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Generative AI Technologies: Shaping the Future of Higher Education
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Generative AI Technologies: Shaping the Future of Higher Education

A special issue of Information (ISSN 2078-2489). This special issue belongs to the section "Artificial Intelligence".

Deadline for manuscript submissions: 30 June 2025 | Viewed by 44312

Special Issue Editors

Prof. Dr. Joao Carlos Lopes Batista

E-Mail Website
Guest Editor
ISCA-UA and Digimedia, University of Aveiro, 3810-500 Aveiro, Portugal
Interests: information and communication technologies in higher education; generative AI in higher education; information and communication overload
Special Issues, Collections and Topics in MDPI journals
Dr. Dimitris Apostolou

E-Mail Website
Guest Editor
Department of Informatics, University of Piraeus, 18534 Piraeus, Greece
Interests: decision support systems; artificial intelligence; information systems
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Anabela Mesquita

E-Mail Website
Guest Editor Assistant
CEOS.PP, ISCAP, Polytechnic of Porto, R. Jaime Lopes Amorim, 4465-004 Porto, Portugal
Interests: information systems; e-learning; technologies; generative AI in higher education; digital transformation
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Generative Artificial Intelligence (GAI) has emerged as a revolutionary technology with significant transformative potential across various sectors, including higher education. GAI tools enable the creation of diverse types of content with impressive precision. These technologies not only facilitate the automation of administrative and educational tasks but also open new possibilities for the teaching and learning process, fostering pedagogical innovation and personalized education.

The use of GAI in higher education encompasses a wide range of applications, ranging from the automatic generation of educational materials and assessments to personalized feedback for students. These technologies also raise important questions about ethics, academic integrity, and institutional issues. Furthermore, significant challenges are posed to educators regarding their roles and readiness, as well as to students in preparing for an evolving job market.

In this Special Issue, we will include studies presenting the latest research findings on the use of Generative AI in higher education contexts. Potential topics include, but are not limited to, the following:

  • Technology Usage Studies: Investigations into how GAI is being implemented and utilized in different higher education institutions;
  • Ethical Aspects: Discussions on the ethical challenges related to the use of GAI;
  • Assessment and Academic Integrity: The impact of GAI on assessment methods and academic integrity practices;
  • Student and Faculty Perceptions: Studies exploring how students and professors perceive and interact with GAI technologies;
  • Institutional Aspects: Analyses of institutional policies and strategies for GAI implementation;
  • Impact on Teaching and Learning: Research on how GAI is transforming teaching and learning processes;
  • Transformation of Teaching and Learning Processes: Case studies on pedagogical innovations facilitated by GAI;
  • Preparation of Students and Faculty: Investigations into how to effectively and ethically prepare educational stakeholders for the use of GAI.

Prof. Dr. Joao Carlos Lopes Batista
Prof. Dr. Anabela Mesquita
Dr. Dimitris Apostolou
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Information is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • generative artificial intelligence (GAI)
  • higher education
  • pedagogical innovation
  • personalized education
  • ethical challenges
  • academic integrity
  • teaching and learning transformation
  • educational stakeholders preparation

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (6 papers)

