From perception to policy: course coordinators’ views on generative artificial intelligence in higher education Open Access

In recent years, education has encountered an unexpected shift with an increasing integration of artificial intelligence into teaching and learning processes, namely with the surge of Generative Artificial Intelligence (GenAI) enabled tools such as ChatGPT, Bard and Midjourney (Villareal et al., 2023). The specific ability of GenAI to not only retrieve but also creatively generate educational materials has driven discussions about its capacity to transform and disrupt educational practices and its impact on educational management (Baidoo-Anu and Ansah, 2023; Zhai, 2022).

While emerging technologies have long influenced higher education, GenAI’s rapid adoption introduces both opportunities and challenges for learning. For example GenAI offers valuable applications, such as providing students with real-time feedback, generating ideas and enhancing critical thinking through conversational engagement (AlAfnan et al., 2023; Kasneci et al., 2023). GenAI can also support students with and without special needs to personalize learning strategies (Pedró et al., 2019). These examples demonstrate GenAI`s potential to support improved learning outcomes and promote greater inclusion and equity in (higher) education, in line with the United Nations Sustainable Development Goal 4 (SDG4), which aims to “ensure inclusive and equitable quality education and promote lifelong learning opportunities for all ” (Pedró et al., 2019, UNESCO, 2019). However, certain concerns and challenges may complicate the achievement of SDG4. The accessibility and ease of use of these tools raise ethical concerns about over-reliance, potential impacts on students’ cognitive development and challenges to academic integrity (Kasneci et al., 2023; Lim et al., 2023). In addition, the potentially disruptive nature of GenAI risks creating a new kind of digital divide, deepening existing inequalities, as disadvantaged students are more likely to be excluded from GenAI tools or lack digital literacy training to use them effectively (Gustilo et al., 2024; Pedró et al., 2019).

Consequently, recent policy developments in Europe highlight the urgency of responsibly integrating AI in education. The European Commission’s Digital Education Action Plan 2021–2027 outlines a vision for high-quality, inclusive digital education, with specific calls for ethical and pedagogically sound AI implementation in higher education. Also at the global level, UNESCO’s Beijing Consensus on Artificial Intelligence and Education (2019) frames AI as a tool for advancing SDG4.

In line with this growing international (political) attention, also research on GenAI in higher education is rapidly emerging. Much of it has focused on student adoption and the potential of these tools to reshape assessment practices and policies (Villareal et al., 2023; Xia et al., 2024). In contrast, less attention has been paid to how educational professionals, particularly university-based course coordinators, make sense of and adapt to GenAI. As key actors in curriculum design and implementation, course coordinators operate at the intersection of institutional policy and classroom practice, balancing strategic goals with the practical needs of students and educators (Stark and Lattuca, 1997). Their role in shaping learning experiences and adapting course content positions them as central to understanding how GenAI integration plays out in practice. While empirical studies on this topic remain limited, a small but growing body of research has begun to explore how course coordinators and related staff engage with GenAI in course development. For example, Conklin et al. (2024) detail a case study in which instructional designers collaborated with ChatGPT to co-develop a 14-week asynchronous graduate-level course. While GenAI-enabled significant time savings, it also required human oversight to address issues in alignment, assessment coherence and content accuracy. Similarly, Meron and Araci (2023) describe the use of ChatGPT as a “virtual colleague” in postgraduate design education, where educators co-created course materials and assignments through reflective dialogue with the tool.

Building on these emerging insights, this study draws on Weick’s (1990) sensemaking framework and the technology acceptance model (TAM) to further examine how university-based course coordinators interpret and respond to GenAI’s integration into curricula and teaching. Sensemaking helps explain how coordinators construct meaning amid these changes, while TAM provides a lens to understand their acceptance and use of GenAI tools.

Through this exploration, this study contributes to the growing body of literature on GenAI in higher education by addressing a key gap, namely, the lack of empirical research on faculty-level adoption and policy framing. While most existing studies focus on student use (Baig and Yadegaridehkordi, 2024), our study highlights the strategic role of course coordinators, offering practical insights for educational management on how to develop effective policies and structures that support – in line with SDG4 – inclusive and equitable quality education during emerging technological challenges.

