Development of a ChatGPT training curriculum to enhance digital information retrieval skills among students at Rajamangala University of Technology Isan

Authors

  • Sirikanjana Pilabutr Faculty of Business Administration and Information Technology, Nakhonratchasima College.
  • Unchalee Tumtong Faculty of Science and Liberal Arts, Rajamangala University of Technology Isan.
  • Onnitcha Thossata Faculty of Business Administration and Information Technology, Nakhonratchasima College.

Keywords:

training curriculum, digital information searching, ChatGPT

Abstract

       The objectives of this study were to: 1) investigate foundational data for developing a training curriculum, 2) design a training curriculum on the use of ChatGPT to enhance information retrieval skills, 3) implement the developed ChatGPT training curriculum, and 4) evaluate and improve the training curriculum. The sample group consisted of 44 undergraduate students. The research instruments included: 1) the training curriculum, 2) a knowledge test, 3) an information retrieval skill assessment, and 4) a satisfaction questionnaire. Data were analyzed using descriptive statistics and t-tests.        

       Research Findings:
       1. Most students reported some familiarity with ChatGPT (42.86%), although the majority had never experimented with it (64.29%) or accessed the platform (46.62%). Students perceived the benefits of using ChatGPT to include time-saving for basic tasks (28.57%) and improved work performance (44.74%).
       2. The developed training curriculum included core components such as principles, objectives, content, learning activities, and assessment and evaluation methods. The curriculum was evaluated as highly appropriate.
       3. The implementation of the ChatGPT training curriculum significantly improved students' post-training knowledge scores, which exceeded the 70% benchmark. Similarly, their information retrieval skills after training also surpassed the 70% threshold. Furthermore, student satisfaction with the curriculum was at the highest level.
       4. Feedback from participants, instructors, and stakeholders indicated that the training curriculum on using ChatGPT for information retrieval in the digital age was highly effective.

References

ภรณี ศิริวิศาลสุวรรณ. (2563). การพัฒนาหลักสูตรเสริมสร้างทักษะชีวิตในยุคดิจิทัลสำหรับนักเรียนระดับมัธยมศึกษาตอนต้น. วารสารศึกษาศาสตร์ มหาวิทยาลัยศิลปากร, 18(1), 1–15.

ภัทรสุดา ยะบุญวัน. (2564). การพัฒนากิจกรรมการเรียนรู้เรื่องการประยุกต์ใช้เทคโนโลยีดิจิทัลสำหรับการเรียนการสอนด้วยสื่ออินโฟกราฟิกที่ส่งผลต่อการรู้ดิจิทัลของนักศึกษาวิชาชีพครู คณะศึกษาศาสตร์ มหาวิทยาลัยศิลปากร [วิทยานิพนธ์ปริญญาดุษฎีบัณฑิต สาขาหลักสูตรและการสอน]. มหาวิทยาลัยศิลปากร.

Annapureddy, R., Fornaroli, A., & Gatica-Perez, D. (2025). Generative AI literacy: Twelve defining competencies. Digital Government: Research and Practice, 6(1), 1–21. https://doi.org/10.1145/3703152

Bang, Y., Yang, J., Zhang, H., Hahn, J., & Hovy, E. (2023). A multitask, multilingual, multimodal evaluation of ChatGPT on reasoning, hallucination, and interactivity. arXiv. https://doi.org/10.48550/arXiv.2302.10797

Borg, W. R., & Gall, M. D. (2003). Educational research: An introduction (7th ed.). Allyn & Bacon.

Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J. D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D., Wu, J., Winter, C., . . . Amodei, D. (2020). Language models are few-shot learners. Advances in Neural Information Processing Systems, 33, 1877–1901.

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(1), Article 43. https://doi.org/10.1186/s41239-023-00411-w

Chiang, Y. H., Li, C. Y., & Liu, S. H. (2023). Digital learning platforms and student information behavior in higher education. Journal of Educational Computing Research, 61(1),45–63. https://doi.org/10.1177/07356331221111111

Federiakin, D., Molerov, D., Zlatkin-Troitschanskaia, O., & Maur, A. (2024). Prompt engineering as a new 21st century skill. Frontiers in Education, 9, Article 1366434. https://doi.org/10.3389/feduc.2024.1366434

Georgieva, M., Kassorla, M., & Papini, A. (2024). AI literacy in teaching and learning: A durable framework for higher education.

