Co-designing Large Language Model Tools for Project-Based Learning with K12 Educators
Fuente:
arXiv
Gespeichert in:
| Hauptverfasser: | , , , , , , , , , |
|---|---|
| Format: | Preprint |
| Veröffentlicht: |
2025
|
| Schlagworte: | |
| Online-Zugang: | |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| _version_ | 1866917922688991232 |
|---|---|
| author | Ravi, Prerna Masla, John Kakoti, Gisella Lin, Grace Anderson, Emma Taylor, Matt Ostrowski, Anastasia Breazeal, Cynthia Klopfer, Eric Abelson, Hal |
| author_facet | Ravi, Prerna Masla, John Kakoti, Gisella Lin, Grace Anderson, Emma Taylor, Matt Ostrowski, Anastasia Breazeal, Cynthia Klopfer, Eric Abelson, Hal |
| contents | The 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_09799 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Co-designing Large Language Model Tools for Project-Based Learning with K12 Educators Ravi, Prerna Masla, John Kakoti, Gisella Lin, Grace Anderson, Emma Taylor, Matt Ostrowski, Anastasia Breazeal, Cynthia Klopfer, Eric Abelson, Hal Human-Computer Interaction Artificial Intelligence Computers and Society The 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. |
| title | Co-designing Large Language Model Tools for Project-Based Learning with K12 Educators |
| topic | Human-Computer Interaction Artificial Intelligence Computers and Society |
| url | https://arxiv.org/abs/2502.09799 |