Integrating Gligglish AI into Project-Based Learning to Improve Speaking Skills of Non-English Major EFL Students

Authors

  • Ari Fajria Novari Universitas Mathla'ul Anwar

DOI:

https://doi.org/10.29408/veles.v9i3.32518

Keywords:

Project-Based Learning (PBL), speaking skills, Gligglish AI, EFL

Abstract

This study examines the effectiveness of integrating Project-Based Learning (PBL) with Gligglish AI in improving the English-speaking skills of second-semester students in the Information Systems Research Program at a private university in Indonesia. In this context, many students experience persistent challenges in speaking development, including linguistic difficulties such as limited vocabulary, grammatical control, and pronunciation accuracy, as well as psychological barriers such as low self-confidence and fear of making mistakes. The study employed a mixed-methods sequential explanatory design. Quantitative data were collected through pre- and post-test assessments using a validated speaking performance rubric. In contrast, qualitative data were obtained through semi-structured interviews to explore students’ learning experiences and perceptions. The instructional intervention involved implementing Project-Based Learning activities supported by Gligglish AI, which facilitated interactive speaking practice, pronunciation feedback, and opportunities for autonomous learning. The participants were two intact classes (n = 52), divided into an experimental and a control group. The quantitative findings indicate substantial improvements in students’ speaking performance following the intervention, with fluency increasing by 31.4%, pronunciation by 28.6%, grammar by 24.8%, and vocabulary by 26.7%. The most pronounced improvement was observed in overall speaking performance, which increased by 47.8% from the pre-test to the post-test. Qualitative findings further reveal that students perceived the integration of PBL and Gligglish AI as engaging, relevant to their academic field, and effective in enhancing confidence, motivation, and active participation. These findings suggest that integrating AI-assisted speaking tools within a project-based learning framework offers a promising instructional approach for improving English-speaking skills in private higher education contexts.

References

Bakar, N. I. A., Noordin, N., & Razali, A. B. (2019). Improving oral communicative competence in English using Project-Based learning activities. English Language Teaching, 12(4), 73. https://doi.org/10.5539/elt.v12n4p73

Bashori, M., Van Hout, R., Strik, H., & Cucchiarini, C. (2021). Effects of ASR-based websites on EFL learners’ vocabulary, speaking anxiety, and language enjoyment. System, 99, 102496. https://doi.org/10.1016/j.system.2021.102496

Bazeley, P. (2024). Conceptualizing integration in mixed methods research. Journal of Mixed Methods Research, 18(3), 225–234. https://doi.org/10.1177/15586898241253636

Bhatnagar, R., Tanguay, C., Sullivan, C., & Many, J. (2021). Observation of field Practice rubric: Establishing content validity and reliability. Georgia Educational Researcher, 18(2). https://doi.org/10.20429/ger.2021.180201

Braun, V., & Clarke, V. (2019). Reflecting on reflexive thematic analysis. Qualitative Research in Sport Exercise and Health, 11(4), 589–597. https://doi.org/10.1080/2159676x.2019.1628806

Castillo-Montoya, M. (2016). Preparing for interview research: The Interview Protocol Refinement Framework. The Qualitative Report. https://doi.org/10.46743/2160-3715/2016.2337

Chatterjee, S., Chaudhuri, R., Vrontis, D., & Giovando, G. (2023). Digital workplace and organization performance: Moderating role of digital leadership capability. Journal of Innovation & Knowledge, 8(1), 100334. https://doi.org/10.1016/j.jik.2023.100334

Cook, T. D., Zhu, N., Klein, A., Starkey, P., & Thomas, J. (2020). How much bias results if a quasi-experimental design combines local comparison groups, a pretest outcome measure and other covariates? A within-study comparison of preschool effects. Psychological Methods, 25, 726–746. https://doi.org/10.1037/met0000260

D’Attoma, I., Camillo, F., & Clark, M. H. (2017). A Comparison of Bias Reduction Methods: Clustering versus Propensity Score Subclassification and Weighting. The Journal of Experimental Education, 87(1), 33–54. https://doi.org/10.1080/00220973.2017.1391161

De Jong, N. H. (2018). Fluency in second language testing: insights from different disciplines. Language Assessment Quarterly, 15(3), 237–254. https://doi.org/10.1080/15434303.2018.1477780

Dennis, N. K. (2024). Using AI-powered speech recognition technology to improve English pronunciation and speaking skills. IAFOR Journal of Education, 12(2), 107–126. https://doi.org/10.22492/ije.12.2.05

Deschênes, A. (2023). Digital literacy, the use of collaborative technologies, and perceived social proximity in a hybrid work environment: Technology as a social binder. Computers in Human Behavior Reports, 13, 100351. https://doi.org/10.1016/j.chbr.2023.100351

