This study investigates the efficacy of task-based chatbots in enhancing speaking skills and shaping the attitudes toward language learning of L2 learners while fostering a translanguaging space. The research employs a mixed-methods approach through quantitative and qualitative data collection methods. A sample of 80 L2 learners participated in the study. Quantitative data were collected through pre- and posttests to measure speaking proficiency and a translanguaging scale to gauge L2 learners’ ability to transition seamlessly between languages. Qualitative data were gathered through semistructured interviews with the participants. The findings reveal a significant improvement in speaking proficiency among the learners following the chatbot interventions. Moreover, there was a positive shift in the learners’ attitudes, from negative to positive, toward L2 speaking proficiency. The qualitative analysis highlights creating a translanguaging space wherein the learners effectively utilize their linguistic repertoire to facilitate communication. The results underscore the importance of integrating chatbot technologies into language instruction to enhance engagement and foster authentic communication.
AdamopoulouE.MoussiadesL. (2020). An overview of chatbot technology. In MaglogiannisI.IliadisL.PimenidisE. (Eds.), Artificial intelligence applications and innovations. AIAI 2020. IFIP advances in information and communication technology (pp. 373–383). Springer.
2.
AwasthiV.VermaR.DhandaN. (2024). Multimodal dialogue systems in the era of artificial intelligence-assisted industry. In KhangA.HajimahmudV. A.MisraA.LitvinovaE. (Eds.), Machine vision and industrial robotics in manufacturing (pp. 178–200). CRC Press.
3.
BoskerH. R.QuenéH.SandersT.De JongN. H. (2014). The perception of fluency in native and nonnative speech. Language Learning, 64(3), 579–614. https://doi.org/10.1111/lang.12067
4.
CanaleM.SwainM. (1980). Theoretical bases of communicative approaches to second language teaching and testing. Applied Linguistics, 1(1), 1–47. https://doi.org/10.1093/applin/I.1.1
5.
Celce-MurciaM.BrintonD. M.GoodwinJ. M. (2010). Teaching pronunciation hardback with audio CDs (2): A course book and reference guide. Cambridge University Press.
6.
ChapelleC. (2003). English language learning and technology: Lectures on applied linguistics in the age of information and communication technology. John Benjamins Publishing. https://doi.org/10.1075/lllt.7
7.
ChenY. C. (2022). Effects of technology-enhanced language learning on reducing EFL learners’ public speaking anxiety. Computer Assisted Language Learning, 37(4), 789–813. https://doi.org/10.1080/09588221.2022.2055083
8.
CreeseA.BlackledgeA. (2015). Translanguaging and identity in educational settings. Annual Review of Applied Linguistics, 35(1), 20–35. https://doi.org/10.1017/S0267190514000233
9.
DaiD. W. (2023). What do second language speakers really need for real-world interaction? A needs analysis of L2 Chinese interactional competence. Language Teaching Research, Article, 13621688221144836. https://doi.org/10.1177/13621688221144836
10.
DörnyeiZ. (2014). Researching complex dynamic systems: ‘Retrodictive qualitative modelling’ in the language classroom. Language Teaching, 47(1), 80–91. https://doi.org/10.1017/S0261444811000516
FølstadA.SkjuveM.BrandtzaegP. B. (2019). Different chatbots for different purposes: Towards a typology of chatbots to understand interaction design. In BodrunovaS.KoltsovaO.FølstadA.HalpinKolozaridiP.YuldashevL.SmoliarovaA.NiedermayerH. (Eds.), Internet science INSCI 2018. Lecture notes in computer science (pp. 1845–1945). Springer.
13.
ForsythC. M.LuceC.Zapata-RiveraD.JacksonG. T.EvaniniK.SoY. (2019). Evaluating English language learners’ conversations: Man vs. machine. Computer Assisted Language Learning, 32(4), 398–417. https://doi.org/10.1080/09588221.2018.1517126
14.
GilmartinE.CowanB. R.VogelC.CampbellN. (2018). Explorations in multiparty casual social talk and its relevance for social human machine dialogue. Journal on Multimodal User Interfaces, 12(4), 297–308. https://doi.org/10.1007/s12193-018-0274-2
15.
GuoK.LiY.LiY.ChuS. K. W. (2023). Understanding EFL students’ chatbot-assisted argumentative writing: An activity theory perspective. Education and Information Technologies, 29(1), 1–20. https://doi.org/10.1007/s10639-023-12230-5
16.
HoW. Y. J.TaiK. W. (2024). Translanguaging in digital learning: The making of translanguaging spaces in online English teaching videos. International Journal of Bilingual Education and Bilingualism, 27(9), 1212–1233. https://doi.org/10.1080/13670050.2021.2001427
17.
HuaZ.WeiL.Jankowicz-PytelD. (2020). Translanguaging and embodied teaching and learning: Lessons from a multilingual karate club in London. International Journal of Bilingual Education and Bilingualism, 23(1), 65–80. https://doi.org/10.1080/13670050.2019.1599811
18.
