Generative AI in higher education: A bibliometric review of emerging trends, power dynamics, and global research landscapes

Purpose Generative AI is changing every facet of life, including higher education. Educators are not immune to this disruption, which challenges their traditional mindsets. This paradigm shift requires critical reflection for educators to unlearn and relearn generative AI’s possibilities. This study explores how higher education educators must approach unlearning traditional teaching practices to adopt generative AI effectively. Design/methodology/approach The study applies a conceptual approach to explore using organizational unlearning theory as a pathway for meaningful generative AI integration. The conceptual integration of organizational unlearning and digital maturity is adopted to map multiple stages for higher education educators to integrate GenAI as an assistant. Findings Five nonlinear maturity stages are proposed for higher education educators to adopt practically: (i) foundation awareness, (ii) educational experimentation, (iii) strategic implementation, (iv) transformational practice, and (v) educational ecosystem architects. Each stage reflects on recognition activities, unlearning levels, and key actions to realize generative AI’s integration. Research limitations/implications The conceptual approach requires empirical exploration to formalize the maturity framework in higher education. Practical implications The maturity stages provide a framework for progressive integration by starting with low-stakes GenAI experiments. The five stages require extensive introspection of how generative AI influences operational and transformational stages to architect an innovative higher education environment. Originality/value Organizational unlearning is an underexplored concept in higher education. This study provides a conceptual framework for initiating higher education unlearning in the generative AI era.

Generative AI (GenAI) has attracted a surge of attention from higher education constituents after OpenAI released ChatGPT in November 2022. While numerous articles discuss applications and perceptions of GenAI in higher education, no comprehensive review has considered commonalities and differences among various educational stakeholder groups and contexts. In this review, we synthesize the applications, capabilities, and perceptions of GenAI in higher education to provide stakeholders (i.e., students, instructors, researchers, staff, and administrators) with insights into this topic to facilitate GenAI integration in higher education. We reviewed 50 relevant empirical articles published from January 2023 to April 2025 on GenAI in higher education. Our findings demonstrate how GenAI has already been applied and present its potential for implementation across teaching and learning, research, and student affairs in higher education. Among various stakeholders in higher education, students hold a more open and positive attitude toward this rising technology, while instructors and researchers hold mixed attitudes toward GenAI usage, and administrators tend to hold an open but cautious attitude toward GenAI implementation. Addressing common stakeholder concerns and needs, we outline institutional strategies for responsible GenAI integration, including launching GenAI learning hubs, formalizing license agreements, redefining academic originality, and implementing pilot programs.

With the rapid evolution and widespread adoption of Generative AI (GenAI), there has been a recent surge in research on students’ perceptions and usage of these tools for learning. However, a critical need remains to understand how students from various demographic groups perceive and utilize GenAI for educational purposes. This research aims to fill this gap by evaluating students’ use of GenAI, comfort level, perception of readiness, benefits and challenges, and demographic differences in their perceptions. Through an online survey of 482 students across diverse institutions of higher education primarily within the United States, the findings indicate a significant majority of students feel comfortable using GenAI tools and recognize their potential to enhance productivity and academic success. However, students also reported concerns related to GenAI use, including concerns about academic integrity, overreliance on AI, and data privacy and security. The analysis highlights differences in the subscales of perceived readiness (GenAI Comprehension; GenAI Utilization and Proficiency), benefits (Impact of GenAI; GenAI Empowerment), and challenges (Negative Impact of GenAI; GenAI Limitation) across demographic groups, including student classification, enrollment status, and institution types. Findings suggest a critical need for the integration of AI literacy into curricula to address opportunities and challenges posed by GenAI in higher education, and this study calls for additional research to explore evolving perceptions of GenAI use over time and its long-term impact on higher education.

