Mitigating Learning Burnout Caused by Generative Artificial Intelligence Misuse in Higher Education: A Case Study in Programming Language Teaching
The advent of generative artificial intelligence (GenAI) has significantly transformed the educational landscape. While GenAI offers benefits such as convenient access to learning resources, it also introduces potential risks. This study explores the phenomenon of learning burnout among university s...
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MDPI AG
2025-05-01
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| author | Xiaorui Dong Zhen Wang Shijing Han |
| author_facet | Xiaorui Dong Zhen Wang Shijing Han |
| author_sort | Xiaorui Dong |
| collection | DOAJ |
| description | The advent of generative artificial intelligence (GenAI) has significantly transformed the educational landscape. While GenAI offers benefits such as convenient access to learning resources, it also introduces potential risks. This study explores the phenomenon of learning burnout among university students resulting from the misuse of GenAI in this context. A questionnaire was designed to assess five key dimensions: information overload and cognitive load, overdependence on technology, limitations of personalized learning, shifts in the role of educators, and declining motivation. Data were collected from 143 students across various majors at Shandong Institute of Petroleum and Chemical Technology in China. In response to the issues identified in the survey, the study proposes several teaching strategies, including cheating detection, peer learning and evaluation, and anonymous feedback mechanisms, which were tested through experimental teaching interventions. The results showed positive outcomes, with students who participated in these strategies demonstrating improved academic performance. Additionally, two rounds of surveys indicated that students’ acceptance of additional learning tasks increased over time. This research enhances our understanding of the complex relationship between GenAI and learning burnout, offering valuable insights for educators, policymakers, and researchers on how to effectively integrate GenAI into education while mitigating its negative impacts and fostering healthier learning environments. The dataset, including detailed survey questions and results, is available for download on GitHub. |
| format | Article |
| id | doaj-art-b2f6385eebd644a4be150263d0b7c063 |
| institution | Kabale University |
| issn | 2227-9709 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | MDPI AG |
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| series | Informatics |
| spelling | doaj-art-b2f6385eebd644a4be150263d0b7c0632025-08-20T03:26:52ZengMDPI AGInformatics2227-97092025-05-011225110.3390/informatics12020051Mitigating Learning Burnout Caused by Generative Artificial Intelligence Misuse in Higher Education: A Case Study in Programming Language TeachingXiaorui Dong0Zhen Wang1Shijing Han2School of Big Data and Basic Sciences, Shandong Institute of Petroleum and Chemical Technology, Dongying 257000, ChinaThe Office of Academic Affairs, Shandong Institute of Petroleum and Chemical Technology, Dongying 257000, ChinaSchool of Natural Resources and Surveying, Nanning Normal University, Nanning 530001, ChinaThe advent of generative artificial intelligence (GenAI) has significantly transformed the educational landscape. While GenAI offers benefits such as convenient access to learning resources, it also introduces potential risks. This study explores the phenomenon of learning burnout among university students resulting from the misuse of GenAI in this context. A questionnaire was designed to assess five key dimensions: information overload and cognitive load, overdependence on technology, limitations of personalized learning, shifts in the role of educators, and declining motivation. Data were collected from 143 students across various majors at Shandong Institute of Petroleum and Chemical Technology in China. In response to the issues identified in the survey, the study proposes several teaching strategies, including cheating detection, peer learning and evaluation, and anonymous feedback mechanisms, which were tested through experimental teaching interventions. The results showed positive outcomes, with students who participated in these strategies demonstrating improved academic performance. Additionally, two rounds of surveys indicated that students’ acceptance of additional learning tasks increased over time. This research enhances our understanding of the complex relationship between GenAI and learning burnout, offering valuable insights for educators, policymakers, and researchers on how to effectively integrate GenAI into education while mitigating its negative impacts and fostering healthier learning environments. The dataset, including detailed survey questions and results, is available for download on GitHub.https://www.mdpi.com/2227-9709/12/2/51generative artificial intelligencelearning burnouthigher educationteaching response strategiescognitive load |
| spellingShingle | Xiaorui Dong Zhen Wang Shijing Han Mitigating Learning Burnout Caused by Generative Artificial Intelligence Misuse in Higher Education: A Case Study in Programming Language Teaching Informatics generative artificial intelligence learning burnout higher education teaching response strategies cognitive load |
| title | Mitigating Learning Burnout Caused by Generative Artificial Intelligence Misuse in Higher Education: A Case Study in Programming Language Teaching |
| title_full | Mitigating Learning Burnout Caused by Generative Artificial Intelligence Misuse in Higher Education: A Case Study in Programming Language Teaching |
| title_fullStr | Mitigating Learning Burnout Caused by Generative Artificial Intelligence Misuse in Higher Education: A Case Study in Programming Language Teaching |
| title_full_unstemmed | Mitigating Learning Burnout Caused by Generative Artificial Intelligence Misuse in Higher Education: A Case Study in Programming Language Teaching |
| title_short | Mitigating Learning Burnout Caused by Generative Artificial Intelligence Misuse in Higher Education: A Case Study in Programming Language Teaching |
| title_sort | mitigating learning burnout caused by generative artificial intelligence misuse in higher education a case study in programming language teaching |
| topic | generative artificial intelligence learning burnout higher education teaching response strategies cognitive load |
| url | https://www.mdpi.com/2227-9709/12/2/51 |
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