GENERATIVE AI IN SECONDARY EDUCATION: STUDENT IMMERSION ACROSS SOCIAL INNOVATION, MARKETING, AND PROGRAMMING

Authors

  • Khinsa Fairuz Zahirah Universitas Ciputra Surabaya
  • Eddy Yusuf School of Information Technology, Universitas Ciputra, Surabaya

DOI:

https://doi.org/10.51878/edutech.v5i4.8077

Keywords:

Kecerdasan Buatan Generatif, pendidikan menengah, literasi AI, analisis sentimen, integrasi AI

Abstract

Integrasi cepat Generative AI (GenAI) ke dalam lingkungan pendidikan menawarkan potensi transformatif bagi pendidikan menengah, namun bukti empiris mengenai penerapan praktisnya masih terbatas. Studi ini menyelidiki efikasi lokakarya GenAI terstruktur yang diselenggarakan di empat sekolah menengah atas di wilayah Jabodetabek, yang melibatkan 229 siswa yang terlibat dalam program sosiopreneurship, pemasaran digital, dan pemrograman. Dengan menggunakan desain metode campuran konvergen, penelitian ini menggabungkan survei kuantitatif dengan analisis sentimen refleksi terbuka untuk mengevaluasi imersi dan persepsi siswa. Hasil kuantitatif menunjukkan penerimaan yang sangat positif, dengan "Kegunaan" GenAI menerima peringkat rata-rata tertinggi sebesar 4,32 (SD = 0,77), dan sekitar 86% peserta menegaskan nilai perangkat tersebut untuk studi akademis mereka. Analisis sentimen selanjutnya mengonfirmasi keterlibatan yang tinggi, terutama dalam tugas pemecahan masalah kreatif dan teknis, meskipun umpan balik kualitatif menyoroti tantangan terkait formulasi cepat, verifikasi data, dan masalah etika seperti halusinasi. Studi ini menyimpulkan bahwa meskipun GenAI berfungsi sebagai katalis yang kuat untuk kreativitas dan efisiensi pembelajaran, integrasi yang sukses dalam pendidikan menengah membutuhkan pendekatan pedagogis yang terstruktur yang memprioritaskan pemikiran kritis, keterampilan rekayasa yang cepat, dan penalaran etis untuk memastikan penggunaan yang bertanggung jawab dan efektif.

ABSTRACT
The rapid integration of Generative AI (GenAI) into educational settings offers transformative potential for secondary education, yet empirical evidence regarding its practical application remains limited. This study investigates the efficacy of structured GenAI workshops conducted across four high schools in the Greater Jakarta area, involving 229 students engaged in sociopreneurship, digital marketing, and coding tracks. Utilizing a convergent mixed-methods design, the research combined quantitative surveys with sentiment analysis of open-ended reflections to evaluate student immersion and perception. The quantitative results demonstrated a strong positive reception, with the "Usefulness" of GenAI receiving the highest mean rating of 4.32 (SD = 0.77), and approximately 86% of participants affirming the tools' value for their academic studies. Sentiment analysis further confirmed high engagement, particularly in creative and technical problem-solving tasks, though qualitative feedback highlighted challenges regarding prompt formulation, data verification, and ethical concerns such as hallucinations. The study concludes that while GenAI serves as a powerful catalyst for creativity and learning efficiency, successful integration in secondary education necessitates a scaffolded pedagogical approach that prioritizes critical thinking, prompt engineering skills, and ethical reasoning to ensure responsible and effective usage.

Downloads

Download data is not yet available.

