Call for Papers: Special Issue on AI in Vocabulary Learning and Teaching

2026-09-08

Special Issue of Technology in Language Teaching & Learning

Beyond Effectiveness: Artificial Intelligence in Vocabulary Learning and Teaching Across Languages and Contexts

Guest Editors: Yijen Wang (Waseda University), Yuzhe Li (University of Macau), & Mark Feng Teng (Macao Polytechnic University)

Vocabulary is fundamental to language learning and use, yet developing rich and usable lexical knowledge is a slow and demanding process. Knowing a word involves far more than recognising its form or recalling a translation. Lexical knowledge encompasses form, meaning, and use, from collocation and phraseology to grammatical behaviour and register, in both receptive and productive dimensions (Nation, 2022; Schmitt, 2019). Artificial intelligence (AI) reaches into every part of vocabulary learning process. Contemporary AI systems can explain and exemplify words in context, translate and define them, turn them into personalised practice, and adapt to individual learners. In doing so, it stands to alter how learners encounter words, how teachers teach them, and how learning is assessed. However, the evidence has not kept pace with the enthusiasm. Vocabulary has long been a testing ground for learning technologies, from electronic dictionaries and corpora to mobile applications, digital games, and machine translation, and this work remains productive in its own right (Elgort, 2018; Lee & Shin, 2025; Naufal et al., 2026). Technology-assisted vocabulary learning generally produces positive effects, but the size of those effects varies substantially with the technology, pedagogical design, and learners and contexts involved (Simonnet & Loiseau, 2025; Yu & Trainin, 2022; Zhou & Zhou, 2026). Research on AI is now repeating the same pattern, as a rapidly growing literature (see Behforouz et al., 2026) reports large overall effects alongside variation that the field cannot yet explain. The evidence base is also narrow, concentrated on university students and formal classrooms, dominated by English, and all but devoid of negative findings (Wang et al., 2026).

The question worth asking is therefore no longer whether AI can improve vocabulary learning, but how, when, for whom, and under what conditions particular AI affordances support particular dimensions of lexical development. Aggregate effects cannot answer this, and neither can studies that reduce vocabulary to form–meaning recall on an immediate post-test while ignoring the multidimensional nature of lexical knowledge. AI can supply definitions and translations, examples and collocations, pronunciation models and personalised exercises, but whether any of this becomes learning depends on what learners do with it. Research on machine translation showed that such resources help primarily when learners are trained to use them strategically (Wang & Stockwell, 2024), a lesson that applies with equal force to generative AI, which also shifts the balance between intentional and incidental vocabulary learning. AI is also redrawing the roles of learners and teachers. As it takes over more of the work of vocabulary teaching, from selecting and explaining items to generating practice, it may support autonomy and personalised learning, or it may encourage dependence and quietly shift important learning decisions away from learners and teachers. Research is needed not only on the effectiveness of AI tools but on how learners and teachers adopt them, adapt them, and sometimes resist them, and on what growing reliance on automated assistance means for cognitive engagement, learner agency, and self-regulation. As AI affordances interact with the linguistic, orthographic, sociocultural, and pedagogical characteristics of particular languages and learning contexts, these questions cannot be settled by English-only, university-only research.

This special issue therefore focuses on artificial intelligence in vocabulary learning and teaching and is deliberately open to all languages and contexts. Research on English remains welcome, and we especially welcome studies of languages other than English, including heritage, less commonly taught, and multilingual and plurilingual contexts, where the evidence base is thinnest. We invite theoretically informed and methodologically rigorous research that advances understanding of AI-mediated vocabulary learning and teaching, whatever the language, learner population, or setting. We particularly welcome work that moves beyond simple comparisons of AI-supported and conventional instruction to investigate the mechanisms and conditions through which AI shapes vocabulary development, and we welcome critical and exploratory work, longitudinal and mixed-methods designs, and replications, including studies reporting null, mixed, or negative findings.

Topics include, but are not limited to:

