GENERATIVE ARTIFICIAL INTELLIGENCE AS AN INNOVATION IN EDUCATIONAL TECHNOLOGY: IMPLICATIONS FOR DIGITAL LEARNING AND TWENTY-FIRST-CENTURY COMPETENCIES

Edi Fitriana Afriza, Imelda Imelda, Teguh Arie Sandy, Muhammad Redo, Wayan Haarits Setiawan, Yohanes Payong, Muhammad Fillah Kurniawan

Abstract


Purpose – This paper aims to analyze Generative Artificial Intelligence (GenAI) as an innovation in educational technology and examine its implications for digital learning and twenty-first-century competencies, particularly within the Indonesian context. 

Methodology – The study employs an integrative literature review method with a conceptual analysis approach. The literature sample comprises relevant international journal articles, official institutional reports, and theoretical frameworks (TPACK, SAMR, TAM, UTAUT, and ADDIE) published between 2020 and 2026. Data analysis followed a systematic four-stage procedure: identifying key concepts, grouping findings by thematic focus, analyzing through educational technology theories, and synthesizing the results into a critical narrative and tabular mapping.

Findings – Results indicate that while GenAI accelerates learning resource production, enriches feedback, and supports adaptive learning, its pedagogical value is realized only when embedded within deliberate instructional design. Unregulated use presents significant risks, including information hallucination, algorithmic bias, data privacy violations, academic dishonesty, and unequal digital access.

Contribution – This study contributes a comprehensive, theory-grounded GenAI integration model. It provides actionable frameworks for positioning educators as pedagogical guides, students as critical learning agents, and institutions as managers of responsible AI governance, offering vital policy and practical guidelines for advancing equitable, ethical, and effective digital education in Indonesia.

Keywords


Generative Artificial Intelligence; educational technology; digital learning; AI literacy; twenty-first-century competencies; TPACK; ADDIE

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References


Akgun, S., & Greenhow, C. (2022). Artificial intelligence in education: Addressing ethical challenges in K-12 settings. AI and Ethics, 2(3), 431–440. https://doi.org/10.1007/s43681-021-00096-7

APJII. (2024). Asosiasi Penyelenggara Jasa Internet Indonesia. https://apjii.or.id/berita/d/apjii-jumlah-pengguna-internet-indonesia-tembus-221-juta-orang

Bearman, M., Ryan, J., & Ajjawi, R. (2023). Discourses of artificial intelligence in higher education: A critical literature review. Higher Education, 86(2), 369–385. https://doi.org/10.1007/s10734-022-00937-2

Bond, M., Khosravi, H., Laat, M., Bergdahl, N., Negrea, V., Oxley, E., Pham, P., Chong, S. W., & Siemens, G. (2024). A meta systematic review of Artificial Intelligence in Higher Education: A call for increased ethics, collaboration, and rigour. International Journal of Educational Technology in Higher Education, 21, 1–41. https://doi.org/10.1186/s41239-023-00436-z

BPS. (2025). Statistik Telekomunikasi Indonesia 2024. https://www.bps.go.id/id/publication/2025/08/29/beaa2be400eda6ce6c636ef8/statistik-telekomunikasi-indonesia-2024.html

Branch, R. M. (2009). Instructional Design: The ADDIE Approach. Springer US. https://doi.org/10.1007/978-0-387-09506-6

Celik, I., Dindar, M., Muukkonen, H., & Järvelä, S. (2022). The Promises and Challenges of Artificial Intelligence for Teachers: A Systematic Review of Research. TechTrends, 66(4), 616–630. https://doi.org/10.1007/s11528-022-00715-y

Chan, C. K. Y., & Hu, W. (2023). Students’ voices on generative AI: Perceptions, benefits, and challenges in higher education. International Journal of Educational Technology in Higher Education, 20(1), 43. https://doi.org/10.1186/s41239-023-00411-8

Chiu, T. K. F. (2024). The impact of Generative AI (GenAI) on practices, policies and research direction in education: A case of ChatGPT and Midjourney. Interactive Learning Environments, 32(10), 6187–6203. https://doi.org/10.1080/10494820.2023.2253861

Choung, H., David, P., & Ross, A. (2023). Trust in AI and Its Role in the Acceptance of AI Technologies. International Journal of Human–Computer Interaction, 39(9), 1727–1739. https://doi.org/10.1080/10447318.2022.2050543

Cotton, D. R. E., Cotton, P. A., & Shipway, J. R. (2024). Chatting and cheating: Ensuring academic integrity in the era of ChatGPT. Innovations in Education and Teaching International, 61(2), 228–239. https://doi.org/10.1080/14703297.2023.2190148

Davis, F. D. (1989). Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Information Technology. MIS Quarterly, 13(3), 319. https://doi.org/10.2307/249008

Farrokhnia, M., Banihashem, S. K., Noroozi, O., & Wals, A. (2024). A SWOT analysis of ChatGPT: Implications for educational practice and research. Innovations in Education and Teaching International, 61(3), 460–474. https://doi.org/10.1080/14703297.2023.2195846

