Topic

Generative Artificial Intelligence (GenAI) is accelerating the digital transformation of higher education, reshaping how knowledge is designed, delivered, and assessed in teaching and learning (Samala et al., 2025; Robert & Muscanell, 2025). Compared with earlier educational technologies, GenAI produces novel outputs, responds dynamically to user prompts, and operates across modalities such as text and video (Sengar et al., 2024). This enables personalisation, curriculum innovation, and efficiencies in routine academic tasks (Hughes et al., 2025). Yet rapid adoption exposes unresolved challenges, including bias in model outputs and integrity risks in assessment (Maxwell et al., 2025). The uneven capability among staff and students, present the possibility that established policies and pedagogies become obsolete (An et al., 2025; Nguyen, 2025). In line with ECER 2026’s theme of ‘Knowing and Acting’, this paper explores how teaching academics understand GenAI’s potentials and limits under changing institutional conditions, and how these understandings inform what they intend to do next. The focus is explicitly future‑oriented: moving beyond from current usage to examine how academics anticipate their future integrating GenAI responsibly and effectively across diverse university contexts (Author et al., 2025; Robert & Muscanell, 2025).

Research Questions

RQ1: What are teaching academics’ attitudes and beliefs about how GenAI will shape future teaching and learning, and how do these attitudes influence their intentions to use GenAI?
RQ2: What contradictions or tensions do teaching academics experience as they plan for future integration of GenAI in their teaching?

Objective

The study explores the interconnections between attitudes, intentions, and anticipated future actions of teaching academics regarding GenAI adoption and future use. It seeks to identify the contradictions and tensions teaching academic are encountering as they move towards the embedding of GenAI tools into teaching practices. By foregrounding future intentions rather than only present practices, the research aims to guide institutions move from reactive compliance toward proactive, pedagogically grounded strategies for GenAI. Specifically, the objective is to clarify where academics perceive value (e.g., personalisation, feedback, efficiency) and where they foresee risks (e.g., bias, over‑automation, equity, teacher role), while surfacing the kinds of policy guidance and training academics consider credible and workable within their disciplines (Kangwa et al., 2025). Current research largely examines perceptions and attitudes towards present uses of GenAI, rather than perceptions of future use in higher education teaching. The ineffectiveness of GenAI ultimately depends on policies that align with academics’ anticipated needs and perspectives.

Theoretical Framework

The analysis is guided by the Theory of Planned Behavior (TPB) (Ajzen, 1991; Ajzen & Fishbein, 2005). This theory suggests that rational considerations, rather than unconscious impulses, tend to guide actions (Ajzen & Fishbein, 2005). According to this theory, attitudes toward a behaviour, subjective norms (perceived uptake and endorsement by peers), and perceived behavioural control (capability and enabling conditions) can jointly shape intentions and subsequent actions (Bosnjak et al., 2020). Applied to GenAI, TPB predicts that academics who hold favourable beliefs about usefulness and trust, observe peer adoption, often feel confident in their capability and are more likely to plan and enact GenAI use (Chiu et al., 2023). However, intentions do not inevitably translate into behaviour; contradictions emerge when capability is uneven, institutional support is limited, or ethical and integrity concerns remain unresolved (Basileo et al., 2024).

From a sociocultural perspective, technology adoption is shaped by institutional culture, disciplinary norms, and policy contexts. Academic uptake of GenAI tools reflects not only individual attitudes but also these broader influences. This study uses the Theory of Planned Behavior (TPB) to examine academics’ future intentions, linking what they know to how they are likely to act (Shata & Hartley, 2025).

Research Design This study adopts a qualitative, exploratory design (Young & Diem, 2023) to examine teaching academics’ beliefs, intentions, and anticipated future practices regarding the use of Generative AI tools (GenAI). The Theory of Planned Behaviour (TPB) provides the conceptual lens. Method An online questionnaire comprising six validated scales and five open-ended questions. Scales assessed: attitudes toward GenAI (48 items); social norms around its use (16 items; Ajzen & Fishbein, 1970); perceived behavioural control (12 items; Ajzen & Fishbein, 1977); authenticity values (12 items; Wood et al., 2008); intended or actual GenAI use (6 items; Ajzen, 1985); and participants’ goals and beliefs (12 items; McElwee & Haugh, 2010). The five open-ended questions (see Table 1) allowed for rich textual data. Data Collection Participants were teaching academics in higher education. Recruitment used professional networks and the Prolific platform. The instrument captured demographic data across 20 disciplines in 24 countries. Table 1. Open-ended questions Questions Response Nos. Q1. How might GenAI affect ‘the future of’ academic teaching in a higher education? n=466 Q2. What are your future plans regarding GenAI in your teaching? n=495 Q3. How might current infrastructure, resources, workload and culture in your institution, support or hinder the use of GenAI in your teaching? n=510 Q4. What are the features of GenAI that you feel could assist your teaching? n=508 Q5. If you are resistant to adopting GenAI in your teaching, can you explain your reservations? n=481 Data Analysis (Quantitative) Using correlations and multiple regressions, attitudes, social norms, and perceived behavioural control significantly predicted intentions to use GenAI (55% of variance) and actual use (61% of variance), with intentions directly predicting behaviour. Individual differences played a nuanced role: future clarity positively predicted favourable attitudes, while fixed mindset and social dominance orientation were unexpectedly positively associated with GenAI attitudes, suggesting these individuals may perceive GenAI as a competitive advantage rather than a threat. Data Analysis (Qualitative) Five researchers conducted inductive thematic analysis within an interpretivist epistemology. Each researcher coded responses, generating initial codes as concise keywords. A lead researcher clustered codes into candidate categories, refined through team consensus meetings. An independent researcher then validated categories by assigning responses and counting frequencies. Investigator triangulation produced six overarching categories: 1. Efficiency and automation; 2. Ethics and authenticity; 3. Quality; 4. Customised learning; 5. Institutional policy–practice; and 6. Socio-cultural implications. Salient excerpts were selected to exemplify each category and examine cross-category tensions.

