Optimalisasi Hak Kekayaan Intelektual dan Hilirisasi Inovasi Pembelajaran melalui Generative Artificial Intelligence
DOI:
https://doi.org/10.47065/jpm.v7i1.3357Keywords:
Digital Literacy; Generative Artificial Intelligence; Intellectual Property Rights; Innovation Commercialization; Educational Digital TransformationAbstract
The digital transformation of education requires teachers to possess digital literacy competencies, the ability to utilize Generative Artificial Intelligence (Generative AI), and an understanding of Intellectual Property Rights (IPR) protection regarding instructional innovations. However, a needs analysis conducted among teachers within the West Java Provincial Education Office Branch (Region X) revealed that the use of Generative AI in the learning process remains limited, understanding of IPR is inadequate, and efforts to commercialize or scale up (downstream) instructional innovations have not been optimally implemented. These conditions potentially hinder the creation of value-added and sustainable instructional innovations. This community service activity aims to optimize teachers' competencies in utilizing Generative AI to produce instructional innovations that are protected by IPR and possess potential for downstream application. The proposed solution was implemented in four stages: participant needs analysis; a workshop on digital literacy and Generative AI utilization; mentoring on IPR documentation and innovation downstreaming strategies; and evaluation using a Likert-scale questionnaire. The results indicate that participants were able to implement Generative AI to develop teaching materials, digital learning media, assessment instruments, and school administrative tools more effectively and innovatively. Evaluation of the activity showed that all indicators fell into the "highly proficient" category, with understanding levels ranging from 89.6% to 95.2%. The highest achievement was recorded in the indicator for readiness to implement training outcomes in schools (95.2%), followed by Generative AI utilization (94.8%) and increased digital literacy (93.2%). Furthermore, participants demonstrated an improved understanding of the importance of IPR protection and the opportunities for downstreaming instructional innovations as part of strengthening the technology-based education ecosystem. Thus, a workshop approach combined with mentoring proved effective in enhancing teacher competence while fostering the creation of instructional innovations that are adaptive, legally protected, and capable of delivering a broader impact on educational development.
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References
Bitakou, E. (2023). Assessing massive open online courses for developing teachers’ digital competence. Education Sciences, 13(9). https://doi.org/10.3390/educsci13090900
Cabero-Almenara, J. (2025). A structural model of distance education teachers’ digital competence. Education Sciences, 15(10). https://doi.org/10.3390/educsci15101271
Chiu, T. K. F. (2024). The impact of generative AI on practices, policies and research direction in education. Interactive Learning Environments. https://doi.org/10.1080/10494820.2023.2253861
Deshen, M. (2026). Teachers’ artificial intelligence literacy: An exploratory study. Smart Learning Environments. https://doi.org/10.1186/s40561-026-00433-5
Dolezal, D. (2025). Pre-service teachers’ digital competence: A call for action. Education Sciences, 15(2). https://doi.org/10.3390/educsci15020160
Dringó-Horváth, I. (2025). University teachers’ digital competence and AI literacy. Education Sciences, 15(7). https://doi.org/10.3390/educsci15070868
García-Delgado, M. Á. (2023). Digital teaching competence among teachers of different educational stages in Spain. Education Sciences, 13(6). https://doi.org/10.3390/educsci13060581
Gulen, H. N. (2026). Integrating artificial intelligence into preservice teacher education. Education and Information Technologies. https://doi.org/10.1007/s10639-026-14020-1
Mah, D. K. (2026). Artificial intelligence in K–12 instruction. Smart Learning Environments. https://doi.org/10.1186/s40561-026-00442-4
Miao, F. (2023). Guidance for generative AI in education and research. UNESCO. https://doi.org/10.54675/EWZM9535
Miao, F. (2024). AI competency framework for teachers. UNESCO. https://doi.org/10.54675/ZJTE2084
OECD. (2026). OECD Digital Education Outlook 2026. OECD Publishing. https://doi.org/10.1787/062a7394-en
Onbasili, Ü. I. (2026). A GenAI-TPACK-based approach to digital storytelling in teacher education. Education and Information Technologies. https://doi.org/10.1007/s10639-026-13991-5
Ren, Y. (2025). Integrating professional intellectual property education into the training system for innovative talents. Frontiers of Digital Education. https://doi.org/10.1007/s44366-025-0065-8
Velander, J. (2024). Artificial intelligence in K–12 education. Education and Information Technologies. https://doi.org/10.1007/s10639-023-11990-4
Walter, Y. (2024). Embracing the future of artificial intelligence in the classroom. International Journal of Educational Technology in Higher Education, 21. https://doi.org/10.1186/s41239-024-00448-3
Younis, B. (2025). The artificial intelligence literacy scale for teachers. Journal of Digital Learning in Teacher Education. https://doi.org/10.1080/21532974.2024.2441682
Zhou, X. (2025). Defining, enhancing, and assessing artificial intelligence literacy in education. Interactive Learning Environments. https://doi.org/10.1080/10494820.2025.2487538
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