Active Learning in IoT-AI Projects: Gradual Development of Technological Competencies
Keywords:
Industrial automation, Active learning, Artificial intelligence, Internet of thingsAbstract
Objectives: Evaluate the effectiveness of an active teaching methodology to develop technological competencies in industrial automation through IoT and AI projects in industrial engineering students. Methodology: A mixed quasi-experimental design was used with 75 fourth-semester industrial engineering students from the Fundación Universitaria del Área Andina during the 2024-2 and 2025-1 periods. Both groups developed an automated beehive monitoring project using sensors, microcontrollers, and AI tools. The experimental group (45 students) followed an active methodology structured in five progressive phases: theoretical contextualization, system design, practical implementation, AI optimization, and final presentation, using project-based learning, graphic organizers, rapid prototyping, and personalized mentoring. The control group (30 students) received traditional teaching through lectures. Evaluation included phase rubrics, satisfaction surveys, and applied technological competency tests. Results: The experimental group showed superior performance in implementing functional prototypes (89% vs 43% success), understanding of IoT-AI technologies (average 4.3/5.0 vs 3.1/5.0), development of device programming competencies (87% vs 52% satisfactory achievement), and methodological satisfaction (average 4.6/5.0). The gradual construction allowed 92% of students to successfully complete complex automation projects. Conclusions: The five-phase active teaching methodology significantly outperforms traditional teaching, demonstrating effectiveness in developing advanced technological competencies in students without prior experience in electronics through structured gradual construction.
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