Faculty perceptions of generative AI in Azerbaijani higher education

Aim/Purpose: This study aimed to investigate the potential of integrating Generative Artificial Intelligence (GenAI) in tertiary education. It examined current practices among teachers and learners regarding GenAI, as well as their perceptions of its benefits and challenges. Background: Higher education worldwide is seeing the increasing use of GenAI. However, its usage patterns and teachers’ and learners’ perceptions of its adoption are yet to be studied. The feasibility and viability of this emerging tool can be assessed by examining early usage patterns as predictors of formal adoption, as supported by the Technology Acceptance Model (TAM) and the Task-Technology Fit (TTF) frameworks. This study aims to fill that gap by examining both teachers’ and students’ practices and perceptions regarding various aspects of AI and its adoption in education. Methodology: A mixed-method approach was employed. Data were collected from 44 teachers and 186 students at Jashore University of Science and Technology through workshops and structured questionnaires based on the TAM and TTF frameworks. Quantitative data were analyzed with SPSS and MS Excel, while qualitative data were thematically analyzed. Contribution: This study contributes empirical evidence on the adoption of GenAI in a South Asian tertiary education context, enriching the body of knowledge on technology acceptance, digital pedagogy, and GenAI in education policy. By revealing the pictures of relevant variables of Generative Artificial Intelligence in Education (GenAIEd) in a unique context, such as Bangladesh, the findings have implications for similar situations. They can inform others about possible challenges and the usefulness of GenAIEd. Findings: Teachers and students are both moderately familiar with GenAI. The teachers primarily use it to prepare courses and materials, while students sporadically engage with GenAI, mainly for academic problem-solving, and they emphasize its role in personalized, learner-centered learning. GenAI familiarity is found to be a strong predictor of usage frequency. However, teachers express concerns about the reliability of GenAI, ethical implications, and the potential for deskilling. While the benefits and usefulness dominate, possible challenges and threats are marginally associated with the future adoption and use of GenAI. This finding is unique because, despite the overpowering ‘ease of use’ of the TAM model, ‘benefits or usefulness’ of the TTF model, challenges, and threats have been found as catalysts for GenAI adoption. Recommendations for Practitioners: Practitioners are to utilize GenAI to support, rather than replace, their teaching expertise. They should also encourage students to strike a balance between GenAI-assisted learning, critical thinking, and independent work. Furthermore, the institutions should introduce guidelines to ensure the ethical use of GenAI and academic integrity. Recommendation for Researchers: Researchers should explore the longitudinal effects of GenAI adoption on learning outcomes and skill development. They can also conduct comparative studies across different universities and disciplines. Investigating the role of GenAI in inclusive education and support for learners from disadvantaged backgrounds also demands research focus. Impact on Society: The findings highlight how GenAI can transform higher education in Bangladesh and similar contexts. It shows the importance of addressing the risks of overreliance and the unethical use of GenAI for effective learning. A balanced adoption could strengthen human–technology collaboration in education. On the other hand, it has revealed the aspects of GenAI, preferred by educators, that AI developers should consider. Future Research: Further studies should examine hybrid learning models that integrate GenAI with human expertise. Cross-cultural perspectives on GenAI in education remain another area of study. Furthermore, studies should be carried out to develop frameworks for maintaining academic authenticity while GenAI is being used in education.

