The integration of artificial intelligence (AI) into higher education is generating growing interest and opening new pathways for innovation. These novel methodologies are particularly relevant in the teaching and learning of engineering disciplines, which face the challenges of rapidly evolving technological demands, problem-solving skills, and diverse student populations. In this context, AI offers promising solutions to enhance the effectiveness and accessibility of instruction, and enables a more personalized, efficient, and scalable educational experience. These tools can support students through real-time feedback, customized learning trajectories, and simulation-based environments that mirror real-world engineering problems.

However, the integration of AI in higher education also raises important questions about equity, data privacy, transparency, and the role of human educators in an increasingly automated context. Understanding the pedagogical, technical, and ethical implications of AI adoption is crucial for its successful and responsible implementation.

This work presents several case studies involving the application of generative AI in the teaching of three courses in engineering programs at the University of Vigo, Spain. Specifically, in the courses “Alternative Fuel Technologies” (3rd year Energy Engineering), and “Industrial Organic Chemistry” (4th year Industrial Chemical Engineering), students were assigned to do a bibliographic review on a specific topic as a preliminary step toward the development of an essay or design project, making use of any AI tool they desired. They were required, in all cases, to include a critical analysis of the actual usefulness and limitations of the chosen AI tool. Particular emphasis was placed on evaluating which aspects of the bibliographic analysis could be effectively supported by AI, and which still necessitated a traditional, human-centered approach. The activity aimed to foster both technical competence in the use of emerging digital tools and reflective thinking regarding the role of AI in academic and engineering research contexts.

In addition, an educational initiative was implemented in the 1st year course “Mathematics: Calculus I” within the Communication Technologies Engineering program. This experience focused on exploring the use of AI-based tools to solve first-year engineering mathematics problems. Students were encouraged to employ AI platforms capable of symbolic computation or step-by-step problem solving and to compare the AI-generated solutions with the analytical methods taught and practiced in class. The activity aimed not only to assess the correctness and completeness of the AI solutions but also to identify discrepancies, underlying assumptions, and potential conceptual gaps.

The preliminary evaluation of these experiences indicates that allowing the use of AI tools contributes to increased student motivation, as learners perceive greater freedom in carrying out academic activities. Additionally, it fosters genuine curiosity and interest in the field of artificial intelligence and in learning how to use such tools effectively. Overall, students' perception of the usefulness of these methodologies was positive, although relevant limitations were noted when applied to very specific tasks or those requiring a high level of technical proficiency.

Keywords: Generative AI, engineering education, educational automation, skill development, case study, critical thinking, personalised learning.