The rapid rise of Generative AI (GenAI) tools is reshaping conversations about assessment and feedback in higher education. While much institutional attention focuses on detection, compliance, and academic integrity (Cotton et al., 2024), this presentation shifts the lens to educators and how they are actually using GenAI in assessment practice. We present findings from a grant-funded initiative at UNSW that explores educator-led innovation through a Postcards of Practice approach. The Postcards of Practice are one-page, practice-based narratives where educators document their use of GenAI tools. These postcards highlight applications including formative feedback generation, student prompting literacy, assessment redesign, and co-creation with AI. They reveal how educators are experimenting with GenAI to support student learning while navigating ethical concerns, transparency, and pedagogical alignment. Our study uses a qualitative interpretive methodology, combining thematic analysis of the postcards with follow-up interviews. The analysis draws on theoretical frameworks including feedback literacy (Carless & Boud, 2018), dialogic assessment (Nicol, 2010), and new paradigm feedback design (Winstone & Carless, 2020). We also apply institutional and national GenAI guidelines (Liu & Bridgeman, 2023; Perkins, 2023) to surface shared values such as authenticity, inclusivity, and responsible innovation that guide educators’ decisions. The aim of this study is to explore how educators are experimenting with GenAI in assessment and feedback, and to capture their emerging practices and reflections through the Postcards of Practice initiative. The central research question guiding this work is: How are educators integrating GenAI into assessment and feedback, and what opportunities, challenges, and support needs arise from these practices? This work advances Technology Enhanced Learning (TEL) by providing empirical insights into how GenAI is actually integrated at the coalface of teaching. Educators describe how GenAI supports more frequent, personalised feedback and builds student agency in learning. At the same time, they raise concerns about over-reliance, AI hallucination, and the need for clear pedagogical scaffolding. These reflections point to the need for professional development that is discipline-sensitive, responsive, and grounded in practice. The postcard approach also functions as a professional learning intervention. It prompts reflection, encourages cross-disciplinary dialogue, and helps build a local community of practice around GenAI use. Through this model, we demonstrate an innovative and scalable method of capturing and supporting TEL innovation in real time. The findings suggest GenAI is prompting a rethinking of assessment: from summative, compliance-driven models to more transparent, formative, and student-centred designs. Educators begin to embed feedback literacy, ethical AI use, and critical prompting into their teaching, with clear implications for program-level assessment and graduate capability development. To strengthen clarity, we propose a concise diagram mapping the emerging practices captured in the postcards against the theoretical frameworks of feedback literacy, dialogic assessment, and new paradigm feedback design. This visual representation illustrates how practical insights align with, extend, or challenge these frameworks, making the study’s contribution accessible across diverse tertiary contexts. This proposal offers exemplary innovation in TEL by foregrounding bottom-up, practice-led experimentation with GenAI. It is grounded in strong theoretical frameworks and applicable across diverse tertiary contexts. The Pecha Kucha format will present key insights through rich visual storytelling, including excerpts from the postcards themselves. We conclude by proposing future directions for research and institutional strategy, including how to embed GenAI into assessment ecosystems in ways that enhance learning, uphold integrity, and empower educators to lead digital transformation from within.

The rapid development of generative AI (GenAI) raises new questions in higher education such as: What should be the university policy regarding GenAI? How ought courses be redesigned for fair and resilient assessment? What the added pedagogical and didactical values when involving GenAI in teaching and learning activities? Different universities have rapidly created and presented contradictory standpoints and draft policies, and teachers show different opinions regarding the pros and cons of GenAI. This study has been carried out with a student perspective, where 16 students have been examining their own Master's programme on sustainable information provision. Students have assessed the assessment in their previous courses in the Master's programme. The aim of the study is to investigate how sustainable course activities and assignment are, and to explore how GenAI tools might support and facilitate teaching and learning activities. Moreover, the students were given the task to test detection software on GenAI generated solutions to assignments in chosen Master's courses. Students conducted these tasks as a part of a 7.5 ECTS project course in the same Master's programme as the investigated courses are a part of. For inspiration and for background information on artificial intelligence to the project work students participated in the first Symposium on AI Opportunities and Challenges (SAIOC) in December 2023. Data have been gathered from reports of 3 group projects where 16 students have investigated 5 freely chosen courses in the programme in each group work. Beside from testing GenAI tools in existing activities and assignments students also interviewed the subject matter experts that are responsible for the chosen courses. Results were firstly analysed and presented in group reports, combined with 16 individual reflection essays. Regarding the individual essays students were instructed to bring up ethical perspectives on GenAI in higher education, and also to present and discuss suggestions for how the current course design and assignments better could be redesigned for improved sustainability and fairness. Finally, all the group reports and the individual reflection essays were thematically analysed by the author, who also is the subject matter expert and main teacher for the project course. Findings show that many of the existing assignments in the Master's programme could be partly solved with different GenAI tools. The AI generated solutions showed different levels of quality and correctness for different types of activities and assignments. An ethical concern that many student essays brought up was the relatively poor quality of the tested detection software. A question in one of the essays was if teachers should use detection software with an accuracy rate just above 50% to evaluate student submissions. The recommendations from both the students and the author are to provide clear instructions about when GenAI is allowed and not in course activities, and to redesign the course structure for continuous assessment. With or without GenAI tools, a continuous assessment where the whole study path through a course is assessed, and not only isolated submissions, would strengthen fairness and sustainability. Finally, several students suggest oral examinations as a complement to the existing assessment methods, even if their findings showed that GenAI tools can be used to prepare oral presentations.