  • AI-mediated vocabulary development includes research on vocabulary breadth and depth, receptive and productive knowledge, collocation and phraseology, lexical fluency, retention, and transfer.
  • AI affordances for vocabulary learning and teaching include research on generative AI, large language models, conversational AI, AI-generated explanations and examples, personalised learning, automated feedback, retrieval practice, assessment, and multimodal AI.
  • Learner engagement and agency in AI-mediated vocabulary learning includes research on learner autonomy, self-regulation, strategy use, AI literacy, decision-making, appropriation, dependence, and overreliance.
  • Teacher roles and pedagogical mediation include research on teacher decision-making, teacher AI literacy, instructional design, human–AI collaboration, and the integration of AI into vocabulary teaching.
  • Intentional, incidental, and informal vocabulary learning with AI includes research on AI-supported learning inside and outside the classroom, self-directed learning, social media, digital games, online communities, and other informal learning environments.
  • AI-mediated vocabulary learning across languages and contexts includes research involving languages other than English, multilingual and plurilingual learners, heritage and additional languages, less commonly taught languages, and diverse educational and sociocultural contexts.
  • Theoretical and methodological perspectives on AI-mediated vocabulary learning include research drawing on theories of lexical development and learning, learner–AI interaction, learning analytics, mixed-methods and longitudinal approaches, and other methodological innovations.
  • Critical perspectives on AI and vocabulary learning include research examining the accuracy and reliability of AI-generated lexical information, linguistic bias and representation, ethics, equity, accessibility, privacy, and unintended consequences.

Submission information

Author guidelines: Technology in Language Teaching & Learning author guidelines

Publication model: Technology in Language Teaching & Learning is fully open access. There is no submission fee; an article processing charge of AUD 990 applies after acceptance. We particularly encourage submissions from researchers working in underrepresented linguistic, geographical, and educational contexts. Authors may find the journal’s current APC discount policies here: https://www.castledown.com/journals/index/article-processing-fees

Timeline
Abstract submission: 30 Nov, 2026
Notification of invited full submissions: 31 Dec, 2026
Full manuscript deadline: 31 July, 2027
Reviews returned to authors: 30 Sep, 2027
Revised manuscripts due: 30 Nov, 2027
Expected publication: Dec, 2027


How to submit

Abstracts of no more than 500 words should state the research questions, context and participants, language(s) and AI technology under study, methodological approach, and, where available, key findings and anticipated contribution. Full manuscripts will be expected to specify the AI system(s) investigated, including the model or tool, version, and period of data collection, so that findings remain interpretable and replicable.

Abstracts should be sent to this form: [Abstract Submission] TLTL SI: AI in Vocabulary Learning and Teaching.

Invitation to submit a full manuscript does not guarantee acceptance. All full submissions will undergo the journal’s standard double-blind peer-review process.

References

Behforouz, B., Al Ghaithi, A., & Alsaadi, A. (2026). The effect of AI-based instruction on vocabulary retention and learner motivation in higher education. Technology in Language Teaching & Learning, 8, 103599. https://doi.org/10.29140/tltl.2026.103599

Elgort, I. (2018). Technology-mediated second language vocabulary development: A review of trends in research methodology. CALICO Journal, 35(1), 1–29. https://doi.org/10.1558/cj.34554

Lee, J. H., & Shin, D. (2025). Vocabulary. In G. Stockwell & Y. Wang (Eds.), The Cambridge Handbook of Technology in Language Teaching and Learning (pp. 479–493). Cambridge University Press. https://doi.org/10.1017/9781009294850.035

Nation, I. S. P. (2022). Learning vocabulary in another language (3rd ed.). Cambridge University Press.

Naufal, A., Hardini, T. I., & Kurniawan, E. (2026). A corpus-based approach to teaching polysemous verbs in French institutional discourse. Technology in Language Teaching & Learning, 8, 103350. https://doi.org/10.29140/tltl.2026.103350

Schmitt, N. (2019). Understanding vocabulary acquisition, instruction, and assessment: A research agenda. Language Teaching, 52(2), 261–274. https://doi.org/10.1017/S0261444819000053

Simonnet, E., & Loiseau, M. (2025). A systematic literature review of technology-assisted vocabulary learning. Journal of Computer Assisted Learning. https://doi.org/10.1111/jcal.13096

Wang, Y., & Stockwell, G. (2024). Training to use machine translation for vocabulary learning. In M. F. Teng, A. Kukulska-Hulme, & J. G. Wu (Eds.), Theory and practice in vocabulary research in digital environments. Routledge. https://doi.org/10.4324/9781003367543-9

Wang, Y., Zhang, Z. J., & Zhou, H. (2026). Artificial intelligence in language learning: A twenty-year scoping review of applications, research methods, and outcomes. Research Synthesis in Applied Linguistics. https://doi.org/10.1080/29984475.2026.2647961

Yu, A., & Trainin, G. (2022). A meta-analysis examining technology-assisted L2 vocabulary learning. ReCALL, 34(2), 235–252. https://doi.org/10.1017/S0958344021000239

Zhou, Y., & Zhou, M. (2026). A meta-analysis on mobile-assisted vocabulary learning: Do mobile applications help? ReCALL, 38(1), 75–93. https://doi.org/10.1017/S0958344025100335