LSTM-driven CLIL: Cybersecurity vocabulary learning with AI

Research Article

• Antonio Nazzaro REPRISE - Register of Expert Peer Reviewers for Italian Scientific Evaluation, Ministry of University and Research (MUR), Italy iD 0009-0001-1463-8940
• Catia Santini Ministero dell' Istruzione e Merito (MIM) iD 0009-0006-9257-1346
• Lidia Nazzaro Università di Fisciano (SA) iD 0000-0002-1858-859X

Published in

Volume 8, Issue 3 (2025)

Pages

313–329

Publication Date

September 30, 2025

Submission Date

April 28, 2025

Acceptance Date

June 18, 2025

Subjects

Information Systems (Other), Instructional Design, Instructional Technologies, Lifelong learning

Abstract

This study presents the development of a custom dataset of L2 gap-fill exercises designed to enhance Long Short-Term Memory (LSTM) neural networks in CLIL (Content and Language Integrated Learning) settings for subject-specific courses. Targeting English for Special Purposes (ESP) vocabulary in cybersecurity, privacy, and data protection, the model addresses the dual challenge of domain-specific context mastery and language practice through structured neural network training. The custom dataset of gap-fill exercises for this LSTM model enables simultaneous prediction of missing words and semantic classification, offering learners contextualized language training that is a core requirement of CLIL methodology. Experimental results validate the model’s efficacy, demonstrating its potential as an adaptive support tool for CLIL-based education. This framework establishes a novel synergy between AI-enhanced language learning and subject-specific instruction, providing a scalable template for integrating neural networks into CLIL pedagogy.

Keywords

• CLIL
• Cibersecurity
• Vocabulary Learning
• AI in Education

References (27)

1. Andujar, A., & Spratt, M. (2023). Using AI to support CLIL teacher language. Journal of Research in Applied Linguistics, 14(2), 7–19. https://doi.org/10.22055/RALS.2023.45267.3177
2. Bengio, Y., Simard, P., & Frasconi, P. (1994). Learning long-term dependencies with gradient descent is difficult. IEEE Transactions on Neural Networks, 5(2), 157–166.
3. Council of the European Union. (2021). Consolidated GDPR text. https://eur-lex.europa.eu
4. Coyle, D., Hood, P., & Marsh, D. (2010). CLIL: Content and language integrated learning. Cambridge University Press.
5. Dalton-Puffer, C. (2007). Discourse in content and language integrated learning (CLIL) classrooms. In D. Marsh & C. J. Ramos (Eds.), CLIL in practice (pp. 153–172). John Benjamins.
6. Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2019). BERT: Pre-training of deep bidirectional transformers for language understanding. Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), 4171–4186.
7. Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep learning. MIT Press.
8. Graves, A., Mohamed, A.-R., & Hinton, G. (2013). Speech recognition with deep recurrent neural networks. Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 6645–6649. https://doi.org/10.1109/ICASSP.2013.6638947
9. Hochreiter, S., & Schmidhuber, J. (1997). Long short-term memory. Neural Computation, 9(8), 1735–1780. https://doi.org/10.1162/neco.1997.9.8.1735
10. Jurafsky, D., & Martin, J. H. (2020). Speech and language processing (3rd ed.). Pearson.
11. Kahraman, A., & Korucu, A. T. (2022). Artificial intelligence applications in education: A content analysis of publications in the Web of Science database. Journal of Educational Technology and Online Learning, 5(2), 312–328. https://doi.org/10.31681/jetol.1100607
12. Karpathy, A. (2015, May 21). The unreasonable effectiveness of recurrent neural networks. Andrej Karpathy blog. http://karpathy.github.io/2015/05/21/rnn-effectiveness/
13. Mageira, K., Pittou, D., Papasalouros, A., Kotis, K., Zangogianni, P., & Daradoumis, A. (2022). Educational AI chatbots for content and language integrated learning. Applied Sciences, 12(7), 3239. https://doi.org/10.3390/app12073239
14. Marsh, D. (2002). CLIL/EMILE: The European dimension: Actions, trends and foresight potential. European Commission.
15. Mehisto, P., & Ting, T. (2017). CLIL essentials for secondary school teachers. Cambridge University Press.
16. Mikolov, T., Chen, K., Corrado, G., & Dean, J. (2013). Efficient estimation of word representations in vector space. arXiv. https://arxiv.org/abs/1301.3781
17. Mikolov, T., Sutskever, I., Chen, K., Corrado, G. S., & Dean, J. (2013). Distributed representations of words and phrases and their compositionality. Advances in Neural Information Processing Systems, 26, 3111–3119.
18. Nazzaro, A., Nazzaro, L., & Santini, C. (2025). LSTM-driven CLIL: Cybersecurity vocabulary learning with AI. Preprints. https://doi.org/10.20944/preprints202504.2124.v2
19. Prechelt, L. (1998). Early stopping—but when? In G. B. Orr & K.-R. Müller (Eds.), Neural networks: Tricks of the trade (pp. 55–69). Springer.
20. Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., & Salakhutdinov, R. (2014). Dropout: A simple way to prevent neural networks from overfitting. Journal of Machine Learning Research, 15, 1929–1958.
21. Swain, M. (2006). Languaging, agency, and collaboration in advanced second language proficiency. In H. Byrnes (Ed.), Advanced language learning: The contribution of Halliday and Vygotsky (pp. 95–108). Continuum.
22. Tonbuloğlu, B. (2023). An evaluation of the use of artificial intelligence applications in online education. Journal of Educational Technology & Online Learning, 6(4), 866–884. https://doi.org/10.31681/jetol.1335906
23. Van Lier, L. (2004). The ecology and semiotics of language learning. Springer.
24. Vygotsky, L. S. (1978a). Mind in society: The development of higher psychological processes (M. Cole, V. John-Steiner, S. Scribner, & E. Souberman, Eds.). Harvard University Press.
25. Vygotsky, L. S. (1978b). Mind in society: The development of higher psychological processes (M. Cole, V. John-Steiner, S. Scribner, & E. Souberman, Eds.). Harvard University Press. (Original works published 1930–1934)
26. Vygotsky, L. S. (1987). Thinking and speech. In R. W. Rieber & A. S. Carton (Eds.), The collected works of L. S. Vygotsky: Vol. 1. Problems of general psychology (pp. 39–285). Plenum Press.
27. Wadden, P., Andujar, A., Mehisto, P., & Ting, T. (2024). Creating and crafting CLIL content learning materials.JALT 2023 Post-Conference Publication, 1–15.

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