Generative AI (GenAI) use is increasing across society in many different industries. Despite widespread adoption in workplaces, there is little consensus on the scope of its benefits and challenges at the level of most industries. Universities are being called upon to equip graduates with important knowledge and skills using GenAI for their professional contexts. Higher education, however, faces issues in effectively and sustainability embedding a use of GenAI in the student experience, which requires adjustments to learning and teaching activities, assessment, and learning outcomes and in access to useful GenAI platforms relevant to the various disciplines. As pedagogical models, ethical debates, and technologies continue to develop in this space, university teachers’ experiences of teaching with GenAI have yet to be explored in detail. Adopting a phenomenographic perspective, this study examines university teachers’ conceptions, perceptions, and approaches to using GenAI in teaching. Leveraging semi-structured interviews with 30 teaching academics, variations of teaching using GenAI were identified. Quantitative analysis was also conducted to capture the associations between these variations. By exploring the qualitative differences between these variations, a nuanced and important contribution to the GenAI discussion from the understanding of university teachers is uncovered. The results show that some ways of understanding and teaching with GenAI are more likely to help students develop effective knowledge and skills for the workplace than others. The findings also offer education leaders evidence to help design effective support for teachers using GenAI to innovate in the student experience. Through investigating the university teacher experience of GenAI, this research adds to the growing debate on the GenAI enabled benefits and challenges that are set to shape the higher education sector.

In the current spring of Artificial Intelligence, the rapid development of Generative AI (GenAI) has initiated vivid discussions in higher education. Opportunities as well as challenges have been identified and to cope with this new situation there is a need for a large-scale teacher professional development. With basic skills about GenAI teachers could use the new technology as an extension of the existing technology enhanced teaching and learning. The aim of this paper is to present and discuss the project FAITH (Frontline Application of AI and Technology-enhanced Learning for Transforming Higher Education). FAITH is a higher education pedagogical development initiative for institutional development for teachers with good fundamental skills in traditional pedagogy. A project with the overall objective of increasing the staff understanding of AI and to develop new competencies in the field of GenAI and technology enhanced learning. The research question that guided this study was: "What are the perceived opportunities, challenges and expectations of involving GenAI in higher education?" The overall research strategy for the FAITH project is design-based research, which involves iterative and cumulative development processes. In the early iteration that this study was a part of has been carried out inspired by Collective Autoethnography where members of the steering group behind the FAITH project, and members of the project team have constituted the main focus group. Data were collected by structured interviews where two GenAI tools also have been interviewed. Findings show that the expectations are high, but that the FAITH ambition of institutional development is depending on teachers’ motivation for taking an active part in the project. Another challenge could be that many teachers see GenAI as something that threatens the current course design, and that a general ban of GenAI is the appropriate solution. One of, several identified opportunities, is that a general revision of syllabi and assessment in an adaptation for GenAI enhanced learning would improve the current course design.

