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Cited by 52Artificial Intelligence in EducationIntelligent Tutoring Systems and Adaptive LearningOnline Learning and Analytics

Shaping the future of higher education: a technology usage study on generative AI innovations

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Pang, Weina, Wei, Zhe·2025·Information
DOI 10.3390/info16020095
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Abstract

Generative Artificial Intelligence (GAI) is rapidly reshaping the landscape of higher education, offering innovative solutions to enhance student engagement, personalize learning experiences, and improve academic performance prediction. This study provides an in-depth exploration of GAI applications in educational contexts, drawing insights from 67 case studies meticulously selected from over 300 papers presented at the AIED 2024 conference. The research focuses on eight key themes from student engagement and behavior analysis to the integration of generative models into educational tools. These case studies illustrate the potential of GAI to optimize teaching practices, enhance student support systems, and provide tailored interventions that address individual learning needs. However, this study also highlights challenges such as scalability, the need for balanced and diverse datasets, and ethical concerns regarding data privacy and bias. Further, it emphasizes the importance of improving model accuracy, transparency, and real-world applicability in educational settings. The findings underscore the need for continued research to refine GAI technologies, ensuring they are scalable, adaptable, and equitable, ultimately enhancing the effectiveness and inclusivity of AI-driven educational tools across diverse higher education environments. It should be noted that this study primarily draws from papers presented at the AIED 2024 conference, which may limit global representativeness and introduce thematic biases. Future studies are encouraged to include broader datasets from diverse conferences and journals to ensure a more comprehensive understanding of GAI applications in higher education.

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  • Empirical research evaluating the effectiveness and impact mechanisms of GenAI-supported MOOC learning environments from the learner's perspective remains relatively limited.

    “empirical research evaluating the effectiveness and impact mechanisms of GenAI-supported MOOC learning environments from the learner’s perspective remains relatively limited (Pang and Wei, 2025)”

    From Exploring the factors influencing college students’ learning satisfaction in generative AI-supported MOOCs learning environment: a learning experience framework perspective (2025) · cites this paper as Pang and Wei, 2025

    Section 5.5, “User Trust and Engagement” (page unavailable), identifies a significant gap in assessing generative AI’s impact on user experience. That supports a general learner-experience research gap, but the passage does not establish that empirical research on GenAI-supported MOOCs, their effectiveness, or their impact mechanisms is limited. (LLM, conf=0.82)

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Cited by

Sorted by citations

Authors
Pang, W., & Wei, Z.
Affiliations
  • Zhejiang Yuexiu University (CN)
  • Civil Aviation Flight University of China (CN)
Corresponding author
  • Pang, Weina
  • Wei, Zhe
ORCID
Wei, Zhe
Venue
Information
Publisher
MDPI AG
Published
2025-01-27
Volume
16
Issue
2
Pages
95
ISSN
2078-2489 (ISSN-L)
DOI
10.3390/info16020095
OpenAlex
W4406904827
Cited by
52
References
1
Citations by year
2026: 37 · 2025: 15
FWCI
54.34
Type
journal article
Language
en
Open access
Licence
Locations
  • Information (publishedVersion, cc-by)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders
  • Fundamental Research Funds for the Central Universities (JG2023-53, J2022-042, PHD2023-027, MHJY2023027, 2023YFG0308, FZ2022ZZ01, 2024JG0226)
  • Civil Aviation Professional Project (JG2023-53, J2022-042, PHD2023-027, MHJY2023027, 2023YFG0308, FZ2022ZZ01, 2024JG0226)
  • Sichuan Science and Technology Program (JG2023-53, J2022-042, PHD2023-027, MHJY2023027, 2023YFG0308, FZ2022ZZ01, 2024JG0226)
  • CAAC Academy of Flight Technology and Safety Project (JG2023-53, J2022-042, PHD2023-027, MHJY2023027, 2023YFG0308, FZ2022ZZ01, 2024JG0226)
  • 2024 Statistical Education Reform Project (JG2023-53, J2022-042, PHD2023-027, MHJY2023027, 2023YFG0308, FZ2022ZZ01, 2024JG0226)
Topics
  • Physical Sciences › Computer Science › Information Systems › Artificial Intelligence in Education
  • Physical Sciences › Computer Science › Artificial Intelligence › Intelligent Tutoring Systems and Adaptive Learning
  • Physical Sciences › Computer Science › Computer Science Applications › Online Learning and Analytics
Keywords
generative AI, higher education, student engagement, personalized learning, student performance prediction, student behavior analysis, student support systems, data privacy, algorithmic bias, model transparency, scalability

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