Knowledge enablers and barriers in generative AI adoption: educator perspectives from higher education

Background The use of generative artificial intelligence (GenAI) in informal digital learning of English (IDLE) foregrounds the need to understand the conditions under which learners adopt and continue using these tools. Objectives This study integrated GenAI literacy into Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) to explain behavioral intention and actual use of GenAI for IDLE. It further examined both net effects and configurational pathways for high GenAI usage. Methods We recruited 475 Chinese university students with prior IDLE experience. We used partial least squares structural equation modelling (PLS‐SEM) to test the extended UTAUT2 model and applied fuzzy‐set qualitative comparative analysis (fsQCA) to identify configurations. Results and Conclusions Effort expectancy, social influence, and habit significantly predicted behavioral intention, whereas performance expectancy, price value, hedonic motivation, and facilitating conditions were not significant. Habit, facilitating conditions, and behavioral intention predicted actual usage of GenAI for IDLE. GenAI literacy also showed direct positive effects on behavioral intention and actual usage. It negatively moderated the relationship between social influence and behavioral intention, but strengthened the effects of habit and behavioral intention on actual usage. Incorporating GenAI literacy improved the explanatory capacity of the UTAUT2 model in GenAI‐IDLE contexts. fsQCA analysis indicated that high levels of GenAI use can emerge from four configurations. Across them, GenAI literacy, hedonic motivation, and habit appeared as core contributors. These findings provide directions for future research and educational design to support effective informal language learning with GenAI.

The rapid advancement of generative artificial intelligence (GAI) has intensified interest in its potential to support English learning in higher education. However, the mechanisms through which students’ perceptions and motivations translate into learning achievement remain unclear. Drawing on the Unified Theory of Acceptance and Use of Technology (UTAUT) and Task–Technology Fit (TTF) theory, this study investigates how undergraduate students’ use of GAI relates to perceived English learning achievement and under what conditions these associations are amplified. Using covariance-based structural equation modeling (CB-SEM), data from 537 undergraduate students across five public universities in China were analyzed. The findings indicate that performance expectancy, effort expectancy, facilitating conditions, perceived competitiveness, and artificial intelligence self-efficacy significantly predict GAI use. In turn, use behavior mediates their relationships with perceived English learning achievement. Task–Technology Fit further moderates the link between use behavior and learning achievement, with stronger associations observed when GAI functionalities are perceived as closely aligned with task requirements. These results highlight the importance of use behavior and task alignment in explaining how GAI is associated with students’ perceived English learning achievement and extend technology acceptance research within AI-supported language learning contexts.

While industry practices evolve rapidly, marketing education in Australia and New Zealand faces challenges in keeping pace, particularly regarding the adoption of current marketing technologies (Harrigan et al., 2022). Generative AI, exemplified by systems like ChatGPT and DALL·E, has demonstrated benefits for learning (Baidoo-Anu & Ansah, 2023). However, despite its potential, there remains a dearth of practical guidance on effectively incorporating these technologies into marketing courses. This gap persists even as general frameworks for responsible and ethical AI use, such as the Australian Framework for Generative AI in Schools (2023), emerge. As the demand for graduates with generative AI skills grows in the job market, educators must explore innovative pedagogical approaches to bridge this gap. This academic poster presents an innovative application of generative artificial intelligence (GenAI) in the context of teaching digital marketing at the postgraduate level. Its purpose is to bridge the gap between academic theory and industry practice by encouraging educators to integrate AI tools into their curriculum through experiential learning pedagogy (Kolb, 2014), characterized by a learning process whereby knowledge is created through hands-on experiences. The poster exemplifies how various types of GenAI technologies — specifically text-based, image-based, and video-based — can enhance teaching content, tutorial exercises, and assessments within the digital marketing course. The poster showcases examples of how these GenAI tools are integrated in the course content, to guide students in generating innovative ideas for using AI in marketing to gain a competitive edge: Text-based GenAI: Tools like ChatGPT and Gemini can automatically generate search keywords for search engine marketing. By integrating text-based GenAI tools with established marketing technology (MarTech) tools such as Google Ads and Google Ads Keyword Planner, students engage in practical exercises that combine AI-generated initial ideas (e.g., search keywords) with further analysis (e.g., search volume, click-through rates, and bidding costs) using established MarTech tools. This hands-on approach enhances their learning experience and prepares them for real-world applications. Image-based GenAI: Platforms such as DALL·E, Midjourney, and Stable Diffusion enable the creation of custom images for display advertising, enhancing visual communication in marketing materials. Through experiential learning activities, students can explore ideas, seek unusual combinations, and inspire creativity faster with image-based GenAI tools, resulting in a greater variety of display ad materials. Video-based GenAI: Applications like Sora and Synthesia facilitate the production of short video clips suitable for social media marketing (e.g., YouTube Shorts, TikTok). By engaging in dynamic content creation exercises, students learn to streamline content creation, reduce manual work, and save both time and budget, thereby gaining practical skills in social media marketing. By incorporating these GenAI technologies through experiential learning pedagogy, educators can enrich the learning experience, foster critical thinking, and prepare students for the evolving landscape of digital marketing. Future research can study the use of GenAI in marketing education using theoretical frameworks such as the Unified Theory of Acceptance and Use of Technology (Venkatesh et al., 2016).

