FUTURE TEACHERS AND AI: COEXISTENCE, ADAPTATION, AND TECHNOLOGY INTEGRATION FOR ENHANCED LEARNING QUALITY

Authors

  • วันวิสา เสียงสนั่น Ban Khalamae School

Keywords:

Artificial Intelligence, , Future Teachers, Adaptation

Abstract

This article analyzes the evolving role and adaptation of Thai teachers in the era of Generative Artificial Intelligence (AI) that is increasingly transforming the educational landscape. AI is no longer a mere tool but is becoming a “teaching assistant” or “co-instructor” capable of designing, analyzing, and assessing personalized learning. This poses significant challenges to traditional teaching roles. The analysis is grounded in key frameworks and theories such as TPACK, DigCompEdu, the Technology Acceptance Model (TAM), Adaptation Theory, and the concept of Human-AI Collaboration. The article argues that teachers must shift from being content transmitters to becoming learning designers who utilize AI ethically and critically. Recommendations include integrating digital competence in teacher education, establishing mentoring systems in schools, and implementing supportive policies to promote ethical and sustainable AI use in education. The ultimate goal is to enhance learning quality in a way that is meaningful, modern, and aligned with the needs of a rapidly changing world.

References

สำนักงานเลขาธิการสภาการศึกษา. (2567). รายงานนโยบายการศึกษาไทยในยุคดิจิทัล. กรุงเทพฯ: สำนักงานเลขาธิการ สภาการศึกษา.

ศิริชัย กาญจนวาสี. (2566). การพัฒนาครูไทยกับการใช้เทคโนโลยี AI อย่างสร้างสรรค์และยั่งยืน. กรุงเทพฯ: จุฬาลงกรณ์มหาวิทยาลัย.

Bender, E. M., Gebru, T., McMillan-Major, A., & Shmitchell, S. (2021). On the dangers of stochastic parrots: Can language models be too big?. Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency (pp. 610–623). ACM. https://doi.org/10.1145/3442188.3445922

Holmes, W., Bialik, M., & Fadel, C. (2019). Artificial intelligence in education: Promises and implications for teaching and learning. Boston, MA: Center for Curriculum Redesign.

Holstein, K., McLaren, B. M., & Aleven, V. (2019). Co-designing a real-time classroom orchestration tool to support teacher–AI collaboration. Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems (pp. 1–15). ACM. https://doi.org/10.1145/3290605.3300843

Li, X., & Wang, Y. (2023). Personalized learning with artificial intelligence: A review of recent research. Computers & Education, 193, 104675. https://doi.org/10.1016/j.compedu.2022.104675

Luckin, R., Holmes, W., Griffiths, M., & Forcier, L. B. (2022). Artificial intelligence and education: Promise and implications for teaching and learning. London: Pearson.

Mollick, E., & Mollick, L. (2023). Assigning AI: Seven approaches for students, with prompts. Computers and Education: Artificial Intelligence, 4, 100161. https://doi.org/10.1016/j.caeai.2023.100161

OECD. (2023). Artificial intelligence in education: A focus on generative AI. Paris: OECD Publishing. https://doi.org/10.1787/79b4d4c8-en

Sadasivan, S., Chen, J., Nayak, A., & Srinivasan, V. J. (2024). The role of generative AI in personalized learning. Journal of Educational Technology Research and Development, 72(2), 355–372. https://doi.org/10.1007/s11423-024-10234-9

UNESCO. (2024). AI and education: Guidance for policy makers. Paris: UNESCO Publishing.

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(39), 1–27. https://doi.org/10.1186/s41239-019-0171-0

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Published

31-08-2025

How to Cite

เสียงสนั่น ว. (2025). FUTURE TEACHERS AND AI: COEXISTENCE, ADAPTATION, AND TECHNOLOGY INTEGRATION FOR ENHANCED LEARNING QUALITY. ratchasimaparithat, 1(2), 22–34. retrieved from https://so09.tci-thaijo.org/index.php/RSMP/article/view/7196