Pengaruh Penggunaan Google NotebookLM terhadap Hasil Belajar Dasar Listrik dan Elektronika (The Effect of Google NotebookLM on Students’ Learning Outcomes in Basic Electricity and Electronics)

Authors

DOI:

https://doi.org/10.68437/jejpte.v6i2.18

Keywords:

Google NotebookLM, learning outcomes, Basic Electricity and Electronics, quasi-experiment, vocational school

Abstract

This study aimed to analyze the effect of using Google NotebookLM on the learning outcomes of Grade X students in the Basic Electricity and Electronics subject at SMK Negeri 3 Tondano. A quantitative approach was employed using a quasi-experimental method with a pretest-posttest control group design. The sample consisted of 29 students selected through purposive sampling, including 15 students in the experimental group and 14 students in the control group. The experimental group learned using Google NotebookLM, while the control group received conventional instruction. Data were collected using a cognitive learning outcome test consisting of 20 multiple-choice items administered before and after the learning intervention. The data were analyzed using descriptive statistics, the Lilliefors normality test, Levene’s homogeneity test, and an independent-samples t-test. The mean score of the experimental group increased from 61.33 to 85.33, whereas the control group increased from 58.92 to 69.28. The t-test yielded t-calculated = 4.191, which was higher than t-critical = 2.05 at the 0.05 significance level. The findings indicate a difference in learning outcomes between the two groups. The study was limited to 29 students from one school and used purposive sampling; therefore, the findings should be interpreted within the study context. Google NotebookLM can serve as a supporting learning medium while teachers remain actively involved in guiding learning activities and reinforcing concepts.

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References

Al Shloul, T., Mazhar, T., Abbas, Q., Iqbal, M., Ghadi, Y. Y., Shahzad, T., Mallek, F., & Hamam, H. (2024). Role of activity-based learning and ChatGPT on students' performance in education. Computers and Education: Artificial Intelligence, 6, 100219. https://doi.org/10.1016/j.caeai.2024.100219

Albadarin, Y., Saqr, M., Pope, N., & Tukiainen, M. (2024). A systematic literature review of empirical research on ChatGPT in education. Discover Education, 3, Article 60. https://doi.org/10.1007/s44217-024-00138-2

Almatrafi, O., Johri, A., & Lee, H. (2024). A systematic review of AI literacy conceptualization, constructs, and implementation and assessment efforts (2019–2023). Computers and Education Open, 6, 100173. https://doi.org/10.1016/j.caeo.2024.100173

Castillo-Martínez, I. M., Flores-Bueno, D., Gómez-Puente, S. M., & Vite-León, V. O. (2024). AI in higher education: A systematic literature review. Frontiers in Education, 9, Article 1391485. https://doi.org/10.3389/feduc.2024.1391485

Chiu, T. K. F., Xia, Q., Zhou, X., Chai, C. S., & Cheng, M. (2023). Systematic literature review on opportunities, challenges, and future research recommendations of artificial intelligence in education. Computers and Education: Artificial Intelligence, 4, Article 100118. https://doi.org/10.1016/j.caeai.2022.100118

Dai, C.-P., & Ke, F. (2022). Educational applications of artificial intelligence in simulation-based learning: A systematic mapping review. Computers and Education: Artificial Intelligence, 3, Article 100087. https://doi.org/10.1016/j.caeai.2022.100087

Dong, Y. (2026). Generative AI technologies and educational outcomes: A comprehensive meta-analysis comparing traditional and AI-driven approaches. Humanities and Social Sciences Communications, 13, Article 559. https://doi.org/10.1057/s41599-026-06903-y

García-Martínez, I., Fernández-Batanero, J. M., Fernández-Cerero, J., & León, S. P. (2023). Analysing the impact of artificial intelligence and computational sciences on student performance: Systematic review and meta-analysis. Journal of New Approaches in Educational Research, 12, 171–197. https://doi.org/10.7821/naer.2023.1.1240

Habibi, A., Muhaimin, M., Danibao, B. K., Wibowo, Y. G., Wahyuni, S., & Octavia, A. (2023). ChatGPT in higher education learning: Acceptance and use. Computers and Education: Artificial Intelligence, 5, Article 100190. https://doi.org/10.1016/j.caeai.2023.100190

Hu, D.-X., Pang, D.-D., & Xing, Z. (2025). Evaluating the effects of Generative AI on student learning outcomes: Insights from a meta-analysis. Educational Technology & Society, 28(3), 226–240. https://doi.org/10.30191/ETS.202507_28(3).TP02

Kalalo, B., Memah, V., & Mamahit, C. (2026). Pengaruh penggunaan smartphone terhadap hasil belajar siswa SMK. JURNAL EDUNITRO Jurnal Pendidikan Teknik Elektro, 6(1), 65–74. https://doi.org/10.53682/eg02zm26

Kong, S.-C., Cheung, M.-Y. W., & Tsang, O. (2024). Developing an artificial intelligence literacy framework: Evaluation of a literacy course for senior secondary students using a project-based learning approach. Computers and Education: Artificial Intelligence, 6, 100214. https://doi.org/10.1016/j.caeai.2024.100214

