Prof. Héctor A. Barrios-Piña
Tecnologico de Monterrey, México
Bio: Héctor A. Barrios Piña is Division Director of the School of Engineering and Sciences at Tecnologico de Monterrey, Campus Guadalajara, Mexico, and a Full Professor of Civil Engineering. He has more than 20 years of experience in higher education, teaching undergraduate and graduate courses in engineering while leading curriculum transformation and educational innovation initiatives. He has played a key role in the design and implementation of the Tec21 Civil Engineering curriculum, integrating challenge-based learning, interdisciplinary education, digital technologies, and artificial intelligence into engineering education. His work has also focused on innovative assessment models, flipped learning, and the development of future-ready engineering competencies, resulting in several international conference publications and Best Paper Awards in engineering education.
Dr. Barrios Piña has received the Tecnologico de Monterrey Inspiring Professor and Distinguished Professor awards, as well as continuous recognition for excellence in teaching. His research spans water resources, environmental engineering, climate resilience, and computational modeling, and he actively promotes the integration of research, innovation, and education to prepare engineers capable of addressing complex societal challenges.
Speech Title: If AI Can Solve the Problem, What Should We Teach Future Engineers?
Abstract: Generative Artificial Intelligence is rapidly transforming the way engineers access knowledge, analyze information, write code, develop models, and solve technical problems. As AI systems become increasingly capable of performing tasks that have traditionally been used to teach and assess engineering students, a fundamental question emerges: If AI can solve the problem, what should we teach future engineers?
This invited talk argues that the rise of Generative AI requires engineering education to move beyond its traditional emphasis on problem solving toward a broader and more demanding educational goal: developing engineers capable of framing the right problems, critically evaluating AI-generated solutions, validating evidence, making informed decisions, and understanding the societal consequences of technological choices.
Rather than treating AI simply as another educational technology, the presentation explores its potential to reshape the relationship between students, professors, knowledge, and assessment. Drawing on experiences with Generative AI-assisted active learning in undergraduate engineering education, the talk discusses how AI can be incorporated into learning environments not as a substitute for human reasoning, but as a catalyst for inquiry, peer discussion, critical thinking, and deeper conceptual understanding.
A five-stage AI-Era Engineering Learning Loop: Frame, Explore, Challenge, Validate, and Decide; is proposed as a conceptual framework for reconsidering learning activities and assessment in engineering education. Within this framework, AI can significantly expand students' capacity to explore ideas and possible solutions, while human learners retain responsibility for defining meaningful problems, challenging assumptions, validating outputs, and making decisions.
The presentation ultimately invites educators and academic leaders to reconsider what it means to educate an engineer in an AI-driven world. The challenge ahead is not simply to teach students how to use increasingly powerful AI tools, but to develop the judgment, curiosity, critical thinking, and sense of responsibility required to work intelligently alongside them.
If AI can solve the problem, perhaps the most important competence we can teach future engineers is how to identify the problem worth solving.