We would like to start this editorial with sincere gratitude. In putting out a call with such a tight turnaround we were acutely aware of the pressure that we were putting on the contributors, the reviewers and ourselves as editors. However, we were equally cognisant of the rapidly changing nature of the world of Generative Artificial Intelligence (GenAI) and its impact on the world of education. Thus, we wanted to publish a timely issue by compressing the whole process from the call, to review, to copyediting and finally to publication into a timeframe of approximately 11 weeks. (Ultimately from call to publication the process took 81 days.) First, thank you to all who took the time to submit manuscripts for consideration. A good portion of academic labour is invisible and unrecognised and we want to acknowledge and thank you for the time you dedicated to creating submissions. Second, thank you to the reviewers who turned things around very quickly in a professional and supportive manner in order to meet our ambitious timetable. Finally, thank you to the authors who appear in this issue and who worked quickly to turn around revisions and edits. As an editorial team, we learned a great deal about our own procedures, processes and patterns which we will carry forward to continue to improve the Irish Journal of Technology Enhanced Learning. In the issue that follows, we hope to provide a snapshot of a moment in time. When ChatGPT was released in November 2022 it created ripples in education that had not been seen in quite some time. Countless articles about it being the downfall of education (Devlin, 2023; Chomsky, 2023) to the solution (Heaven, 2023; Seetharaman, 2023) and all things in-between (Leaver & Srdarov, 2023) flash across our screens daily. Places of education are scrambling to create policies and there has been a swift reaction to GenAI at national, European, and global level. In Ireland, the Quality and Qualification Agency (QQI) issued broad advice for tertiary education providers on GenAI in the context of assessment and academic integrity and reworking assessment strategies (National Academic Integrity Network, 2023). In Europe, The European Network for Academic Integrity (ENAI) published very useful recommendations on the Ethical use of Artificial Intelligence in Education in May (Foltynek et al., 2023). At the global level, UNESCO (2023) published a simple guide for educators called ChatGPT and Artificial Intelligence in higher education: Quick start guide in April. In November, Australia produced a national framework for the use of GenAI in schools (Commonwealth of Australia, 2023.) One clear throughline has been the need for faculty to increase their digital literacy and understanding of GenAI (Laupichler et al., 2022; Farrelly & Baker, 2023; Southworth et al., 2023). This was the driving force for this special issue. As a journal, we wanted to create a safe, open and scholarly platform for engaging with GenAI. The hope is that this issue can serve as a mentor text for discussion and experimentation.

Advances in generative artificial intelligence (GenAI) have created uncertainties and tensions in higher education, particularly concerning learning, equity and quality. Despite emerging empirical research, much current policy is based on assumptions about how and why students are using GenAI. This Pecha Kucha reports on 20 online focus groups involving 79 students from four Australian universities. Each focus group represents a mix of disciplines and levels of study (including undergraduate and postgraduate). We conducted reflexive thematic analysis, adopting a relational view of AI (Bearman & Ajjawi, 2023) that supports a nuanced examination of how AI uses are enacted, understood, and contested within educational settings. Our study shows that students use GenAI in diverse and complex ways and their beliefs about GenAI contain ambiguity, contradictions, and tensions. In this pecha kucha we focus on five interrelated tensions, identified across participants, and selected as particularly significant and challenging for educators. The salience of these tensions varied across participants but, together, they paint a complex picture of student engagement with GenAI. Tension 1 is between student perceptions of AI in terms of enhanced efficiency and concerns about academic integrity. Students reported that GenAI tools could speed up writing, editing, summarising, and simplifying complex materials. However, many also feared that short-cuts and efficiencies could lead to accusations of cheating. Tension 2 is between widespread adoption of GenAI tools and ambiguous policy around acceptable use. Many students used a diverse range of GenAI tools, yet a number of participants voiced uncertainty about allowable use of GenAI in assessments. A perceived lack of clear and detailed guidance from universities created confusion and anxiety, and the development of personal rules to avoid accusations of academic misconduct. Tension 3 is between empowerment and dependency. AI tools were sometimes seen as reducing inequalities (e.g. for international students or those requiring language support). On the other hand, some students expressed concerns about becoming dependent on GenAI tools where tasks were made too easy, undermining learning and skill development. Tension 4 is between access and equity. Closely related to tension 2, here, the reduction of barriers to academic writing and accessing educational resources is contrasted with concerns around exacerbating inequalities due to variation in access and support. These concerns are amplified through diversity of engagement, beliefs of students and educators around acceptability, and contextual pressures (e.g. fear of being left behind, time pressures, the perceived stakes of assessment). Tension 5 is between beliefs about deepened engagement with learning materials and reduced quality or accuracy of GenAI output. Some students reported that GenAI tools could provide useful perspectives on resources or simplify complex texts. However, many voiced frustration that GenAI tools sometimes provided incorrect information, required verification or “missed the point”, which could lead to significant additional work. These tensions highlight areas where students need additional support and guidance. The overlaps and entanglements of these tensions make their navigation in higher education particularly complex. These findings suggest practical implications for educators, policymakers, and institutions. For instance, to better support students, institutions should continue to develop clear, context-sensitive guidelines that resolve ambiguities around acceptable use (Tensions 1 and 2) and provide concrete strategies to balance the benefits of efficiency with concerns over academic integrity and dependency (Tensions 1 and 3). Additionally, efforts should be made to ensure equitable access to GenAI tools and support (Tension 4) while helping students critically assess the quality of AI-generated content (Tension 5).

