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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Bibliographic Details
Published in:Informatics (Basel) Vol. 12; no. 2; p. 51
Main Authors: Dong, Xiaorui, Wang, Zhen, Han, Shijing
Format: Journal Article
Language:English
Published: Basel MDPI AG 01.06.2025
Subjects:
Academic achievement
Analysis
Artificial intelligence
Burn out (Psychology)
Burnout
Chatbots
Chemical technology
cognitive load
Cognitive tasks
Collaboration
Colleges & universities
Critical thinking
Education
Education, Higher
Feedback
Generative artificial intelligence
Higher education
Information overload
Large language models
Learning
learning burnout
Likert scale
Pedagogy
Personalized learning
Problem solving
Programming languages
Questionnaires
Students
Study and teaching
Task complexity
Teachers
Teaching
teaching response strategies
Technology application
University students
China
ISSN:2227-9709, 2227-9709
Online Access:Get full text
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Summary: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.
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ISSN:2227-9709
2227-9709
DOI:10.3390/informatics12020051