Enhancing Computational Thinking of Islamic Education Students through CT-Based Prompt Engineering: A Quasi-Experimental Study on AI Multimodal Media Design

Authors

  • Imam Kharits Najibulloh Institut Agama Islam K.H. Sufyan Tsauri Majenang
  • Ahmad Latif Universitas Komputama Cilacap
  • Muhtyas Yugi Universitas Komputama Cilacap
  • Beny Riswanto Universitas Komputama Cilacap

DOI:

https://doi.org/10.33394/jtp.v11i2.20197

Keywords:

AI Multimodal, computational thinking, Prompt Engineering, Learning Media, Theological Validity

Abstract

This study evaluates the effectiveness of Prompt Engineering strategies based on Computational Thinking (CT) in enhancing the ability of Islamic Religious Education (PAI) students to design multimodal learning media (images, videos, and games) powered by Artificial Intelligence (AI). Using a quasi-experimental design involving 35 students, the research integrates the four pillars of CT decomposition, pattern recognition, abstraction, and algorithm design as logical foundations for composing AI instructions. The results show a significant increase in students’ CT scores, from an average of 56.80 to 85.10, with an N-Gain Score of 0.65 (effective category). Students successfully produced educational image media (92%), interactive game logic (84%), and animated videos (78%) with high theological accuracy. The abstraction pillar was found to be the most crucial in minimizing AI “hallucinations” in sensitive religious content, while algorithm design enabled the creation of systematic game flows. This strategy successfully transformed students’ roles from mere users to logical and critical instruction designers (Prompt Engineers). The study recommends integrating CT-Prompting into the PAI curriculum as a core competency to produce innovative and valid digital content in the era of artificial intelligence.

References

Abdul Aziz, L. (n.d.). Analisis Kemampuan Computational Thinking Mahasiswa Dalam Menyelesaikan Masalah Matematika. Juni 2021, 9(1). Retrieved https://e-journal.undikma.ac.id/index.php/jmpm

Al Husaeni, D. F., Widiaty, I., Mulyanti, B., Abdullah, A. G., Riza, L. S., Suherman, A., & Al Husaeni, D. N. (2025). Trends and Impacts of Artificial Intelligence Application in the Development of Computational Thinking Skills. Informatics in Education, 24(2), 261–298. https://doi.org/10.15388/infedu.2025.14

Arias, J. C., & Esteve-Mon, F. M. (2025). Gamified flipped classroom: application of a didactic strategy to work on computational thinking in future teachers. RIED-Revista Iberoamericana de Educacion a Distancia, 28(2). https://doi.org/10.5944/ried.28.2.43532

Azmi, R. D., & Ummah, S. K. (2021). Analisis Kemampuan Computational Thinking Dalam Pembuatan Media Pembelajaran Matematika. Jurnal Pendidikan Matematika (JUDIKA EDUCATION), 4(1), 34–40. https://doi.org/10.31539/judika.v4i1.2273

del Olmo-Muñoz, J., Bueno-Baquero, A., Cózar-Gutiérrez, R., & González-Calero, J. A. (2023). Exploring Gamification Approaches for Enhancing Computational Thinking in Young Learners. Education Sciences, 13(5). https://doi.org/10.3390/educsci13050487

Ginige, J. A., Rana, K., Tague, R., Lum, B., Foxwell, H., & Ienna, M. (2026). An Australian cross-sectional study exploring the potential role of generative AI in high school computer programming education. Computer Science Education. https://doi.org/10.1080/08993408.2026.2621720

Hanifah, U., Niar, S. &, Universitas, A., & Dahlan Yogyakarta, A. (2021). PERAN TEKNOLOGI PENDIDIKAN DALAM PEMBELAJARAN. In Jurnal Keislaman dan Ilmu Pendidikan (Vol. 3, Number 1). https://ejournal.stitpn.ac.id/index.php/islamika

Hijón-Neira, R., Connolly, C., Pizarro, C., & Pérez-Marín, D. (2023). Prototype of a Recommendation Model with Artificial Intelligence for Computational Thinking Improvement of Secondary Education Students. Computers, 12(6). https://doi.org/10.3390/computers12060113

Hijón-Neira, R., Pizarro, C., Borrás-Gené, O., & Cavero, S. (2024). AI-Generated Context for Teaching Robotics to Improve Computational Thinking in Early Childhood Education. Education Sciences, 14(12). https://doi.org/10.3390/educsci14121401

Kim, S.-W., Go, H., Hong, S.-J., & Lee, Y. (2023). An Approach to the Utilization of Design Thinking in Artificial Intelligence Education. 13(6).