Kovalchuk, V. I., Maslich, S. V., & Movchan, L. H. (2023). Digitalization of vocational education under crisis conditions. Educational Technology Quarterly, 2023(1), 1–17. https://doi.org/10.55056/etq.49

Lee, S. J., & Kwon, K. (2024). A systematic review of AI education in K-12 classrooms from 2018 to 2023: Topics, strategies, and learning outcomes. Computers and Education: Artificial Intelligence, 6, Article 100211. https://doi.org/10.1016/j.caeai.2024.100211

Lin, C.-C., Huang, A. Y. Q., & Lu, O. H. T. (2023). Artificial intelligence in intelligent tutoring systems toward sustainable education: A systematic review. Smart Learning Environments, 10, 41. https://doi.org/10.1186/s40561-023-00260-y

Lintner, T. (2024). A systematic review of AI literacy scales. npj Science of Learning, 9, 50. https://doi.org/10.1038/s41539-024-00264-4

Liu, Z., Zhao, Y., Zuo, H., & Lu, Y. (2025). Perceived satisfaction, perceived usefulness, and interactive learning environments as predictors of university students’ self-regulation in the context of GenAI-assisted learning: An empirical study in mainland China. Frontiers in Psychology, 16, 1599478. https://doi.org/10.3389/fpsyg.2025.1599478

Nasr, N. R., Tu, C.-H., Werner, J., Bauer, T., Yen, C.-J., & Sujo-Montes, L. (2025). Exploring the impact of generative AI ChatGPT on critical thinking in higher education: Passive AI-directed use or human–AI supported collaboration? Education Sciences, 15(9), 1198. https://doi.org/10.3390/educsci15091198

Rambing, J. C., Angmalisang, H., & Seke, F. R. (2025). Efektivitas blended learning terhadap hasil belajar dasar teknik ketenagalistrikan di SMK Negeri 3 Tondano. JURNAL EDUNITRO Jurnal Pendidikan Teknik Elektro, 5(2). https://doi.org/10.53682/edunitro.v5i2.12683

Rasyid, M., Algaus, I., & Waode, H. (2023). The application of problem based learning improves the learning outcomes of electric motor installation for students of SMKN 1 Kulisusu. JURNAL EDUNITRO Jurnal Pendidikan Teknik Elektro, 3(2), 79–88. https://doi.org/10.53682/edunitro.v3i2.6574

Rizvi, S., Waite, J., & Sentance, S. (2023). Artificial intelligence teaching and learning in K-12 from 2019 to 2022: A systematic literature review. Computers and Education: Artificial Intelligence, 4, 100145. https://doi.org/10.1016/j.caeai.2023.100145

Rodway, P., & Schepman, A. (2023). The impact of adopting AI educational technologies on projected course satisfaction in university students. Computers and Education: Artificial Intelligence, 5, Article 100150. https://doi.org/10.1016/j.caeai.2023.100150

Shi, J., Liu, W., & Hu, K. (2025). Exploring how AI literacy and self-regulated learning relate to student writing performance and well-being in generative AI-supported higher education. Behavioral Sciences, 15(5), 705. https://doi.org/10.3390/bs15050705

Steele, J. L. (2023). To GPT or not GPT? Empowering our students to learn with AI. Computers and Education: Artificial Intelligence, 5, Article 100160. https://doi.org/10.1016/j.caeai.2023.100160

Stolpe, K., & Hallström, J. (2024). Artificial intelligence literacy for technology education. Computers and Education Open, 6, 100159. https://doi.org/10.1016/j.caeo.2024.100159

Tomisu, H., Ueda, J., & Yamanaka, T. (2025). The cognitive mirror: A framework for AI-powered metacognition and self-regulated learning. Frontiers in Education, 10, 1697554. https://doi.org/10.3389/feduc.2025.1697554

Weng, X., Xia, Q., Ahmad, Z., & Chiu, T. K. F. (2024). Personality traits for self-regulated learning with generative artificial intelligence: The case of ChatGPT. Computers and Education: Artificial Intelligence, 7, 100315. https://doi.org/10.1016/j.caeai.2024.100315

Yim, I. H. Y. (2024). Artificial intelligence literacy in primary education: An arts-based approach to overcoming age and gender barriers. Computers and Education: Artificial Intelligence, 7, 100321. https://doi.org/10.1016/j.caeai.2024.100321

Zhang, T., Lai, Y. C., & Yu, P. L. H. (2026). Generative artificial intelligence in K-12 education: A systematic review. Research and Practice in Technology Enhanced Learning. https://doi.org/10.58459/rptel.2026.21034

Published

2026-09-22

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Section

Research Articles

How to Cite

Alopa, S., Kilis, B., & Mahendra, I. G. B. (2026). Pengaruh Penggunaan Google NotebookLM terhadap Hasil Belajar Dasar Listrik dan Elektronika (The Effect of Google NotebookLM on Students’ Learning Outcomes in Basic Electricity and Electronics). JURNAL EDUNITRO Jurnal Pendidikan Teknik Elektro, 6(2), 217-224. https://doi.org/10.68437/jejpte.v6i2.18