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Research

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14 pages, 1318 KiB  
Article
Exploring the Application of Text-to-Image Generation Technology in Art Education at Vocational Senior High Schools in Taiwan
by Chin-Wen Liao, Hsiang-Wei Chen, Bo-Siang Chen, I-Chi Wang, Wei-Sho Ho and Wei-Lun Huang
Information 2025, 16(5), 341; https://doi.org/10.3390/info16050341 - 23 Apr 2025
Viewed by 132
Abstract
Exploring the potential of text-to-image generation technology in Taiwanese vocational high school art courses, this study employs a conceptual framework of technology integration, creative thinking, and metacognitive abilities, focusing on its effects on teaching strategies as well as students’ digital art creation skills [...] Read more.
Exploring the potential of text-to-image generation technology in Taiwanese vocational high school art courses, this study employs a conceptual framework of technology integration, creative thinking, and metacognitive abilities, focusing on its effects on teaching strategies as well as students’ digital art creation skills and cognitive and creative development. The study was conducted through a multi-methodological approach that includes a systematic literature review plus participatory action research and qualitative analysis. The results showed that integrating text-to-image technology with education boosted students’ interest in activities such as prompt design and project creation and suited themes like landscapes and conceptual art. Testing AI tools enhanced technical proficiency (average of 3.95/5), while pedagogy shifted to project-based learning, increasing engagement. Students’ digital art skills improved from 3.26 to 3.78 (16% growth), with creativity and originality (3.82/5), style diversity, visual complexity, and divergent thinking notably advanced. The technology also fostered metacognitive skills and critical thinking, proving to be an effective teaching aid beyond a mere digital tool. This discovery provides a fresh theoretical viewpoint and instructional procedures for high school art education curricula, anchored in technology, and highlights the importance of nurturing students’ innovativeness and adaptability within the contemporary digital age. Full article
(This article belongs to the Special Issue Generative AI Technologies: Shaping the Future of Higher Education)
17 pages, 214 KiB  
Article
Finding Your Voice: Using Generative AI to Help International Students Improve Their Writing
by Leon Sterling, Chunchun Ye, Haoxuan Ying and Zhe Chen
Information 2025, 16(4), 289; https://doi.org/10.3390/info16040289 - 3 Apr 2025
Viewed by 399
Abstract
Students are faced with a wide range of writing tasks during their studies, including writing literature reviews, summarising papers and producing reflective reports. Writing tasks present a challenge for students who are not writing in their native language due to studying overseas. Indeed, [...] Read more.
Students are faced with a wide range of writing tasks during their studies, including writing literature reviews, summarising papers and producing reflective reports. Writing tasks present a challenge for students who are not writing in their native language due to studying overseas. Indeed, students writing in their native language have a distinct advantage in assignments involving writing. The rapid emergence of Generative Artificial Intelligence (Gen-AI) over the past three years has the potential to significantly impact the quality and efficiency of writing of non-native English speakers by providing international students with an opportunity to minimise the language barrier when writing in academia. This paper reports on a series of structured exercises we developed to determine how using Gen-AI tools built on large language models (LLMs) such as ChatGPT and Claude might improve student writing in the context of computing degrees. Two of the exercises were successfully repeated with a second and independent group of students. We analyse some issues to be aware of when using Gen-AI tools and make suggestions as to their effective use. The key underlying message is that students need to develop their own distinct voice. Full article
(This article belongs to the Special Issue Generative AI Technologies: Shaping the Future of Higher Education)
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19 pages, 3360 KiB  
Article
The Future of Higher Education: Trends, Challenges and Opportunities in AI-Driven Lifelong Learning in Peru
by Pablo Lara-Navarra, Antonia Ferrer-Sapena, Eduardo Ismodes-Cascón, Carlos Fosca-Pastor and Enrique A. Sánchez-Pérez
Information 2025, 16(3), 224; https://doi.org/10.3390/info16030224 - 14 Mar 2025
Viewed by 1014
Abstract
This study analyses future trends in lifelong learning in the Peruvian context. Using the DeflyCompass model, an artificial intelligence tool, the main trends affecting the evolution of postgraduate studies were identified, including the impact of generative AI on the personalisation of education, the [...] Read more.
This study analyses future trends in lifelong learning in the Peruvian context. Using the DeflyCompass model, an artificial intelligence tool, the main trends affecting the evolution of postgraduate studies were identified, including the impact of generative AI on the personalisation of education, the transformation of work and the growth of Generation Z as key players in the educational environment. The methodology applied combines a mixed qualitative and quantitative approach, based on the opinion of experts—45 participants from seven public/private universities in Peru—the technique of semantic projections, the use of generalist search engines and specialised databases, and other digital management resources such as Google Scholar profile analysis and online marketing campaign design tools. In particular, a total of 150 scientific papers and 300 articles from generalist sources were analysed. This approach made it possible to select, analyse and quantify the main trends in higher education in Peru and to assess their potential impact on the future development of graduate schools, specifically in the case of the Pontificia Universidad Católica de Perú (PUCP). The results highlight the importance of adapting postgraduate studies to new demands, such as the adoption of generative AI, the adaptation of personalised education and the integration of digital technologies to enhance the personal and professional growth of students. It also highlights the need to incorporate strategies that address the transformation of work, with a focus on developing digital skills and preparing for an ever-changing work environment. The study thus provides a guide for Peruvian universities on how to adapt their graduate programmes to emerging trends, promoting a flexible and technologically advanced education that responds to the needs of future professionals. Full article
(This article belongs to the Special Issue Generative AI Technologies: Shaping the Future of Higher Education)
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28 pages, 724 KiB  
Article
Determinants of ChatGPT Adoption Intention in Higher Education: Expanding on TAM with the Mediating Roles of Trust and Risk
by Stefanos Balaskas, Vassilios Tsiantos, Sevaste Chatzifotiou and Maria Rigou
Information 2025, 16(2), 82; https://doi.org/10.3390/info16020082 - 22 Jan 2025
Viewed by 2578
Abstract
Generative AI, particularly tools like ChatGPT, is reshaping higher education by enhancing academic engagement, streamlining processes, and fostering innovation. This study investigates the determinants of ChatGPT adoption intentions (CGPTAIs) by extending the Technology Acceptance Model (TAM) to include the mediating roles of perceived [...] Read more.
Generative AI, particularly tools like ChatGPT, is reshaping higher education by enhancing academic engagement, streamlining processes, and fostering innovation. This study investigates the determinants of ChatGPT adoption intentions (CGPTAIs) by extending the Technology Acceptance Model (TAM) to include the mediating roles of perceived trust (PT) and perceived risk (PR). Using a quantitative cross-sectional design, the data from 435 participants were analyzed using structural equation modeling (SEM) to explore the relationships among the perceived ease of use (PE), perceived intelligence (PI), perceived usefulness (PUSE), PT, and PR. Τhe findings reveal that the perceived ease of use (PE) and perceived intelligence (PI) significantly drive adoption intentions, while perceived usefulness (PUSE) plays a limited role. PR fully mediates the relationship between PUSE and CGPTAI and partially mediates PE and PI, while PT fully mediates PUSE and partially mediates PE, but not PI. Multi-group analysis highlights demographic differences, such as age and prior AI experience, in adoption pathways. These results challenge traditional TAM assumptions, advancing the model to account for the interplay of usability, intelligence, trust, and risk. Practical insights are provided for fostering ethical and responsible ChatGPT integration, safeguarding academic integrity, and promoting equitable access in higher education. Full article
(This article belongs to the Special Issue Generative AI Technologies: Shaping the Future of Higher Education)
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Review