Godwin-Jones, R. (2023). AI in language education: Language partners, writing assistants, or cheating tools? Language Learning & Technology, 27(1), 1–15. http://hdl.handle.net/10125/103807

Han, S., & Bui, T. M. (2024). Challenges in AI-assisted information retrieval: Relevance, trustworthiness, and user intent. Information Processing & Management, 61(2), Article 103598. https://doi.org/10.1016/j.ipm.2023.103598

Holmes, W., Bialik, M., & Fadel, C. (2024). Artificial intelligence in education: Promises and implications for teaching and learning (Updated ed.). Center for Curriculum Redesign.

Kasneci, E., Sessler, K., Kübler, R. V., 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, Article 102274. https://doi.org/10.1016/j.lindif.2023.102274

Kasneci, E., Sessler, K., Krosse, H., & Kasneci, G. (2024). ChatGPT in education: Bridging awareness and application among university students. Computers and Education: Artificial Intelligence, 6, Article 100204. https://doi.org/10.1016/j.caeai.2024.100204

Knowles, M. S. (1975). Self-directed learning: A guide for learners and teachers. Association Press.

Koohang, A., Sargent, C. S., & Svanadze, S. (2024). Students' perceptions of benefits and opportunities of artificial intelligence (AI). Issues in Information Systems, 25(2).

LaFlamme, K. A. (2025). Scaffolding AI literacy: An instructional model for academic librarianship. The Journal of Academic Librarianship, 51(3), Article 103041. https://doi.org/10.1016/j.acalib.2024.103041

Lee, D., & Palmer, E. (2025). Prompt engineering in higher education: A systematic review to help inform curricula. International Journal of Educational Technology in Higher Education, 22(1), Article 7. https://doi.org/10.1186/s41239-025-00512-y

Lee, K.-W., Mills, K., Ruiz, P., Coenraad, M., Fusco, J., Roschelle, J., & Weisgrau, J. (2024). AI literacy: A framework to understand, evaluate, and use emerging technology. Digital Promise. https://digitalpromise.org/

Liu, J., Xu, Q., & Wang, H. (2021). Machine learning for information retrieval: Recent advances and future trends. ACM Computing Surveys, 54(8), 1–39. https://doi.org/10.1145/3458345

OpenAI. (2023). ChatGPT: Optimizing language models for dialogue. https://openai.com/chatgpt

Rogers, E. M. (2003). Diffusion of innovations (5th ed.). Free Press.

Tan, C. L., & Ng, J. Y. (2024). Personalizing learning pathways through AI-powered learning analytics: A model for higher education. Journal of Learning Analytics and Educational Technology, 8(1), 15–32.

Woo, D. J., Wang, D., Yung, T., & Guo, K. (2024). Effects of a prompt engineering intervention on undergraduate students' AI self-efficacy,

AI knowledge and prompt engineering ability: A mixed methods study. arXiv. https://doi.org/10.48550/arXiv.2408.07302

Zawacki-Richter, O., Marín, V. I., Bond, M., & Gouverneur, F. (2024). Systematic review of research on artificial intelligence applications in higher education: Emerging trends and implications. International Journal of Educational Technology in Higher Education, 21, Article 10. https://doi.org/10.1186/s41239-024-00397-z

Zhai, C. (2022). The future of information retrieval: Towards conversational, explainable, and responsible intelligent information access. Foundations and Trends in Information Retrieval, 16(1), 1–153. https://doi.org

/10.1561/1500000078

Zhang, H., & Chen, Y. (2024). Student perceptions of AI-powered learning tools: Benefits, concerns, and the path forward. Journal of Educational Computing Research, 62(1), 112–136. https://doi.org/10.1177/07356331231119325

Zhang, X., Qian, W., & Chen, C. (2024). The effect of digital technology usage on higher vocational student satisfaction: The mediating role of learning experience and learning engagement. Frontiers in Education, 9, Article 1508119. https://doi.org/10.3389

/feduc.2024.1508119

Downloads

Published

2025-12-23

How to Cite

Pilabutr, S. ., Tumtong, U. ., & Thossata, O. . (2025). Development of a ChatGPT training curriculum to enhance digital information retrieval skills among students at Rajamangala University of Technology Isan. Academic Journal of North Bangkok University, 14(2), 144–156. retrieved from https://so01.tci-thaijo.org/index.php/NBU/article/view/282175

Issue

Section

บทความวิจัย