HuenschA. (2023). Effects of speaking task and proficiency on the midclause pausing characteristics of L1 and L2 speech from the same speakers. Studies in Second Language Acquisition, 45(4), 1031–1055. https://doi.org/10.1017/S0272263123000323
19.
JeonJ. (2024). Exploring AI chatbot affordances in the EFL classroom: Young learners’ experiences and perspectives. Computer Assisted Language Learning, 37(1-2), 1–26. https://doi.org/10.1080/09588221.2021.2021241
20.
JeonJ.LeeS.ChoiS. (2024). A systematic review of research on speech-recognition chatbots for language learning: Implications for future directions in the era of large language models. Interactive Learning Environments, 32(8), 4613–4631. https://doi.org/10.1080/10494820.2023.2204343
21.
JurafskyD.MartinJ. (2020). Speech and language processing: An introduction to natural language processing, computational linguistics, and speech recognition. Dorling Kindersley Pvt., Limited.
22.
KatchapakirinK.AnutariyaC.SupnithiT. (2022). ScratchThAI: A conversation-based learning support framework for computational thinking development. Education and Information Technologies, 27(6), 8533–8560. https://doi.org/10.1007/s10639-021-10870-z
23.
KhatoonR. (2025). AI-powered task-based learning for cross-border higher education. In NaseerC.YuC.DullooR.Abdul Kader JilaniM. M.ShaheenM. (Eds.), Bridging global divides for transnational higher education in the AI era (pp. 141–164). IGI Global Scientific Publishing. https://doi.org/10.4018/979-8-3693-7016-2.ch007
24.
KırkgözY.KüçükC. (2021). Investigating translanguaging practices in an English medium higher education context in Turkey: A case of two lecturers. In Di SabatoB.HughesB. (Eds.), Multilingual perspectives from Europe and beyond on language policy and practice (pp. 135–155). Routledge.
LanE. M. (2024). Unveiling the silent struggle: Investigating the effects of flipped classroom instruction models on business English oral presentation development through online learning: A case of learner engagement, emotions, and anxiety. Education and Information Technologies, 29(17), 23299–23328. https://doi.org/10.1007/s10639-024-12746-4
27.
LauS. M. C.TianZ.LinA. M. Y. (2021). Critical literacy and additional language learning: An expansive view of translanguaging for change-enhancing possibilities. In PandyaJ. Z.MoraR. A.AlfordJ. H.GoldenN. A.de RockR. S. (Eds.), The handbook of critical literacies (pp. 381–390). Routledge. https://doi.org/10.4324/9781003214908
28.
LefebvreH.Nicholson-SmithD. (1991). The production of space. Oxford Blackwell.
LitmanD.StrikH.LimG. S. (2018). Speech technologies and the assessment of second language speaking: Approaches, challenges, and opportunities. Language Assessment Quarterly, 15(3), 294–309. https://doi.org/10.1080/15434303.2018.1472265
MannS. (2011). A critical review of qualitative interviews in applied linguistics. Applied Linguistics, 32(1), 6–24. https://doi.org/10.1093/applin/amq043
34.
MarashiH.KhatamiH. (2017). Using cooperative learning to boost creativity and motivation in language learning. Journal of Language and Translation, 7(1), 43–58.
35.
MetrukR. (2018). Comparing holistic and analytic ways of scoring in the assessment of speaking skills. Journal of Teaching English for Specific and Academic Purposes, 6(1), 179–189. https://doi.org/10.22190/JTESAP1801179M
36.
MohammedS. J.KhalidM. W. (2025). Under the world of AI-generated feedback on writing: Mirroring motivation, foreign language peace of mind, trait emotional intelligence, and writing development. Language Testing in Asia, 15(1), 7. https://doi.org/10.1186/s40468-025-00343-2
37.
NicklinC.PlonskyL. (2020). Outliers in L2 research in applied linguistics: A synthesis and data re-analysis. Annual Review of Applied Linguistics, 40(1), 26–55. https://doi.org/10.1017/S0267190520000057
38.
NicolaidouI.PissasP.BoglouD. (2023). Comparing immersive virtual reality to mobile applications in foreign language learning in higher education: A quasi-experiment. Interactive Learning Environments, 31(4), 2001–2015. https://doi.org/10.1080/10494820.2020.1870504
39.
OckeyG.Chukharev-HudilainenE. (2021). Human versus computer partner in the paired oral discussion test. Applied Linguistics, 42(5), 924–944. https://doi.org/10.1093/applin/amaa067
40.
PituraJ. (2022). Developing L2 speaking skills in English-medium EFL higher education. Innovation in Language Learning and Teaching, 16(2), 118–143. https://doi.org/10.1080/17501229.2021.1884688
41.
PozaL. E. (2018). The language of ciencia: Translanguaging and learning in a bilingual science classroom. International Journal of Bilingual Education and Bilingualism, 21(1), 1–19. https://doi.org/10.1080/13670050.2015.1125849
42.
ReseA.TränknerP. (2024). Perceived conversational ability of task-based chatbots–Which conversational elements influence the success of text-based dialogues?International Journal of Information Management, 74(1), Article 102699. https://doi.org/10.1016/j.ijinfomgt.2023.102699
43.
