Design and Preliminary Evaluation of an AI-Enabled Physical Systems Workshop

Students’ use of artificial intelligence (AI) tools does not necessarily provide understanding of how AI-enabled systems are developed and evaluated. This study presents the design and preliminary evaluation of a guided experiential workshop on AI-enabled physical systems for secondary-school students. Students worked with programmable electronics, camera input, image datasets, machine-learning classification, and actuator responses. A one-group pre-test/post-test design examined immediate changes in workshop-specific foundational knowledge and exploratory associations with student-profile indicators. Pre-test data were available for 44 students, with 38 matched pre/post pairs. Mean scores increased from 7.21/16 to 11.55/16 (mean gain = 4.34 points; mean individual normalised gain = 0.50), t(37) = 8.35, p < 0.001, Cohen’s dz = 1.35. This standardised change describes performance on the workshop-aligned test. Post-test scores were higher in both electronics/programming and AI/ML/data domains. Exploratory analyses detected no significant associations between knowledge gains and initial STEM interest or ICT self-concept. Students reported positive perceptions of the workshop. The implementations provide preliminary evidence of practical feasibility with instructional support. These findings support further refinement and evaluation; they do not establish causal effects, broader AI literacy gains, or independent design competence.