Optimization of AI Usage in Learning Materials Application of Prompt Engineering Techniques for Learning Management Systems (LMS)

Muhammad Fauzan Gustafi

doi:10.30595/jrst.v9i2.23393

Authors

Muhammad Fauzan Gustafi Universitas Siber Muhammadiyah

DOI:

https://doi.org/10.30595/jrst.v9i2.23393

Keywords:

ChatGPT-4o, Prompt Engineering, LMS, Instructional Materials, AI Accuracy, Mean Absolute Error (MAE)

Abstract

The application of artificial intelligence (AI) in education opens new opportunities to enhance the quality and effectiveness of learning materials. This study integrates the capabilities of ChatGPT-4o with Prompt Engineering techniques, including persona, context, task, format, exemplar, and tone, to develop AI-based adaptive and interactive instructional content within Learning Management Systems (LMS). By employing these techniques, AI can generate more relevant, personalized, and academically aligned learning materials. The evaluation results using Mean Absolute Error (MAE) indicate that the error rate in generating learning outcomes is 17, syllabus design 14, quiz generation 12, and retrieval of CPL & CPMK 9. These values demonstrate that while ChatGPT-4o is reasonably accurate in generating instructional materials, deviations still exist, particularly in completeness of information and material specification. Furthermore, manual validation remains necessary to ensure alignment with academic standards. Thus, this study confirms that AI-based Prompt Engineering can be an effective and efficient tool for supporting digital learning, yet human supervision is essential to maintain accuracy, credibility, and the quality of educational content.

References

Ahmed, I., Jeon, G., & Piccialli, F. (2022). From Artificial Intelligence to Explainable Artificial Intelligence in Industry 4.0: A Survey on What, How, and Where. IEEE Transactions on Industrial Informatics, 18(8), 5031–5042. IEEE Computer Society. https://doi.org/10.1109/tii.2022.3146552

Annas, A. N., Wijayanto, G., Cahyono, D., Safar, M., & Ilham. (2024). Pelatihan Teknis Penggunaan Aplikasi Artificial Intelligences (AI). Journal of Human And Education, 4(1), 332–340. https://doi.org/10.31004/jh.v4i1.617

Baidoo-Anu, D., & Owusu Ansah, L. (2023). Education in the Era of Generative Artificial Intelligence (AI): Understanding the Potential Benefits of ChatGPT in Promoting Teaching and Learning. Journal of AI (Vol. 52). http://dx.doi.org/10.61969/jai.1337500

Basuki, S., Faiqurrahman, M., Marthasari, G. I., Indrabayu, R., Zachra, F., & Effendy, N. A. (2024). Assistance in Preparing Engineering Prompts for Muhammadiyah School Teachers to Optimize the Use of ChatGPT in the World of Education. DINAMISIA: Jurnal Pengabdian Kepada Masyarakat, 8(3), 721–729. https://doi.org/10.31849/dinamisia.v8i3.19522

Dermawan, R. D., & Herdianto. (2024). Meningkatkan Kinerja Output ChatGPT Melalui Teknik Prompt Engineering Yang Dapat Dikustomisasi. Journal Of Social Science Research, 4(1), 10646–10664.

George, A. S., Hovan George, A. S., & Baskar, T. (2023). Exploring the Potential of Prompt Engineering in India: A Study on the Future of AI-Driven Job Market and the Role of Higher Education. Retrieved from www.puirp.com https://doi.org/10.5281/zenodo.10121998

Giray, L. (2023, December 1). Prompt Engineering with ChatGPT: A Guide for Academic Writers. Annals of Biomedical Engineering. Springer. https://doi.org/10.1007/s10439-023-03272-4

Hodson, T. O. (2022). Root-mean-square error (RMSE) or mean absolute error (MAE): when to use them or not. Geoscientific Model Development, 15(14), 5481–5487. https://doi.org/10.5194/gmd-15-5481-2022

Jacobsen, L. J., & Weber, K. E. (n.d.). The Promises and Pitfalls of ChatGPT as a Feedback Provider in Higher Education: An Exploratory Study of Prompt Engineering and the Quality of AI-Driven Feedback 1. https://doi.org/10.3390/ai6020035

Liu, P., Yuan, W., Fu, J., Jiang, Z., Hayashi, H., & Neubig, G. (2023). Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language Processing. ACM Computing Surveys, 55(9). Association for Computing Machinery. https://doi.org/10.1145/3560815

Oppenlaender, J. (2022). A Taxonomy of Prompt Modifiers for Text-To-Image Generation. Retrieved from http://arxiv.org/abs/2204.13988 https://doi.org/10.1080/0144929X.2023.2286532

Oppenlaender, J., Linder, R., & Silvennoinen, J. (2023). Prompting AI Art: An Investigation into the Creative Skill of Prompt Engineering. Retrieved from http://arxiv.org/abs/2303.13534 https://doi.org/10.48550/arXiv.2303.13534

Park, D., An, G. T., Kamyod, C., & Kim, C. G. (2023). A Study on Performance Improvement of Prompt Engineering for Generative AI with a Large Language Model. Journal of Web Engineering, 22(8), 1187–1206. River Publishers. https://doi.org/10.13052/jwe1540-9589.2285

Prananta, A. W., Susanto, N., Purwantoro, A., & Fuadah, N. (2023). ChatGPT Artificial Intelligence Integration in Science Learning Media: Systematic Literature Review. Jurnal Penelitian Pendidikan IPA, 9(7), 315–321. Universitas Mataram. http://dx.doi.org/10.29303/jppipa.v9i7.4386

Relmasira, S. C., Lai, Y. C., & Donaldson, J. P. (2023). Fostering AI Literacy in Elementary Science, Technology, Engineering, Art, and Mathematics (STEAM) Education in the Age of Generative AI. Sustainability (Switzerland), 15(18). Multidisciplinary Digital Publishing Institute (MDPI). https://doi.org/10.3390/su151813595

Short, C. E., & Short, J. C. (2023). The artificially intelligent entrepreneur: ChatGPT, prompt engineering, and entrepreneurial rhetoric creation. Journal of Business Venturing Insights, 19. Elsevier Inc. https://doi.org/10.1016/j.jbvi.2023.e00388

Zulfikasari, S., Sulistio, B., & Aprilianasari, W. (2024). Utilization of Chat GPT Artificial Intelligence (AI) in Student’s Learning Experience Gen-Z Class. Lectura : Jurnal Pendidikan, 15(1), 259–272. Universitas Lancang Kuning. https://doi.org/10.31849/lectura.v15i1.18840