References

Abdelghani, R., Sauzéon, H., & Oudeyer, P.-Y. (2023). Generative AI in the classroom: Can students remain active learners? arXiv. https://doi.org/10.48550/arXiv.2310.03192

Bolender, B., et al. (2024). Generative AI in K12: Analytics from early adoption. Journal of Measurement and Evaluation in Education and Psychology, 15(Special Issue), 361–377. https://doi.org/10.21031/epod.1539710

Cheah, Y. H., Lu, J., & Kim, J. (2025). Integrating generative artificial intelligence in K–12 education: Examining teachers’ preparedness, practices, and barriers. Computers and Education: Artificial Intelligence, 8, Article 100363. https://doi.org/10.1016/j.caeai.2025.100363

Chen, S.-Y. (2023). Generative AI, learning and new literacies. Journal of Educational Technology Development and Exchange, 16(2), 1–19. https://doi.org/10.18785/jetde.1602.01

Chiu, T. K. F. (2024). A framework for using GenAI to support student engagement in interdisciplinary learning from self-determination theory. ASCILITE Publications. https://doi.org/10.14742/apubs.2024.1053

Demir, S. B., & Pismek, N. (2018). A convergent parallel mixed-methods study of controversial issues in social studies classes: A clash of ideologies. Educational Sciences: Theory & Practice, 18(1). https://jestp.com/menuscript/index.php/estp/article/view/395

DePaolo, C. A., & Wilkinson, K. (2014). Get your head into the clouds: Using word clouds for analyzing qualitative assessment data. TechTrends, 58(3), 38–44. https://doi.org/10.1007/s11528-014-0750-9

Dila, P., Nurlela, N., & Rahmawaty, I. (2025). Peningkatkan Keterampilan Sosial Melalui Layanan Bimbingan Kelompok Teknik Problem Based Learning. MANAJERIAL Jurnal Inovasi Manajemen Dan Supervisi Pendidikan, 5(1), 190. https://doi.org/10.51878/manajerial.v5i1.4917

Duong, C. D. (2025). How AI-enabled drivers inspire sustainability-oriented entrepreneurial intentions: Unraveling the (in)congruent effects of perceived desirability and feasibility from the entrepreneurial event model perspective. Sustainable Development. Advance online publication. https://doi.org/10.1002/sd.3461

Durães, D., Bezerra, R., & Novais, P. (2024, May). AI driven educational transformation in secondary schools: Leveraging data insights for inclusive learning environments. In Proceedings of the IEEE Global Engineering Education Conference (EDUCON 2024) (pp. 1–9). IEEE. https://doi.org/10.1109/EDUCON60312.2024.10578910

Ferreira, T. M. (2024). A new educational reality: Active methodologies empowered by generative AI. Preprints. https://doi.org/10.20944/preprints202408.1933.v1

Fleckenstein, J., et al. (2024). Do teachers spot AI? Evaluating the detectability of AI-generated texts among student essays. Computers and Education: Artificial Intelligence, 6, Article 100209. https://doi.org/10.1016/j.caeai.2024.100209

Haidar, A. (2024). ChatGPT and generative AI in educational ecosystems: Transforming student engagement and ensuring digital safety. In F. Al Husseiny & A. Munna (Eds.), Preparing students for the future educational paradigm (pp. 70–100). IGI Global. https://doi.org/10.4018/979-8-3693-1536-1.ch004

Hayashi, M. (2024, November). Generative AI and XR in education: Student co created metaverse worlds in an international virtual exchange. Proceedings of the 32nd International Conference on Computers in Education (ICCE 2024). https://doi.org/10.58459/icce.2024.5032

Helsinki-NLP. (2019). Helsinki-NLP/opus-mt-en-id [Neural machine translation model]. Hugging Face. https://huggingface.co/Helsinki-NLP/opus-mt-en-id

Horvat, N., Becattini, N., & Škec, S. (2021). Use Of Information And Communication Technology Tools In Distributed Product Design Student Teams. Proceedings of the Design Society, 1, 3329. https://doi.org/10.1017/pds.2021.594

Hutto, C. J., & Gilbert, E. E. (2014). VADER: A parsimonious rule-based model for sentiment analysis of social media text. In Proceedings of the International AAAI Conference on Web and Social Media, 8(1). https://doi.org/10.1609/icwsm.v8i1.14550

