The rapid transformation of education, driven by digitalization, has positioned artificial intelligence (AI) as a key enabler in addressing some of the most pressing challenges in higher education [1]. However, the accelerated integration of AI into teaching and learning, coupled with its distinct characteristics—particularly its ethical implications—necessitates competencies that extend beyond conventional digital literacy [2]. Therefore, a comprehensive and systematic assessment of AI’s effectiveness, particularly from the students’ perspective, is imperative.

The emergence of the first «teaching machines», as conceptualized by Skinner in 1958 [3], marked the inception of this transformation. These early systems, designed to optimize learning by enhancing efficiency and structure, have evolved into sophisticated AI-driven teaching tools that actively engage both learners and educators.

As with all technological advancements, the development of AI-based teaching systems has been driven by the pursuit of enhanced pedagogical effectiveness. However, there exists a significant risk of premature overpromotion and misapplication of AI in education before its actual capabilities and limitations are fully understood [4, 5]. This study aims to provide a balanced perspective, contextualizing the historical use of this technology in education, analyzing current needs and practices, and exploring future expectancies.

Traditional education models have primarily focused on equipping students with knowledge for stable, long-term careers, often prioritizing content acquisition over adaptive learning skills. However, as a recent report by the Institute for the Future (IFTF) predicts that 85% of the jobs that will exist in 2030 have yet to be created, it is essential to reconsider the role of AI in preparing students for an increasingly dynamic job market. This paper examines the potential opportunities, challenges, and risks associated with the integration of AI in higher education, with a particular emphasis on engineering programs.

To illustrate these concepts, this paper presents two case studies from automatic control courses within engineering degree programs at the University of Extremadura, Spain. These cases focus on:
(1) fostering an understanding of the principles underlying generative artificial intelligence (GenAI),
(2) leveraging GenAI to solve a specific control problem, including a comparative analysis of different AI tools.

References:
[1] B. George, O. Wooden, “Managing the Strategic Transformation of Higher Education through Artificial Intelligence,” Administrative Sciences, vol. 13, no. 9, 196, 2023.
[2] UNESCO, AI and education: guidance for policy-makers, 2021. Accessed March 5, 2025. Retrieved from https://unesdoc.unesco.org/ark:/48223/pf0000376709.
[3] B. F. Skinner, “Teaching machines,” Science, vol. 128, no. 3330, pp. 969-977, 1958.
[4] K. Michael, J. Pitt, J. Sargent, E. Scornavacca, "Automating Higher Education Through Artificial Intelligence?," IEEE Transactions on Technology and Society, vol. 5, no. 3, pp. 264-271, 2024.
[5] R. Watermeyer, L. Phipps, D. Lanclos, C. Knight, “Generative AI and the Automating of Academia,” Postdigital Science and Education, vol. 6, pp. 446-466, 2024.

Keywords: Artificial intelligence, education, engineering, new learning technologies.