INTRODUCTION

The use of AI in university teaching holds great potential for enhancing educational quality and efficacy, enabling more effective and personalized learning experiences for students (^{Ren et al., 2024}). This phase is essential for the modernization of education. However, it requires ongoing professional training, ethical considerations, and a strategic implementation within higher education (^{Elsakova & Markus’, 2024}). Likewise, theoretical and practical implications arise for teaching practices, including the need to develop AI knowledge and digital competencies, as well as fostering positive perceptions and practical strategies-pedagogical principles-and innovation opportunities (^{Gómez-Rodríguez et al., 2024}). In this regard, ^{Zambrano & Pérez (2024}) state that university professors possess basic knowledge of AI tools-such as ChatGPT and Copilot-which is essential for effective integration. The origins of AI trace back to various disciplines such as philosophy, mathematics, and early computer science. Initially, AI was grounded in the ideas of the Greek philosopher Aristotle, who introduced the syllogism-a deductive logical reasoning structure consisting of three propositions: two premises and one conclusion-laying the foundation for logical reasoning (^{Fodor, 2023}). Furthermore, scholars such as Thomas Bayes, George Boole, and Charles Babbage further developed the mathematical and logical foundations necessary for AI (^{Vitanova, 2024}). A major milestone occurred at the Dartmouth Conference, where the term “artificial intelligence” was coined, setting the stage for future research. Recent advances in AI are significantly impacting university teaching, particularly through technologies in the teaching-learning process, learning experiences, and adaptive assessment techniques. These developments also raise ethical considerations that must be addressed for responsible integration (^{Castro & Padilla, 2024}). In this respect, ^{Yatsenko et al. (2024}) place special emphasis on the concept of competencies, encompassing a wide range of skills and practical experiences. Similarly, ^{Wang (2022}) clarified that such competencies are essential to adapt to the demands of modern education and the labor market.

These skills are commonly present among educators and tend to develop progressively throughout higher education (^{Gazit et al., 2023}; ^{Tavares et al., 2023}). University educators must explore the diverse skill sets and knowledge areas emphasized in academic programs (^{Chee et al., 2024}). Moreover, higher education institutions are currently addressing faculty preparation and resource availability to close existing gaps and ensure students are well-prepared to leverage AI in their future careers. ^{Ren et al. (2024}) stress that faculty should become familiar with tutoring systems, adaptive learning platforms-both commercial and open-source tools-and automated grading systems (^{Karmakar & Das, 2024}; ^{Sharma et al., 2024}). At the international level, the European Union's Digital Competence Framework for Educators (DigCompEdu) stands out as a major initiative aimed at enhancing educators’ digital competencies.

This framework has been recognized and adopted by various institutions to improve digital literacy and teaching practices (^{Grosseck et al., 2024}; ^{Cabero-Almenara et al., 2020}). In Taiwan, for example, the Ministry of Education promotes teaching quality through the integration of competency-based training models and systematic programs with curriculum design (^{Chen et al., 2024}, p. 2). Similarly, a 2022 study in Romania involving 60 higher education teachers highlighted the importance of continuous training and reported significant variability in the development of digital skills, emphasizing the need for ongoing professional development. Along the same lines, a 2023 survey in Ethiopia involving a sample of 498 university teachers revealed that most had low levels of digital competency, with only 7.2% demonstrating high levels (^{Amare et al., 2024}).

In Peru, the National System for Evaluation, Accreditation, and Certification of Educational Quality (Sineace) ensures that university institutions meet established standards, focusing on faculty selection, evaluation, training, qualifications, academic recognition, and curriculum plans (^{Ñañez-Silva et al., 2023}). Additionally, the National Superintendency of Higher Education (Sunedu) guarantees educational quality, which includes the development of faculty competencies (^{Hinojosa, 2024}). Some studies, such as ^{Iraola-Real et al. (2022}), evaluated 29 instructors from a private university in Lima, revealing optimal pedagogical management but weaknesses in intellectual production.

