Influence of Teachers’ TPACK, AI Utilization, and Students’ Metacognitive Practices on Academic Writing Performance among Senior High School Students

Authors

  • Raymund G. Lumacang, LPT Professor, Graduate School Department, Lourdes College, Inc. Cagayan de Oro City, Misamis Oriental, Philippines Author
  • Kriscentti Exzur P. Barcelona, PhD Professor, Graduate School Department, Lourdes College, Inc. Cagayan de Oro City, Misamis Oriental, Philippines Author

DOI:

https://doi.org/10.65339/ijsair.V2.I2.197

Keywords:

Teachers’ TPACK, AI Utilization, Metacognitive Practices, Academic Writing Skills, Senior High School Students

Abstract

The integration of educational technology and artificial intelligence in writing instruction offers new opportunities to support students’ academic writing; however, evidence explaining how students’ assessment of teachers’ Technological Pedagogical Content Knowledge (TPACK), AI utilization, and metacognitive practices relate to writing skills remains limited. Anchored on Flavell’s Metacognitive Theory and supported by the TPACK Framework and Technology Acceptance Model, this study examined if metacognitive practices shape AI utilization, teachers’ TPACK, and academic writing among senior high school learners. Using a quantitative descriptive-correlational design. This study collected from 176 Grade 11 STEM students in a private Catholic institution in Cagayan de Oro City through validated questionnaires and a rubric-based writing output. Findings revealed generally high teacher TPACK, high AI utilization, and high metacognitive practices, while academic writing was average, with organization and coherence as the weakest dimension. Metacognitive practices had significant direct effects on AI utilization and teachers’ TPACK, whereas AI utilization and TPACK had very weak direct contributions to writing performance. The study concludes that improving academic writing requires explicit instruction, guided feedback, deliberate practice, and stronger metacognitive scaffolding. It is recommended that educators integrate structured metacognitive strategies and guided AI-supported writing activities to help students translate their cognitive and technological readiness into measurable improvements in writing performance.

References

Du, J., & Nordin, N. R. (2025). A systematic review of automated writing evaluation (AWE) systems on university students’ English writing performance. Forum for Linguistic Studies, 7(11).

Gao, J., Zhang, J., & Li, Y. (2025). Do AI chatbot-integrated writing tasks influence writing self-efficacy and critical thinking ability? An exploratory study. Computers and Education: Artificial Intelligence, 8, 100472.

Goshu, K. C., & Gebremariam, H. T. (2024). Revisiting writing feedback: Using teacher-student writing conferences to enhance learners’ L2 writing skills. Ampersand, 13, 100195.

Greenhow, C., Graham, C. R., & Koehler, M. J. (2022). Foundations of online learning: Challenges and opportunities. Educational Psychologist, 57(3), 131-147.

Iqbal, J., Hashmi, Z. F., Asghar, M. Z., & Abid, M. N. (2025). Generative AI tool use enhances academic achievement in sustainable education through shared metacognition and cognitive offloading among preservice teachers. Scientific Reports, 15, Article 16610. https://doi.org/10.1038/s41598 025 01676 x

Lappe, J. M. (2000). Taking the mystery out of research: Descriptive correlational design. Orthopaedic Nursing, 19(2), 81.

Roxas, M. J. D. (2020). Exploring senior high school students’ academic writing difficulties: Towards an academic writing model. IOER International Multidisciplinary Research Journal, 2(1), 10–19. https://doi.org/10.54476/iimrj376

Shi, J., Li, Y., & Liu, X. (2025). Exploring how AI literacy and self-regulated learning relate to student writing performance and well-being. Behavioral Sciences, 15(5), 705.

Siloterio, K. T., & Cajandig, A. J. S. (2025). Teachers’ technological pedagogical content knowledge (TPACK) and readiness for implementing the MATATAG curriculum: Context for developing a TPACK based intervention framework. International Journal of Research and Innovation in Social Science, 5(90400143), 1896–1910.

Taye, T., & Mengesha, M. (2024). Identifying and analyzing common English writing challenges among regular undergraduate students. Heliyon, 10(17), e36876.

Teng, M. F., & Zhan, Y. (2023). Assessing self-regulated writing strategies, self-efficacy, task complexity, and performance in English academic writing. Assessing Writing, 57, 100728.

United Nations. (n.d.). Goal 4: Quality education. United Nations Sustainable Development. https://sdgs.un.org/goals/goal4

Von Kotzebue, L. (2022). Beliefs, self-reported or performance-assessed TPACK: What can predict the quality of technology-enhanced biology lesson plans? Journal of Science Education and Technology, 31, 570–582.

Voogt, A. (2021). The effect of zoom fatigue on consumers’ group creative performance (Doctoral dissertation, Radboud University, Netherlands).

Wang, D., et al. (2025). Catalyst for future education: An empirical study on the Impact of artificial intelligence generated content on college students’ innovation ability. Education and Information Technologies, 30(8), 9949-9968.

Yu, C. (2025). Revisiting the relationship between information literacy and academic writing: Evidence from a self-assessment scale. The Journal of Academic Librarianship, 51(6), 103138.

Zahari, M., et al. (2025). Optimizing student writing performance in higher education: A quantitative study of teacher feedback and classroom environment. Social Sciences & Humanities Open, 11, 101286.

Zhai, C., Wibowo, S., & Li, L. D. (2024). The effects of over-reliance on AI dialogue systems on students’ cognitive abilities: A systematic review. Smart Learning Environments, 11(1), 28

Downloads

Published

2026-04-10

Issue

Section

Articles

How to Cite

Lumacang, R., & Barcelona, K. E. (2026). Influence of Teachers’ TPACK, AI Utilization, and Students’ Metacognitive Practices on Academic Writing Performance among Senior High School Students. International Journal of Sustainability and Advanced Integrated Research, 2(2), 344-351. https://doi.org/10.65339/ijsair.V2.I2.197