Phanichsiti, S., Nilsook, P., & Piriyasurawong, P. (2025a). Advance Organizer Integrating Visual-Based Programming via Artificial Intelligence of Things to Enhance Advanced Computational Thinking Competency. International Journal of Information and Education Technology, 15(7), 1355–1367. https://doi.org/10.18178/ijiet.2025.15.7.2337

Phanichsiti, S., Nilsook, P., & Piriyasurawong, P. (2025b). Advance Organizer Integrating Visual-Based Programming via Artificial Intelligence of Things to Enhance Advanced Computational Thinking Competency. International Journal of Information and Education Technology, 15(7), 1355–1367. https://doi.org/10.18178/ijiet.2025.15.7.2337

Rachmayanti, I., & Alatas, M. A. (2023). Pemanfaatan AI sebagai Media Pembelajaran Digital dalam Foreign Language Development Program (FLDP) IAIN Madura. GHANCARAN: Jurnal Pendidikan Bahasa Dan Sastra Indonesia. https://doi.org/10.19105/ghancaran.vi.11752

Sari, A. P., & Munir, M. (2024). Pemanfaatan Teknologi Digital dalam Inovasi Pembelajaran untuk Meningkatkan Efektivitas Kegiatan di Kelas. Digital Transformation Technology, 4(2), 977–983. https://doi.org/10.47709/digitech.v4i2.5127

Susanti, R. D., & Taufik, M. (2021). Analysis of Student Computational Thinking in Solving Social Statistics Problems. SJME (Supremum Journal of Mathematics Education), 5(1). https://doi.org/10.35706/sjme.v5i1.4376

Syafi, N., Fathurohim, F., & Muliyah, P. (2023). PENGARUH GAME ONLINE TERHADAP KONSENTRASI BELAJAR SISWA. Jurnal Asy-Syukriyyah, 24(2), 143–151. https://doi.org/10.36769/asy.v24i2.348

Tian, J. (2025). A Practice-Oriented Computational Thinking Framework for Teaching Neural Networks to Working Professionals. AI (Switzerland), 6(7). https://doi.org/10.3390/ai6070140

Weng, X., Ye, H., Dai, Y., & Ng, O. L. (2024). Integrating Artificial Intelligence and Computational Thinking in Educational Contexts: A Systematic Review of Instructional Design and Student Learning Outcomes. Journal of Educational Computing Research, 62(6), 1640–1670. https://doi.org/10.1177/07356331241248686

Wongla, A., Chatwattana, P., & Piriyasurawong, P. (2025). Study of Perspectives towards the Development of the Gamified Computational Thinking Platform with AI Prompt Engineering. TEM Journal, 3206. https://doi.org/10.18421/tem144-29

Zaus, M. A., Eliza, F., Andriani, C., Candra, O., Jalil, S. A., Zaus, A. A., & Islami, S. (2025). Leveraging Algorithm Instruction with AI Chatbots: A Detailed Exploration of Their Impact on Students’ Computational Thinking. International Journal of Information and Education Technology, 15(5), 891–901. https://doi.org/10.18178/ijiet.2025.15.5.2295

Downloads

Published

2026-04-30

How to Cite

Imam Kharits Najibulloh, Ahmad Latif, Muhtyas Yugi, & Beny Riswanto. (2026). Enhancing Computational Thinking of Islamic Education Students through CT-Based Prompt Engineering: A Quasi-Experimental Study on AI Multimodal Media Design. Jurnal Teknologi Pendidikan : Jurnal Penelitian Dan Pengembangan Pembelajaran, 11(2), 329–339. https://doi.org/10.33394/jtp.v11i2.20197

Issue

Vol. 11 No. 2 (2026): April

Section

Articles

License

Copyright (c) 2026 Imam Kharits Najibulloh, Ahmad Latif, Muhtyas Yugi, Beny Riswanto

Creative Commons License

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

License and Publishing Agreement
In submitting the manuscript to the journal, the authors certify that:

  • They are authorized by their co-authors to enter into these arrangements.
  • The work described has not been formally published before, except in the form of an abstract or as part of a published lecture, review, thesis, or overlay journal.
  • That it is not under consideration for publication elsewhere,
  • That its publication has been approved by all the author(s) and by the responsible authorities – tacitly or explicitly – of the institutes where the work has been carried out.
  • They secure the right to reproduce any material that has already been published or copyrighted elsewhere.
  • They agree to the following license and publishing agreement.

Copyright

  1. Authors who publish with JTP agree to the following terms:
  2. Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License (CC BY-SA 4.0) that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal.
  3. Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this journal.
  4. Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work.
  5. Licensing for Data Publication

Licensing for Data Publication

JTP use a variety of waivers and licenses, that are specifically designed for and appropriate for the treatment of data:
Open Data Commons Attribution License, http://www.opendatacommons.org/licenses/by/1.0/ (default)