Shafiee RadH.AlipourJ.JafarpourA.HashemianM. (2024). Crafting with AI: Personalized pathways to boost critical language awareness and spark creativity in writing through digital adaptive learning. Language Awareness, 1–27. https://doi.org/10.1080/09658416.2024.2431071
44.
ShiJ.SitthiworachartJ.HongJ. C. (2024). Supporting project-based learning for students’ oral English skill and engagement with immersive virtual reality. Education and Information Technologies, 29(11), 14127–14150. https://doi.org/10.1007/s10639-023-12433-w
45.
ShiL. (2025). The integration of advanced AI-enabled emotion detection and adaptive learning systems for improved emotional regulation. Journal of Educational Computing Research, 63(1), 173–201. https://doi.org/10.1177/07356331241296890
TaiK. W. (2024). Classroom interactional competence in an English medium instruction mathematics classroom: A creation of a technology-mediated translanguaging space. Learning and Instruction, 90(1), Article 101849. https://doi.org/10.1016/j.learninstruc.2023.101849
48.
TaiK. W.LiW. (2021a). Constructing playful talk through translanguaging in English medium instruction mathematics classrooms. Applied Linguistics, 42(4), 607–640. https://doi.org/10.1093/applin/amaa043
49.
TaiK. W.WeiL. (2024). The affordances of iPad for constructing a technology-mediated space in Hong Kong English medium instruction secondary classrooms: A translanguaging view. Language Teaching Research, 28(4), 1501–1551. https://doi.org/10.1177/13621688211027851
50.
TaiK. W. H.LiW. (2021b). Co-Learning in Hong Kong English medium instruction mathematics secondary classrooms: A translanguaging perspective. Language and Education, 35(3), 241–267. https://doi.org/10.1080/09500782.2020.1837860
51.
TaiT. Y.ChenH. H. J. (2022). The impact of intelligent personal assistants on adolescent EFL learners’ listening comprehension. Computer Assisted Language Learning, 37(3), 433–460. https://doi.org/10.1080/09588221.2022.2040536
52.
Vega-MendozaM.IvanovaI.McLeanJ. F.PickeringM. J.BraniganH. P. (2024). Lexically-specific syntactic restrictions in second-language speakers. Journal of Memory and Language, 134(1), Article 104470. https://doi.org/10.1016/j.jml.2023.104470
53.
VerhagenT.Van NesJ.FeldbergF.Van DolenW. (2014). Virtual customer service agents: Using social presence and personalization to shape online service encounters. Journal of Computer-Mediated Communication, 19(3), 529–545. https://doi.org/10.1111/jcc4.12066
54.
WangX.PangH.WallaceM. P.WangQ.ChenW. (2024). Learners’ perceived AI presences in AI-supported language learning: A study of AI as a humanized agent from community of inquiry. Computer Assisted Language Learning, 37(4), 814–840. https://doi.org/10.1080/09588221.2022.2056203
55.
WeiL. (2011). Moment analysis and translanguaging space: Discursive construction of identities by multilingual Chinese youth in Britain. Journal of Pragmatics, 43(5), 1222–1235. https://doi.org/10.1016/j.pragma.2010.07.035
56.
WerdiningsihI.MarzukiRusdinD. (2024). Balancing AI and authenticity: EFL students’ experiences with ChatGPT in academic writing. Cogent Arts & Humanities, 11(1), Article 2392388. https://doi.org/10.1080/23311983.2024.2392388
57.
XuW.ZammitK. (2020). Applying thematic analysis to education: A hybrid approach to interpreting data in practitioner research. International Journal of Qualitative Methods, 19(1), Article 1609406920918810. https://doi.org/10.1177/1609406920918810
58.
YangH.KimH.LeeJ. H.ShinD. (2022). Implementation of an AI chatbot as an English conversation partner in EFL speaking classes. ReCALL, 34(3), 327–343. https://doi.org/10.1017/S0958344022000039
59.
YuenC. L.SchloteN. (2024). Learner experiences of mobile apps and artificial intelligence to support additional language learning in education. Journal of Educational Technology Systems, 52(4), 507–525. https://doi.org/10.1177/00472395241238693
60.
ZhangR.ZouD.ChengG. (2023). A review of chatbot-assisted learning: Pedagogical approaches, implementations, factors leading to effectiveness, theories, and future directions. Interactive Learning Environments, 32(8), 4529–4557. https://doi.org/10.1080/10494820.2023.2202704
61.
ZhengY. B.ZhouY. X.ChenX. D.YeX. D. (2025). The influence of large language models as collaborative dialogue partners on EFL English oral proficiency and foreign language anxiety. Computer Assisted Language Learning, 1–27. https://doi.org/10.1080/09588221.2025.2453191
62.
ZhuM.LiuO. L.LeeH. S. (2020). The effect of automated feedback on revision behavior and learning gains in formative assessment of scientific argument writing. Computers & Education, 143(1), Article 103668. https://doi.org/10.1016/j.compedu.2019.103668
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