Purpose. This article aims to analyze the role of generative artificial intelligence (GenAI), specifically ChatGPT, in educational activities while addressing concerns regarding academic integrity. The study explores the ambiguous boundaries of GenAI’s involvement in coursework, its potential ethical and technological challenges, and the need for clear policies regulating its use in education. Methodology. This study employs a mixed-methods approach, combining bibliometric analysis, direct interaction with ChatGPT, and a survey of Ukrainian students. Findings. The findings of this study reveal several key insights into the use of GenAI, specifically ChatGPT, in educational settings and its impact on academic integrity. The findings underscore the need for educational institutions to develop and implement policies that regulate GenAI’s role in academic activities. While GenAI offers significant potential as a technological assistant, there are risks associated with its misuse, particularly concerning academic dishonesty and the erosion of academic standards. Originality. The study’s originality lies in the comprehensive analysis of the problem of integrating GenAI, in particular ChatGPT, into the educational process from the point of view of academic integrity. For the first time, a systematic view of the stages of user interaction with GenAI has been proposed, potential points of violation of academic integrity at each of these stages are identified, and a “white box” concept has been developed to describe the use of GenAI, which allows controlling input and output parameters, minimizing risks. In addition, the study contains empirical data obtained as a result of a large-scale survey of Ukrainian students on their attitude to the use of GenAI in education, the level of awareness of university policies regarding GenAI, and support for the use of GenAI provided that academic integrity is observed. This outcome allows identifying the gap between existing practices and the need to develop effective strategies for integrating GenAI into the educational process. Practical value. The practical value of the work lies in the fact that the study’s results can serve as the basis for the development of clear recommendations and policies on using GenAI in higher education institutions. The proposed “white box” model can be applied to create practical tools that will help students and teachers understand the potential risks and consequences of using GenAI and develop skills for responsible use of these technologies. The student survey results can be used to inform and ensure dialogue between stakeholders on the optimal ways of integrating GenAI into the educational space, taking into account ethical aspects and the need to maintain academic integrity.

The use of Generative Artificial Intelligence (GAI) in higher education has garnered significant attention from scholars and researchers since the release of ChatGPT, one of the prominent GAI tools, in late 2022. While academic communities are increasingly recognizing the potential of GAI in teaching and learning, concerns persist regarding the impact of individual backgrounds and employment statuses on attitudes toward GAI, particularly in private higher education. This paper examines the perspectives of academic staff across different disciplines and employment statuses on their familiarity with and incorporation of GAI technologies in teaching. It emphasizes how to integrate GAI technologies effectively into teaching while upholding academic integrity and ensuring the quality of education. The findings, derived from an online survey and descriptive analysis, reveal significant variation in GAI familiarity among disciplines, as well as differing approaches to integrating GAI tools into teaching practices and formulating policies to maintain academic integrity. Notably, full-time staff are generally more familiar with GAI than their casual counterparts. While most teaching staff are open to students using GAI in their studies, concerns about potential breaches of academic integrity, particularly in assessments, remain prominent. To address these concerns, we recommend developing a transparent academic integrity policy along with clear guidelines for GAI use tailored to different disciplines and employment statuses. Such measures would foster an innovative and creative learning environment while safeguarding the quality of education.