Dong, N., & Lipsey, M. W. (2018). Can Propensity Score analysis approximate randomized experiments using pretest and demographic information in Pre-K intervention research? Evaluation Review, 42(1), 34–70. https://doi.org/10.1177/0193841x17749824

Ekayati, R., Manurung, I. D., & Yenni, E. (2020). Need analysis of esp for non-English study program. language literacy Journal of Linguistics Literature and Language Teaching, 4(2), 322–332. https://doi.org/10.30743/ll.v4i2.3152

Ginusti, G. N. (2023). The Implementation of Digital Technology in Online Project-Based Learning during Pandemic: EFL Students’ Perspectives. J-SHMIC Journal of English for Academic, 10(1), 13–25. https://doi.org/10.25299/jshmic.2023.vol10(1).10220

Guaillas Gualán, G. M., & Armijos Ramírez, M. R. (2024). Uso de la aplicación Gliglish en las habilidades de habla inglesa entre estudiantes de educación secundaria superior en una institución pública (Using Gliglish Application on English speaking skills among upper secondary education students at a public institution). Revista Científica Multidisciplinar G-nerando, 5(2). https://doi.org/10.60100/rcmg.v5i2.386 DOI

Gualán, G. M. G., & Ramírez, M. R. A. (2024). Uso de la aplicación Gliglish en las habilidades de habla inglesa entre estudiantes de educación secundaria superior en una institución pública. Revista Científica Multidisciplinar G-nerando, 5(2). https://doi.org/10.60100/rcmg.v5i2.386

Guo, P., Saab, N., Post, L. S., & Admiraal, W. (2020). A review of project-based learning in higher education: Student outcomes and measures. International Journal of Educational Research, 102, 101586. https://doi.org/10.1016/j.ijer.2020.101586

Hidayatullah, N. E. (2024). The impact of TalkPal.AI on English speaking proficiency: an academic inquiry. Journal of Insan Mulia Education, 2(1), 19–25. https://doi.org/10.59923/joinme.v2i1.98

Hofweber, J. & Jaworska, S. (2022). Polite impoliteness? How power, gender and language background shape request strategies in English as a Business Lingua Franca (BELF) in corporate email exchanges. Journal of English as a Lingua Franca, 11(2), 223-253. https://doi.org/10.1515/jelf-2022-2085

Hwang, G., Rahimi, M., & Fathi, J. (2024). Enhancing EFL learners’ speaking skills, foreign language enjoyment, and language-specific grit utilising the affordances of a MALL app: A microgenetic perspective. Computers & Education, 214, 105015. https://doi.org/10.1016/j.compedu.2024.105015

Iswati, L., & Triastuti, A. (2021). Voicing the challenges of ESP teaching: Lessons from ESP in non-English departments. Studies in English Language and Education, 8(1), 276–293. https://doi.org/10.24815/siele.v8i1.17301

Jackson-Gordon, R., & Clark, V. L. P. (2023). Using a joint display for building integration in a sequential study: informing data collection for a participatory second phase. Journal of Mixed Methods Research, 18(2), 137–146. https://doi.org/10.1177/15586898231179848

Junining, E., Alif, S., & Setiarini, N. (2020). Automatic speech recognition in computer-assisted language learning for individual learning in speaking. JEES (Journal of English Educators Society), 5(2), 219–223. https://doi.org/10.21070/jees.v5i2.867

Kafryawan, W. (2023). The effectiveness of Computer-Assisted Language Learning (CALL) by smartphones to increase English proficiency of Papuan EFL students. ENGLISH FRANCA Academic Journal of English Language and Education, 7(1), 217. https://doi.org/10.29240/ef.v7i1.7090

Khalizah, N., & Damanik, E. S. D. (2024). ELSA speak: Piquing demotivated students to self-improve their pronunciation with an AI-powered English speaking coach. ELSYA Journal of English Language Studies, 6(1), 92–102. https://doi.org/10.31849/elsya.v6i1.18727

Khoudri, I. (2024). Revolutionizing English Language Learning with AI: Boosting Student Receptive and Productive Skills. Pakistan Journal of Life and Social Sciences (PJLSS), 22(2). https://doi.org/10.57239/pjlss-2024-22.2.00115

Lou, L., Xu, W., & Liu, R. (2024). A Bibliometric Approach and Meta-Analysis of Effects of Automatic Speech Recognition on second Language learning. International Journal of Web-Based Learning and Teaching Technologies, 19(1), 1–20. https://doi.org/10.4018/ijwltt.349959

McCrudden, M. T., & McTigue, E. M. (2018). Implementing integration in an explanatory sequential mixed methods study of belief bias about climate change with high school students. Journal of Mixed Methods Research, 13(3), 381–400. https://doi.org/10.1177/1558689818762576

Mustamir, M. (2024). Indonesian EFL learners and speaking anxiety: Insights from a meta-synthetic analysis. English Review Journal of English Education, 12(2), 509–518. https://doi.org/10.25134/erjee.v12i2.9950

