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dc.contributor.authorRavi, Prerna
dc.contributor.authorMasla, John
dc.contributor.authorKakoti, Gisella
dc.contributor.authorLin, Grace
dc.contributor.authorAnderson, Emma
dc.contributor.authorTaylor, Matt
dc.contributor.authorOstrowski, Anastasia
dc.contributor.authorBreazeal, Cynthia
dc.contributor.authorKlopfer, Eric
dc.contributor.authorAbelson, Hal
dc.date.accessioned2025-09-26T20:21:24Z
dc.date.available2025-09-26T20:21:24Z
dc.date.issued2025-04-25
dc.identifier.isbn979-8-4007-1394-1
dc.identifier.urihttps://hdl.handle.net/1721.1/162820
dc.descriptionCHI ’25, Yokohama, Japanen_US
dc.description.abstractThe emergence of generative AI, particularly large language models (LLMs), has opened the door for student-centered and active learning methods like project-based learning (PBL). However, PBL poses practical implementation challenges for educators around project design and management, assessment, and balancing student guidance with student autonomy. The following research documents a co-design process with interdisciplinary K-12 teachers to explore and address the current PBL challenges they face. Through teacher-driven interviews, collaborative workshops, and iterative design of wireframes, we gathered evidence for ways LLMs can support teachers in implementing high-quality PBL pedagogy by automating routine tasks and enhancing personalized learning. Teachers in the study advocated for supporting their professional growth and augmenting their current roles without replacing them. They also identified affordances and challenges around classroom integration, including resource requirements and constraints, ethical concerns, and potential immediate and long-term impacts. Drawing on these, we propose design guidelines for future deployment of LLM tools in PBL.en_US
dc.publisherACM|CHI Conference on Human Factors in Computing Systemsen_US
dc.relation.isversionofhttps://doi.org/10.1145/3706598.3713971en_US
dc.rightsCreative Commons Attributionen_US
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/en_US
dc.sourceAssociation for Computing Machineryen_US
dc.titleCo-designing Large Language Model Tools for Project-Based Learning with K12 Educatorsen_US
dc.typeArticleen_US
dc.identifier.citationPrerna Ravi, John Masla, Gisella Kakoti, Grace C. Lin, Emma Anderson, Matt Taylor, Anastasia K. Ostrowski, Cynthia Breazeal, Eric Klopfer, and Hal Abelson. 2025. Co-designing Large Language Model Tools for Project-Based Learning with K12 Educators. In Proceedings of the 2025 CHI Conference on Human Factors in Computing Systems (CHI '25). Association for Computing Machinery, New York, NY, USA, Article 138, 1–25.en_US
dc.contributor.departmentMassachusetts Institute of Technology. Department of Electrical Engineering and Computer Scienceen_US
dc.contributor.departmentMassachusetts Institute of Technology. Program in Comparative Media Studies/Writingen_US
dc.identifier.mitlicensePUBLISHER_POLICY
dc.eprint.versionFinal published versionen_US
dc.type.urihttp://purl.org/eprint/type/ConferencePaperen_US
eprint.statushttp://purl.org/eprint/status/NonPeerRevieweden_US
dc.date.updated2025-08-01T08:13:18Z
dc.language.rfc3066en
dc.rights.holderThe author(s)
dspace.date.submission2025-08-01T08:13:18Z
mit.licensePUBLISHER_CC
mit.metadata.statusAuthority Work and Publication Information Neededen_US


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