THE ROLE OF NOTEBOOKLM IN ENHANCING ARTIFICIAL INTELLIGENCE LITERACY AMONG VOCATIONAL STUDENTS
DOI:
https://doi.org/10.36618/merpati.v8i1.5253Keywords:
artificial intelligence literacy, NotebookLM, Retrieval-Augmented Generation, vocational education, project-based learningAbstract
The Society 5.0 era requires vocational high school students to use artificial intelligence productively, independently, and ethically, yet many still rely on open-domain generative AI without verifying sources. This community service program addressed that gap by introducing Google NotebookLM, a Retrieval-Augmented Generation-based learning assistant, to Software Engineering students at SMK Bakti Nusantara 666. Using a Project-Based Learning approach, a single-day workshop covered concept delivery, feature demonstration, hands-on practice, and the development of a personal knowledge base from participants’ own vocational materials. A pre-test on general artificial intelligence literacy was administered to 26 participants and a post-test on NotebookLM-specific operational knowledge to 17. Baseline general artificial intelligence literacy was already strong (mean 89.7%), although half of the participants had never heard of NotebookLM. After the workshop, 16 of the 17 post-test respondents (94.1%) scored at least 75, exceeding the targeted success indicator, with the strongest mastery in source-grounded answering, in-line citation, and ethical use. Only one item discriminated: although 82.4% identified the primary function of the Study Guide feature, most also extended it to a summarisation task for which Summary is the intended tool, indicating over-generalisation of a demonstrated feature rather than lack of exposure. Because the two instruments assessed related but distinct constructs, a normalised gain could not be validly computed and the administrations are reported descriptively. A hands-on, document-grounded artificial intelligence workshop can therefore strengthen the operational artificial intelligence literacy of vocational students, provided that closely adjacent generative features are demonstrated contrastively.
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