The integration of Generative Artificial Intelligence (GenAI) into health professions education (HPE) is rapidly transforming learning environments, raising questions about its impact on teaching and learning. This mixed methods study explores clinical educators’ and undergraduate students’ perceptions and attitudes about using GenAI tools in HPE at a tertiary hospital in Singapore. Using the Technology Acceptance Model (TAM) and the Unified Theory of Acceptance and Use of Technology (UTAUT) as theoretical frameworks, we designed and administered a survey and conducted interviews to assess participants’ perceived usefulness, ease of use, and concerns related to GenAI adoption. Quantitative survey data were analyzed for frequencies and percentages, while qualitative responses underwent thematic analysis. Results showed that students demonstrated higher GenAI adoption rates (68.7%) compared to educators (38.5%), with GenAI perceived as valuable for efficiency, research, and personalized learning. However, concerns included over-reliance on GenAI, diminished critical thinking, and ethical implications. Educators emphasized the need for institutional guidelines and training to support responsible GenAI integration. Our findings suggest that while GenAI holds great potential for enhancing education, structured institutional policies and ethical oversight are crucial for its effective use. These insights contribute to the ongoing discourse on GenAI adoption in HPE.

Purpose This study explores how information and communication technology (ICT) professionals adopt and integrate generative artificial intelligence (GenAI) applications in organizational settings. It examines perceived benefits and challenges, the influence of social and organizational dynamics and the role of professional function in shaping adoption practices. Design/methodology/approach Drawing on the Unified Theory of Acceptance and Use of Technology (UTAUT), the study employs a qualitative–interpretive approach based on semi-structured interviews with 20 ICT professionals across diverse sectors. Inductive content analysis was used to identify thematic patterns and role-based distinction. Findings The results affirm UTAUT's core constructs: performance expectancy, effort expectancy, social influence and facilitating conditions, while introducing “local filters”: contextual mediators shaped by professional role, sectoral norms and organizational culture. These filters influence how GenAI is evaluated and adopted across occupational domains. While some organizations support strategic integration, others impose restrictions that lead to covert usage. The findings also highlight the dynamic nature of GenAI technologies and the need for flexible, role-sensitive integration strategies. Practical implications This study provides actionable insights for business and organizational leaders on integrating Generative AI (GenAI) technologies. It highlights the importance of fostering user acceptance, providing targeted training, and cultivating a supportive organizational culture. These findings help businesses and organizations navigate GenAI adoption effectively, ensuring both operational efficiency and employee alignment with technological advancements. Originality/value The study extends UTAUT by incorporating role-based differentiation and contextual sensitivity, offering a more nuanced understanding of GenAI adoption. Practically, it highlights the need for clear institutional policies, robust AI literacy programs, and differentiated deployment models tailored to professional roles. By foregrounding the interplay between technological capabilities, professional identity, and institutional readiness, the research contributes to the evolving discourse on AI integration in the workplace.

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.

Psychological adaptation to scaffolded generative AI use in higher education: SEM and thematic evidence from a private university's new media marketing course.

