A design-informed framework for integrating generative AI into IoT engineering education: a single-cycle technical implementation case study
Antonio Carlos Bento, Elisa Yolanda Torres-Torres, Sergio Camacho-León, Enrique González, Raul Peña, Daniel Couto Gatti, José Reinaldo Silva
- Journal
- Frontiers in Education
- Autor
- Antonio Carlos Bento, Elisa Yolanda Torres-Torres, Sergio Camacho-León, Enrique González, Raul Peña, Daniel Couto Gatti, José Reinaldo Silva
- Book Title
- Volume
- 11
- Página(s)
- DOI
- 10.3389/feduc.2026.1919886
- Publicada
- 2003
- Month
- Tipo de Documento
- Artigo em Revista
- Abstract
- The rapid adoption of generative artificial intelligence (AI) has created new opportunities for engineering education, particularly in project-based Internet of Things (IoT) courses that require students to integrate software, hardware, and embedded systems. This paper presents a technical case description of a design-informed, AI-augmented instructional framework for integrating Google's Gemini into undergraduate IoT engineering education, structured around mobile and web application tiers, with exploratory microcontroller-level AI integration attempted by a subset of teams. The framework was informed by prior classroom experience at Tecnologico de Monterrey and is illustrated here through a single ten-week course offering conducted in the second semester of 2024, involving 14 project teams and 73 third-semester engineering students, of whom 66 were based in Mexico and 7 were based in Brazil; the Brazil-based students joined for the final five weeks of the software module only. This paper reports technical and project-level evidence only – system architecture, tool usage, project artifacts, and Azure DevOps activity logs (e.g., debugging-time timestamps) – and does not report individual-level human-subjects data such as competency assessments, examination results, or student quotations, as documented informed consent for research use of that data was not obtained (see Ethics Statement). Results indicate that participating teams incorporated AI into multidisciplinary IoT projects through the proposed mobile/web/embedded architecture, with all 14 teams producing a functional prototype aligned with at least one Sustainable Development Goal (SDG) and completing their projects using freely available AI tools at no additional software cost. These findings describe the technical feasibility of the framework in a single, non-controlled implementation; they do not constitute an evaluation of student learning outcomes. Future work should include a properly consented study of learning outcomes, controlled comparisons, and replication across additional course cycles.
- Keywords
- Publisher
- MDPI
- URL