Another study conducted during the COVID-19 pandemic by ^{Madrid et al. (2024}), with a sample of 201 faculty members from a private university in Lima, found that digital competencies were a predictive factor in teaching performance. ^{Castillo & Herhuay (2025}) emphasized the need to improve digital skills in rural areas due to technological and connectivity limitations. In general, university faculty acknowledge AI’s potential to enhance teaching and learning processes. Nonetheless, there is considerable variation in their knowledge and understanding of AI applications (^{Kallunki et al., 2024}; ^{Bhojak et al., 2025}). According to ^{Güneyli et al. (2024}), teachers’ perceptions are influenced by prior knowledge, beliefs, and attitudes toward AI, with practical knowledge being the critical factor. Other studies, such as ^{Nirchi et al. (2024}), highlight benefits such as learning personalization, cognitive process enhancement, classroom management, and improved teaching practice.

^{Zipf et al. (2024}) conducted a study with 273 university professors and emphasized the dual impact of AI, highlighting both challenges and opportunities. A similar study, conducted between 2023 and 2024 across various education levels, found that university professors were more likely to incorporate AI into their teaching compared to primary and secondary teachers (^{Üzüm et al., 2025}). Technological integration in university teaching has seen significant advances between 2021 and 2025. ^{Ren et al. (2024}) noted that “AI enables personalized teaching by adapting materials to each student’s learning style.”

This personalization clearly enhances student engagement (^{Alzakwani et al., 2025}). Another important aspect is assessment and feedback; AI tools such as virtual labs and simulators provide enriched learning experiences, supporting decision-making and fostering critical-reflective thinking.

From a pedagogical standpoint, AI supports innovative teaching methods, such as intelligent tutoring systems and adaptive learning platforms, which improve student comprehension (^{Gupta et al., 2024}; ^{Asad et al., 2023}). In light of the existing academic literature, the general objective of this study was to describe the use of AI in university teaching based on competencies, perception, and technological integration among faculty members.

METHODOLOGY

This research adopted a quantitative approach, as it focused on the collection of numerical data to describe behavioral patterns (^{Bernal, 2000}). It was basic in type, given that it emphasized the expansion of knowledge and the generation of new theories (^{Paitán et al., 2014}). The research design was non-experimental and cross-sectional, since the variables were not manipulated and data were collected at a single point in time.

Regarding the data collection technique, a survey was employed, and the instrument used was a questionnaire consisting of 27 items, structured on a Likert-type scale where: 1 = Strongly disagree, 2 = Disagree, 3 = Neither agree nor disagree, 4 = Agree, and 5 = Strongly agree.

Initially, for the application of the instruments, 72 university professors from the Ica Region participated voluntarily, having signed an informed consent form that explained the purpose of the study and its estimated duration.

Data were compiled using Google Forms as well as in printed format. Finally, Microsoft Excel was used to compile the data, and the statistical analysis was conducted using RStudio version 2024.04.2+764, which enabled the presentation of results through tables and figures.

RESULTS

Figure 1 Previous experience with the use of AI tools 

In Figure 1, two bars are shown: orange (yes) and blue (no), illustrating university professors' previous experience with the use of AI tools. The data indicate that 13.9% (n = 10) reported no prior experience with AI tools, while 86.1% (n = 62) stated that they do have prior experience.

The results obtained indicate that university faculty demonstrate an acceptable level of proficiency regarding the use of AI. Specifically, 89.5% of respondents reported having an initial conceptual understanding of the basic concepts and applications of AI in the educational field. Likewise, 73.7% stated that they are familiar with AI tools for teaching.

Furthermore, 63.2% indicated that they have already incorporated these technologies into their pedagogical practices. These data reflect a growing trend toward the active use of AI. However, it is noteworthy that approximately 30% of the participants have not yet achieved full integration of AI into their teaching practice. Additionally, a significant finding is that 89.5% of faculty members reported evaluating the effectiveness of these tools, revealing a reflective attitude toward teaching and learning processes in relation to the impact of AI.

In the same vein, regarding their perception of AI use, the findings reflect a broadly positive assessment. Notably, 79% of faculty members believe that AI enhances the quality of instruction, while 73.7% believe it facilitates personalized learning. Furthermore, 81.6% consider AI tools to be intuitive, and 73.7% find their use easy. Another relevant aspect is that 89.5% either agreed or strongly agreed that AI helps optimize the time dedicated to planning and assessment, making it a perceived advantage. Moreover, 84.2% affirmed that AI improves the learning experience, and 73.7% acknowledged that it promotes student engagement and participation. Despite this, there are still ethical concerns, including issues of originality (68.5%) and the risk of technological dependence (78.9%).

