Digital Technologies and Virtual Environments in Language Learning
DOI:
https://doi.org/10.69760/gsrh.0260304028Keywords:
language learning, digital technologies, automatic speech recognition, conversational agents, machine translation, virtual environmentsAbstract
Digital technologies can expand opportunities for language practice, feedback, and access to learning materials, but their educational value depends on how they are integrated into instruction. This conceptual article draws on a selective narrative review to examine automatic speech recognition, conversational agents, machine translation, and virtual environments in language learning. It distinguishes the functions of these technologies from the learning outcomes that teachers must evaluate. The discussion identifies opportunities for pronunciation practice, sustained interaction, reflective revision, and contextualized communication, alongside problems involving unreliable feedback, cognitive demands, unequal access, and personal data. Alpha School is considered briefly as an institutional illustration of adaptive learning and human guidance, rather than as evidence of foreign-language learning effectiveness. Four proposed classroom applications show how teachers can connect digital activity to observable language performance. The article argues that responsible integration requires clear learning objectives, appropriate scaffolding, human review of feedback, and opportunities to demonstrate learning beyond the original tool. Its contribution is a practical framework for evaluating instructional use; the proposed applications require subsequent classroom investigation.
References
Alpha School. (n.d.). Overview. Retrieved October 29, 2026, from https://alpha.school/overview/
Bibauw, S., Van den Noortgate, W., François, T., & Desmet, P. (2022). Dialogue systems for language learning: A meta-analysis. Language Learning & Technology, 26(1), 1–24. https://doi.org/10.64152/10125/73488
Chen, J., Dai, J., Zhu, K., & Xu, L. (2022). Effects of extended reality on language learning: A meta-analysis. Frontiers in Psychology, 13, Article 1016519. https://doi.org/10.3389/fpsyg.2022.1016519
Global Education Monitoring Report Team. (2023). Global education monitoring report 2023: Technology in education: A tool on whose terms? UNESCO. https://doi.org/10.54676/UZQV8501
Hua, C., & Wang, J. (2023). Virtual reality-assisted language learning: A follow-up review (2018–2022). Frontiers in Psychology, 14, Article 1153642. https://doi.org/10.3389/fpsyg.2023.1153642
Huang, W., Hew, K. F., & Fryer, L. K. (2022). 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. https://doi.org/10.1111/jcal.12610
Inceoglu, S., Chen, W. H., & Lim, H. (2024). Monitoring student behavior in autonomous automatic speech recognition-based pronunciation practice. System, 124, Article 103387. https://doi.org/10.1016/j.system.2024.103387
Jolley, J. R., & Maimone, L. (2022). Thirty years of machine translation in language teaching and learning: A review of the literature. L2 Journal, 14(1), 26–44. https://doi.org/10.5070/L214151760
Jurafsky, D., & Martin, J. H. (2026). Speech and language processing: An introduction to natural language processing, computational linguistics, and speech recognition with language models (3rd ed. draft, August 19). https://web.stanford.edu/~jurafsky/slp3/
Kohnke, L., Moorhouse, B. L., & Zou, D. (2023). ChatGPT for language teaching and learning. RELC Journal, 54(2), 537–550. https://doi.org/10.1177/00336882231162868
Kuhail, M. A., Alturki, N., Alramlawi, S., & Alhejori, K. (2023). Interacting with educational chatbots: A systematic review. Education and Information Technologies, 28, 973–1018. https://doi.org/10.1007/s10639-022-11177-3
Lai, W. Y. W., & Lee, J. S. (2024). A systematic review of conversational AI tools in ELT: Publication trends, tools, research methods, learning outcomes, and antecedents. Computers and Education: Artificial Intelligence, 7, Article 100291. https://doi.org/10.1016/j.caeai.2024.100291
Makransky, G., & Petersen, G. B. (2021). The Cognitive Affective Model of Immersive Learning (CAMIL): A theoretical research-based model of learning in immersive virtual reality. Educational Psychology Review, 33, 937–958. https://doi.org/10.1007/s10648-020-09586-2
Miao, F., & Holmes, W. (2023). Guidance for generative AI in education and research. UNESCO. https://doi.org/10.54675/EWZM9535
Nickolai, D., Schaefer, E., & Figueroa, P. (2024). Aggregating the evidence of automatic speech recognition research claims in CALL. System, 121, Article 103250. https://doi.org/10.1016/j.system.2024.103250
Downloads
Published
Issue
Section
License

This work is licensed under a Creative Commons Attribution 4.0 International License.
This journal publishes all articles under the Creative Commons Attribution 4.0 International License (CC BY 4.0). Authors retain copyright of their work. Anyone may freely share, copy, distribute, adapt, and build upon the published material for any purpose, including commercial use, provided that appropriate credit is given to the original author(s) and source, a link to the license is provided, and any changes made are indicated. License URL: https://creativecommons.org/licenses/by/4.0/