With a pretest/posttest/delayed posttest design, this quasi-experimental study compares the effectiveness of corpus use versus generative AI (GenAI) use in helping undergraduate students learn multiword constructions (MWCs) in written academic English and examines the moderating effects of learners’ learning styles. Six intact writing classes with a total of 210 students were assigned to one of three instructional groups with each group including two intact classes: Experimental Group 1 (using data-driven learning [DDL] only), Experimental Group 2 (employing GenAI-assisted learning combined with corpus analysis of teacher-generated concordance lines), and Control Group. MWC learning was measured by a MWC production test and a writing test that assessed both the overall writing quality and the frequency and number of types of MWCs used. Statistical analyses revealed that while both experimental groups made significantly more improvements in their MWC use than the control group across all four learning measures in both posttests, Experimental Group 2 (GenAI combined with corpus analysis) significantly outperformed Experimental Group 1 (DDL only) on nearly all measures in both posttests. Survey results also show that while these two instructional methods were well-received by most participants, the combined instruction better accommodated diverse learning styles than the DDL-only instruction did. Together, these findings indicate that combining DDL and GenAI can significantly enhance the effectiveness of MWC instruction in academic writing.
ÄdelA.ErmanB. (2012). Recurrent word combinations in academic writing by native and nonnative speakers of English: A lexical bundles approach. English for Specific Purposes, 31(2), 81–92.
2.
AlHassanL.WoodD. (2015). The effectiveness of focused instruction of formulaic sequences in augmenting L2 learners’ academic writing skills: A quantitative research study. Journal of English for Academic Purposes, 20, 31–48.
AndersA. (2023, October19–21). Generative AI: Experiments in writing and learning [plenary speech]. Technology for Second Language Learning Conference, Ames, IA.
Bazán-PerkinsB.Santibañez-SalgadoJ.A. (2025). Relationship between the learning gains and learning style preferences among students from the school of medicine and health sciences. BMC Medical Education, 25, 71. https://doi.org/10.1186/s12909-024-06554-0
7.
BiberD.ConradS. (1999). Lexical bundles in conversation and academic prose. In HasselgardH.OskserjellS. (Eds.), Out of corpora: Studies in honour of Stig Johansson (pp. 181–190). Rodopi.
8.
BiberD.ConradS.CortesV. (2004). If you look at. . .: Lexical bundles in university teaching and textbooks. Applied Linguistics, 25(3), 371–405.
9.
BiberD.JohanssonS.LeechG.ConradS.FineganE. (1999). Longman grammar of spoken and written English. Longman.
10.
BirhanA.T. (2021). Effects of teaching lexical bundles on EFL students’ abstract genre academic writing skills improvement: Corpus-based research design. International Journal of Language Education, 5(1), 585–597.
11.
BoultonA.VyatkinaN. (2021). Thirty years of data-driven learning: Taking stock and charting new directions over time. Language Learning & Technology, 25(3), 66–89.
CarifioJ.PerlaR. (2007). Ten common misunderstandings, misconceptions, persistent myths and urban legends about Likert scales and Likert response formats and their antidotes. Journal of Social Sciences, 3(3), 106–116.
14.
CarterR.McCarthyM. (2006). This that and the other: Multi-word clusters in spoken English as visible patterns of interaction. Journal of Linguistics, 42(2), 287–311.
15.
ChenB.BaoL.ZhangR.ZhangJ.LiuF.WangS.LiM. (2024). A multi-strategy computer-assisted EFL writing learning system with deep learning incorporated and its effects on learning: A writing feedback perspective. Journal of Educational Computing Research, 61(8), 60–102.
16.
ChenY. (2019). Proficient learners’ use of lexical bundles in academic writing: Implications for L2 acquisition and instruction. Journal of Second Language Writing, 45, 1–13.
17.
CheungL.CrosthwaiteP. (2025). CorpusChat: integrating corpus linguistics and generative AI for academic writing development. Computer Assisted Language Learning. Advance online publication. https://doi.org/10.1080/09588221.2025.2506480
18.
ChunD.M.SmithB.KernR. (2016). Technology in language use, language teaching, and language learning. Modern Language Journal, 100(s1), 64–80.
19.
CohenJ. (1988). Statistical power analysis for the behavioral sciences (2nd ed.). Lawrence Erlbaum.
20.