“Most people in the field of education are unaware of what AI is all about, even though it is expected to make big changes to their lives within the next decade” (Hossain et al., 2021, p. 4). Sudden technological advancements, such as GenAI, can lead to a state of professional disruption where institutional arrangements of a profession are influenced (Hasselbalch, 2015). Understanding how educational professionals interpret and respond to the rise of GenAI is considered crucial as these perceptions shape the integration and utilization of these technologies (Hinings et al., 2018; Luckin et al., 2022).

Sensemaking, a process defined by Weick (1990) as “the way people understand issues or events that are novel, ambiguous, or confusing” offers a theoretical lens for exploring professionals' responses to technological shifts (Maitlis and Christianson, 2014). The importance of sensemaking is highlighted by Fleming (2018), stating that early interpretations of technology during the adoption phase are considered critical and lay the foundation for decisions that significantly impact digitalization outcomes. Educational institutions worldwide show varying responses to GenAI, reflecting different sensemaking processes. For instance, while some institutions embrace GenAI for its perceived benefits, others show caution or resistance due to concerns about learning quality and digital inequality (Tapalova and Zhiyenbayeva, 2022).

As the implications of GenAI for policy and practice in higher education become more profound, it becomes urgent for educators and students to construct a collective understanding of its capabilities and ethical implications (Luckin et al., 2022; Giannini, 2023). Educators and policymakers are therefore engaged in a continuous process of interpreting and reinterpreting the evolving landscape of GenAI, developing and refining policies and practices that address new challenges and opportunities along the way (Pedro et al., 2019; Flogie and Aberšek, 2022).

Sensemaking offers a framework to explore course coordinators initial perceptions and interpretations of GenAI, while the TAM offers a structured approach to understanding these perceptions (Deslonde and Becerra, 2018). TAM, proposed by Davis (1993), focuses on two key factors: perceived ease of use (PEOU) and perceived usefulness (PU) of new technologies. Individuals are more likely to adopt technologies they find easy to use and beneficial (Tarhini et al., 2014). In this study, PEOU refers to the belief that GenAI is easy to use, and PU refers to the belief that GenAI adds value to education (Davis, 1993; Nair and Das, 2011).

However, delays in AI integration due to hesitance around its educational value have been noted (Tapalova and Zhiyenbayeva, 2022). Studies by Villareal et al. (2023) and Chen et al. (2022) reveal the challenge of rapid student adoption of GenAI, which often exceeds institutional readiness and support.

TAM has been widely used to understand attitudes toward emerging technologies. Barakat et al. (2024) applied TAM to investigate university educators' acceptance of ChatGPT, finding that perceived usefulness and ease of use were significant factors. Perceived usefulness is shaped by factors like educational relevance and institutional support, while ease of use is influenced by technical support, user-friendly design and ethical concerns.

In this study’s context, sensemaking can be seen as the starting point for the attitudes and perceptions central to TAM. The narratives, interpretations and meanings that coordinators construct around GenAI influence their perceived usefulness and ease of use of the technology and shape the likelihood of technology acceptance or rejection (Rouse, 2004). Specifically, sensemaking helps course coordinators make initial judgments about GenAI’s relevance and potential challenges, which informs their beliefs about its usefulness in meeting educational goals. These early interpretations also affect how coordinators assess the ease of integrating GenAI into their existing practices.

Combining the theoretical frameworks of sensemaking and TAM enables us to get a deeper understanding that spans from course coordinators' initial perception and interpretation of GenAI in higher education (sensemaking), through acceptance and decision-making (TAM). In this way, we aim to provide a holistic understanding of the research questions that follow:

1. How do university-based course coordinators make sense of GenAI`s (potential) benefits and dangers in higher education, and additionally the integration process of GenAI into educational practices and curricula?

2. What are the perceived impacts of integrating GenAI on educational practices and policies at higher education?

Thus, this study aims to gain insights into the complexities of how university-based course coordinators make sense of GenAI. Ultimately, contributing to a deeper understanding, more informed policymaking and improved educational management in higher education.

