Towards humanized English learning: AI tools and speaking outcomes among university students

Muhamad Rifqi Bakhtiar, Hendi Pratama, Seful Bahri, Rudi Hartono, Sri Wahyuni, Kurniawan Yudhi Nugroho, Treenuch Chaowanakritsanakul

Abstract


The rapid advancement of artificial intelligence in higher education has significantly transformed English language teaching and learning processes. This study investigates the integration of humanizing pedagogy into AI-supported learning environments, particularly via platforms such as ChatGPT, Gemini, and Bing AI/Copilot, to enhance university students' speaking outcomes. Using a cross-sectional survey design involving 54 university students from Elementary School Teacher Education, Accounting, and English Education programs, data were collected via a structured questionnaire during May 2025 and analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). The findings reveal that while artificial intelligence provides efficient feedback and structured practice, the human element remains essential for creating an authentic and supportive instructional atmosphere. Students who perceived psychological benefits from AI use showed the strongest intention to continue using these tools (β = 0.368, p < 0.01), followed by learning effectiveness (β = 0.289) and technology usability (β = 0.241), with the model accounting for 61.4% of variance in adoption intention. This research provides practical implications for curriculum designers to incorporate humanized artificial intelligence strategies into English education programs to ensure more effective and empathetic classroom instruction.


Keywords


Artificial intelligence; English speaking practice; higher education; humanizing pedagogy; university students

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References


Ayedoun, E. (2019). Adding Communicative and Affective Strategies to an Embodied Conversational Agent to Enhance Second Language Learners ’ Willingness to Communicate. 29–57.

Bearman, M., & Ajjawi, R. (2023). Learning to work with the black box : Pedagogy for a world with artificial intelligence. April, 1160–1173. https://doi.org/10.1111/bjet.13337

Chen, J., Lai, P., Chan, A., Man, V., & Chan, C. (2023). AI-Assisted Enhancement of Student Presentation Skills : Challenges and Opportunities.

Chen, Y.-C. (2022). Effects of technology-enhanced language learning on reducing EFL learners’ public speaking anxiety. Computer Assisted Language Learning, 1–25. https://doi.org/10.1080/09588221.2022.2055083

De, L., & Kucha, P. (2017). The Effect of Pecha Kucha Presentations on Students ’ English Public Speaking Anxiety. 19, 11–22.

Freire, P. (1970). Pedagogy of the oppressed. Herder and Herder.

Gershon, W. S., & Helfenbein, R. J. (2023). Curriculum matters: educational tools for troubled times. Journal of Curriculum Studies, 55(3), 251–269. https://doi.org/10.1080/00220272.2023.2218466

Godwin-Jones, R. (2022). Partnering with AI: Intelligent writing assistance and instructed language learning. Language Learning & Technology, 26(2), 5–24. https://doi.org/10.64152/10125/73474

Ringle, C. M., & Sarstedt, M. (2014). A Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM). Sage Publications . 211–213.

Jia, F., Sun, D., Ma, Q., & Looi, C. (2022). Developing an AI-Based Learning System for L2 Learners ’ Authentic and Ubiquitous Learning in English Language.

Lancaster, T. (2023). Artificial intelligence , text generation tools and ChatGPT – does digital watermarking offer a solution ? International Journal for Educational Integrity, 6, 1–14. https://doi.org/10.1007/s40979-023-00131-6

Lee, D., Seok, K., & Sung, H. (2023). Development research on an AI English learning support system to facilitate learner ‑ generated ‑ context ‑ based learning. In Educational technology research and development (Vol. 71, Issue 2). Springer US. https://doi.org/10.1007/s11423-022-10172-2

Libbrecht, P., Declerck, T., Schlippe, T., Mandl, T., & Schiffner, D. (n.d.). NLP for Student and Teacher : Concept for an AI based Information Literacy Tutoring System.

Liu, Y. (2022). Paradigmatic Compatibility Matters: A Critical Review of Qualitative-Quantitative Debate in Mixed Methods Research. SAGE Open, 12(1). https://doi.org/10.1177/21582440221079922

Maher, K., & King, J. (2022). ‘ The Silence Kills Me .’: ‘ Silence ’ as a Trigger of Speaking ‑ Related Anxiety in the English ‑ Medium. English Teaching & Learning, 46(3), 213–234. https://doi.org/10.1007/s42321-022-00119-4

Maier, C., Thatcher, J. B., Grover, V., & Dwivedi, Y. K. (2023). Cross-sectional research: A critical perspective, use cases, and recommendations for IS research. International Journal of Information Management, 70, 102625. https://doi.org/https://doi.org/10.1016/j.ijinfomgt.2023.102625

Mariani, M. M., Hashemi, N., & Wirtz, J. (2023). Artificial intelligence empowered conversational agents : A systematic literature review and research agenda. Journal of Business Research, 161(June 2022), 113838. https://doi.org/10.1016/j.jbusres.2023.113838

Markus, K. A. (2012). Principles and Practice of Structural Equation Modeling by Rex B. Kline. Structural Equation Modeling: A Multidisciplinary Journal, 19(3), 509–512. https://doi.org/10.1080/10705511.2012.687667

Noddings, N. (2005). The challenge to care in schools: An alternative approach to education (2nd ed.). Teachers College Press.

Ogunleye, B., Zakariyyah, K. I., Ajao, O., & Olayinka, O. (2024). education sciences A Systematic Review of Generative AI for Teaching and Learning Practice.

Salih, A. A., & Omar, L. I. (2021). Season of Migration to Remote Language Learning Platforms : Voices from EFL University Learners. 10(2), 62–73. https://doi.org/10.5430/ijhe.v10n2p62

Shadiev, R., & Yang, M. (2020). Review of studies on technology-enhanced language learning and teaching. Sustainability, 12(2), 524. https://doi.org/10.3390/su12020524

Silke, C., Davitt, E., Flynn, N., Shaw, A., Brady, B., Murray, C., & Dolan, P. (2024). Social and Emotional Learning : Research , Practice , and Policy Activating Social Empathy : An evaluation of a school-based social and emotional learning programme. Social and Emotional Learning: Research, Practice, and Policy, 3(August 2023), 100021. https://doi.org/10.1016/j.sel.2023.100021

Sperling, K., Stenberg, C., Mcgrath, C., Åkerfeldt, A., & Heintz, F. (2024). In search of artificial intelligence ( AI ) literacy in teacher education : A scoping review. 6(December 2023). https://doi.org/10.1016/j.caeo.2024.100169

Steven, L. (2018). Refining pragmatically-appropriate oral communication via computer-simulated conversations. https://doi.org/10.1080/09588221.2017.1394326

Venkatesh, V., Davis, F. D., & Studies, F. (2000). A Theoretical Extension of the Technology Acceptance Model : Four Longitudinal. January 2015.

Walter, Y. (2024). Embracing the future of Artificial Intelligence in the classroom : the relevance of AI literacy , prompt engineering , and critical thinking in modern education. International Journal of Educational Technology in Higher Education. https://doi.org/10.1186/s41239-024-00448-3

Wang, N., & Lester, J. (2023). K-12 Education in the Age of AI : A Call to Action for K-12 AI Literacy. 228–232.




DOI: http://dx.doi.org/10.30659/e.11.2.435-449

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