CrosthwaiteP.BaisaV. (2023). Generative AI and the end of corpus-assisted data-driven learning? Not so fast!Applied Corpus Linguistics, 3(3), 100066.
DörnyeiZ. (2005). The psychology of the language learner: Individual differences in second language acquisition. Lawrence Erlbaum.
24.
EllisN.C.Simpson-VlachR. (2009). Formulaic language in native speakers: Triangulating psycholinguistics, corpus linguistics, and education. Corpus Linguistics and Linguistic Theory, 5(1), 61–78.
25.
El-SabaghH.A. (2021). Adaptive e-learning environment based on learning styles and its impact on development students’ engagement. International Journal of Educational Technology in Higher Education, 18(1), 1–15.
26.
EngelbartD. (2023). Augmenting human intellect: A conceptual framework. In ArayaD.MarberP. (Eds.), Augmented education in the global age: Artificial intelligence and the future of learning and work (pp. 13–29). Routledge. (Originally published 1962)
27.
ErmanB.WarrenB. (2000). The idiom principle and the open choice principle. Text & Talk, 20(1), 29–62.
28.
FathiJ.RahimiM.DerakhshanA. (2024). Improving EFL learners’ speaking skills and willingness to communicate via artificial intelligence-mediated interactions. System, 121, 103254.
29.
FlowerdewJ. (2025). Data-driven learning: FromCollins Cobuild Dictionary to ChatGPT. Language Teaching, 58(3), 359–376.
30.
FryerL.K.AinleyM.ThompsonA.GibsonA.SherlockZ. (2017). Stimulating and sustaining interest in a language course: An experimental comparison of chatbot and human task partners. Computers in Human Behavior, 75, 461–468.
31.
GeorgeD.MalleryM. (2010). SPSS for Windows step by step: A simple guide and reference (10th ed.). Pearson.
32.
GoldbergA.E. (1995). Constructions: A construction grammar approach to argument structure. University of Chicago Press.
33.
GoodarziR.GholamiJ.AbdollahpourZ. (2024). Lexical bundles in the discussion sections of medical sciences articles: Frequencies, syntactic structures, and discourse functions. Language Teaching Research Quarterly, 40, 1–28.
34.
HuangW.HewK.F.FryerL.K. (2021). Chatbots for language learning—are they really useful? A systematic review of chatbot-supported language learning. Journal of Computer Assisted Learning, 38(1), 237–257.
35.
HuangZ. (2014). The effect of paper-based DDL on the acquisition of lexico-grammatical patterns in L2 writing. ReCALL, 26, 163–183.
36.
HylandK. (2008). As can be seen: Lexical bundles and disciplinary variation. English for Specific Purposes, 27, 4–21.
37.
JalaliZ.S.MoiniM.R. (2014). Structure of lexical bundles in introduction section of medical research articles. Procedia - Social and Behavioral Sciences, 98, 719–726.
38.
JonesA.BrownM. (2021). Enhancing language learning through adaptive technologies. Journal of Educational Technology & Society, 24(2), 123–135.
39.
JonesM.HaywoodS. (2004). Facilitating the acquisition of formulaic sequences: An exploratory study in an EAP context. In SchmittN. (Ed.), Formulaic sequences: Acquisition, processing, and use (pp. 269–300). John Benjamins.
40.
KazemiaM.KatiraeibS.Eslami RasekhA. (2014). The impact of teaching lexical bundles on improving Iranian EFL students’ writing skill. Procedia - Social and Behavioral Sciences, 98, 864–869.
41.
KondosS. (2020). The impact of teaching formulaic sequences on improving the writing skills of freshman students at an American university in the Middle East. Journal of Higher Education Theory and Practice, 20(11), 184–197.
42.
KuhailF.Al-KhalidiM.Al-KhalidiM.Al-KhalidiM. (2023). Leveraging generative AI for language learning: A systematic review. Journal of Educational Technology and Society, 26(2), 123–145.
43.
LangackerR.W. (1987). Foundations of cognitive grammar. Stanford University Press.
44.
LawL. (2024). Application of generative artificial intelligence(GenAI) in language teaching and learning: A scoping literature review. Computers and Education Open, 6, 100174.
45.
LeeJ.-Y.HwangY. (2022). A meta-analysis of the effects of using AI chatbot in Korean EFL education. Studies in English Language & Literature, 48(1), 213–243.
46.
LiJ.SchmittN. (2009). The acquisition of lexical phrases in academic writing: A longitudinal case study. Journal of Second Language Writing, 18(2), 85–102.