A qualitative case study methodology was adopted which facilitated an in-depth exploration within a real-world context (Stake, 1994). In this instance, it provided a comprehensive understanding of the processes, challenges and opportunities faced by university-based course coordinators as they adapted to GenAI at a prominent higher education institution in the Netherlands, selected for its position at the forefront of educational innovation. Representing new educational strategies, an interdisciplinary framework and a forward-thinking approach to technology integration, this institution served as a “microcosm” of broader trends in higher education (Crowe et al., 2011).

To recruit participants, purposive and snowball sampling methods were employed, focusing on course coordinators with direct roles in or influence over GenAI integration. This targeted sampling approach ensured that participants had relevant experience and insight into the study’s objectives (Tongco, 2007).

Throughout the research process, confidentiality and data security were prioritized. All participants' information was anonymized, and data were handled and stored securely according to a data management plan. The study design and procedure were reviewed and approved by the Ethics Committee of the Faculty of Social and Behavioral Sciences of the specific University where the research took place and filed under number 24–0042.

The study involved N = 11 course coordinators working in various disciplines, including History and Philosophy of Science (N = 1), Sociology (N = 2), Educational Sciences (N = 4), Law (N = 2) and Mathematics and Natural Sciences (N = 2), employed at a large university in the Netherlands. While all participants served as course coordinators, some also held additional roles, such as department or faculty directors, program coordinators or educational advisors. Their professional experience varied from 3 to over 10 years, with several bringing extensive expertise in educational leadership and instructional practices.

The decision to recruit 11 participants was guided by practical research considerations, as prior studies indicate that data saturation often occurs within approximately 12 interviews (Guest et al., 2006). This sample size allowed for a balance between depth of insight and analytical manageability, providing a robust basis for examining diverse perspectives within the case study.

The primary data collection method was semi-structured interviews, with 11 main questions and 7 sub-questions grounded in the Sensemaking (SENS) and Technology Acceptance Model (TAM) frameworks designed to capture various dimensions of the theoretical frameworks. For Sensemaking (SENS), four questions explored participants' perceptions of GenAI in education, focusing on cognitive and interpretative processes as described by Weick (1990). This approach was inspired by Fairchild et al. (2016), who adapted sensemaking questions for new technology adoption. In this study, these questions were tailored to course coordinators’ experiences with GenAI to ensure relevance to the educational context. For example, “How do you perceive the long-term role and impact of GenAI on teaching and learning within higher education?”

For TAM, five questions assessed the perceived usefulness (PU) and ease of use (PEOU) of GenAI, following Davis’s (1993) framework and drawing on Vogelsang et al.'s (2013) study. The goal was to understand how these perceptions influenced decisions about GenAI integration in education. For example, “From your perspective, how do you interpret the benefits of GenAI for education?” (PU) and “How easy do you think it would be to integrate GenAI into your courses’ teaching practices and/or curriculum?” (PEOU)

The procedure began with initial contact via institutional email, providing an overview of the study’s purpose and relevance to participants' roles in integrating GenAI into educational settings. Interested participants received a detailed information letter, outlining the study’s aims, confidentiality measures and their right to withdraw. Informed consent was obtained through a signed consent form. Semi-structured interviews were then conducted between March and May 2024, lasting approximately 60 min and scheduled at the participants' convenience, either in person or online. The interview guide, which was piloted with a former course coordinator, allowed for flexible, in-depth exploration of participants' experiences and perceptions of GenAI (Kvale, 1996). All interviews were recorded and transcribed for accuracy.

This study used reflexive thematic analysis (RTA) as conceptualized by Braun and Clarke (2012), emphasizing the researcher’s role in constructing themes from data. Aligned with constructivist epistemology, knowledge was seen as co-constructed between researcher and data (Williams and Moser, 2019; Byrne, 2021; Schwandt, 1994). While themes were developed inductively, their interpretation was guided by the research questions and theoretical framework (Braun and Clarke, 2006).

