AI benefits, challenges, and pedagogical implications on speaking task completion: A multiple case study of two vocational programs in Indonesia

Arini Sabrina; Ajar Pradika Ananta Tur

doi:10.12928/notion.v8i2.14271

Authors

Arini Sabrina Politeknik LPP Yogyakarta, Yogyakarta, Indonesia
Ajar Pradika Ananta Tur Universitas Ahmad Dahlan, Yogyakarta, Indonesia

DOI:

https://doi.org/10.12928/notion.v8i2.14271

Keywords:

Artificial intelligence (AI), English for specific purpose, Speaking skills, Vocational context, Politeknik LPP Yogyakarta

Abstract

This study investigates spoken English performance in vocational education contexts. Conducted at Politeknik LPP Yogyakarta, a plantation-focused vocational institution, the research adopts a multiple case study design with an embedded quantitative component involving two distinct student groups: 30 Accountancy students and 28 Plantation Crop Farming students. All data were collected with voluntary, informed participation and reported using anonymized participant codes. Findings show that AI use supported time efficiency, task completion, material comprehension, and communicative confidence across both groups. At the same time, the participants showed overreliance on AI-generated language, shallow comprehension of AI-generated content, limited preparation for adapting and internalizing AI-assisted material, and occasional mismatches between AI output and task requirements, leading to the AKT cohort's AI-assisted mean (72.93) falling below its own prior non-AI mean (79.67) as its paradox to the ‘material comprehension’ as AI benefit aforementioned. These findings suggest that AI functions best as a supportive tool rather than a substitute for learner preparation and instructor guidance, particularly in technical vocational fields where accuracy of field-specific vocabulary matters. For the pedagogical implication more broadly, the study highlights the need for structured guidance on AI use alongside speaking skill development. Educators are encouraged to pair AI tools with explicit structured discourse-building and motivation-building strategies. Future research should examine a broader range of AI tools, larger samples, and longer-term effects on spoken language development.

References

Adelita, D., & Daulay, S. H. (2023). Postgraduate students’ difficulties and strategies on speaking. NOTION: Journal of Linguistics, Literature, and Culture, 5(1), 1-17. https://doi.org/10.12928/notion.v5i1.5827

Ahimsa, A. F. H., & Khawa, D. (2025). The impact of artificial intelligence on English language teaching: Opportunities and challenges in technology era. IDEAS: Journal on English Language Teaching and Learning, Linguistics and Literature, 13(1), 191–201. https://doi.org/10.24256/ideas.v13i1.6270

Ali, J. K. M., Shamsan, M. A. A., Hezam, T. A., & Mohammed, A. A. (2023). Impact of ChatGPT on learning motivation: Teachers and students' voices. Journal of English Studies in Arabia Felix, 2(1), 41–49. https://doi.org/10.56540/jesaf.v2i1.51

Al-khresheh, M. H. (2024). The future of artificial intelligence in English language teaching: Pros and cons of ChatGPT implementation through a systematic review. Language Teaching Research Quarterly, 43, 54–80. https://doi.org/10.32038/ltrq.2024.43.04

Almehmadi, W. (2024). Exploring the potential of AI techniques in teaching English as a foreign language: A systematic literature review. Asian Journal of Social Sciences and Management Studies, 11(2), 22–31. https://doi.org/10.20448/ajssms.v11i2.5576

Al-Raimi, M., Mudhsh, B. A., Muqaibal, M. H., & Al-Yafaei, Y. (2024). To what extent has artificial intelligence impacted EFL teaching and learning? A systematic review. Jurnal Arbitrer, 11(3), 399–412. https://doi.org/10.25077/ar.11.3.399-412.2024

Bećirović, S., Dubravac, V., & Brdarević-Čeljo, A. (2022). Cooperative learning as a pathway to strengthening motivation and improving achievement in an EFL classroom. Sage Open, 12(1). https://doi.org/10.1177/21582440221078016

Chea, P., & Xiao, Y. (2024). Artificial intelligence in higher education: The power and damage of AI-assisted tools on academic English reading skills. Journal of General Education and Humanities, 3(3), 287–306. https://doi.org/10.58421/gehu.v3i3.242

Deci, E. L., & Ryan, R. M. (2016). Optimizing students’ motivation in the era of testing and pressure: A self-determination theory perspective. In Building autonomous learners: Perspectives from research and practice using self-determination theory (pp. 9-29). Singapore: Springer Singapore. https://doi.org/10.1007/978-981-287-630-0_2

Dja'far, V. H., & Hamidah, F. N. (2024). Improving English pronunciation skills through AI-based speech recognition technology. Ethical Lingua: Journal of Language Teaching and Literature, 11(2). https://doi.org/10.30605/25409190.747

