Using Gemini AI on the Interest and Learning Outcomes of Computer and Network Engineering Students in Vocational High Schools

In the digital age and the 4.0 industrial revolution, the use of artificial intelligence (AI) technology in education has become increasingly urgent, particularly in technical vocational learning such as Network Security. This study aims to analyze the effect of using Gemini AI on students' interest and learning outcomes in Network Security at vocational high schools. This study employs a quantitative approach with a quasi-experimental design in the form of a non-randomized control group pre-test post-test design. The sample consists of two Grade XI TKJ classes at SMKN 1 Banyuanyar selected through purposive sampling. Data collection techniques include a questionnaire to measure learning interest and pre-test and post-test assessments to evaluate learning outcomes. Data were analyzed using the independent samples t-test, and effectiveness was assessed using Cohen’s d. The results showed a significant increase in both learning interest (d = 1.821) and learning outcomes (d = 3.560) in the experimental class after implementing Gemini AI, with a significance level of p < 0.001. These findings confirm that AI-based learning can be an effective solution in improving the quality of vocational education in the field of technology. This study contributes practically and theoretically to the development of adaptive learning methods and is recommended to be expanded to other contexts and subjects.