Reconfiguration of Teaching Practice through Generative Artificial Intelligence: evidence from Engineering and Architecture Degree Programs

Abstract

This article presents the results of a mixed-methods study examining the uses, perceptions, and instructional integration practices of generative artificial intelligence (GAI) in teaching. The research was conducted with faculty members from three undergraduate programs in the fields of Engineering and Architecture at a private university in Nicaragua. Data were collected through a questionnaire and three focus groups. The most common uses of GAI include instructional planning, the search for specialized and up-to-date information and the design of exercises and case studies. Teaching strategies are aimed at fostering critical analysis and mitigating academic plagiarism. Among the perceived benefits, time savings and increased student motivation associated with the use of these technologies stand out. However, faculty members also express concerns regarding potential cognitive dependency, the risk of plagiarism, insufficient development of fundamental cognitive skills, as well as issues related to the reliability and quality of the generated information. In conclusion, the findings underscore the urgent need to establish a clear framework for action and to provide institutional training for both faculty and students—measures that are crucial to ensuring a transition from a purely empirical use toward a pedagogical and ethical application of artificial intelligence.