The analysis in NVivo (v14) followed Braun and Clarke’s (2012) six-phase process. The first author familiarized with the data by reading transcripts and listening to recordings to generate initial codes. Significant text segments were annotated and codes were grouped into themes: GenAI Sensemaking (course coordinators` understanding of GenAI, including initial responses, benefits and dangers within a higher education context), GenAI Integration Sensemaking (integration challenges, policy interpretations), Implications for Policies and Practices (institutional impacts, guideline needs) and Suggestions for Further Integration (training, collaboration, policy development). While themes were largely developed inductively, their interpretation was informed by the research questions and theoretical framework, blending inductive and deductive elements (Braun and Clarke, 2006). For example, the subcodes “educational relevance” and “GenAI benefits” referred to PU and one code covered the “Ease of use of GenAI” (see Table 1 for a complete overview of the codes and frequencies). Themes were reviewed for coherence and named for clarity in the fifth phase. In the final phase, the findings were documented and connected to relevant literature on education and technology.

Validity and reliability were ensured through transparency and reflexivity in data collection and analysis (Byrne, 2021). Reflexivity involved documenting decision-making and acknowledging the researcher’s influence on interpretation, using NVivo for continuous annotations and tracking changes. Transferability was addressed by providing detailed descriptions of context and participants, allowing others to assess the applicability of findings (Byrne, 2021). Member checking was conducted to validate findings, and the researcher practiced reflexivity to reduce personal biases during coding.

A collaborative coding approach was used to ensure reliability in thematic analysis. The coding scheme was reviewed by an independent researcher, refined for clarity and logic and then applied independently by both researchers. Discrepancies were discussed and resolved, improving consistency and refining the coding process.

The results are presented through four themes. Theme 1: Sensemaking of GenAI`s benefits and dangers in higher education describes how course coordinators make sense of GenAI`s benefits and dangers within a higher education context, in line with answering the first research question. Theme 2: GenAI Integration Sensemaking presents how course coordinators make sense of the integration regarding GenAI. Aligning with the first research question too, this theme examines how course coordinators comprehend and interpret the challenges and supports for integrating GenAI in higher education. Theme 3: Implications for Policies and Practices addresses the broader institutional impact GenAI already has or might have on policies and practices and answers the second research question. Lastly, Theme 4: Suggestions includes participants’ advice and suggestions for the higher education institution to further accommodate the integration of GenAI, informing the practical implications of this study.

This theme explores how university-based course coordinators make sense of (potential) benefits, dangers and concerns of GenAI in higher education. The perceived benefits, directly link to concept of Perceived Usefulness of the TAM framework, focusing on educational relevance and benefits to strengthen inclusive quality education (SDG4).

First, the participants highlighted GenAI’s capacity to enhance writing and coding skills, streamline the learning process and foster students’ creativity.

The practical utility GenAI could have in an academic setting was noted by nine participants, emphasizing the efficiency GenAI introduced as it shortened the work process dramatically, highlighting the technologies’ ability to accelerate and simplify complex tasks:

Furthermore, seven participants mentioned GenAI’s role in enhancing accessibility and inclusivity in education. Assisting with language barriers and providing support for students with disabilities, making learning more adaptable and inclusive. For instance, one participant illustrated the benefit of accessibility, stating:

The benefits mentioned by the participants came with skeptical follow-up answers. All 11 participants expressed concerns or dangers of using or integrating GenAI. Concerns ranged from ethical, environmental and skill deterioration to broader educational relevance. While most respondents acknowledged GenAI to be an impressive tool its output is considered by some as unreliable:

Seven participants addressed environmental and ethical concerns, namely that the technology is not considered to be environmentally friendly while also affecting academic integrity:

Where seven participants mentioned that GenAI could lead to levelling the playing field in terms of access, others say that it’s impossible to get equal access, and potentially even widen the inequality gap:

This theme describes how course coordinators make sense of integrating GenAI within higher education. The theme refers to the variety of practical support and challenges participants associate with adopting GenAI technologies, highlighting how these interpretations reflect sensemaking processes regarding new technological tools. Participants draw parallels with past technological integrations to shape their views on the current integration. Additionally, this theme describes practical aspects of technology adoption, such as the ease of use of GenAI and the difficulties associated with transitioning to a more GenAI-oriented higher education environment.