This research examines the integration of generative artificial intelligence (AI) technologies in educational settings through the lens of educator perspectives. As tools like ChatGPT, Claude, and other large language models become increasingly prevalent, understanding how educators perceive, adopt, and integrate these technologies is critical for effective implementation. We conducted a comprehensive mixed-methods study involving 285 educators from K-12 and higher education institutions across six countries. Through online surveys and semi-structured interviews, we assessed awareness levels, sentiment patterns, adoption barriers, and key factors influencing educator attitudes toward generative AI. Our findings reveal that 91% of educators are aware of generative AI tools, with 68% having experimented with them in professional contexts. Sentiment analysis indicates cautious optimism (mean score: 3.7/5), with educators recognizing potential benefits for personalized learning, content creation, and administrative efficiency while expressing concerns about academic integrity, equity, and pedagogical soundness. Regression analysis identified technological self-efficacy, institutional support, and perceived usefulness as primary positive predictors of adoption, while ethical concerns and fear of job displacement emerged as significant barriers. The study contributes actionable insights for policymakers, administrators, and educational technology developers. We recommend comprehensive professional development programs, establishment of clear ethical guidelines, and creation of supportive institutional frameworks that balance innovation with academic integrity. These findings provide a foundation for evidence-based decision-making as educational institutions navigate the transformative potential of generative AI. Keywords: Generative AI, Educational Technology, Teacher Attitudes, Technology Adoption, ChatGPT, Digital Pedagogy

The inclusion of Generative Artificial Intelligence (GAI) in higher education is revolutionizing teaching, learning, and research processes, presenting new opportunities and challenges to institutions worldwide. This paper explores the multidimensional inclusion of GAI in transforming higher education, with an emphasis on its applications in content development, individualized learning, and academic support systems. By utilizing algorithms capable of producing creative outputs such as text, images, and simulations, GAI enables the automation of administrative processes, increasing efficiency while promoting personalized learning experiences. This paper also looks at how GAI is utilized to enhance traditional pedagogical frameworks, giving educators new tools for curriculum creation and assessment. However, in addition to its potential benefits, GAI inclusion raises important ethical, pedagogical, and technological challenges, such as data privacy, academic integrity, and the digital divide. This paper examines the growing significance of GAI with a review of existing literature, case studies, and expert perspectives, highlighting its potential to alter educational practices while advocating appropriate applications. The findings are intended to provide an exhaustive framework for policymakers, educators, and technology developers to guide the effective and ethical integration of GAI into higher education institutions. Finally, this paper contributes to the discussion of how GAI might improve academic experiences and prepare future generations for a fast-changing technological landscape.

Authenticity and academic integrity in Generative Artificial Intelligence (GenAI) use among undergraduate nursing students.

Generative artificial intelligence (GAI) has emerged as one of the most disruptive technologies in higher education, transforming how students learn, write, research, and complete assessments. While GAI offers significant academic benefits, including improved access to information, writing support, personalized assistance, and productivity enhancement, its rapid use in higher education has generated serious concerns regarding academic integrity and misconduct. The ability of GAI tools to produce essays, summaries, code, answers, and other forms of academic content has challenged long-standing assumptions about authorship, originality, independent learning, and fair assessment. This article examines the academic integrity and misconduct risks associated with GAI in higher education. Using a narrative literature review approach, the study synthesizes recent scholarship on institutional policies, student behaviors, misconduct patterns, personality predictors, ethical concerns, and preventive strategies related to GAI use in academic contexts. The review finds that GAI-related misconduct is not limited to plagiarism but includes unauthorized assistance, concealed authorship, fabrication, contract-like substitution of academic labor, manipulation of assessments, and misuse of AI-generated content in research and publication. The findings further show that misconduct risks are shaped by institutional ambiguity, weak policy enforcement, assessment design flaws, student perceptions, personality traits, and uneven AI literacy. The article argues that academic integrity in the age of GAI must be addressed through a comprehensive framework that combines authentic assessment, clear governance policies, ethical literacy, early-warning systems, due process, and context-sensitive enforcement mechanisms. It concludes that higher education institutions must move beyond narrow anti-cheating responses and adopt a broader academic integrity strategy that recognizes the complexity of GAI use while preserving fairness, originality, trust, and educational purpose.

