Edge AI Gas-Sensing Project in Vocational Education: A Mixed-Methods Study of Problem-Solving Attitudes

This exploratory mixed-methods study examines how an edge artificial intelligence (edge AI) gas-sensing project is associated with vocational upper-secondary students’ self-reported problem-solving attitudes in an authentic classroom setting. Thirty-eight students specializing in Electrical, Electronics, and Automation addressed the same design problem: developing a gas-sensing prototype by collecting sensor data, training a machine-learning classifier, and deploying the model on embedded hardware for local inference and real-time decision support without continuous reliance on cloud processing. Three instructional pathways were compared: (i) Maker Learning (ML; n = 12) with hands-on prototyping and on-device testing, (ii) Virtual Learning (VL; n = 13) with simulation-based activities, and (iii) Traditional Learning (C; n = 13) with teacher-guided instruction. Quantitative data were collected through three administrations of the Problem-Solving Inventory (PSI), complemented by post-intervention semi-structured interviews. Exploratory analyses indicated statistically significant changes in total PSI scores across all pathways (p ≤ 0.003), reflecting more favourable problem-solving appraisals. At post-test, the ML and VL pathways showed more approach-oriented profiles than the Traditional Learning pathway, with large effect-size estimates (ML − C: r = 0.714; VL − C: r = 0.611), while for problem-solving confidence, the pattern favoured the Maker Learning pathway (ML − VL: r = 0.496). Interviews provided complementary interpretive context, highlighting iterative debugging, feedback, collaboration, and artefact ownership. Within the limits of this exploratory vocational-school classroom implementation, the findings and effect-size estimates were interpreted cautiously. Overall, the study contributes by depicting how edge AI gas-sensing projects may support positive problem-solving appraisals and outlining a meaningful pathway for the integration of edge AI technologies in vocational education.