Ebneyamini, S., & Sadeghi Moghadam, M. R. (2018). Toward developing a framework for conducting case study research. International journal of qualitative methods, 17(1). https://doi.org/10.1177/160940691881795

Haenlein, M., & Kaplan, A. (2019). A brief history of artificial intelligence: On the past, present, and future of artificial intelligence. California Management Review, 61(4), 5–14. https://doi.org/10.1177/0008125619864952

Halimah, A. N., Budianto, A., Hoesny, M. U., Cabuquin, J. C., & Ulfa, S. (2025). Improving English speaking skills through AI-based voice chatbots: A systematic review of vocational education studies. Studies in Learning and Teaching, 6(3), 785-797. https://doi.org/10.46627/silet.v7i1.684

Kavgić, A. (2024). Effects of using AI in teaching English: A workplace-English case study. Годишњак Филозофског факултета у Новом Саду, 49(2), 19–40. https://doi.org/10.19090/gff.v49i2.2534

Kazu, I. Y., & Kuvvetli, M. (2023). The influence of pronunciation education via artificial intelligence technology on vocabulary acquisition in learning English. International Journal of Psychology and Educational Studies, 10(2), 480–493. https://doi.org/10.52380/ijpes.2023.10.2.1044

Marwan, A. (2009). ESP teaching challenges in an Indonesian vocational higher institution. The English Teacher, 38(1), 1–10. https://meltajournals.com/index.php/TET/article/

view/184

Ningrum, F. A., Mulyati, Y., Sumiyadi, S., Halimah, H., & Ye, Z. (2026). The role of artificial intelligence in contemporary literary practice: Perceptions and implications. NOTION: Journal of Linguistics, Literature, and Culture, 8(1), 21–34. https://doi.org/10.12928/notion.v8i1.15292

Patall, E. A., Cooper, H., & Robinson, J. C. (2008). The effects of choice on intrinsic motivation and related outcomes: A meta-analysis of research findings. Psychological Bulletin, 134(2), 270–300. https://doi.org/10.1037/0033-2909.134.2.270

Purbasari, D. A. (2025). Students’ perspective and challenge towards utilization of Talkpal.AI in ESP classroom. ICEETE Conference Series, 3(1), 18–30. https://doi.org/10.36728/iceete.v3i1.226

Romadhon, R. (2025). Replika AI Chatbot as a tool for enhancing ESP business vocabulary acquisition: A study on polytechnic students. SALEE: Study of Applied Linguistics and English Education, 6(1), 183–201. https://doi.org/10.35961/salee.v6i1.1671

Sabrina, A. (2020). The implementation of communicative language teaching in English practice. NOTION: Journal of Linguistics, Literature, and Culture, 2(1), 17-27. https://doi.org/10.12928/notion.v2i1.1750

Sariani, Miladiyenti, F., Rozi, F., Haslina, W., & Marzuki, D. (2022). Incorporating mobile-based artificial intelligence to English pronunciation learning in tertiary-level students: developing autonomous learning. International Journal of Advanced Science Computing and Engineering, 4(3), 220-232. https://doi.org/10.62527/ijasce.4.3.92

Sheikh, H., Prins, C., & Schrijvers, E. (2023). Artificial intelligence: Definition and background. In Mission AI: The new system technology (pp. 15–41). Springer International Publishing. https://doi.org/10.1007/978-3-031-21448-6_2

Siahaan, A., Ardayati, A., & Ramasari, M. (2022). The importance of instrumental motivation among students in teaching English as a foreign language (TEFL) process. Education of English as a Foreign Language Journal, 5(1), 1–12. https://doi.org/10.21776/ub.educafl.2022.005.01.01

Stander, M. (2022). The relationship between language learning strategies, affective factors and language proficiency. Language Learning in Higher Education, 12(2), 391–408. https://doi.org/10.1515/cercles-2022-2053

Wang, X., & Yan, K. (2022). College English reading teaching integrating production-oriented approach from the perspective of artificial intelligence. Computational Intelligence and Neuroscience, 2022, 3411203. https://doi.org/10.1155/2022/3411203

Wass, R., Harland, T., & Mercer, A. (2011). Scaffolding critical thinking in the zone of proximal development. Higher Education Research & Development, 30(3), 317–328. https://doi.org/10.1080/07294360.2010.489237

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

Xatamova, N., & Ashurov, J. (2024). The future of legal English learning: Integrating AI into ESP education. SPAST Reports, 1(7). https://doi.org/10.69848/sreports.v1i7.5081

Yang, S., & Wang, W. (2022). The role of academic resilience, motivational intensity and their relationship in EFL learners' academic achievement. Frontiers in psychology, 12, 823537. https://doi.org/10.3389/fpsyg.2021.823537

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(1), 39. https://link.springer.com/article/10.1186/s41239-019-0171-0