AI-Assisted Case Method Learning for Scientific Writing: A Design and Development Study
DOI:
https://doi.org/10.21009/jtp.v28i2.71033Keywords:
case method, artificial intelligence, Perplexity ai, scientific writing, design and developmentAbstract
work, yet writing instruction remains largely lecture-driven and disconnected from students’ digital tools. This study designed, validated, and tested instructional products for an undergraduate Scientific Writing course that integrate the case method with Perplexity.ai, an AI answer engine providing source-linked responses. Combining an adapted Recursive Reflective Design and Development (R2D2) model with the Research-Development-Research (RDR) cycle, the study produced lesson plans, thirteen teaching-material units, a four-stage case-method syntax with explicit operational and ethical rules for AI use, and authentic assessment instruments, including a 22-indicator writing rubric (0–100 scale). Three practitioners and three experts judged all five products valid (M = 3.40 on a 4-point scale), and their feedback guided revisions through small-group and large-group trials and a semester-long implementation at Universitas Negeri Padang. Both the model class (n = 21; M = 65.24 to 83.33) and a conventionally taught class (n = 18; M = 62.22 to 72.78) improved significantly (Wilcoxon p ≤ .001). With classes equivalent at pre-test, analysis of covariance confirmed the model class’s advantage, F(1, 36) = 14.19, p = .001, partial η² = .28, adjusted difference 8.86 points, corroborated non-parametrically (p = .005). Students reported high knowledge gains (90.5%) and positive attitudes (81.0%). The findings establish the products’ validity and implementability, provide non-randomised evidence that the model outperformed conventional teaching, and show how responsible AI use can become an explicit object of writing instruction.
References
Abbas, M., Jam, F. A., & Khan, T. I. (2024). Is it harmful or helpful? Examining the causes and consequences of generative AI usage among university students. International Journal of Educational Technology in Higher Education, 21, Article 10.
Bonwell, C. C., & Eison, J. A. (1991). Active learning: Creating excitement in the classroom (ASHE-ERIC Higher Education Report No. 1). The George Washington University.
Chan, C. K. Y. (2023). A comprehensive AI policy education framework for university teaching and learning. International Journal of Educational Technology in Higher Education, 20, Article 38.
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, Article 43.
Chiu, T. K. F. (2024). Future research recommendations for transforming higher education with generative AI. Computers and Education: Artificial Intelligence, 6, 100197.
Chiu, T. K. F., Xia, Q., Zhou, X., Chai, C. S., & Cheng, M. (2023). Systematic literature review on opportunities, challenges, and future research recommendations of artificial intelligence in education. Computers and Education: Artificial Intelligence, 4, 100118.
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.
Flower, L., & Hayes, J. R. (1981). A cognitive process theory of writing. College Composition and Communication, 32(4), 365–387.
Gall, M. D., Gall, J. P., & Borg, W. R. (2003). Educational research: An introduction (7th ed.). Allyn & Bacon.
Garvin, D. A. (2003). Making the case: Professional education for the world of practice. Harvard Magazine, 106(1), 56–65.
Graham, S., & Perin, D. (2007). A meta-analysis of writing instruction for adolescent students. Journal of Educational Psychology, 99(3), 445–476.
Herreid, C. F. (2007). Start with a story: The case study method of teaching college science. NSTA Press.
Hwang, Y., & Lee, J. H. (2025). Exploring students’ experiences and perceptions of human–AI collaboration in digital content making. International Journal of Educational Technology in Higher Education, 22, Article 44.
Kasneci, E., Sessler, K., Küchemann, S., Bannert, M., Dementieva, D., Fischer, F., … Kasneci, G. (2023). ChatGPT for good? On opportunities and challenges of large language models for education. Learning and Individual Differences, 103, 102274.
Krain, M. (2016). Putting the learning in case learning? The effects of case-based approaches on student knowledge, attitudes, and engagement. Journal on Excellence in College Teaching, 27(2), 131–153.
Luo, J. (2024). A critical review of GenAI policies in higher education assessment: A call to reconsider the ‘originality’ of students’ work. Assessment & Evaluation in Higher Education, 49(5), 651–664.
Lynn, M. R. (1986). Determination and quantification of content validity. Nursing Research, 35(6), 382–385.
Molenaar, I. (2022). Towards hybrid human–AI learning technologies. European Journal of Education, 57(4), 632–645.
Richey, R. C., & Klein, J. D. (2007). Design and development research: Methods, strategies, and issues. Lawrence Erlbaum Associates.
Vygotsky, L. S. (1978). Mind in society: The development of higher psychological processes. Harvard University Press.
Willis, J. (1995). A recursive, reflective instructional design model based on constructivist-interpretivist theory. Educational Technology, 35(6), 5–23.
Willis, J. (2000). The maturing of constructivist instructional design: Some basic principles that can guide practice. Educational Technology, 40(1), 5–16.
Yadav, A., Lundeberg, M., DeSchryver, M., Dirkin, K., Schiller, N. A., Maier, K., & Herreid, C. F. (2007). Teaching science with case studies: A national survey of faculty perceptions of the benefits and challenges of using cases. Journal of College Science Teaching, 37(1), 34–38.
Zawacki-Richter, O., Marín, V. I., Bond, M., & Gouverneur, F. (2019). Systematic review of research on artificial intelligence applications in higher education: Where are the educators? International Journal of Educational Technology in Higher Education, 16, Article 39.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2026 Nurul Inayah Hutasuhut, Reska Mayefis, Fitri Yasih

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
Jurnal Teknologi Pendidikan is an Open Access Journal. The authors who publish the manuscript in Jurnal Teknologi Pendidikan agree to the following terms.
Attribution-ShareAlike 4.0 International (CC BY-SA 4.0)
-
Attribution — You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.
-
ShareAlike — If you remix, transform, or build upon the material, you must distribute your contributions under the same license as the original.
- No additional restrictions — You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.
Notices:
- You do not have to comply with the license for elements of the material in the public domain or where your use is permitted by an applicable exception or limitation.
- No warranties are given. The license may not give you all of the permissions necessary for your intended use. For example, other rights such as publicity, privacy, or moral rights may limit how you use the material.




