Examining the Flow Dynamics of Artificial Intelligence in Real-Time Classroom Applications
The integration of artificial intelligence (AI) into educational environments is fundamentally transforming the learning process, raising new questions regarding student engagement and motivation. This empirical study investigates the relationship between AI-based learning support and the experience...
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MDPI AG
2025-07-01
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| Series: | Computers |
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| Online Access: | https://www.mdpi.com/2073-431X/14/7/275 |
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| author | Zoltán Szűts Tünde Lengyelné Molnár Réka Racskó Geoffrey Vaughan Szabolcs Ceglédi Dalma Lilla Dominek |
| author_facet | Zoltán Szűts Tünde Lengyelné Molnár Réka Racskó Geoffrey Vaughan Szabolcs Ceglédi Dalma Lilla Dominek |
| author_sort | Zoltán Szűts |
| collection | DOAJ |
| description | The integration of artificial intelligence (AI) into educational environments is fundamentally transforming the learning process, raising new questions regarding student engagement and motivation. This empirical study investigates the relationship between AI-based learning support and the experience of flow, defined as the optimal state of deep attention and intrinsic motivation, among university students. Building on Csíkszentmihályi’s flow theory and current models of technology-enhanced learning, we applied a validated, purposefully developed AI questionnaire (AIFLQ) to 142 students from two Hungarian universities: the Ludovika University of Public Service and Eszterházy Károly Catholic University. The participants used generative AI tools (e.g., ChatGPT 4, SUNO) during their academic tasks. Based on the results of the Mann–Whitney U test, significant differences were found between students from the two universities in the immersion and balance factors, as well as in the overall flow score, while the AI-related factor showed no statistically significant differences. The sustainability of the flow experience appears to be linked more to pedagogical methodological factors than to institutional ones, highlighting the importance of instructional support in fostering optimal learning experiences. Demographic variables also influenced the flow experience. In gender comparisons, female students showed significantly higher values for the immersion factor. According to the Kruskal–Wallis test, educational attainment also affected the flow experience, with students holding higher education degrees achieving higher flow scores. Our findings suggest that through the conscious design of AI tools and learning environments, taking into account instructional support and learner characteristics, it is possible to promote the development of optimal learning states. This research provides empirical evidence at the intersection of AI and motivational psychology, contributing to both domestic and international discourse in educational psychology and digital pedagogy. |
| format | Article |
| id | doaj-art-dbe912cb86a14c79b4fd5e9b284d7ac7 |
| institution | Kabale University |
| issn | 2073-431X |
| language | English |
| publishDate | 2025-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Computers |
| spelling | doaj-art-dbe912cb86a14c79b4fd5e9b284d7ac72025-08-20T03:36:19ZengMDPI AGComputers2073-431X2025-07-0114727510.3390/computers14070275Examining the Flow Dynamics of Artificial Intelligence in Real-Time Classroom ApplicationsZoltán Szűts0Tünde Lengyelné Molnár1Réka Racskó2Geoffrey Vaughan3Szabolcs Ceglédi4Dalma Lilla Dominek5Doctoral School of Education, Faculty of Pedagogy, Eszterházy Károly Catholic University, Eszterházy Square 1, 3300 Eger, HungaryDoctoral School of Education, Faculty of Pedagogy, Eszterházy Károly Catholic University, Eszterházy Square 1, 3300 Eger, HungaryDoctoral School of Education, Faculty of Pedagogy, Eszterházy Károly Catholic University, Eszterházy Square 1, 3300 Eger, HungaryDoctoral School of Education, Faculty of Pedagogy, Eszterházy Károly Catholic University, Eszterházy Square 1, 3300 Eger, HungaryDepartment of Social Communication, Ludovika University of Public Service, 1083 Budapest, HungaryDepartment of Social Communication, Ludovika University of Public Service, 1083 Budapest, HungaryThe integration of artificial intelligence (AI) into educational environments is fundamentally transforming the learning process, raising new questions regarding student engagement and motivation. This empirical study investigates the relationship between AI-based learning support and the experience of flow, defined as the optimal state of deep attention and intrinsic motivation, among university students. Building on Csíkszentmihályi’s flow theory and current models of technology-enhanced learning, we applied a validated, purposefully developed AI questionnaire (AIFLQ) to 142 students from two Hungarian universities: the Ludovika University of Public Service and Eszterházy Károly Catholic University. The participants used generative AI tools (e.g., ChatGPT 4, SUNO) during their academic tasks. Based on the results of the Mann–Whitney U test, significant differences were found between students from the two universities in the immersion and balance factors, as well as in the overall flow score, while the AI-related factor showed no statistically significant differences. The sustainability of the flow experience appears to be linked more to pedagogical methodological factors than to institutional ones, highlighting the importance of instructional support in fostering optimal learning experiences. Demographic variables also influenced the flow experience. In gender comparisons, female students showed significantly higher values for the immersion factor. According to the Kruskal–Wallis test, educational attainment also affected the flow experience, with students holding higher education degrees achieving higher flow scores. Our findings suggest that through the conscious design of AI tools and learning environments, taking into account instructional support and learner characteristics, it is possible to promote the development of optimal learning states. This research provides empirical evidence at the intersection of AI and motivational psychology, contributing to both domestic and international discourse in educational psychology and digital pedagogy.https://www.mdpi.com/2073-431X/14/7/275artificial intelligence (AI)flow experiencehigher educationdigital pedagogygenerative AI |
| spellingShingle | Zoltán Szűts Tünde Lengyelné Molnár Réka Racskó Geoffrey Vaughan Szabolcs Ceglédi Dalma Lilla Dominek Examining the Flow Dynamics of Artificial Intelligence in Real-Time Classroom Applications Computers artificial intelligence (AI) flow experience higher education digital pedagogy generative AI |
| title | Examining the Flow Dynamics of Artificial Intelligence in Real-Time Classroom Applications |
| title_full | Examining the Flow Dynamics of Artificial Intelligence in Real-Time Classroom Applications |
| title_fullStr | Examining the Flow Dynamics of Artificial Intelligence in Real-Time Classroom Applications |
| title_full_unstemmed | Examining the Flow Dynamics of Artificial Intelligence in Real-Time Classroom Applications |
| title_short | Examining the Flow Dynamics of Artificial Intelligence in Real-Time Classroom Applications |
| title_sort | examining the flow dynamics of artificial intelligence in real time classroom applications |
| topic | artificial intelligence (AI) flow experience higher education digital pedagogy generative AI |
| url | https://www.mdpi.com/2073-431X/14/7/275 |
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