The irruption of Generative AI (GenAI) is rapidly reshaping higher education. The widespread availability of tools capable of generating text, images, code or feedback has introduced profound challenges for teaching and assessment, including concerns about academic integrity, authorship, bias or data privacy. At the same time, GenAI may bring significant pedagogical potentialities, such as enhanced support for learning processes, instructional design, content creation and student engagement. In this context, higher education teachers play a pivotal role, yet their position remains insufficiently understood. The rapid diffusion of GenAI raises critical questions regarding teachers’ knowledge, use and stance: What do teachers know about GenAI? Are they using it for teaching, and how? How do they position themselves on the integration of GenAI in higher education? 

To address these issues, researchers have begun to investigate GenAI in higher education, focusing on institutional responses, ethical debates, student perspectives, and so on. With regards to their knowledge, studies consistently report high general awareness of GenAI tools among university teachers, with many having experimented with them at least occasionally (Ghimire et al., 2024). However, this familiarity often masks important gaps in AI literacy. While teachers tend to understand GenAI’s basic capabilities and recognize its potential for efficiency and pedagogical support, deeper technical understanding remains limited outside the specialized fields. Additionally, research highlights persistent uncertainty regarding pedagogical integration: many teachers report difficulties redesigning assessment, feedback, and learning outcomes in ways that meaningfully and responsibly incorporate GenAI (Cordero et al., 2024; Nikolic et al., 2024). 

As for the teachers’ use, recent research shows that higher education teachers are already engaging with GenAI, primarily to augment or ease existing teaching practices rather than to transform pedagogical models. The most common uses involve content creation and course design, such as generating teaching materials, examples, quizzes or media resources, as well as providing automated or semi-automated feedback in some cases. GenAI is also increasingly framed as a learning support tool, for instance as a “virtual tutor” to assist students with brainstorming, drafting, explanation, or research tasks, often within teacher-guided activities aimed at fostering critical and ethical use. While more advanced, collaborative student-AI-teacher models are emerging, these remain limited and uneven across disciplines, with stronger uptake reported in engineering, health, language, and creative fields (Quian, 2025).

Regarding stance, the literature portrays higher education teachers as cautiously positive rather than resistant. Across contexts, most view GenAI as a supportive tool that can enhance teaching and learning, not as a replacement for educators (Nikolic, 2024). At the same time, they express strong concerns about academic integrity, data privacy, bias, over-reliance by students, and the reliability of AI outputs. Faculty acceptance of GenAI appears closely tied to perceived pedagogical value, ease of use, ethical clarity, and the availability of institutional support (Tovar & Ocegueda, 2025). Overall, existing studies suggest a landscape characterized by experimentation, uneven skills, and ambivalent optimism, underscoring the need for further empirical research into teachers’ knowledge, use and stance in relation to GenAI in higher education.

Even though some incipient findings have been reported about higher education teachers’ knowledge, use and stance, there are still some questions about if these dimensions are interconnected. In this scenario, the objective of this paper is to analyse whether the teacher’s knowledge, uses and stances on GenAI are somehow related. The research questions are:

• Are the items within each dimension (teachers’ knowledge, stance, and use) significantly associated with one another?
• Is teachers’ knowledge of GenAI associated with their use of GenAI in teaching?
• Is teachers’ stance toward GenAI associated with their use of GenAI?
• Is teachers’ stance toward GenAI associated with their knowledge of GenAI?

