The research paper is related to an analysis of the impact of AI-guided intervention in the course of performing Arduino programming projects for undergraduate students on innovation functionality and technical skills. Two sets of students were taken for assessment, where one set was AI-assisted, and the other set underwent traditional methods of teaching using a quasi-experimental design. The primary objectives were to evaluate the effectiveness of AI tools in improving student performance, to determine the inter-rater reliabilities and, based on the evidence, to recommend how AI tools can be integrated into Creative Technology Curricula. The paired t-test of the means of the two groups showed a significant difference t(58) = 5.322 p < .001 with the intervention group performing better than the control group. Correlation analysis indicated that all the components of the final project assessment were related in a positive and moderately strong way: innovativeness and functionality (r = 0.435), innovativeness and technical skills (r = 0.561), and functionality and technical skills (r = 0.418). The regression analysis revealed that the model explained 64.4% of the variance in the total score (R-square = 0.644) with good predictors being innovation (β = 0.901, p = 0.002) and technical skills (β = 0.975, p = 0.001). As measured by the Intraclass Correlation Coefficient (ICC), inter-rater reliability was very good, with ICC values for single measures and average measures coming out greater than 0.75. Results indicate that AI-supported intervention has significantly increased the student’s performance in Arduino programming projects, particularly in innovation and technical skills. These findings suggest that embedding AI tools in creative technology education could serve as a very effective means of enhancing both creative and technical skills among students. Long-term effects and applicability across diverse educational contexts need to be examined by future research.