How Students (Really) Use ChatGPT: Uncovering Experiences Among Undergraduate Students
Fuente:
arXiv
Guardado en:
| Autores principales: | , , , |
|---|---|
| Formato: | Preprint |
| Publicado: |
2025
|
| Materias: | |
| Acceso en línea: | |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
| _version_ | 1866912832457539584 |
|---|---|
| author | Ammari, Tawfiq Chen, Meilun Zaman, S M Mehedi Garimella, Kiran |
| author_facet | Ammari, Tawfiq Chen, Meilun Zaman, S M Mehedi Garimella, Kiran |
| contents | This study investigates how undergraduate students engage with ChatGPT in self-directed learning contexts. Analyzing naturalistic interaction logs, we identify five dominant use categories of ChatGPT: information seeking, content generation, language refinement, metacognitive engagement, and conversational repair. Behavioral modeling reveals that structured, goal-driven tasks like coding, multiple-choice solving, and job application writing are strong predictors of continued use. Drawing on Self-Directed Learning (SDL) and the Uses and Gratifications Theory (UGT), we show how students actively manage ChatGPT's affordances and limitations through prompt adaptation, follow-ups, and emotional regulation. Rather than disengaging after breakdowns, students often persist through clarification and repair, treating the assistant as both tool and learning partner. We also offer design and policy recommendations to support transparent, responsive, and pedagogically grounded integration of generative AI in higher education. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_24126 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | How Students (Really) Use ChatGPT: Uncovering Experiences Among Undergraduate Students Ammari, Tawfiq Chen, Meilun Zaman, S M Mehedi Garimella, Kiran Human-Computer Interaction This study investigates how undergraduate students engage with ChatGPT in self-directed learning contexts. Analyzing naturalistic interaction logs, we identify five dominant use categories of ChatGPT: information seeking, content generation, language refinement, metacognitive engagement, and conversational repair. Behavioral modeling reveals that structured, goal-driven tasks like coding, multiple-choice solving, and job application writing are strong predictors of continued use. Drawing on Self-Directed Learning (SDL) and the Uses and Gratifications Theory (UGT), we show how students actively manage ChatGPT's affordances and limitations through prompt adaptation, follow-ups, and emotional regulation. Rather than disengaging after breakdowns, students often persist through clarification and repair, treating the assistant as both tool and learning partner. We also offer design and policy recommendations to support transparent, responsive, and pedagogically grounded integration of generative AI in higher education. |
| title | How Students (Really) Use ChatGPT: Uncovering Experiences Among Undergraduate Students |
| topic | Human-Computer Interaction |
| url | https://arxiv.org/abs/2505.24126 |