Research shows that feedback practices significantly impact key student outcomes, including performance, engagement, and satisfaction (Esmaeeli, Shandiz, Shojaei, Fazli & Ahmadi 2023). Feedback is a crucial component of learning in Higher Education (HE) and plays a vital role in developing critical thinking, improving retention, and enhancing student engagement. The importance of timely dialogic feedback in enhancing student engagement and potentially improving retention is well understood (Advance HE 2020). However, academic staff are increasingly time-poor, with reduced opportunities to provide regular in-depth quality feedback outside of that given for summative assessment (Henderson, Ryan & Phillips 2019). Early experimentations with using Generative AI (GenAI) such as ChatGPT to provide feedback for formative assessment recognises that students will learn and work in an AI-enabled world beyond their university studies (Bowditch 2023). GenAI can be leveraged inside and outside the classroom to achieve positive student engagement and improved skill development thereby affording them the skills and knowledge necessary to succeed (Hooda et al. 2022). Engaging with GenAI for feedback purposes offers an opportunity to increase equitable access to feedback across the student cohort, to support and further develop their critical skills and learning outcomes. As Verhoeven and Rana (2023) note, “AI disruption may present an opportunity to shift the focus from assessment of learning to assessment for learning”. Utilising GenAI for feedback purposes can provide rapid, personalised learning support, and aid with planning, drafting, and revising student work. However, this adoption of GenAI for feedback must be driven and developed by the educator, keeping the human in the loop to ensure quality (Atchley, Pannell, Wofford, Hopkins & Atchley). Our project draws on the principles of feedback literacy, current research on using AI as learning tool (Verhoeven & Rana 2023b; Tubino & Adachi 2022) and emphasises student-centred learning through dialogic feedback practices. The project draws on scholarship from Mollick and Mollick’s seven approaches to student use of AI (2023), Perkins, Furze, Roe and MacVaugh’s framework for ethical integration of AI in assessment (2024), and emerging work from Liu, Brightman, and Miller on GenAI and feedback (2023). This presentation addresses the conference theme of Technology, providing an overview and reflection on staff development and adoption of GenAI for feedback processes for the benefit of student learning. We will showcase four use cases of the use of GenAI to design and implement feedback creation for undergraduate formative assessment across the three Colleges at the University of Newcastle. All cases engage innovation in Technology Enhanced Learning (TEL) practice in developing GenAI tools to support student learning via feedback. The presentation addresses the benefits and challenges of each approach. Recognising the value of feedback in student learning, this PechaKucha is aimed at a diverse audience in HE. Our presentation will demonstrate applicability and adaptability to a range of disciplines as we explore the impact new and emerging GenAI technologies can have on HE. We will introduce the possibilities of using GenAI for feedback purposes, and encourage staff to consider experimenting with and adopting their own innovative TEL practices.

The rapid development of generative AI (GenAI) technologies in recent years has enabled new opportunities as well as new challenges in higher education. While many studies in computer science have focused on GenAI in programming education, fewer have examined its possibilities and challenges in requirements engineering (RE). This study aims to explore the impact of GenAI on the pedagogical aspects of RE in higher education, focusing on the student perspective, to analyse how GenAI might influence learning experiences, knowledge acquisition, and skill development. The main research question to answer was: "What are the students’ perspectives of the integration of GenAI in the educational practices of requirements engineering?" An Action research strategy was employed, with one of the authors also serving as teacher in the investigated course. A mixed-methods approach was used to collect both qualitative and quantitative data from workshops and surveys. During the workshops, students used ChatGPT to generate and evaluate software requirements and compared these to manually crafted requirements. Thematic analysis of the qualitative data captured students’ perspectives, while survey data identified trends and preferences. Findings show that while students generally had a positive experience with GenAI, valuing its efficiency and the quality of generated requirements, they also recognized the need for human oversight to maintain accuracy. The study highlights both opportunities and challenges of using GenAI in RE education. While GenAI increased learning engagement and helped with brainstorming, students faced difficulties in creating effective prompts and found it time-consuming to refine AI-generated requirements. A hybrid approach, combining AI-generated and manually created requirements, proved most effective by balancing AI's advantages with human insights. Further research is needed on how GenAI could be effectively integrated into computer science education.

