The Effect of K-NN Algorithm Practice Chatbot Tutors on the Speed and Accuracy of Solving AI Problems in Vocational Schools - Andi Asfar; Andi Winda Purnamasari; Angriani Nur; Aprilia; Arnis; Ashabul Kahfi (Journal of Embedded Systems, Security and Intelligent Systems, 2025)
Learning Artificial Intelligence (AI) concepts at the vocational high school level often presents challenges, particularly in understanding procedural algorithms such as K-Nearest Neighbor (K-NN). Students frequently experience difficulties in calculating distances, selecting the optimal value of k, and determining classification results accurately and efficiently. This study investigates the effect of a chatbot tutor designed for K-NN practice on students’ speed and accuracy in solving AI problems. A quasi-experimental design with a pretest–posttest control group model was employed. Sixty students from a vocational high school majoring in Software Engineering were divided into an experimental group receiving chatbot-assisted practice and a control group receiving conventional instruction. Data were collected through validated K-NN problem-solving tests and task completion time measurements. Statistical analysis using paired and independent sample t-tests revealed that the experimental group demonstrated significantly faster completion times and higher accuracy scores compared to the control group (p < 0.05). The effect size indicated a moderate to high practical impact. The findings suggest that chatbot-based tutoring can enhance both efficiency and precision in learning K-NN algorithms. This study contributes empirical evidence supporting the integration of chatbot tutors into Artificial Intelligence instruction in vocational education to improve problem-solving performance.