With regard to institutional technological integration, faculty members reported the existence of favorable conditions, as 73.3% stated that their institution promotes the use of AI. Among the most significant findings were the availability of training and resources (73.7%). However, 57.9% indicated a moderate gap in technical support, as they lack direct assistance. In terms of peer collaboration, 94.8% of the respondents affirmed that their colleagues use AI in class, suggesting the presence of a digitally integrated environment. Moreover, faculty members expressed a strong willingness to continue exploring new AI applications (84.2%), while 68.4% stated that they intend to increase their use of AI in the coming academic terms. This reflects a proactive attitude toward pedagogical innovation. Finally, 84.2% of respondents consider AI to be a key element in the future of education, thus consolidating a positive perception of its potential.

DISCUSSION

In relation to the initial objective-to describe the use of artificial intelligence in university teaching based on competencies, perception, and technological integration among faculty members-the results contribute to enriching the theoretical foundations surrounding AI implementation. These findings align with ^{Ren et al. (2024}), who stated that AI use enhances both the quality and effectiveness of teaching. Notably, 73.7% of respondents reported that AI improves the educational experience, while 89.5% affirmed, as highlighted by ^{Zambrano & Pérez (2024}) and ^{Elsakova & Markus’ (2024}), that university professors possess basic knowledge of AI tools such as ChatGPT and Copilot.

With respect to digital competencies, the findings of this study are consistent with authors such as ^{Gazit et al. (2023}), ^{Tavares et al. (2023}), and ^{Gómez-Rodríguez et al. (2024}), who emphasize that these skills develop progressively. Furthermore, 63.2% of faculty members reported already incorporating AI into their teaching and learning practices-an especially significant finding.

Nevertheless, a gap remains among those who have not yet integrated such tools. In addition, 89.5% reported critically and reflectively evaluating the use of AI tools, demonstrating not only technical but also ethical and pedagogical appropriation, as proposed by ^{Elsakova & Markus (2024}), ^{Castro & Padilla (2024}), and ^{Fodor (2023}). Regarding perception, the results are in line with ^{Nirchi et al. (2024}), ^{Vitanova (2024}), and ^{Yatsenko et al. (2024}), who underscored the benefits of AI in learning personalization, classroom management, and the improvement of didactic processes. A total of 79% and 89.5% of faculty members considered that AI enhances the quality of teaching and optimizes time spent on planning and assessment. These results are consistent with findings by ^{Zipf et al. (2024}) and ^{Wang (2022}), who emphasize the functional opportunities of AI.

However, serious concerns also emerged regarding originality (68.5%) and the risk of technological dependence (78.9%), in line with the warnings by ^{Kallunki et al. (2024}) and ^{Sharma et al. (2024}), who pointed to the variability and ethical challenges in educational practice. The findings related to technological integration support the assertions of ^{Chee et al. (2024}) and ^{Grosseck et al. (2024}), who stressed the importance of institutional training and support for the optimal implementation of AI. While 73.7% of respondents reported receiving institutional support and 57.9% acknowledged having access to technical support, these results coincide with studies conducted in Ethiopia by ^{Amare et al. (2024}), as well as by ^{Cabero-Almenara et al. (2020}) and ^{Hinojosa (2024}). Additionally, the high levels of collaboration among faculty (94.8%) and their exploration of new AI applications (84.2%) suggest the presence of a supportive and proactive environment. These outcomes align with findings by ^{Üzüm et al. (2025}) and ^{Ñañez-Silva et al. (2023}), who highlighted faculty members’ proactive disposition.

CONCLUSIONS

In relation to the stated objective-describing the use of artificial intelligence in university teaching in terms of competencies, perception, and technological integration among faculty members-it can be concluded that university professors possess foundational knowledge of AI. They demonstrate critical and reflective attitudes regarding its applications and pedagogical implications.However, the study also revealed ongoing challenges, such as the need for technical support and continuous professional development. These findings highlight the importance of providing pedagogical guidance to higher education institutions, with the aim of promoting the implementation of more appropriate policies, as well as ensuring the availability of adequate resources to support the digital transformation of higher education