Generative AI in Higher Education: Applications, Implications, and Future Directions

A special issue of Informatics (ISSN 2227-9709).

Deadline for manuscript submissions: 31 October 2025 | Viewed by 377

Special Issue Editors


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Guest Editor
College of Health, Sport and Engineering, Victoria University, Melbourne, Australia
Interests: generative AI in industry and higher education; information systems adoption; educational technologies and innovations; health informatics and analytics; decision making

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Guest Editor
Faculty of Science and Engineering, Southern Cross University, Gold Coast, Australia
Interests: technology/innovation diffusion and planning; software engineering; evolutionary computation; human–computer interaction; information system adoption; user experience

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Guest Editor
College of Arts, Business, Law, Education and IT, Victoria University, Melbourne, Australia
Interests: business analytics; artificial intelligence adoption; educational technologies; health analytics and informatics

Special Issue Information

Dear Colleagues,

The rapid advancement and integration of Generative Artificial Intelligence (Gen AI) in educational settings marks a pivotal transformation in the higher education landscape. As universities worldwide adapt to these emerging technologies, fundamental questions arise about teaching methodologies, learning assessment, and academic integrity. This Special Issue of Informatics aims to explore the applications, implications, and future directions of Gen AI in higher education, with a particular focus on how these technologies are reshaping traditional educational paradigms.

We welcome submissions addressing, but not limited to, the following topics:

  • Applications of Gen AI in curriculum design and delivery;
  • Impact of Gen AI on assessment strategies and academic integrity;
  • Integration of Gen AI tools in teaching and learning practices;
  • Institutional policies and frameworks for Gen AI adoption;
  • Student engagement and learning outcomes with Gen AI;
  • Faculty development and adaptation to Gen AI technologies;
  • Equity and accessibility considerations in Gen AI implementation;
  • Future skills and graduate employability in a Gen AI-enhanced workplace;
  • Ethical considerations and responsible use of Gen AI in education;
  • Pedagogical innovations and transformations enabled by Gen AI.

Dr. Amir Ghapanchi
Dr. Reza Ghanbarzadeh
Dr. Purarjomandlangrudi Afrooz
Guest Editors

Manuscript Submission Information

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Keywords

  • generative AI
  • higher education
  • educational technology
  • academic integrity
  • learning analytics
  • artificial intelligence in education
  • digital pedagogy
  • educational innovation
  • student assessment
  • educational policy

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Published Papers (1 paper)

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Research

17 pages, 880 KiB  
Article
Mitigating Learning Burnout Caused by Generative Artificial Intelligence Misuse in Higher Education: A Case Study in Programming Language Teaching
by Xiaorui Dong, Zhen Wang and Shijing Han
Informatics 2025, 12(2), 51; https://doi.org/10.3390/informatics12020051 - 20 May 2025
Abstract
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 [...] Read more.
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. Full article
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