The emergence of generative artificial intelligence in 2022 has been described by some scholars as the fourth industrial revolution (Serrano & Sánchez-Vera, 2024). This technology has become firmly embedded in personal, professional, and educational contexts, and universities are no exception. Evidence of this can be found in the guidance issued by several international organisations (CRUE, 2024; EUA, 2023; UNESCO, 2023) on the appropriate use of artificial intelligence (AI) within higher education.

The implementation, ongoing development, and use of AI in universities pose significant challenges for the entire academic community. These challenges include ensuring pedagogical coherence and aligning institutional strategies and technological policies with the achievement of educational objectives (García-Peñalvo et al., 2024). Furthermore, as noted by CRUE (2024), the adoption of AI in higher education must address four key dimensions: ethics and accessibility; data security and privacy; training and digital literacy across the academic community; and the safeguarding of academic quality.

According to Bruner (2011), organisations must adapt their management systems to changes in their environment if they are to maintain their position and achieve their strategic goals. In this regard, higher education institutions need to examine the impact that AI is having on their day-to-day management practices and to propose changes that enable them to adapt to the use of this emerging technology by members of the academic community.

Given that university managers play a central role in driving such changes, this contribution seeks to address the following research question: What concerns do university managers have regarding the implementation of AI in university teaching?

Based on the results of the EdU-InA project (Ref. PID2023-149069OA-I00), this paper aims to analyse the concerns expressed by university managers in relation to the need to adapt teaching management models in higher education to the use of artificial intelligence.

This study adopted a qualitative research design based on focus groups, as this technique is particularly appropriate for exploring participants’ opinions, perceptions, and discourses. As an exploratory study, it sought to identify university managers’ perceptions regarding the use of artificial intelligence in university teaching, as well as the need to ensure that AI is used in an ethical and secure manner and oriented towards academic quality. Fieldwork was conducted during the first semester of 2025 through six focus groups, each lasting between 70 and 90 minutes and involving from 6 to 11 participants (total of 51 university managers). Each focus group was held at a different Spanish university and included participants (55% men and 45% women) holding a range of academic leadership positions: vice-rectors (14%), deputy vice-rectors (14%), deans (6%), vice-deans (26%), programme coordinators (22%), and expert academic staff (18%). Universities were selected to ensure a comprehensive institutional profile, offering degree programmes across multiple areas (sciences, engineering, social sciences, and humanities). Participants selection focused on academic leadership roles with direct responsibility for the organisation and management of university teaching. Accordingly, vice-rectors and their deputies with responsibility for academic affairs, educational innovation, or quality assurance were included, as well as vice-deans and programme coordinators representing different fields of knowledge. The focus group protocol was structured around seven thematic sections: Leadership and Governance Practices; Infrastructure; Professional Development; Teaching and Learning Practices; Assessment Practices; Content and Curriculum; and Collaboration and Networking. All focus group sessions were transcribed literally. Content analysis was conducted using ATLAS.ti software, following a systematic coding process (Miles et al., 2014). The coding framework combined a deductive approach, based on a preliminary set of codes developed by the research team, with an inductive process that incorporated emergent codes arising from the analysis. Six researchers participated in the coding process and held regular meetings to review and agree upon the codes used. Each textual quote was coded by considering the profile of the participant expressing the view and the orientation of the comment (positive, negative, interrogative or neutral). Where possible, references to other members of the academic community (teaching staff, students, administrative staff, or managers) and the emotion expressed by the informant were also coded using the emotion classification proposed by Beaudry and Pinsonneault (2010). Quotes were categorised according to the substantive content of the information provided. Finally, the analysis was made through co-occurrences that shows the relationship between various codes.

The initial findings of the research highlight the high prevalence with which institutional leaders express concerns regarding how should be the use of artificial intelligence in higher education. The first area of concern that emerges relates to the ethical use of AI. Participants highlight the risk that the use of AI may undermine the development of students’ values, as this technology is perceived as capable of providing knowledge, but not of offering a humanistic or ethical interpretation of that knowledge. They also express concern about distinguishing between work that has been critically produced by students and work generated by AI, raising questions about the academic integrity of student outputs. Finally, in connection with ethical considerations, participants voice doubts about the ethical implications of using AI as a tool for student assessment. A second area of concern relates to the secure use of AI. In this regard, participants discuss how AI providers handle the data supplied by students and academic staff. This leads to concerns about which platforms should be adopted within the university context, as well as the procedures required to ensure that these platforms comply with European data protection and security regulations. Closely linked to this issue is the concern that both students and academic staff should be able to use AI with confidence. Confidence is understood here as the capacity to use AI with sufficient knowledge to act in an ethical, secure, and lawful manner. For this reason, participants emphasise the need to develop targeted training plans for both groups, ensuring that they acquire the necessary competencies to use AI in ways that do not expose themselves or others to potential risks.

Brunner, J. J. (2011). Gobernanza universitaria: tipología, dinámicas y tendencias. Revista de Educación, (355), 137-159. Beaudry, A. & Pinsonneault, A. (2010). The Other Side of Acceptance: Studying the Direct and Indirect Effects of Emotions on Information Technology Use. MIS Quarterly, 34 (4), 689-710. https://www.jstor.org/stable/25750701 CRUE (2024). La inteligencia artificia generativa en la docencia universitaria. Oportunidades, desafíos y recomendaciones. CRUE. Available in: https://bit.ly/3Qb0Qtg EUA (2023). Artificial intelligence tolos and their responsabile use in higher education learning and teaching. European University Association. Available in: https://bit.ly/3CzPJaj García-Peñalvo, F.J. Alier, M., Pereira, J. & Casany., M.J. (2024). Inteligencia Artificial Segura, Transparente y Ética: Claves para una Educación Sostenible de calidad (ODS4). International Journal of Educational Research and Inovation, 22, 1-21. https://doi.org/10.46661/ijeri.11036 Miles, M., Huberman, M. & Saldaña, J. (2014). Qualitative Data Analysis. A Methods Sourcebook (3rd Ed.). SAGE Serrano, J.L. & Sánchez-Vera, M.M. (2024). ¿A qué promesas y desafíos me enfrento como docente con la IA? En: A. Arroyo (coord). Inteligencia artificial y educación: construyendo puentes. (pp.57-4070). Graó. ISBN: 978-84-128529-1-2. UNESCO. (2023). Guidance for generative AI in education and research. UNESCO. Available in https://doi.org/10.54675/ewzm9535