Introduction. This paper uses the affordances framework to investigate how Generation Z (GenZ) students in higher education use generative AI (GenAI). There is an increasing need to gain a deeper understanding of GenZ’s interaction with artificial intelligence tools to better support their integration into higher education and the workforce. Method. Data was collated from semi-structured interviews with 34 GenZ students in higher education. Analysis. Thematic analysis was conducted on the qualitative data collected from the semi-structured interviews. Results. The findings suggest GenZ students have seamlessly integrated GenAI into diverse aspects of their lives. This study highlighted three main GenAI affordances that resonate with GenZ students: a) content searching and curation b) content generation and ideation, and c) content enhancement and refinement, revealing new opportunities for information access. Conclusions. This study shed light on the perceived affordances of GenAI for GenZs, addressing a gap in the current literature on GenAI. The findings underscore the significant extent to which GenAI has been integrated into GenZ students’ daily lives. Our study contributes to a better understanding of how GenAI’s affordances facilitate and support GenZ students, providing invaluable insights that can inform future policies on developing literacy for AI use tailored to this group.

The examination of the impact of Generative AI (GenAI) on higher education, especially from the viewpoint of students, is gaining significance. Although prior research has underscored GenAI's potential advantages in higher education, there exists a discernible research gap concerning the determinants that affect its adoption. In the present study, we aim to enhance our comprehension of the factors influencing the willingness of higher education students to adopt GenAI tools. To achieve this, we have developed an extended Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) model incorporating specific GenAI constructs. Our research methodology entailed the selection of a diverse sample of 374 students through random sampling. We then analyzed their data using Structural Equation Modeling (SEM) to gain insights into the complex relationships between various variables. The study found that students are more likely to use GenAI tools when they view them as supplemental resource and effort expectancy. It also revealed that perceived costs negatively impact adoption intentions, highlighting that financial factors are a significant barrier. Interestingly, Factors like information accuracy and hedonic motivation did not significantly affect students' adoption intentions. This study offers key insights for eLearning practitioners on integrating Generative AI (GenAI) tools into educational settings. It emphasizes the significance of resource perception and effort expectancy, demonstrating GenAI's potential to personalize learning experiences. eLearning platforms can utilize GenAI to enhance active learning through engaging methods and streamline course development. Addressing cost barriers is crucial for equitable access and inclusivity. A gradual approach to integration aligned with learning objectives is recommended, along with fostering critical engagement with GenAI tools to enhance digital literacy. Lastly, the study is constrained by its specific context, potential biases in self-reported data, a narrow focus on factors influencing students' intent to use GenAI tools and a cross-sectional design. Future research should encompass a broader range of factors, employ objective measures, and integrate observational data. Longitudinal studies or experimental designs could offer more comprehensive insights into how students' perceptions and intentions develop, thus promoting a more inclusive educational environment for all students.

This paper reports preliminary findings from an ongoing, campus wide research project on effective methods for generative AI applicability in pursuit of effective and engaging teaching and learning activities. Generative AI has had a tremendous adoption rate since the public release of ChatGPT 3.5 on November 30th 2022. This has necessitated that educators and administrators consider the potential opportunities and threats usage of generative AI by students and faculty may have on higher education. Recognizing the inevitability of generative AI, the researchers have proposed a university-wide research project to ascertain the changes in faculty and students perspectives when using generative AI The research project is two-fold. First, a longitudinal survey has been developed to address research questions about usage and perceptions of generative AI change over time. The second prong of this research project focuses on the implementation of new and continuing generative AI professional development workshops. These “AI Institutes” are targeted educational opportunities to provide faculty, staff, and students with hands-on experiences that model appropriate ways to teach and learn with generative AI tools. Workshops change based on audience needs, but will be designed to support such processes as introductory and advanced lessons on building learning activities which engage students with generative AI, administrative shortcuts, best practices for writing, and our university’s AI policy and principles. The longitudinal survey, thus, allows the research team to gauge changes in perspectives as the “AI Institutes'' are deployed and widespread adoption of generative AI tools become more mainstream. This paper reports on the first year of this research project, including one survey and one AI Institute. This research on integrating generative AI technologies into teaching and learning has important implications for the field of networked learning. As the paper explores, rapid advances in AI are changing how students and faculty interact with content and each other. Findings from the longitudinal survey and AI Institutes could provide insights into how to thoughtfully leverage these emerging tools to enhance connections, dialogue, collaboration, and co-creation of knowledge within digital learning networks. While further research is needed, this project takes an important first step in assessing faculty and student perceptions that can inform appropriate AI integration. Lessons learned could guide other institutions exploring the potentials and pitfalls of weaving generative AI into networked learning ecosystems.