Holmes, W., & Tuomi, I. (2022). State of the art and practice in AI in education. European Journal of Education, 57(4), 542–570. https://doi.org/10.1111/ejed.12533

Kasneci, E., Sessler, K., Küchemann, S., Bannert, M., Dementieva, D., Fischer, F., Gasser, U., Groh, G., Günnemann, S., Hüllermeier, E., Krusche, S., Kutyniok, G., Michaeli, T., Nerdel, C., Pfeffer, J., Poquet, O., Sailer, M., Schmidt, A., Seidel, T., … Kasneci, G. (2023). ChatGPT for good? On opportunities and challenges of large language models for education. Learning and Individual Differences, 103, 102274. https://doi.org/10.1016/j.lindif.2023.102274

Liang, W., Yuksekgonul, M., Mao, Y., Wu, E., & Zou, J. (2023). GPT detectors are biased against non-native English writers. Patterns, 4(7), 100779. https://doi.org/10.1016/j.patter.2023.100779

Long, D., & Magerko, B. (2020). What is AI Literacy? Competencies and Design Considerations. Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, 1–16. https://doi.org/10.1145/3313831.3376727

Miao, F. (with Giannini, S., & Holmes, W.). (2023). Guidance for generative AI in education and research. UNESCO.

Miao, F., & Cukurova, M. (2024). AI competency framework for teachers. UNESCO. https://doi.org/10.54675/ZJTE2084

Miao, F., Shiohira, K., & Lao, N. (2024). AI competency framework for students. UNESCO.

Mishra, P., & Koehler, M. J. (2006). Technological Pedagogical Content Knowledge: A Framework for Teacher Knowledge. Teachers College Record: The Voice of Scholarship in Education, 108(6), 1017–1054. https://doi.org/10.1111/j.1467-9620.2006.00684.x

Ng, D. T. K., Leung, J. K. L., Chu, S. K. W., & Qiao, M. S. (2021). Conceptualizing AI literacy: An exploratory review. Computers and Education: Artificial Intelligence, 2, 100041. https://doi.org/10.1016/j.caeai.2021.100041

OECD. (2023, December 4). PISA 2022 Results, Volume I and II: Country notes, Indonesia. OECD Publishing. OECD. https://www.oecd.org/en/publications/pisa-2022-results-volume-i-and-ii-country-notes_ed6fbcc5-en/indonesia_c2e1ae0e-en.html

OECD. (2024, June 17). PISA 2022 Results, Volume III: Creative Minds, Creative Schools, factsheets, Indonesia. OECD Publishing. OECD. https://www.oecd.org/en/publications/pisa-results-2022-volume-iii-factsheets_041a90f1-en/indonesia_a7090b49-en.html

Ouyang, F., Zheng, L., & Jiao, P. (2022). Artificial intelligence in online higher education: A systematic review of empirical research from 2011 to 2020. Education and Information Technologies, 27(6), 7893–7925. https://doi.org/10.1007/s10639-022-10925-9

Perkins, M. (2023). Academic Integrity considerations of AI Large Language Models in the post-pandemic era: ChatGPT and beyond. Journal of University Teaching and Learning Practice, 20(2). https://doi.org/10.53761/1.20.02.07

Puentedura, R. R. (2006). Transformation, Technology, and Education. http://hippasus.com/resources/tte/

Rudolph, J., Tan, S., & Tan, S. (2023). ChatGPT: Bullshit spewer or the end of traditional assessments in higher education? Journal of Applied Learning & Teaching, 6(1). https://doi.org/10.37074/jalt.2023.6.1.9

Sullivan, M., Kelly, A., & McLaughlan, P. (2023). ChatGPT in higher education: Considerations for academic integrity and student learning. Journal of Applied Learning & Teaching, 6. https://doi.org/10.37074/jalt.2023.6.1.17

Tlili, A., Shehata, B., Adarkwah, M. A., Bozkurt, A., Hickey, D. T., Huang, R., & Agyemang, B. (2023). What if the devil is my guardian angel: ChatGPT as a case study of using chatbots in education. Smart Learning Environments, 10(1), 15. https://doi.org/10.1186/s40561-023-00237-x

Torraco, R. J. (2016). Writing Integrative Literature Reviews: Using the Past and Present to Explore the Future. Human Resource Development Review, 15(4), 404–428. https://doi.org/10.1177/1534484316671606

Venkatesh, Morris, Davis, & Davis. (2003). User Acceptance of Information Technology: Toward a Unified View. MIS Quarterly, 27(3), 425. https://doi.org/10.2307/30036540

Weng, X., Xia, Q., Gu, M., Rajaram, K., & Chiu, T. K. F. (2024). Assessment and learning outcomes for generative AI in higher education: A scoping review on current research status and trends. Australasian Journal of Educational Technology. https://doi.org/10.14742/ajet.9540

World Economic Forum. (2025). The Future of Jobs Report 2025. World Economic Forum. https://www.weforum.org/publications/the-future-of-jobs-report-2025/




DOI: https://doi.org/10.36987/jes.v13i4.9420

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