การเรียนรู้กับ AI ในปี 2568 : ครูไทยปรับตัวอย่างไรในยุค Generative AI

กัญชลารักษ์  ทีปกากร

Authors

กัญชลารักษ์ ทีปกากร -

Keywords:

Learning with AI, Thai teachers, adaptation

Abstract

The emergence of Generative AI technologies—such as ChatGPT, Gemini, and Claude—has significantly reshaped the landscape of education in Thailand. These tools have evolved from simple aids to active “co-instructors,” capable of generating content, designing personalized learning experiences, analyzing student data, and providing instant feedback at scale. In this context, Thai teachers are facing both unprecedented opportunities and critical challenges. Their role must shift from knowledge transmitters to facilitators, critical thinkers, and ethical guardians of AI-generated content. Essential competencies in the AI era include prompt engineering, evaluating AI outputs, designing individualized learning plans, and promoting ethical use of AI in education. Nevertheless, barriers such as teacher anxiety, lack of sustained professional development, and technological disparities persist. This paper proposes strategies for empowering Thai teachers to adapt effectively, including active learning-based training, integration of AI ethics into teacher education, the establishment of AI teacher-coaches, and multi-sector collaboration. These approaches aim to ensure that Thai educators not only keep pace with AI but thrive alongside it—ethically, sustainably, and equitably.

References

สำนักงานปลัดกระทรวงศึกษาธิการ. (2567). รายงานนโยบายการศึกษาแห่งชาติว่าด้วยการใช้ปัญญาประดิษฐ์เพื่อการเรียนรู้. สืบค้นจาก https://bps.moe.go.th/

สำนักงานเลขาธิการสภาการศึกษา. (2566). รายงานการจัดทำหลักสูตรอบรมครูเกี่ยวกับปัญญาประดิษฐ์. กรุงเทพฯ: กระทรวงศึกษาธิการ.

สำนักงานเลขาธิการสภาการศึกษา. (2567). ทักษะครูในศตวรรษที่ 21 กับการเรียนรู้ในยุค AI. กรุงเทพฯ: กระทรวงศึกษาธิการ.

Songsiengchai, P., Jongsuwat, S., & Nithimapakorn, A. (2025). The impact of ChatGPT in enhancing motivation and language skills in Thai university students. ResearchGate. https://www.researchgate.net/publication/xxx

Bender, E. M., Gebru, T., McMillan-Major, A., & Shmitchell, S. (2021). On the dangers of stochastic parrots: Can language models be too big? FAccT '21: Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency, 610–623. https://doi.org/10.1145/3442188.3445922

Brown, P., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., ... & Amodei, D. (2023). Language models are few-shot learners. arXiv preprint arXiv:2005.14165. https://arxiv.org/abs/2005.14165

Calvani, A., Fini, A., & Ranieri, M. (2021). DigCompEdu framework and teacher digital competence: A critical perspective. Journal of E-Learning and Knowledge Society, 17(2), 23–33.

Chai, C. S., Koh, J. H. L., & Tsai, C.-C. (2020). A review of technological pedagogical content knowledge. Journal of Educational Technology & Society, 23(3), 31–51.

Collins, A., Brown, J. S., & Newman, S. E. (1989). Cognitive apprenticeship: Teaching the crafts of reading, writing, and mathematics. In L. Resnick (Ed.), Knowing, learning, and instruction: Essays in honor of Robert Glaser (pp. 453–494). Lawrence Erlbaum Associates.

Dweck, C. S. (2016). Mindset: The new psychology of success. Ballantine Books.

Etzkowitz, H., & Leydesdorff, L. (2000). The dynamics of innovation: From National Systems and “Mode 2” to a Triple Helix of university–industry–government relations. Research Policy, 29(2), 109–123.

Holstein, K., McLaren, B. M., & Aleven, V. (2019). Co-designing a real-time classroom orchestration tool to support teacher–AI complementarity. Journal of Learning Analytics, 6(2), 27–52.

Li, X., & Wang, M. (2023). The impact of generative AI on personalized learning in Asia-Pacific classrooms. AI in Education Journal, 11(1), 45–60.

Livingstone, S. (2004). Media literacy and the challenge of new information and communication technologies. The Communication Review, 7(1), 3–14.

Luckin, R., Holmes, W., Griffiths, M., & Forcier, L. B. (2022). Intelligence unleashed: An argument for AI in education. Pearson Education.

Mollick, E., & Mollick, L. (2023). Using AI to level the playing field in education. arXiv preprint arXiv:2301.08913. https://arxiv.org/abs/2301.08913

Mishra, P., & Koehler, M. J. (2006). Technological pedagogical content knowledge: A framework for teacher knowledge. Teachers College Record, 108(6), 1017–1054.

Mishra, P., & Koehler, M. J. (2009). Too cool for school? No way! Using the TPACK framework. Learning & Leading with Technology, 36(7), 14–18.

OECD. (2023). Artificial intelligence in education: Challenges and opportunities for policy. OECD Publishing. https://www.oecd.org

Redecker, C. (2017). European Framework for the Digital Competence of Educators: DigCompEdu. Publications Office of the European Union. https://doi.org/10.2760/159770

Sadasivan, M., Jin, L., & Chen, Y. (2024). Generative AI and its role in next-generation educational technologies. Computers & Education: Artificial Intelligence, 5, 100132.

Schuwer, R., & Sjoer, E. (2021). Lifelong learning and digital transformation in education. International Journal of Educational Technology in Higher Education, 18(1), 1–17.

UNESCO. (2024). Guidelines for the ethical use of AI in education. United Nations Educational, Scientific and Cultural Organization. https://unesdoc.unesco.org

Zawacki-Richter, O., Marín, V. I., Bond, M., & Gouverneur, F. (2019). Systematic review of research on artificial intelligence applications in higher education. International Journal of Educational Technology in Higher Education, 16(1), 1–27.

Zhao, Y. (2024). What works may hurt: Side effects in education and the role of AI. Harvard Education Press.