Future research recommendations for transforming higher education with generative AI

Despite the potential and challenges of generative AI (GenAI) in higher education, there remains a significant gap in empirical research to guide strategic decision-making. Current studies are often conceptual or small-scale explorations of staff and student experiences, ranging from analyses of the potential advantages of GenAI, through to expressions of serious concerns about risks for higher education (e.g. Chan & Hu, 2023). Large-scale and comprehensive, data-driven investigations remain rare, however. In particular, there is a need to explore the experiences of students, to better understand what they know, how they are using GenAI, why they are using it, and their attitudes about the use of AI in their studies, by their educators and in their future lives (e.g. Lodge et al., 2023; O’Dea, 2024). This Pecha Kucha reports on the second phase of a multi-institutional collaborative project. The project’s overall research question is ‘How do university students make meaning about GenAI in relation to themselves as learners?’ The first phase involved focus groups at each university, followed by a large-scale online survey. The analysis of the focus group data informed the design of a cross-sectional survey to explore students’ perspectives on GenAI on topics such as knowledge and access, use and usefulness, and attitudes. The survey development was an iterative and significant undertaking. Current literature was unable to provide a comprehensive picture of student use and attitudes. Given the rapidly changing GenAI landscape, we were also aware that student experience and attitudes are contextually dependent and may also evolve quickly. Whilst valuable, existing studies have not connected the varied dimensions of the student experience we seek to investigate. While keeping these studies in mind, this project used a grounded thematic approach to build its own survey instrument, beginning with 4 focus groups of 11 scholars to identify the key issues which needed to be explored within the Australian university context. This stage was followed by iterative development of the survey, in which over 200 suggested edits were provided by 17 scholars as well as a reference group of Deputy/Pro-Vice Chancellors. When completed, the survey was piloted by undergraduate students. The anonymous online survey was conducted in the second half of 2024 at four Australian Universities located in Victoria, New South Wales, and Queensland. Human Research Ethics Committee approval was granted at each University prior to conducting this study. The data is currently being collected. To date, the survey has been completed by 8,340 students. The Pecha Kucha will present key descriptive data on selected components of the survey including student knowledge and access, GenAI literacies and skills, perceptions of use and usefulness, and motivations and barriers for when they choose to (not) use GenAI. We will present findings of the whole sample, as well specific equity groups who may have experienced disadvantage or under-representation. For example, preliminary analysis already indicates significant differences in the ways in which international students use and trust generative AI. The findings will provide an evidence base to inform policies and practices that leverage GenAI's benefits while mitigating its risks.

Generative AI (GenAI) is penetrating all areas of life, including higher education, where teachers and examiners are debating how to respond. While there are legitimate concerns about its potential negative impact on academic integrity, there are also learning and efficiency benefits that this technological advancement brings (Crompton and Burke, 2023; Abdaljaleel et al., 2024; Bond et al., 2024). We must develop a workforce that can confidently, effectively, efficiently, and responsibly use technological advancements, including GenAI. Our study relates to a GenAI-related activity in a postgraduate Finance course. It explored the role GenAI played when students worked in groups and how it affected their interaction, engagement, learning, efficiency, and confidence. Does GenAI enhance or hinder group work, which is an essential graduate skill? Moreover, how do students perceive the role of GenAI in enabling them to engage with the task, complete it better, and interact with group members? While the ability to use GenAI effectively might be an important graduate skill, it is crucial to train students to become responsible citizens who use this technology ethically. In our presentation, we described how the activity was conducted and shared our findings, including how many students used GenAI when allowed, what they used it for, the types of GenAI used, how effectively prompts were used, what students learnt, and how GenAI affected their interaction and engagement in the task. Some insights about the benefits and challenges of GenAI would be obtained from this study. The design of our practical study in the classroom and the students’ experiences and perceptions about GenAI will be useful to the teaching community and policy makers in higher education. Moreover, the insight into the use of GenAI in the classroom will also be useful. Such an activity can be replicated and tailored to suit different classes.