The methodology of this paper is quantitative. The instrument used to collect the information needed to answer the research questions is EdU-P-InA survey (Mercader et al., 2025). This survey was elaborated adhoc in order to collect information about the knowledge, uses and stances of university teachers regarding GenAI in education. The initial survey underwent a pilot test (N= 14) and its improved version was validated by 18 judges regarding its clarity, appropriateness, importance and sufficiency. The final version of the survey consists of 36 questions distributed in 3 dimensions: Knowledge (11 questions), Uses (14 questions) and Stances (11 questions). Regarding its reliability, Cronbach’s Alfa of EdU-P-InA shows a strong consistency with an Alfa of .906. The data was collected between March and June 2025. Data analysis was carried out with the support of SPSS software (v31) and consisted of descriptive analysis (means and standard deviation) as well as inferential analysis (Pearsons’ correlation) to explore the possible relationships within the same dimension and between dimensions. The population of the study were university teachers from 6 public universities in Spain with different territory reach (North, South, East, West, Center and Online). The sample obtained was 730 teachers, distributed according to the size of their universities. With regards to the field of knowledge, representation across academic disciplines is balanced, considering that some disciplines have more teaching staff than others. In this regard, 38.6% are from Social Sciences and Law, 24.1% from Science and Engineering, 20.1% from Arts and Humanities, and 17.1% from Health Sciences. In terms of gender, the sample is mainly female (47.4%) and male (50%), although non-binary individuals (1.1%), individuals who prefer not to answer (1.4%), and others (0.1%) are also included. The mode in age and teaching experience are 50 and 10 years, respectively, although the average is 48.28 years (SD = 10.36) and 17.09 years of experience (SD = 11.08). The teaching staff who participated are mainly full-time and permanent employees (60.4%), representing the different professional categories (pre-doctoral, post-doctoral, assistant, tenured, professor, visiting, associate, substitute and others).

The internal correlations of each dimension (Knowledge, Uses and Stances) show significant association (p < .050), which are moderate in Uses and Stances, and strong in the Knowledge dimension. All these associations are positive, except for concerns about bias, plagiarism and GenAI limitations related with the perception of usefulness and efficiency of GenAI for teaching and learning. Although the relations are significant, the correlations are very weak (r < .200). The correlations between Knowledge and Stances items are significant but all of them are weak or very weak, as well as Stances and Use. The exception is considering GenAI useful for teaching, which moderately correlates with using GenAI generative text [r(728 = .474), p = .000] and using it frequently to teach, [r(728 = .403), p = .000]. Knowledge and Use are two dimensions that have greater force in their relationship, although only five of them are moderate: knowing how to help students to use GenAI confidently, [r(728 = .411), p = .000] and having greater GenAI competency correlates with using it for planning [r(728 = .435), p = .000], teaching [r(728 = .456), p = .000], with students [r(728 = .474), p = .000] and generating text [r(728 = .472), p = .000]. Therefore, teachers considering that GenAI is helpful, or being worried about it is not based on their knowledge. The fact that teachers are used to implement technology without needed to know how it works might also be a reason why there are no correlations. However, GenAI is not the same as previous technological resources so teachers having basic GenAI literacy is a must to be able to implement it in their teaching with confidence, security, consistency and ethically.

Ghimire, A., Prather, J., & Edwards, J. (2024). Generative AI in Education: A Study of Educators' Awareness, Sentiments, and Influencing Factors. 2024 IEEE Frontiers in Education Conference (FIE), 1-9. https://doi.org/10.1109/fie61694.2024.10892891 Nikolic, S., Wentworth, I., Sheridan, L., Moss, S., Duursma, E., Jones, R., Ros, M., & Middleton, R. (2024). A systematic literature review of attitudes, intentions and behaviours of teaching academics pertaining to AI and generative AI (GenAI) in higher education: An analysis of GenAI adoption using the UTAUT framework. Australasian Journal of Educational Technology. https://doi.org/10.14742/ajet.9643 Cordero, J., Torres-Zambrano, J., & Cordero-Castillo, A. (2024). Integration of Generative Artificial Intelligence in Higher Education: Best Practices. Education Sciences. https://doi.org/10.3390/educsci15010032 Mishra, P., & Koehler, M. J. (2006). Technological Pedagogical Content Knowledge: A Framework for Teacher Knowledge. Teachers College Record, 108(6), 1017–1054. https://doi.org/10.1111/j.1467-9620.2006.00684.x Qian, Y. (2025). Pedagogical Applications of Generative AI in Higher Education: A Systematic Review of the Field. TechTrends, 69, 1105 - 1120. https://doi.org/10.1007/s11528-025-01100-1. Tovar, I., & Ocegueda, G. (2025). Attitudes of University Professors towards the Use of Artificial Intelligence in Teaching and Learning. INTERNATIONAL JOURNAL OF MULTIDISCIPLINARY RESEARCH AND ANALYSIS. https://doi.org/10.47191/ijmra/v8-i01-46