Kadaruddin, K. (2023). Empowering education through generative AI: Innovative instructional strategies for tomorrow’s learners. International Journal of Business, Law, and Education, 4(2), 618–625. https://doi.org/10.56442/ijble.v4i2.215

Kilde-Westberg, S., Johansson, A., & Enger, J. (2024). Generative AI as a lab partner: A case study. arXiv. https://doi.org/10.48550/arXiv.2412.11300

Lawitta, R., & Najdah, T. (2025). The Role Of Critical Thinking As A Predictor Of Students’ Digital Literacy Skills. SOCIAL Jurnal Inovasi Pendidikan IPS, 5(1), 247. https://doi.org/10.51878/social.v5i1.5150

Noroozi, O., Soleimani, S., Farrokhnia, M., & Banihashem, S. K. (2024). Generative AI in education: Pedagogical, theoretical, and methodological perspectives. International Journal of Technology in Education, 7(3), 373–385. https://doi.org/10.46328/ijte.845

Ortega-Ochoa, E., et al. (2024). Exploring the utilization and deficiencies of generative artificial intelligence in students’ cognitive and emotional needs: A systematic mini review. Frontiers in Artificial Intelligence, 7, Article 1493566. https://doi.org/10.3389/frai.2024.1493566

Pepin, M., Tremblay, M., Audebrand, L. K., & Chassé, S. (2024). The responsible business model canvas: Designing and assessing a sustainable business modeling tool for students and start-up entrepreneurs. International Journal of Sustainability in Higher Education, 25(3), 514–538. https://doi.org/10.1108/IJSHE-01-2023-0008

Rosati, F., Rodrigues, V. P., Cosenz, F., & Li-Ying, J. (2022). Business model innovation for the Sustainable Development Goals. Business Strategy and the Environment, 32(6), 2986–2998. https://doi.org/10.1002/bse.3334

Tiedemann, J., et al. (2023). Democratizing neural machine translation with OPUS-MT. Language Resources and Evaluation, 58, 713–755. https://doi.org/10.1007/s10579-023-09704-w

Tzimiris, S., et al. (2025). A comparative evaluation of transformer-based language models for topic-based sentiment analysis. Electronics, 14(15), Article 2957. https://doi.org/10.3390/electronics14152957

Villena Zapata, L. I., et al. (2024). Employment of generative artificial intelligence in classroom environments to improve financial education in secondary school students. Academic Journal of Interdisciplinary Studies, 13(3), 121. https://doi.org/10.36941/ajis-2024-0069

Wolf, T., et al. (2020). Transformers: State-of-the-art natural language processing. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): System Demonstrations (pp. 38–45). Association for Computational Linguistics. https://doi.org/10.18653/v1/2020.emnlp-demos.6

Wu, D., & Zhang, J. (2025). Generative artificial intelligence in secondary education: Applications and effects on students’ innovation skills and digital literacy. PLoS ONE, 20(5), Article e0323349. https://doi.org/10.1371/journal.pone.0323349

Yang, T.-C., Hsu, Y.-C., & Wu, J.-Y. (2025). The effectiveness of ChatGPT in assisting high school students in programming learning: Evidence from a quasi experimental study. Interactive Learning Environments. Advance online publication. https://doi.org/10.1080/10494820.2025.2450659

Zapata-Rivera, D., et al. (2024). Editorial: Generative AI in education. Frontiers in Artificial Intelligence, 7, Article 1532896. https://doi.org/10.3389/frai.2024.1532896

Downloads

Published

2025-12-16

How to Cite

Zahirah, K. F., & Yusuf, E. . (2025). GENERATIVE AI IN SECONDARY EDUCATION: STUDENT IMMERSION ACROSS SOCIAL INNOVATION, MARKETING, AND PROGRAMMING. EDUTECH : Jurnal Inovasi Pendidikan Berbantuan Teknologi, 5(4), 837-851. https://doi.org/10.51878/edutech.v5i4.8077

Issue

Section

Articles