1. Efficiency and automation (n=737). GenAI could expedite teaching activities and reduce demands of teaching or raise workload and job demands. "GenAI has the potential to free up educators' time to focus on more meaningful interactions ..." 2. Ethics and authenticity (n=326). The circumvent of academic rules or unethical behaviours. “It's a great tool but it's so easy to abuse, I worry …” "Students are paying for my knowledge; they should get my knowledge." 3. Quality (n=424). Improved quality – more extensive knowledge, information, and wisdom; over-reliance might diminish human judgment and experience. "AI also has its own limitations in terms of accuracy and … its trustworthiness." 4. Customised learning (n=197). Students’ information – tailored to needs, preferences, or interests. “Creates personalised study plans based on students' strengths, weaknesses, and progress”. 5. Institutional policy and practices (n=305 comments). Limitations – policies, training, infrastructure, resources, or funding. "There is no coherent policy on the use of GenAI in my institution …." 6. Sociocultural implications as well (n=217). The rapid decline of some jobs, and the eroding of the human connection in education. "I just don’t think [GenAI] should replace the human essence of content creation." Teaching academics expressed strong, nuanced, and conflicting opinions about GenAI in teaching and a spectrum of perspectives; enthusiasm and optimism to caution and resistance. The attitudes, and perceived behavioural controls are multifaceted and ambivalent. To utilise GenAI in the future, institutions must acknowledge and accommodate complexities. Rational deliberations rather than unconscious impulse will guide future actions. GenAI’s future use will hinge on academics’ attitudes and institutional conditions. e.g., policy, ethics, and capability. Selective, pedagogically justified integration that safeguards authenticity and relational in teaching is necessary for efficiency and personalisation in teaching. Investment in coherent governance, infrastructure, and discipline‑sensitive professional development will support teaching academics usage of GenAI.

Author et al., (2025) An, Y., Yu, J. H., and James, S. (2025). Investigating the higher education institutions’ guidelines and policies regarding the use of generative AI in teaching, learning, research, and administration. International Journal of Educational Technology in Higher Education, 22(1), 10. 10.1186/s41239-025-00507-3 Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50(2), 179-211. https://doi.org/10.1016/0749-5978(91)90020-T Ajzen, I., and Fishbein, M. (2005). The influence of attitudes on behavior. In D. Albarracín, B. T. Johnson Hughes, L., Malik, T., Dettmer, S., Al-Busaidi, A. S., and Dwivedi, Y. K. (2025). Reimagining higher education: Navigating the challenges of generative AI adoption. Information Systems Frontiers. https://doi.org/10.1007/s10796-025-10582-6 Basileo, L. D., Otto, B., Lyons, M., Vannini, N., and Toth, M. D. (2024). The role of self-efficacy, motivation, and perceived support of students' basic psychological needs in academic achievement. Frontiers in Education (Lausanne), 910.3389/feduc.2024.1385442 Chiu, T.K.F., Xia, Q., Zhou, X, Chai, C.S., Cheng, M. (2023). Systematic literature review on opportunities, challenges, and future research recommendations or artificial intelligence in education. Computers and Education: Artificial Intelligence, 4, 100118. https://doi.org/10.1016/j.caeai.2022.100118 Kangwa, D., Msafiri, M. M., and Fute, A. (2025). Balancing innovation and ethics: promote academic integrity through support and effective use of GenAI tools in higher education. AI and Ethics, 10.1007/s43681-025-00689-6 Maxwell, D., Oyarzun, B., Kim, S., and Bong, J. Y. (2025). Generative AI in Higher Education: Demographic Differences in Student Perceived Readiness, Benefits, and Challenges. TechTrends, 10.1007/s11528-025-01109-6 Nguyen, K. V. (2025). The use of generative AI tools in higher education: Ethical and pedagogical principles. Journal of Academic Ethics,1–21. https://doi.org/10.1007/s10805-025-09607-1 Robert, J., and Muscanell, N. (2023). *2023 EDUCAUSE Horizon Action Plan: Generative AI*. EDUCAUSE. https://library.educause.edu/resources/2023/9/2023-educause-horizon-action-plan-generative-ai Samala, A. D., Rawas, S., Wang, T., Reed, J. M., Kim, J., Howard, N.-J., and Ertz, M. (2025). Unveiling the landscape of generative artificial intelligence in education: a comprehensive taxonomy of applications, challenges, and future prospects. Education and Information Technologies, 30(3), 3239-3278. https://doi.org/10.1007/s10639-024-12936-0 Sengar, S. S., Hasan, A. B., Kumar, S., and Carroll, F. (2024). Generative artificial intelligence: a systematic review and applications. Multimedia Tools and Applications, 10.1007/s11042-024-20016-1 Shata, A., and Hartley, K. (2025). Artificial intelligence and communication technologies in academia: faculty perceptions and the adoption of generative AI. International Journal of Educational Technology in Higher Education, 22(1),14. https://doi.org/10.1186/s41239-025-00511-7 Young, MD & Diem, S (2023), Handbook of critical education research: qualitative, quantitative, and emerging approaches, (eds) Routledge, Taylor & Francis Group, New York, NY.