Nine participants referred to historical technological shifts to frame their understanding of the current integration of GenAI in higher education. They drew on experiences with past educational tools to describe their perceptions of the integration of GenAI, reflecting skepticism and the slow pace some of these revolutions were handled within the field of education:

Ten participants acknowledge that they feel a need to integrate GenAI in future courses. Integrating GenAI within the educational framework presents multiple challenges, as described by all 11 respondents. Issues include lack of time and the rapid pace of technological advancements which educators struggle to keep up with. Some participants also addressed the feeling that students are always one step ahead of teachers with current technologies. Additionally, seven participants mention that there is a notable deficiency in the necessary knowledge and expertise needed around this technology, while also noting that a lack of flexible mindset among colleagues provides challenges to further integration of GenAI:

Nine participants emphasized the absence of clear vision, frameworks or guidelines from university administrations in effectively adopting GenAI technologies, noting that this lack of uniform institutional support and direction leaves participants uncertain about how to proceed with integration in a structured and effective manner:

Course coordinators highlighted the general ease of use associated with GenAI tools, suggesting that these technologies are inherently user-friendly and accessible. Six participants pointed out the simplicity of the tools. Despite the basic operational ease, four respondents raised concerns about the depth of understanding and effective utilization of these tools beyond simple tasks, emphasizing the need for critical engagement with this technology:

Eight participants noted a proactive movement regarding the integration of GenAI within the university through various initiatives such as projects, meetings and conferences. These initiatives are aimed at enhancing awareness and understanding, ethical considerations and practical application of GenAI technologies.

This theme describes how course coordinators interpret the impact GenAI currently has, or will have, on both policies and educational practices. The theme refers to experiences course coordinators have with encountering GenAI while educating or shaping courses, albeit with students or by integrating GenAI in courses themselves. Course coordinators reflect on these experiences and make sense of how GenAI’s impact could alter the shaping of education in the future.

All 11 participants highlighted that the increased use of GenAI necessitates a re-evaluation of educational values and goals, acknowledging a significant disruption at the core of current educational frameworks. They emphasized the urgent need to reconsider what skills and knowledge are essential for students and how GenAI aligns with longstanding educational values. This reflection is considered critical, as noted by participants, in assessing the sustainability of current educational practices:

Additionally, all participants recognized the impact of GenAI on their educational practices, particularly in terms of assessment. They expressed concerns over maintaining academic integrity, as the difficulty in detecting AI assistance in student work increases:

In response to these challenges, all 11-course coordinators reported either already adapting their courses or planning adaptations to integrate integrity checks and modify assessment methods to make them “GenAI proof.” One coordinator shared their experience:

Furthermore, eight participants discussed considering assessment reforms to ensure that evaluations accurately reflect students' understanding and abilities, rather than just their proficiency in using AI tools. Proposed changes by participants included more oral exams or practical tests that require a demonstration of knowledge rather than reliance on written outputs that could be easily generated by GenAI.

Ten participants mentioned that there was no clear unified policy framework around GenAI as of writing. Participants stated that the lack thereof leads to course coordinators making individual decisions on the use of GenAI without a consistent framework, leading to varied approaches across disciplines and courses:

Moreover, nine participants expressed a need for more clear and structured policy guidelines and frameworks around GenAI, stating that these frameworks should address aspects such as academic integrity, student use of GenAI, ethical use of the technology, and ensuring uniformity and fairness across departments. Course coordinators address that without clear frameworks, guidelines, vision or a stance from the university regarding GenAI it’s challenging to come up with decentralized policies per course, resulting in no policy at all, or a more trial-and-error approach to how courses deal with or integrate with GenAI.