Ngo, T. T., Chen, H. H., & Lai, K. K. (2023). The effectiveness of automatic speech recognition in ESL/EFL pronunciation: A meta-analysis. ReCALL, 36(1), 4–21. https://doi.org/10.1017/s0958344023000113

Nowell, L. S., Norris, J. M., White, D. E., & Moules, N. J. (2017). Thematic analysis. International Journal of Qualitative Methods, 16(1). https://doi.org/10.1177/1609406917733847

Purnami, N. I. a. O., & Widiadnya, N. I. G. N. B. Y. (2024). Speaking Confidently: How Project-Based Learning Can Improve Student Communication. Jurnal Pendidikan Bahasa Inggris Undiksha, 12(2), 184–190. https://doi.org/10.23887/jpbi.v12i2.86587

Qassrawi, N. R. M., ElMashharawi, N. A., Itmeizeh, N. M., & Tamimi, N. M. H. M. (2024). AI-Powered Applications for improving EFL students’ speaking proficiency in higher Education. Forum for Linguistic Studies, 6(5), 535–549. https://doi.org/10.30564/fls.v6i5.6966

Radhiyya, F. P., Nasution, D. K., & Ginting, P. (2025). Exploring AI Gliglish to enhance English speaking skills among secondary school students in Thailand: A classroom action research study on confidence and proficiency. Journal of English Language and Education, 10(2), 270–283. https://doi.org/10.31004/jele.v10i2.750

Roberts, R. (2020). Qualitative Interview Questions: Guidance for novice researchers. The Qualitative Report. https://doi.org/10.46743/2160-3715/2020.4640

Shazly, R. E. (2021). Effects of artificial intelligence on English speaking anxiety and speaking performance: A case study. Expert Systems, 38(3). https://doi.org/10.1111/exsy.12667

Sirisrimangkorn, L. (2021). Improving EFL Undergraduate learners’ speaking skills through Project-Based Learning using Presentation. Advances in Language and Literary Studies, 12(3), 65. https://doi.org/10.7575/aiac.alls.v.12n.3.p.65

Suryani, L., & Argawati, N. O. (2023). Teaching speaking through project-based learning with ICT. Indonesian EFL Journal, 9(1), 35–42. https://doi.org/10.25134/ieflj.v9i1.7134

Trinh, N. B., & Pham, D. T. T. (2021). Challenges in Speaking Classrooms among Non-English Majors. Vietnam Journal of Education (Online)/Vietnam Journal of Education/GiáO DụC, 5(2), 37–42. https://doi.org/10.52296/vje.2021.52

Tunarosa, A., & Glynn, M. A. (2016). Strategies of Integration in Mixed Methods research. Organizational Research Methods, 20(2), 224–242. https://doi.org/10.1177/1094428116637197

Turner, S. F., Cardinal, L. B., & Burton, R. M. (2015). Research design for mixed methods. Organizational Research Methods, 20(2), 243–267. https://doi.org/10.1177/1094428115610808

Van Ha, X., Nguyen, L. T., & Hung, B. P. (2021). Oral corrective feedback in English as a foreign language classrooms: A teaching and learning perspective. Heliyon, 7(7), e07550. https://doi.org/10.1016/j.heliyon.2021.e07550

Wallwey, C., & Kajfez, R. L. (2023). Quantitative research artifacts as qualitative data collection techniques in a mixed methods research study. Methods in Psychology, 8, 100115. https://doi.org/10.1016/j.metip.2023.100115

Wind, S. A. (2019). Do raters use rating scale categories consistently across analytic rubric domains in writing assessment? Assessing Writing, 43, 100416. https://doi.org/10.1016/j.asw.2019.100416

Wood, D. (2016). Willingness to communicate and second language speech fluency: An idiodynamic investigation. System, 60, 11–28. https://doi.org/10.1016/j.system.2016.05.003

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). https://doi.org/10.1186/s41239-019-0171-0

Zhang, C., Meng, Y., & Ma, X. (2024). Artificial intelligence in EFL speaking: Impact on enjoyment, anxiety, and willingness to communicate. System, 121, 103259. https://doi.org/10.1016/j.system.2024.103259

Zhang, X. (2019). Foreign Language Anxiety and Foreign Language Performance: A Meta‐Analysis. Modern Language Journal, 103(4), 763–781. https://doi.org/10.1111/modl.12590

Downloads

Published

2026-01-01

How to Cite

Novari, A. F. (2026). Integrating Gligglish AI into Project-Based Learning to Improve Speaking Skills of Non-English Major EFL Students. Voices of English Language Education Society, 9(3), 611–621. https://doi.org/10.29408/veles.v9i3.32518

Similar Articles

<< < 17 18 19 20 21 22 23 24 25 26 > >> 

You may also start an advanced similarity search for this article.