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Mindset in Vocational Students Through AI-Assisted Business Model Canvas Workshop
Abstract
Vocational High School (SMK) graduates in Indonesia experience persistently high unemployment, partly because many students’ competencies remain oriented toward formal employment rather than business creation. Preliminary interviews at SMK Saraswati 1 Denpasar indicated that among 51 grade-12 students enrolled in the entrepreneurship subject, only five reported owning or intending to start a business after graduation. This community service program aimed to strengthen students’ technopreneurship understanding and their ability to produce a structured business plan using the Business Model Canvas (BMC), supported by Generative AI (ChatGPT). The intervention was delivered in a 2-hour workshop consisting of: (1) a 30-minute session on entrepreneurship and technopreneurship concepts, (2) a 50-minute step-by-step BMC development integrated with ChatGPT and guided by the speaker with facilitator mentoring and observation, and (3) a 40-minute group presentation with feedback. Outcomes were assessed using a descriptive evaluation framework (pre–post concept quiz, BMC rubric, and structured observations). Before the program, all students recognized the term “entrepreneur,” but only 3.92% demonstrated adequate understanding of “technopreneurship”; after the workshop, 100% of participants could define both concepts, explain differences, and provide examples. All groups completed the nine BMC blocks (100%), with 80% of group outputs reflecting technopreneurship-based business ideas. Rubric review identified recurring weaknesses in articulating value propositions (30%) and distinguishing customer relationships from channels (40%). Overall, integrating BMC practice with AI-assisted ideation provides a practical and replicable approach to improve structured business thinking and digital literacy among SMK students.
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DOI: https://doi.org/10.37905/ljpmt.v5i1.36640
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Lamahu : Jurnal Pengabdian Masyarakat Terintegrasi (P-ISSN: 2828-6839, E-ISSN: 2828-6677) is licensed under a Creative Commons Attribution 4.0 International License.