In the digital era, generative artificial intelligence (GAI) is increasingly used in higher education, yet the psychological factors influencing its adoption are underexplored. This study examines the role of a growth mindset towards technology, defined as the belief that technological abilities can be developed in predicting GAI usage among Chinese undergraduates. Using the Unified Theory of Acceptance and Use of Technology (UTAUT), this study explored the mediating roles of performance expectancy, effort expectancy, and technology anxiety. A total of 500 students participated in an online survey. Mediation analysis showed that a growth mindset predicted GAI usage through performance expectancy, effort expectancy, and technology anxiety, even when perceived external resources and gender were statistically controlled. The findings underscore the importance of psychological readiness, alongside technical skills, in fostering GAI adoption in education. Future research should use longitudinal and experimental designs to validate these results.

The rapid rise of Generative Artificial Intelligence (GenAI) has transformed the way humans interact with technology and has revealed cognitive mechanisms that extend beyond the explanatory scope of traditional technology acceptance models, such as the Technology Acceptance Model (TAM), Technology Acceptance Model 2 (TAM2), and the Unified Theory of Acceptance and Use of Technology (UTAUT). This theoretical review examines the combined role of the Technology Acceptance Model (TAM) and Cognitive Response Theory (CRT) in explaining GenAI-related user behaviors. The increasing involvement of GenAI in knowledge production triggers complex cognitive reactions, including cognitive trust, curiosity, ambivalence, epistemic suspicion, and resistance, which fundamentally shape technology acceptance processes. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 guidelines, a systematic literature search was conducted in the Web of Science and Scopus databases. From 3,842 records published between 2014 and 2025, duplicates were removed, and the remaining studies underwent title–abstract and full-text screening. In the final stage, 69 publications were included in the review corpus. The findings indicate that, while perceived usefulness and perceived ease of use remain core determinants of GenAI adoption within the TAM framework, integrating CRT highlights the importance of deeper internal mechanisms, such as cognitive reappraisal, epistemic trust, algorithmic scepticism, cognitive load, and curiosity. Post-ChatGPT literature further emphasizes the influence of anthropomorphic cues and cognitive tension on user attitudes, trust calibration, and engagement. Overall, the combined application of TAM and CRT provides a more comprehensive theoretical lens for understanding GenAI interactions by concurrently capturing cognitive, emotional, and behavioural processes. This integrative approach offers a comprehensive lens for understanding cognitive, emotional, and behavioral processes in GenAI interactions.

This paper analyses the perceived influence of Generative Artificial Intelligence (GenAI) on student engagement in online higher education using the Self-Determination Theory (SDT) framework. Drawing on qualitative data from 27 experienced academics across the Australian tertiary sector, the study investigates the perspectives of online educators on how GenAI may influence three core psychological needs that are considered central to student engagement: autonomy, competence, and relatedness. The findings reveal that GenAI can enhance student autonomy through personalised learning opportunities, improve competence through real-time feedback and writing support, and support relatedness by enabling inclusive participation for linguistically diverse learners. Nevertheless, the study also identifies key risks, including over-reliance on GenAI, diminished critical thinking, reduced interaction with peers and instructors, reduced collaboration, and concerns around academic integrity. The paper argues that to harness GenAI’s pedagogical potential, higher education institutions must integrate GenAI literacy, student-centred instructional design, and actionable ethical frameworks. With such measures in place, GenAI can evolve from an emerging tool into a major driver of engagement, inclusion, and transformational learning in higher education.

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

As generative artificial intelligence (GenAI) reshapes digital journalism, questions about its consequences for media credibility and audience selectivity have intensified. Drawing on the Technology Acceptance Model (TAM), the Unified Theory of Acceptance and Use of Technology (UTAUT), and research on algorithm aversion, this study examines how distinct GenAI applications influence users’ evaluations of news organizations. We conducted a pre-registered choice-based conjoint experiment in Chile (N = 2145), a context marked by declining trust in news and increasing AI adoption. The design varied seven domains of GenAI use—including supporting tasks, content creation, personalization, human oversight, and transparency—to estimate their causal effects on perceived media credibility and outlet selection. Results show that users differentiate sharply across GenAI functions. Human oversight and disclosure emerge as the strongest positive predictors of both credibility and selection. In contrast, using GenAI for menial tasks or personalization does not significantly affect evaluations, while automated content production modestly reduces credibility and selection. Attitudes toward AI moderate effects on selection but not credibility. These findings indicate that audience responses are shaped less by the mere presence of GenAI than by the accountability structures governing its use.

