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Generative AI Technologies: Shaping the Future of Higher Education

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

Deadline for manuscript submissions: 30 June 2026 | Viewed by 110912

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Special Issue Editors

Prof. Dr. Joao Carlos Lopes Batista

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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

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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

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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 250 words) can be sent to the Editorial Office for assessment.

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 1800 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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Published Papers (10 papers)

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Research

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22 pages, 1329 KB

Open AccessArticle

Voices of Researchers: Ethics and Artificial Intelligence in Qualitative Inquiry

by Juan Luis Cabanillas-García, María Cruz Sánchez-Gómez and Irene del Brío-Alonso

Information 2025, 16(11), 938; https://doi.org/10.3390/info16110938 - 28 Oct 2025

Viewed by 1209

Abstract

The rapid emergence of Generative Artificial Intelligence (GenAI) has sparked a growing debate about its ethical, methodological, and epistemological implications for qualitative research. This study aimed to examine and deeply understand researchers’ perceptions regarding the use of GenAI tools in different phases of the qualitative research process. The study involved a sample of 214 researchers from diverse disciplinary areas, with publications indexed in Web of Science or Scopus that apply qualitative methods. Data collection was conducted using an open-ended questionnaire, and analysis was carried out using coding and thematic analysis procedures, which allowed us to identify patterns of perception, user experiences, and barriers. The findings show that, while GenAI is valued for its ability to optimize tasks such as corpus organization, initial coding, transcription, translation, and information synthesis, its implementation raises concerns regarding privacy, consent, authorship, the reliability of results, and the loss of interpretive depth. Furthermore, a dual ecosystem is observed, where some researchers already incorporate it, mainly generative text assistants like ChatGPT, while others have yet to use it or are unfamiliar with it. Overall, the results suggest that the most solid path is an assisted model, supported by clear ethical frameworks, adapted methodological guidelines, and critical training for responsible and humanistic use. Full article

(This article belongs to the Special Issue Generative AI Technologies: Shaping the Future of Higher Education)

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25 pages, 1768 KB

Open AccessArticle

Generative AI in Education: Mapping the Research Landscape Through Bibliometric Analysis

by Sai-Leung Ng and Chih-Chung Ho

Information 2025, 16(8), 657; https://doi.org/10.3390/info16080657 - 31 Jul 2025

Cited by 3 | Viewed by 7282

Abstract

The rapid emergence of generative AI technologies has sparked significant transformation across educational landscapes worldwide. This study presents a comprehensive bibliometric analysis of GAI in education, mapping scholarly trends from 2022 to 2025. Drawing on 3808 peer-reviewed journal articles indexed in Scopus, the analysis reveals exponential growth in publications, with dominant contributions from the United States, China, and Hong Kong. Using VOSviewer, the study identifies six major thematic clusters, including GAI in higher education, ethics, technological foundations, writing support, and assessment. Prominent tools, especially ChatGPT, are shown to influence pedagogical design, academic integrity, and learner engagement. The study highlights interdisciplinary integration, regional research ecosystems, and evolving keyword patterns reflecting the field’s transition from tool-based inquiry to learner-centered concerns. This review offers strategic insights for educators, researchers, and policymakers navigating AI’s transformative role in education. Full article

(This article belongs to the Special Issue Generative AI Technologies: Shaping the Future of Higher Education)

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22 pages, 1448 KB

Open AccessArticle

A Framework for Generative AI-Driven Assessment in Higher Education

by Galina Ilieva, Tania Yankova, Margarita Ruseva and Stanimir Kabaivanov

Information 2025, 16(6), 472; https://doi.org/10.3390/info16060472 - 3 Jun 2025

Cited by 4 | Viewed by 10781

Abstract

The rapid integration of generative artificial intelligence (AI) into educational environments raises both opportunities and concerns regarding assessment design, academic integrity, and quality assurance. While new generation AI tools offer new modes of interactivity, feedback, and content generation, their use in assessment remains insufficiently pedagogically framed and regulated. In this study, we propose a new framework for generative AI-supported assessment in higher education, structured around the needs and responsibilities of three key stakeholders (branches): instructors, students, and control authorities. The framework outlines how teaching staff can design adaptive and AI-informed tasks and provide feedback, how learners can engage with these tools transparently, and how institutional bodies can ensure accountability through compliance standards, policies, and audits. This three-branch multi-level model contributes to the emerging discourse on responsible AI adoption in higher education by offering a holistic approach for integrating AI-based systems into assessment practices while safeguarding academic values and quality. Full article

(This article belongs to the Special Issue Generative AI Technologies: Shaping the Future of Higher Education)

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14 pages, 1318 KB

Open AccessArticle

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 2134

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 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 KB

Open AccessArticle

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

Cited by 1 | Viewed by 2692

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, 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 KB

Open AccessArticle

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

Cited by 3 | Viewed by 6608

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 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 KB

Open AccessArticle

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

Cited by 11 | Viewed by 9568

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 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 KB

Open AccessReview

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

Cited by 6 | Viewed by 7867

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. 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 KB

Open AccessReview

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 61 | Viewed by 58514

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 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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Other

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32 pages, 2243 KB

Open AccessSystematic Review

Empowering Student Learning in Higher Education with Generative AI Art Applications: A Systematic Review

by Weihan Rong, Mengyun Xiao, Long Zhao and Xiaolong Zhou

Information 2025, 16(12), 1070; https://doi.org/10.3390/info16121070 - 4 Dec 2025

Viewed by 283

Abstract

Generative Artificial intelligence (AI) art is increasingly integrated into higher education (HE). While its creative potential has been discussed, its actual pedagogical impact and implications for educational equity remain underexplored. This study conducts a systematic review to evaluate how AI art has been applied in HE settings, what teaching and learning outcomes it supports, and what structural barriers exist in its integration. Using the PRISMA framework, 65 peer-reviewed articles published Scopus and Web of Science. The included studies were synthesized thematically and find that generative AI tools are being used to support ideation, multimodal expression, and interdisciplinary projects. However, barriers such as limited faculty training and unclear evaluation standards may hinder equitable access and long-term integration. This review contributes a conceptual framework for understanding the integration of generative AI art, highlighting opportunities and structural limitations. It offers insights for curriculum designers, educators aiming to support responsible, creative, and inclusive uses of AI in arts education. Full article

(This article belongs to the Special Issue Generative AI Technologies: Shaping the Future of Higher Education)

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