The purpose of the study is to predict possible trends in the impact of generative artificial intelligence, in particular ChatGPT technologies, on education. Predictive estimates are formed on the basis of expert discussions of the consequences of using these digital technologies in education, which are currently going on in the public space and in the scientific community. The main groups of expert opinions and scientific approaches are being identified and compared, which makes it possible to present a perspective vision of the processes of integrating generative artificial intelligence into education. Analysis and forecasting are mostly carried out on the example of the practice by using generative artificial intelligence in higher education, however, the main provisions and conclusions can be extrapolated to other levels of education.Materials and methods. In the course of the study, methods of qualitative analysis of expert opinions presented in the public space (in the media, social networks, on the websites of educational organizations and analytical agencies, in public speeches), as well as methods of meaningful analysis of scientific publications, were used. Grouping and classification of expert opinions and scientific approaches were carried out. The analysis also used the results of a sociological study conducted by means of online survey of students from the Plekhanov Russian University of Economics on a sample of more than 3 thousand people. Methods of social forecasting were used to form predictive estimates.Results. The analysis made it possible to conclude that public discourse on employing generative artificial intelligence in education is controversial. Five groups of expert opinions were identified regarding the impact of generative artificial intelligence on education, which differ as to the need for its use in educational organizations and the scope of educational transformations that can occur under its influence. The analysis of scientific discussions showed that scientific community has not finally determined the consequences of the practical impact of generative artificial intelligence on the field of education. At the same time, possible promising areas and problem areas of its use are being identified, as well as its potential to initiate new reforms in education. The following possible trends in the integration of generative artificial intelligence into education are predicted: gradual change in the paradigm of education towards creativity-oriented education; increase of the share and scope of using artificial intelligence technologies in education; formation of new legal and ethical standards governing the use of generative artificial intelligence in education; increasing the importance and changing the role of the lecturer.Conclusions. Generative artificial intelligence has all the potential for solving long-term tasks of developing education. However, rapid technological development is inevitably associated with numerous risks, which require the creation of a methodology for using generative artificial intelligence in education, improvement of regulatory framework and solution of ethical problems. A new qualitative level of integration of a human being and artificial intelligence in the educational sphere is the thing of the future. Such integration will contribute to improving the quality of human capital in line with rapidly developing technologies of 5.0 Industrial Revolution.

The emergence of Generative Artificial Intelligence (GenAI) has reshaped higher education with both promising opportunities and significant challenges.Yet little is known about how global educational policies are evolving to address the assessment challenges posed by GenAI in terms of 'ethical considerations.'Additionally, current research has yet to thoroughly pinpoint potential pitfalls in existing policy areas that require further policy development attention.This review seeks to fill this gap by reviewing the existing literature over the last three years since GenAI's emergence, focusing on ethical guidelines in assessment.It also aims to offer policy recommendations to address these issues through this central research question: "What ethical guidelines can be established to leverage generative AI in higher education assessment while ensuring academic honesty and reconsidering the concept of academic integrity?"We followed PRISMA to select articles for this literature review.This review revealed that GenAI has greatly impacted assessment in three main ways: disrupting traditional ways of assessment, raising concerns about academic integrity and ethics, and necessitating the urgent need for a clear ethical framework for the responsible and productive usage of GenAI in the higher education context.This review also offers four critical insights into the existing research on policies: advocate for assessment strategies in adapting policies to encourage the ethical usage of GenAI, consider that reliance on AI detectors is inadequate, reconsider 'originality' and 'academic integrity' in the context of the GenAI era, and suggest frameworks to establish ethical guidance.