The third theme describes participants’ propositions, advice and suggestions for the university to further accommodate the integration of GenAI. Firstly, four participants mentioned that frameworks or guidelines around GenAI could be constructed with the knowledge that is already there. Faculties are already coming up with their guidelines based on experiences of recent semesters, similar-like faculties could benefit from sharing experiences and knowledge:

Finally, three participants proposed promoting expertise through accessible training or modules, emphasizing the importance of collaboration between teachers and policymakers. Additionally, the sharing of practices between peers was addressed:

This study explored how course coordinators make sense of GenAI in higher education and how this affects educational practices and institutional policies. First, the findings align with previous research and the views of educational professionals, indicating that GenAI is perceived as a tool with the potential to enhance efficiency, inclusivity and creativity in teaching (AlAfnan et al., 2023; Kasneci et al., 2023; Pedró et al., 2019; UNESCO, 2019). However, participants also expressed concerns about potential over-reliance on the technology, threats to academic integrity, the erosion of critical thinking skills and the risk of exacerbating existing educational inequalities-concerns that have also been highlighted in earlier publications, including Pedró et al. (2019). Moreover, interviews revealed coordinators shared perception of GenAI as a transformative and inevitable force, prompting a re-evaluation of core educational values, essential skills and academic integrity. This finding is in line with previous studies (e.g. Hasselbalch, 2015) that support the notion that rapid technological advancements can disrupt established pedagogical and professional norms. Course coordinators recognized various challenges in the integration of GenAI to their practice and emphasized the need for clear policies and guidelines. They viewed GenAI impact as extending beyond that of previous technologies, reflecting skepticism that may complicate integration. As suggested in previous research (Patvardhan et al., 2018) educators’ acceptance can be affected by both the novelty and pace of technological change.

Integration of GenAI varied across faculties. Some had begun developing new guidelines while others lacked time, institutional direction or expertise. This disparity highlights the need for clearer policies for GenAI integration echoing the work of Flogie and Aberšek (2022), who found that the lack of coherent policies hinders effective integration. For successful AI integration educators need to be able to feel in control, have the necessary expertise and institutional support (Conklin et al., 2024; Meron and Araci, 2023).

Participants also expressed uncertainty about the future of curricula and professional development. Traditional frameworks where seen as outdated for preparing students for a GenAI driven workforce. Participants also shared their concerns about the misalignment between the rapid advancements in GenAI implementation and the slow pace of adaptation within educational institutions. This gap is consistent with prior studies (Chen et al., 2022), which identified a disconnect between GenAI’s perceived potential impact and its actual implementation in educational settings.

From a theoretical point of view, our study highlights the need to reconsider or further adapt traditional technology acceptance models like the TAM. Based on our findings, we understand that GenAI integration is an inevitable force in education and may shape teaching practices regardless of teachers readiness or preference. Alternative models such as the Unified Theory of Acceptance and Use of Technology (UTAUT) (Venkatesh et al., 2003) may better reflect the drivers of adoption in contexts where external forces and urgency play a significant role. The UTAUT integrates constructs like social influence and facilitating conditions, both of which play a role in the institutional pressures surrounding GenAI. However, UTAUT, while more comprehensive, does not directly address the notion of perceived inevitability, an important driver identified in this study.

To explore this further, we propose an extension of UTAUT, incorporating constructs such as perceived inevitability, institutional pressure and perceived preparedness. For example, perceived inevitability may affect perceived usefulness by prompting educators to recognize potential benefits in GenAI due to its inescapable presence. Future research could examine how these constructs interact within UTAUT to influence attitudes and behaviors toward GenAI, particularly in contexts where adoption is driven more by external pressures than by intrinsic technology characteristics. This extended framework could provide a more accurate understanding of technology acceptance dynamics in educational settings, where the urgency and inevitability of GenAI integration create unique challenges.

Participants also emphasized the need for institutional support through shared guidelines, collaborative communities of practice and target training. Structured professional development efforts such as peer workshops and policy co-creation were also suggested as ways to navigate current uncertainty and foster consistent approaches through various disciplines. These findings are consistent with earlier studies that underscored the urgency to build a collective understanding of GenAI (Luckin et al., 2022; Gianini, 2023).

This study encountered several limitations that should be addressed in future research. First, the recruitment process for course coordinators was targeted rather than random, potentially introducing selection bias. Participants interested in or already dealing with GenAI integration may have been more inclined to participate, which could skew the findings toward a more favorable perception of GenAI integration. Furthermore, the sample consisted of only 11 course coordinators. Despite the relatively small sample size, the purposeful sampling enables an in-depth analysis and uncovers the complexity of sense-making within an institution. Such insights may apply to other institutional contexts and help to inform practices in similar higher education settings. Nonetheless, future studies could employ random sampling and a larger sample size to capture a broader range of perspectives.