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37 pages, 1867 KiB  
Review
Shaping the Future of Higher Education: A Technology Usage Study on Generative AI Innovations
by Weina Pang and Zhe Wei
Information 2025, 16(2), 95; https://doi.org/10.3390/info16020095 - 27 Jan 2025
Viewed by 1816
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 [...] Read more.
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. Full article
(This article belongs to the Special Issue Generative AI Technologies: Shaping the Future of Higher Education)
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27 pages, 2734 KiB  
Review
Generative AI and Higher Education: Trends, Challenges, and Future Directions from a Systematic Literature Review
by João Batista, Anabela Mesquita and Gonçalo Carnaz
Information 2024, 15(11), 676; https://doi.org/10.3390/info15110676 - 28 Oct 2024
Cited by 9 | Viewed by 37927
Abstract
(1) Background: The development of generative artificial intelligence (GAI) is transforming higher education. This systematic literature review synthesizes recent empirical studies on the use of GAI, focusing on its impact on teaching, learning, and institutional practices. (2) Methods: Following PRISMA guidelines, a comprehensive [...] Read more.
(1) Background: The development of generative artificial intelligence (GAI) is transforming higher education. This systematic literature review synthesizes recent empirical studies on the use of GAI, focusing on its impact on teaching, learning, and institutional practices. (2) Methods: Following PRISMA guidelines, a comprehensive search strategy was employed to locate scientific articles on GAI in higher education published by Scopus and Web of Science between January 2023 and January 2024. (3) Results: The search identified 102 articles, with 37 meeting the inclusion criteria. These studies were grouped into three themes: the application of GAI technologies, stakeholder acceptance and perceptions, and specific use situations. (4) Discussion: Key findings include GAI’s versatility and potential use, student acceptance, and educational enhancement. However, challenges such as assessment practices, institutional strategies, and risks to academic integrity were also noted. (5) Conclusions: The findings help identify potential directions for future research, including assessment integrity and pedagogical strategies, ethical considerations and policy development, the impact on teaching and learning processes, the perceptions of students and instructors, technological advancements, and the preparation of future skills and workforce readiness. The study has certain limitations, particularly due to the short time frame and the search criteria, which might have varied if conducted by different researchers. Full article
(This article belongs to the Special Issue Generative AI Technologies: Shaping the Future of Higher Education)
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