Development of Conversational AI through Situation-Based Learning to Enhance General Staff Operational Competency for Student Officers in the Joint Operations Course
Article Sidebar
Main Article Content
Abstract
This research aimed to develop a conversational Artificial Intelligence (AI) system integrated with a five-step Situation-Based Learning (SBL) framework to enhance the general staff operational capabilities of student officers in the Joint Operations Course. The study compared learning achievement before and after the intervention and analyzed
the development of general staff operational skills throughout the learning process. A quasi-experimental design was employed with 30 student officers from the Fiscal Year 2026 Joint Operations Course, selected through purposive sampling. The research instruments included a conversational AI system developed on the n8n platform, with integrated Gemini AI with Retrieval-Augmented Generation (RAG) technology. This system provided high-precision responses and was accessible via a LINE Official Account (LINE OA). Data collection tools comprised a learning achievement test and a general staff operations capability assessment form. Data were statistically analyzed using mean (M), standard deviation (S.D.), and paired-samples t-test.
The research findings were as follows: 1) The conversational AI system, developed according to the seven-stage Software Development Life Cycle (SDLC), demonstrated “Very Good” quality. It was particularly highly rated for its immediate feedback and the modernity of its learning materials, both of which received the highest evaluation score (M = 5.00). The system successfully provided accurate military doctrine information through RAG technology. 2) The post-test learning achievement (T2 ; M = 20.27) was significantly higher than pre-test scores (T1 ; M =16.67) at the .05 level (p < .05). 3) The post-learning general staff operational capabilities in Personnel, Intelligence, and Logistics increased significantly at the .05 level (p < .05), and development across the three experimental phases (X1 + X2 + X3) showed a consistent upward trend. In conclusion, the integration of conversational AI with SBL effectively elevates the operational readiness and capabilities of student officers.
Article Details
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
The articles, images, tables, graphs, written content, and opinions published in this journal are solely those of the authors and do not necessarily reflect the views or positions of the National Defence Studies Institute or its academic affiliates.
References
จักรพันธุ์ กิตตินรรัตน์. (2568). ระเบียบวิธีวิจัยทางสังคมศาสตร์. มหาวิทยาลัยเกษตรศาสตร์.
ทิศนา แขมมณี. (2552). ศาสตร์การสอน: องค์ความรู้ เพื่อการจัดกระบวนการเรียนรู้ที่มีประสิทธิภาพ. จุฬาลงกรณ์มหาวิทยาลัย.
วิทยาลัยเสนาธิการทหาร. (2567). เอกสารประกอบการบรรยาย วิชา การจัดและการดำเนินงานของฝ่ายอำนวยการ. วิทยาลัยเสนาธิการทหาร สถาบันวิชาการป้องกันประเทศ.
สำนักการศึกษาทหาร. (2567). แบบฟอร์มการจัดทำหลักสูตร 7 ตอน: หลักสูตรการปฏิบัติการร่วม. สำนักการศึกษาทหาร สถาบันวิชาการป้องกันประเทศ.
ศรีนวล ฟองมณี. (2556). การวิเคราะห์และออกแบบระบบ. มหาวิทยาลัยราชภัฏเชียงราย.
Balaguer, A., Vinamra, b., Cunha, R. L. de F., Filho, R.de M. E., Hendry, T., Holstein, D., Jennifer, M., Mecklenburg, N., Malvar, S., Nunes, L. O., Padilha, R., Sharp, M., Silva, B., Sharma, S., Aski, V., & Chandra, R. (2024). RAG vs Fine-tuning: Pipelines, Tradeoffs, and a Case Study on Agriculture. arXiv. https://doi.org/10.48550/arXiv.2401.08406
Beal, S. A., & Christ, R. E. (2017). Simulation-based military training. In Handbook of military and veteran psychology (pp. 1-21). Oxford University Press.
Bennett, N., & Lemoine, G. J. (2014). What a difference a word makes: Understanding threats to performance in a VUCA world. Business Horizons, 57(3), 311-317.
Emran, M. A., & Shaalan, K. (2014). A survey of intelligent language tutoring systems. In 2014 International Conference on Advances in Computing, Communications and Informatics (ICACCI), 393-399.
Gao, Y., Xiong, Y., Gao, X., Jia, K., Pan, J., Bi, Y., Dai, Y., Sun, J., Wang, M., & Wang, H. (2023). Retrievalaugmented generation for large language models: A survey. arXiv. https://doi.org/10.48550/arXiv.2312.10997
Jones, L., & Baeyer, V. C. (1983). Functions of American English: Communication activities for the classroom: Student’s book. Cambridge University Press.
Minaee, S., Mikolov, T., Nikzad, N., Chenaghlu, M., Socher, R., Amatriain, X., & Gao, J. (2024). Large language models: A survey. arXiv preprint. https://doi.org/10.48550/arXiv.2402.06196
Sturtridge, G. (1984). Procedures and techniques. Role-play and simulations. In Johnson, K. and Morrow, K. (Eds.), Communication in the Classroom (pp. 126-130). Longman.
