discussion on opportunities and considerations in using generative AI tools to support SRL.

Place, publisher, year, edition, pages
CEUR-WS , 2024. p. 223-229
Keywords [en]
generative AI, higher education, personalized support, Self-regulated learning
National Category
Educational Sciences Human Computer Interaction
Identifiers
URN: urn:nbn:se:kth:diva-350557Scopus ID: 2-s2.0-85191997398OAI: oai:DiVA.org:kth-350557DiVA, id: diva2:1884468
Conference
2024 Joint of International Conference on Learning Analytics and Knowledge Workshops, LAK-WS 2024, Kyoto, Japan, Mar 18 2024 - Mar 22 2024
Note

QC 20240716

Available from: 2024-07-16 Created: 2024-07-16 Last updated: 2025-02-18Bibliographically approved

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Viberg, Olga

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CiteExportLink to record
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Citation style
  • apa
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