While qualitative research does not aim to generalize findings, this study’s scope was limited to a single university, which might affect the transferability of the findings. Future comparative studies across multiple institutions would help explore broader educational trends and identify best practices for GenAI integration.

This study suggests multiple actionable implications for higher education institutions considering GenAI integration. First, course coordinators emphasized the importance of structured sensemaking. Universities could facilitate regular workshops and collaborative sessions where educators share experiences and explore GenAI’s potential and risks. Such activities may involve case studies, scenario analysis and group discussions to explore GenAI’s potential and challenges. Open discussions, grounded in practical examples, can support shared understanding and help educators better adapt to technological change (Weick, 1990).

Second, course coordinators expressed concerns about the sustainability of the current educational framework especially regarding academic integrity. Course coordinators expressed a need for re-evaluation of core educational values to ensure alignment with a GenAI-oriented future. A concrete example on how to foster sustainability calls for institutions to establish task forces to assess how GenAI may reshape curricula and core competencies (Hasselbalch, 2015).

Third, course coordinators felt unprepared for GenAI’s impact and pointed to fragmented or ad hoc approaches across faculties. Several participants also emphasized the need for clearer policies on issues such as academic integrity, ethical use and student engagement with GenAI. To move beyond reactive, course-level decisions, higher education management should align their internal strategies with broader policy frameworks. For example, the European Commission’s Digital Education Action Plan 2021–2027 and UNESCO’s AI and Education Guidance for Policymakers both call for institution-wide educational leadership, capacity-building and shared ethical standards for AI use in education (European Commission, 2020; Miao et al., 2021). Referencing such frameworks when shaping internal policies could help institutions offer more cohesive, future-oriented support for staff and ensure that GenAI integration contributes meaningfully to SDG4’s goals for inclusive, equitable and high-quality education. To ensure the responsible use of GenAI in higher education and enhance all students’ and staff GenAI literacy, universities can, for example, provide digital literacy training and tools so all students -regardless of background- learn how to use GenAI effectively and responsibly. Taking motivational literature into account (Deci et al., 1991), such training and tools may also be helpful in reducing potential resistance toward AI, as this may impact both students’ and staff’s perceived ease of use, and thus also feelings of competence and motivation to effectively integrate and reflect on the use of GenAI in daily practice.

Fourth, given the fast-paced development of GenAI, universities need to be able to adapt in ways that support educators keep up with change. Making sense of new technologies requires ongoing reflection and adaptation (Weick, 1990) to shifts in educational technology (Pedro et al., 2019). Conklin et al. (2024) show that even with support, educators must constantly revise how they use GenAI to stay aligned with teaching goals. Similarly, Meron and Araci (2023) describe how educators engaging with GenAI as a design collaborator undergo a progressive normalization of the tool, reflecting its growing entrenchment in course development. These studies affirm participants’ sense that GenAI is becoming a permanent fixture in education. Since many see GenAI as unavoidable, institutions should treat its integration as a way to improve education, not as a threat to it (Luckin et al., 2022).

This study contributes valuable empirical insights into how course coordinators make sense of GenAI integration within higher education. Course coordinators view GenAI with a mix of optimism and concern, recognizing its transformative potential while navigating challenges related to academic integrity and traditional educational frameworks. This study highlights the need for collective sensemaking, institutional support and adaptive strategies to better adapt to GenAI developments. We also emphasize the need to reconsider traditional technology acceptance frameworks when interpreting results on GenAI adaptation and propose an extended UTAUT model to better capture educators needs. Aside from the study’s limitations and its qualitative, constructivist approach, we offer a first comprehensive step in bringing forward the voices of course coordinators across disciplines and propose practice-oriented guidelines for more effective GenAI adaptation in higher education.

The authors acknowledge the use of generative AI in the preparation of this study, while remaining mindful of its limitations and the potential for unreliable output. Specifically, AI tools were employed to identify any gaps or omissions in sections such as the methods section and to refine the readability of text originally authored by the researchers. All substantive decisions and interpretations were made by the authors to ensure the study’s academic integrity.