The rapid adoption of generative artificial intelligence (GenAI) technologies in higher education has raised concerns about academic integrity, assessment practices and student learning. Banning or blocking GenAI tools has proven ineffective, and punitive approaches ignore the potential benefits of these technologies. As a result, assessment reform has become a pressing topic in the GenAI era. This paper presents the findings of a pilot study conducted at British University Vietnam exploring the implementation of the Artificial Intelligence Assessment Scale (AIAS), a flexible framework for incorporating GenAI into educational assessments. The AIAS consists of five levels, ranging from “no AI” to “full AI,” enabling educators to design assessments that focus on areas requiring human input and critical thinking. The pilot study results indicate a significant reduction in academic misconduct cases related to GenAI and enhanced student engagement with GenAI technology. The AIAS facilitated a shift in pedagogical practices, with faculty members incorporating GenAI tools into their modules and students producing innovative multimodal submissions. The findings suggest that the AIAS can support the effective integration of GenAI in higher education, promoting academic integrity while leveraging technology’s potential to enhance learning experiences. Implications for practice or policy: Higher education institutions should adopt flexible frameworks like the AIAS to guide ethical integration of GenAI into assessment practices. Educators should design assessments that leverage GenAI capabilities, while supporting critical thinking and human input. Institutional policies related to GenAI should be developed in consultation with stakeholders and regularly updated to keep pace with technological advancements. Policymakers should prioritise research funding into the impacts of GenAI on higher education to inform evidence-based practices.

The increasing use of generative artificial intelligence (GenAI) technologies by students in assessment practices has drawn considerable attention in higher education (HE). This paper examines how HE students in Nepal perceive and experience GenAI use in assessment practices and how they associate its impacts with academic integrity, originality of student work, and assessment validity. Based on a longitudinal design, this study employed semi-structured interviews to generate the overall study data. Our findings showed that, while Nepali HE students appreciated the wide-ranging capabilities of GenAI, their justification of self-assumed frameworks and denial to acknowledge GenAI use in their work produced more concerning findings. In particular, as these students’ conceptions of GenAI emerged, how they subjectively drew ethical frameworks for GenAI use and their false impressions that text prompts and modifications of GenAI’s resulting outputs could retain the creative and intellectual values of their work threatened traditional notions of academic integrity and originality. Further, how they overestimated GenAI capabilities for reducing cognitive loads undermined the core visions of HE assessment systems. Our findings contribute to the practical understanding that urgent policy interventions and GenAI literacy programmes are required to motivate thoughtful, responsible, and transparent GenAI use for effective assessment practices in HE.

The rapid adoption of generative artificial intelligence (GenAI) in higher education has raised questions about student use, academic integrity, and institutional regulation. This study examines students' perceptions of and compliance with GenAI regulations in higher education, using a Singaporean university as a case study. Adopting a mixed‐methods approach, the research combines thematic analysis of survey responses and quantitative modelling based on the Theory of Planned Behaviour. Qualitative results reveal that students value GenAI for its learning support, time efficiency, and advanced capabilities, yet emphasise the need for clearer guidelines and improved education on appropriate usage. Quantitative analysis highlights the positive influence of guideline understanding on compliance and declaration honesty but notes the negative impacts of perceived restrictiveness and increased GenAI experience. Faculty influence promotes compliance but minimally affects honesty, indicating the need for distinct strategies to address visible and ethical adherence. This research underscores the importance of balanced, flexible regulatory frameworks that integrate educational clarity and faculty engagement, advancing the discourse on GenAI governance in higher education.

How Can <i>IJDS</i> Authors, Reviewers, and Editors Use (and Misuse) Generative AI?