Students Collaboratively Prompting ChatGPT
This study investigated how undergraduate students collaborated when working with ChatGPT and what teamwork approaches they used, focusing on students’ preferences, conflict resolution, reliance on AI-generated content, and perceived learning outcomes. In a course on the Applications of Information...
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| Format: | Article |
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MDPI AG
2025-04-01
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| Series: | Computers |
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| Online Access: | https://www.mdpi.com/2073-431X/14/5/156 |
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| author | Maria Perifanou Anastasios A. Economides |
| author_facet | Maria Perifanou Anastasios A. Economides |
| author_sort | Maria Perifanou |
| collection | DOAJ |
| description | This study investigated how undergraduate students collaborated when working with ChatGPT and what teamwork approaches they used, focusing on students’ preferences, conflict resolution, reliance on AI-generated content, and perceived learning outcomes. In a course on the Applications of Information Systems, 153 undergraduate students were organized into teams of 3. Team members worked together to create a report and a presentation on a specific data mining technique, exploiting ChatGPT, internet resources, and class materials. The findings revealed no strong preference for a single collaborative mode, though <i>Modes #2, #4</i>, and <i>#5</i> were marginally favored due to clearer structures, role clarity, or increased individual autonomy. Students reasonably encountered initial disagreements (averaging 30.44%), which were eventually resolved—indicating constructive debates that improve critical thinking. Data also showed that students moderately modified ChatGPT’s responses (50% on average) and based nearly half (44%) of their overall output on AI-generated content, suggesting a balanced yet varied level of reliance on AI. Notably, a statistically significant relationship emerged between students’ perceived learning and actual performance, implying that self-assessment can complement objective academic measures. Students also employed a diverse mix of communication tools, from synchronous (phone calls) to asynchronous (Instagram) and collaborative platforms (Google Drive), valuing their ease of use but facing scheduling, technical, and engagement issues. Overall, these results reveal the need for flexible collaborative patterns, more supportive AI use policies, and versatile communication methods so that educators can apply collaborative learning effectively and maintain academic integrity. |
| format | Article |
| id | doaj-art-bdcbb74165224133a681255ade9cd5fe |
| institution | Kabale University |
| issn | 2073-431X |
| language | English |
| publishDate | 2025-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Computers |
| spelling | doaj-art-bdcbb74165224133a681255ade9cd5fe2025-08-20T03:47:53ZengMDPI AGComputers2073-431X2025-04-0114515610.3390/computers14050156Students Collaboratively Prompting ChatGPTMaria Perifanou0Anastasios A. Economides1SMILE Lab, University of Macedonia, 54636 Thessaloniki, GreeceSMILE Lab, University of Macedonia, 54636 Thessaloniki, GreeceThis study investigated how undergraduate students collaborated when working with ChatGPT and what teamwork approaches they used, focusing on students’ preferences, conflict resolution, reliance on AI-generated content, and perceived learning outcomes. In a course on the Applications of Information Systems, 153 undergraduate students were organized into teams of 3. Team members worked together to create a report and a presentation on a specific data mining technique, exploiting ChatGPT, internet resources, and class materials. The findings revealed no strong preference for a single collaborative mode, though <i>Modes #2, #4</i>, and <i>#5</i> were marginally favored due to clearer structures, role clarity, or increased individual autonomy. Students reasonably encountered initial disagreements (averaging 30.44%), which were eventually resolved—indicating constructive debates that improve critical thinking. Data also showed that students moderately modified ChatGPT’s responses (50% on average) and based nearly half (44%) of their overall output on AI-generated content, suggesting a balanced yet varied level of reliance on AI. Notably, a statistically significant relationship emerged between students’ perceived learning and actual performance, implying that self-assessment can complement objective academic measures. Students also employed a diverse mix of communication tools, from synchronous (phone calls) to asynchronous (Instagram) and collaborative platforms (Google Drive), valuing their ease of use but facing scheduling, technical, and engagement issues. Overall, these results reveal the need for flexible collaborative patterns, more supportive AI use policies, and versatile communication methods so that educators can apply collaborative learning effectively and maintain academic integrity.https://www.mdpi.com/2073-431X/14/5/156ChatGPTcollaborative learningcollaborative modescollaborative promptingGenAIproject-based learning |
| spellingShingle | Maria Perifanou Anastasios A. Economides Students Collaboratively Prompting ChatGPT Computers ChatGPT collaborative learning collaborative modes collaborative prompting GenAI project-based learning |
| title | Students Collaboratively Prompting ChatGPT |
| title_full | Students Collaboratively Prompting ChatGPT |
| title_fullStr | Students Collaboratively Prompting ChatGPT |
| title_full_unstemmed | Students Collaboratively Prompting ChatGPT |
| title_short | Students Collaboratively Prompting ChatGPT |
| title_sort | students collaboratively prompting chatgpt |
| topic | ChatGPT collaborative learning collaborative modes collaborative prompting GenAI project-based learning |
| url | https://www.mdpi.com/2073-431X/14/5/156 |
| work_keys_str_mv | AT mariaperifanou studentscollaborativelypromptingchatgpt AT anastasiosaeconomides studentscollaborativelypromptingchatgpt |