47.
LiuD. (2012). The most frequently-used multi-word constructions in academic written English: A multi-corpus study. English for Specific Purposes, 31(1), 25–35.
48.
LiuD.JiangP. (2009). Using a corpus-based lexicogrammatical approach to grammar instruction in EFL and ESL contexts. The Modern Language Journal, 93, 61–78.
49.
LiuK.AfzaalM. (2020). Lexical bundles: A corpus-driven investigation of academic writing teaching to ESL undergraduates. International Journal on Emerging Technologies, 11(5), 476–482.
50.
LiuM. (2025). Learning style preferences, strategy use and Chinese undergraduate EFL students’ English achievements. Theory and Practice of Second Language Acquisition, 11(1), 1–23.
51.
MiltonJ.AlexiouT. (2009). Vocabulary size and the Common European Framework of Reference for languages. In RichardsB.DallerH.M.MalvernD.D.MearaP.MiltonJ.Treffers-DallerJ. (Eds.), Vocabulary studies in first and second language acquisition (pp. 194–211). Palgrave Macmillan.
52.
MulalicA.ShahP.M.AhmadF. (2009). Learning-style preference of ESL students. AJTLHE: ASEAN Journal of Teaching and Learning in Higher Education, 1(2), 9–17.
53.
PashlerH.McDanielM.RohrerD.BjorkR. (2008). Learning styles: Concepts and evidence. Psychological Science in the Public Interest, 9(3), 105–119.
54.
PlonskyL.OswaldF.L. (2014). How big is “big”? Interpreting effect sizes in L2 research. Language Learning, 64(4), 878–912.
55.
QiuX. (2024). Exploring the effect of corpus-based writing instruction on learner-corpus interaction in L2 revision: A study of Chinese EFL disciplinary writers. TESOL Quarterly, 58(3), 1108–1137.
56.
ReidM.J. (1987). The learning style preferences of ESL students. TESOL Quarterly. 21(1), 87–111.
57.
Salam El-DakhsD.PrueT.T.IjazA. (2017). The effect of the explicit instruction of formulaic sequences in pre-writing vocabulary activities on foreign language writing. International Journal of Applied Linguistics & English Literature, 6(4), 21–31.
58.
Simpson-VlachR.EllisN.C. (2010). An academic formulas list: New methods in phraseology research. Applied Linguistics, 31(4), 487–512.
59.
SunP.P.TengL.S. (2017). Profiling perceptual learning styles of Chinese as a second language learners in university Settings. Journal of Psycholinguistic Research, 46(6), 1529–1548.
60.
UçarS.ZarfsazE. (2023). A corpus-based teaching of lexical bundles to enhance writing skills of prospective Turkish EFL teachers. International Journal of Learning and Teaching, 15(1), 42–54.
61.
WangF.CheungA.C.K.NeitzelA.J.ChaiC.S. (2024). Does chatting with chatbots improve language learning performance? A meta-analysis of chatbot-assisted language learning. Review of Educational Research, 95(4), 623–660.
62.
WingateU.TribbleC. (2012). The Best of both worlds? Towards an English for academic purposes/academic literacies writing pedagogy. Studies in Higher Education, 37(4), 481–495.
63.
YilmazN.Koban KocD. (2020). Developing pragmatic comprehension and production: Corpus-based teaching of formulaic sequences in an EFL setting. Journal of Language and Linguistic Studies, 16(1), 474–488.
64.
YottaE.G. (2023). Accommodating students’ learning styles differences in English language classroom. Heliyon, 9(6), e17497.
65.
Zawacki-RichterO.MarínV.I.BondM.GouverneurF. (2019). Systematic review: Systematic review of research on artificial intelligence applications in higher education – where are the educators?International Journal of Educational Technology in Higher Education, 16, 39. https://doi.org/10.1186/s41239-019-0171-0
66.
ZhuM.WangC. (2025). A systematic review of research on AI in language education: Current status and future implications. Language Learning & Technology, 29(1), 1–29.
Supplementary Material
Please find the following supplemental material available below.
For Open Access articles published under a Creative Commons License, all supplemental material carries the same license as the article it is associated with.
For non-Open Access articles published, all supplemental material carries a non-exclusive license, and permission requests for re-use of supplemental material or any part of supplemental material shall be sent directly to the copyright owner as specified in the copyright notice associated with the article.
