Agentic AI as Process Scaffolding in Undergraduate ESL Instruction: Effects on Proficiency, Engagement, and Self-Regulated Language Learning

Authors

  • Roland A. Niez Biliran Province State University
  • Joedel O. Peñaranda Biliran Province State University
  • Maribel N. Zipagan Biliran Province State University
  • John Cavin M. Sabonsolin Biliran Province State University
  • Cristie Ann L. Jaca Cebu Technological University
  • Noel P. Tancinco Biliran Province State University
  • Jelyn M. Magno Biliran Province State University
  • Mary Joy B. Gelizon Biliran Province State University
  • Jed Priam Cerera Biliran Province State University

DOI:

https://doi.org/10.17507/tpls.1610.04

Keywords:

agentic artificial intelligence, ESL instruction, learner engagement, self-regulated language learning, language pedagogy

Abstract

Recent AI-assisted language learning research has focused mainly on reactive chatbots that respond to prompts but offer limited support for planning, monitoring, and follow-up. This study examined whether a bounded agentic AI environment designed as process scaffolding would be associated with stronger ESL outcomes than a reactive AI interface. Using an explanatory sequential mixed-methods, quasi-experimental design, 128 undergraduates from four intact academic communication sections at two private universities in the Philippines participated in a 12-week intervention. Students in the experimental condition used an Agentic English Coach that supported weekly goal setting, task decomposition, reminders, reflection prompts, adaptive resources, and teacher escalation; the comparison group used a reactive chat interface to complete the same course tasks. ANCOVA results controlling for pretest scores showed that the agentic AI group outperformed the reactive AI group in posttest English proficiency, learner engagement, self-regulated language learning, and agentic engagement. Within the experimental group, plan completion, reflection completion, revision cycles, and speaking practice turns positively predicted posttest proficiency. Interview data indicated that agentic AI reduced task uncertainty, supported sustained practice, made progress more visible, and shifted teachers toward orchestration and oversight of feedback rather than constant procedural prompting. The findings suggest that AI is most pedagogically valuable in ESL instruction when it structures learning processes over time rather than only generating on-demand responses.

References

Chiu, T. K. F. (2024). A classification tool to foster self-regulated learning with generative artificial intelligence by applying self-determination theory: A case of ChatGPT. Educational Technology Research and Development, 72, 2401-2416. https://doi.org/10.1007/s11423-024-10366-w

Dizon, G., Gold, J., & Barnes, R. (2025). ChatGPT for self-regulated language learning: University English as a foreign language students’ practices and perceptions. Digital Applied Linguistics, 3, 102510. https://doi.org/10.29140/dal.v3.102510

Fathi, J., Rahimi, M., & Derakhshan, A. (2024). Improving EFL learners’ speaking skills and willingness to communicate via artificial intelligence-mediated interactions. System, 121, 103254. https://doi.org/10.1016/j.system.2024.103254

Fredricks, J. A., Blumenfeld, P. C., & Paris, A. H. (2004). School engagement: Potential of the concept, state of the evidence. Review of Educational Research, 74(1), 59-109. https://doi.org/10.3102/00346543074001059

Guan, R., Raković, M., Chen, G., & Gašević, D. (2025). How educational chatbots support self-regulated learning? A systematic review of the literature. Education and Information Technologies, 30(4), 4493-4518. https://doi.org/10.1007/s10639-024-12881-y

Kasneci, E., Seßler, K., Küchemann, S., 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, 102274. https://doi.org/10.1016/j.lindif.2023.102274

Liu, G. L., Lee, J. S., & Zhao, X. (2025). Critical digital literacies, agentic practices, and AI-mediated informal digital learning of English. System, 134, 103797. https://doi.org/10.1016/j.system.2025.103797

Molenaar, I. (2022). The concept of hybrid human-AI regulation: Exemplifying how to support young learners’ self-regulated learning. Computers and Education: Artificial Intelligence, 3, 100070. https://doi.org/10.1016/j.caeai.2022.100070

Panadero, E. (2017). A review of self-regulated learning: Six models and four directions for research. Frontiers in Psychology, 8, 422. https://doi.org/10.3389/fpsyg.2017.00422

Plaat, A., van Duijn, M., van Stein, N., Preuss, M., van der Putten, P., & Batenburg, K. J. (2025). Agentic large language models, a survey. Journal of Artificial Intelligence Research, 84, Article 29. https://doi.org/10.1613/jair.1.18675

Reeve, J., & Shin, S. H. (2020). How teachers can support students’ agentic engagement. Theory Into Practice, 59(2), 150-161. https://doi.org/10.1080/00405841.2019.1702451

Reeve, J., & Tseng, C.-M. (2011). Agency as a fourth aspect of students’ engagement during learning activities. Contemporary Educational Psychology, 36(4), 257-267. https://doi.org/10.1016/j.cedpsych.2011.05.002

Tram, N. H. M., Nguyen, T. T., & Tran, C. D. (2024). ChatGPT as a tool for self-learning English among EFL learners: A multi-methods study. System, 127, 103528. https://doi.org/10.1016/j.system.2024.103528

Wang, Y., & Xue, L. (2024). Using AI-driven chatbots to foster Chinese EFL students’ academic engagement: An intervention study. Computers in Human Behavior, 159, 108353. https://doi.org/10.1016/j.chb.2024.108353

Wei, L. (2023). Artificial intelligence in language instruction: Impact on English learning achievement, L2 motivation, and self-regulated learning. Frontiers in Psychology, 14, 1261955. https://doi.org/10.3389/fpsyg.2023.1261955

Wu, X.-Y. (2024). Artificial intelligence in L2 learning: A meta-analysis of contextual, instructional, and social-emotional moderators. System, 126, 103498. https://doi.org/10.1016/j.system.2024.103498

Xia, Q., Chiu, T. K. F., Chai, C. S., & Xie, K. (2023). The mediating effects of needs satisfaction on the relationships between prior knowledge and self-regulated learning through artificial intelligence chatbot. British Journal of Educational Technology, 54(4), 967-986. https://doi.org/10.1111/bjet.13305

Xu, T., & Wang, H. (2024). The effectiveness of artificial intelligence on English language learning achievement. System, 125, 103428. https://doi.org/10.1016/j.system.2024.103428

Yang, L., & Li, R. (2024). ChatGPT for L2 learning: Current status and implications. System, 124, 103351. https://doi.org/10.1016/j.system.2024.103351

Zhang, D., Wu, J. G., & Fu, Z. (2024). From shy to fly: Facilitating EFL learners’ willingness to communicate with an AI chatbot and an intelligent tutoring system. System, 127, 103501. https://doi.org/10.1016/j.system.2024.103501

Zimmerman, B. J. (2002). Becoming a self-regulated learner: An overview. Theory Into Practice, 41(2), 64-70. https://doi.org/10.1207/S15430421TIP4102_2

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Published

2026-10-02

Issue

Vol. 16 No. 10 (2026)

Section

Articles