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

Purpose As key figures in curriculum design and implementation, course coordinators operate at the intersection of institutional goals, the needs of students, institutional policy and classroom practice, balancing educators alike (Stark and Lattuca, 1997). Their perspective is especially valuable in understanding the broader implications of GenAI integration, as they are often responsible for shaping educational experiences and adapting course content in response to new technologies. Therefore, this study examines how course coordinators view and integrate GenAI into curriculum and teaching practices. The study contributes to the ongoing discourse on GenAI in education, providing practical implications for how higher education institutions might develop policies and support structures that foster responsible and effective GenAI integration. Design/methodology/approach 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). Findings The findings illustrate that course coordinators at a Dutch university have mixed reactions to GenAI, viewing it as both an educational innovator and a disruptor. They raised concerns about its impact on academic integrity, curriculum relevance and educational values. Coordinators stressed the need for a re-evaluation of skills and knowledge taught in response to GenAI’s disruptive potential. While some faculties experimented with GenAI, many lacked clear policies or strategies for integration, highlighting a knowledge gap. Course coordinators also acknowledged GenAI’s inevitability in education, suggesting a shift toward adoption driven by external pressures rather than perceived usefulness. They proposed collaborative efforts and training programs to support GenAI integration. Research limitations/implications This study had several limitations. The recruitment of course coordinators was targeted, potentially introducing selection bias, as participants with prior interest in GenAI may have skewed the findings. Future studies should use random sampling to capture diverse perspectives. Additionally, the rapid evolution of GenAI technology may limit the long-term relevance of the findings. Future research could adopt a longitudinal design to continuously update the framework and data collection methods. Finally, the study’s focus on a single university limits transferability; comparative studies across multiple institutions would provide broader insights into GenAI integration in different educational contexts. Practical implications Our study offers several practical implications for higher education. First, our findings suggests facilitating structured collective sensemaking sessions through workshops and discussions. Additionally, it calls for a re-evaluation of educational values due to GenAI’s disruptive potential and the need for clearer top-down strategies alongside bottom-up approaches. The study advocates for targeted training programs as well to bridge knowledge gaps, while also proposing adaptive strategies to prepare institutions for the inevitable integration of GenAI into education. Originality/value The study offers 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 grappling with challenges to academic integrity and the traditional educational framework. The findings highlight the need for collective sensemaking, institutional support and adaptive strategies to harness GenAI effectively. The study’s limitations underscore the need for ongoing research to adapt to technological shifts and include diverse perspectives in the GenAI discourse.