Generative AI as a Teaching Ally: Pedagogical Design for Engineering Education

DOI: https://doi.org/10.54674/ess.v37i2.1106
Keywords: Generative Artificial Intelligence; Active Learning; Pedagogical Design; Engineering Education; Design-Based Research

Abstract

The emergence of Generative Artificial Intelligence Chatbots (GAIC) represents an unprecedented challenge and opportunity for higher education. In the face of the need to overcome traditional teaching paradigms, educators require the design of pedagogical models that allow them to effectively integrate these tools. This article develops and substantiates the design of a pedagogical strategy based on an active learning approach that empowers teachers as designers of learning environments, using GAIC to create innovative instructional experiences in engineering education. The methodology follows the Design-Based Research (DBR) approach. It began with a diagnostic phase that included the application of the Unified Theory of Acceptance and Use of Technology (UTAUT) scales and Kolb’s Learning Style Inventory to 80 students, which served as the foundation for the design of the learning activities. The result was a pedagogical model that employs GAIC through the teacher’s mediation, incorporating active learning and gamification dynamics. The model redefines roles, positioning the teacher as an architect of learning, GAIC as a design assistant, and the student as an active and self-regulated agent. It is concluded that the integration of GAIC into teaching is feasible when focused on pedagogical design. The proposed model offers a practical and theoretically grounded guide for educators to innovate in their teaching practice.

Author Biographies

Idalia Salceda-Flores, Sonora Institute of Technology, Obregón, Mexico

Adjunct professor at the Sonora Institute of Technology and a doctoral student. An engineer by training, her research focuses on the acceptance of Generative Artificial Intelligence (GAI) and active learning in STEM education, using structural equation modeling (SEM) and the UTAUT2 model. Her recent work includes the study “The Generative Artificial Intelligence Chatbot in Higher Education: Perceptions and Practices of STEM Students from a Phenomenological Approach” and “Strategies and Impact of Generative Artificial Intelligence Chatbots on Higher Education Students: A Systematic Literature Review.”

Armando Lozano-Rodríguez, Sonora Institute of Technology, Obregón, Mexico

Full-time professor in the Department of Education at the Sonora Institute of Technology. Ph.D. in Educational Innovation and Technology; research interests: educational technology, learning and teaching styles, competency development, and active learning. Most recent publications: Active Teaching Strategies in Higher Education: A Systematic Literature Review (The International Journal of Learning in Higher Education, 2025) and Psychometric properties of a scale to measure online teaching styles in university teachers (RIED-Ibero-American Journal of Distance Education, 2025).

Ramona Imelda Garcia-Lopez, Sonora Institute of Technology, Obregón, Mexico

Ph.D. in Education and full-time professor in the Department of Education at the Sonora Institute of Technology. Her research interests include technology-mediated learning, teacher education, and curriculum development. Most recent publications: Psychometric Properties of an Academic Reading-Writing Scale for Graduate Students (Comunicar Journal, 2026) and Trends in Research on Attitudes Toward Learning Mathematics: A Bibliometric Analysis (International Journal of Education in Mathematics, Science and Technology, 2026).

Emmanuel Contreras, Autonomous University of Coahuila, Torreón, Mexico

Professor and researcher affiliated with the Autonomous University of Coahuila. He holds a Ph.D. in Strategic Planning and Technology Management; his research interests include strategic planning, multi-criteria ordinal classification for financial decision-making, and the alignment of information technology with business strategy. He has authored several publications, notably A New Methodology Based on Multicriteria Ordinal Classification for the Management of Financial Resources with Application to Real Data from the Stock Market.

Published
2026-05-30
How to Cite
Salceda-Flores, I., Lozano-Rodríguez, A., Garcia-Lopez, R. I., & Contreras, E. (2026). Generative AI as a Teaching Ally: Pedagogical Design for Engineering Education. Higher Education and Society Journal (ESS), 37(2), 90-109. https://doi.org/10.54674/ess.v37i2.1106
Issue
Vol 37 No 2 (2025): Artificial Generative Intelligence (GenAI) in teaching and research: best practices, challenges, and prospects
Section
Articles Thematic Dossier

Copyright (c) 2025 Idalia Salceda-Flores, Armando Lozano-Rodríguez , Ramona Imelda García-López, Emmanuel Contreras

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