This paper explored students' use of Generative Artificial Intelligence (GenAI) for Objective Structured Clinical Examinations (OSCE) preparation using a retrospective cohort study conducted over two years (2023–2024) across Australia and Malaysia. Results analysed OSCE grades and written self-reflections from students. The Unified Theory of Acceptance and Use of Technology (UTAUT) model was applied as a lens to interpret qualitative data using summative content analysis. Of 997 students, 163 (16.3%) stated they did use GenAI to prepare for OSCEs. Across both campuses, there was no significant difference in mean OSCE grades between GenAI users (79.9%, SD = 15.3) and non-GenAI users (79.6%, SD = 17.4; p = .6); however, non-GenAI users performed significantly better (p &lt; .05) in four of the seven communication rubric criteria. Themes around mistrust or perceived inaccuracy of GenAI data for clinical application deterred use in non-users. From our results, the use of GenAI did not demonstrate additional benefits for overall OSCE preparation, suggesting the potential need for more pedagogically aligned applications of GenAI tools to maximise their utility for clinical assessment preparation.

Background The application of generative artificial intelligence (GenAI) in education has been deepening. However, at the same time, behaviours that jeopardise academic health, such as learners' over‐reliance on generative AI and massive plagiarism of generated content of generative AI in essay writing, have begun to emerge, and the issue of generative AI ethics should not be underestimated. It is necessary to develop an in‐depth understanding of the issue of ethical adoption of generative AI for learners. Objectives This article examines the determinants of ethical adoption of generative artificial intelligence (GenAI) learning applications among college students. It explores the mechanisms through which these factors operate and investigates the moderating effects of key variables. Based on these findings, the study proposes targeted recommendations to foster responsible GenAI integration in education, offering valuable insights for the wider adoption of GenAI technologies in educational contexts. Methods This study constructs an ethical adoption model for college students' use of GenAI learning applications, integrating the technology acceptance model and the unified theory of acceptance and use of technology. Following the theoretical model development, empirical research was conducted—encompassing questionnaire surveys, quantitative data analysis and results interpretation—to validate the proposed framework. Results The results demonstrate that college students' intention to adopt ethical practices regarding generative AI, facilitating conditions and the management system exhibit a positive correlation with actual compliance with ethical norms. Among these factors, ethical intention exerts the strongest effect. Furthermore, students' performance expectation concerning the ethical adoption of generative AI is positively correlated with their ethical adoption intention. Gender, grade level, voluntariness of use and prior experience significantly moderate these influence pathways. Conclusions This study identifies key factors influencing college students' adoption of generative artificial intelligence (GenAI) in learning applications. The findings offer theoretical and practical insights to inform the responsible integration of GenAI technologies in educational settings.

While Generative Artificial Intelligence (GenAI) technologies like ChatGPT are revolutionizing education by offering unique interaction opportunities and prompting legislative shifts toward AI literacy, there remains a significant gap in understanding the factors that influence their acceptance and effective use in educational settings. In particular, the impact of students’ digital competencies and motivational factors on their acceptance and utilization of GenAI tools is not well understood. This study investigates how these variables influence undergraduate students’ acceptance and use of ChatGPT as an informal learning tool, aiming to advance understanding of GenAI adoption in higher education. By integrating the Digital Competence Framework (DigComp) with the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2), this research provides a comprehensive analysis of factors affecting students’ intention to use and actual use of ChatGPT. Data were collected from 544 undergraduate students using adapted UTAUT2 scales and digital competence measures aligned with DigComp. Confirmatory Factor Analysis ( n = 140) validated the adapted UTAUT2, and Structural Equation Modeling ( n = 404) explored relationships between variables. The findings reveal that habit, hedonic motivation, performance expectancy, and facilitating conditions significantly predict students’ intention to use ChatGPT, while behavioral intention, problem-solving skills, and ethical considerations positively influence actual use. Notably, the study highlights the critical role of problem-solving and ethical awareness—in the adoption of GenAI tools. These results suggest that existing digital competence frameworks may need updating to include GenAI-specific competencies such as prompt-writing skills, managing ongoing dialogs with GenAI tools, and critically evaluating AI-generated content.