The Use of AI in Analysing Engineers Written Skills in English

Corpus-Based Approach to Error Analysis in University Students

Authors

DOI:

https://doi.org/10.62701/revedu.v14.5510

Keywords:

AI in Academic Communication, L2 Acquisition at university level, L1 Interference in L2 acquisition, Corpus Linguistics AI enhanced analysis, STEM Education and Communication, AI solutions for Communicative purposes, Engineering and Communication

Abstract

This study leverages artificial intelligence (AI) to enhance second language (L2) acquisition among Spanish-speaking telecommunication engineering students with B2-level English proficiency, focusing on mitigating first language (L1) interference. Through a neurolinguistic and corpus-based analysis of 120 application letters (60 in Spanish, 60 in English), AI tools like AntConc and Grammarly identify structural, pragmatic, and cognitive errors—such as syntactic transfers, overly formal tones, and convoluted sentences—stemming from cognitive overload and underdeveloped L1 competence. Integrating interference theory with neuroconstructivist and social/pragmatic frameworks, the research proposes AI-driven interventions, including real-time feedback and scaffolded learning, to foster linguistic precision and professional communication skills, offering educators innovative strategies for inclusive L2 learning in STEM context

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References

Baddeley, A. (2003). Working memory: Looking back and looking forward. Nature Reviews Neuroscience, 4(10), 829–839. https://doi.org/10.1038/nrn1201 DOI: https://doi.org/10.1038/nrn1201

Black, P., & Wiliam, D. (1998). Assessment and classroom learning. Assessment in Education: Principles, Policy & Practice, 5(1), 7–74. https://doi.org/10.1080/0969595980050102 DOI: https://doi.org/10.1080/0969595980050102

Byram, M. (1997). Teaching and assessing intercultural communicative competence. Multilingual Matters.

CAST. (2018). Universal Design for Learning guidelines version 2.2. http://udlguidelines.cast.org

Cenoz, J. (2001). The effect of linguistic distance on L2 influence in third language acquisition. In J. Cenoz, B. Hufeisen, & U. Jessner (Eds.), Cross-linguistic influence in third language acquisition: Psycholinguistic perspectives (pp. 8–20). Multilingual Matters. https://doi.org/10.21832/9781853595509-003 DOI: https://doi.org/10.2307/jj.29278493.4

de Bot, K., Lowie, W., & Verspoor, M. (2007). A Dynamic Systems Theory approach to second language acquisition. Bilingualism: Language and Cognition, 10(1), 7–21. https://doi.org/10.1017/S1366728906002732 DOI: https://doi.org/10.1017/S1366728906002732

DeepSeek AI. (2025). DeepSeek: Advanced language model for research and analysis. https://www.deepseek.ai

Ellis, N. C. (2006). Selective attention and transfer phenomena in L2 acquisition: Contingency, cue competition, salience, interference, overshadowing, blocking, and perceptual learning. Applied Linguistics, 27(2), 164–194. https://doi.org/10.1093/applin/aml015 DOI: https://doi.org/10.1093/applin/aml015

Ellis, R. (2015). Understanding second language acquisition (2nd ed.). Oxford University Press.

FECYT. (2019). Informe sobre la comunicación científica en el ámbito de la ingeniería. Fundación Española para la Ciencia y la Tecnología.

Hyland, K. (2016). Academic publishing: Issues and challenges in the construction of knowledge. Oxford University Press. DOI: https://doi.org/10.65961/AJELT-2015-1-006

Jarvis, S., & Pavlenko, A. (2008). Crosslinguistic influence in language and cognition. Routledge. https://doi.org/10.4324/9780203936221 DOI: https://doi.org/10.4324/9780203935927

Karpathy, A. (2025). Advances in AI reasoning for quantitative analysis. Journal of Artificial Intelligence Research, 82, 123–145. https://doi.org/10.1613/jair.1.14567

Kilgarriff, A., Rychlý, P., Smrž, P., & Tugwell, D. (2004). The Sketch Engine. In Proceedings of the Eleventh EURALEX International Congress (pp. 105–116). EURALEX.

Kramsch, C. (2002). Language acquisition and language socialization: Ecological perspectives. Continuum.

Miloradovich, A. (2025). DeepSeek: A cost-effective solution for multilingual corpus analysis. Computational Linguistics Journal, 51(3), 89–104. https://doi.org/10.1162/coli_a_00512

Paradis, M. (2004). A neurolinguistic theory of bilingualism. John Benjamins Publishing. https://doi.org/10.1075/sibil.18 DOI: https://doi.org/10.1075/sibil.18

Paradis, M. (2009). Declarative and procedural determinants of second languages. John Benjamins Publishing. https://doi.org/10.1075/sibil.40 DOI: https://doi.org/10.1075/sibil.40

Swales, J. M. (1990). Genre analysis: English in academic and research settings. Cambridge University Press.

United Nations. (2015). Transforming our world: The 2030 Agenda for Sustainable Development. https://sdgs.un.org/2030agenda

Ure, J. (1971). Lexical density and register differentiation. In G. E. Perren & J. L. M. Trim (Eds.), Applications of linguistics (pp. 443–452). Cambridge University Press.

van Lier, L. (2004). The ecology and semiotics of language learning: A sociocultural perspective. Kluwer Academic Publishers. https://doi.org/10.1007/1-4020-7912-5 DOI: https://doi.org/10.1007/1-4020-7912-5

Weinreich, U. (1953). Languages in contact: Findings and problems. Linguistic Circle of New York.

Wing, J. M. (2006). Computational thinking. Communications of the ACM, 49(3), 33–35. https://doi.org/10.1145/1118178.1118215 DOI: https://doi.org/10.1145/1118178.1118215

xAI. (2025). Grok 3: Advancing reasoning for academic applications. https://x.ai/grok

Yule, G. (1996). Pragmatics. Oxford University Press.

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Published

30-06-2026

How to Cite

Avilés Mariño, E., & Zimbroiano, C. (2026). The Use of AI in Analysing Engineers Written Skills in English: Corpus-Based Approach to Error Analysis in University Students. EDU REVIEW. International Education and Learning Review Revista Internacional De Educación Y Aprendizaje, 14(1), 1–9. https://doi.org/10.62701/revedu.v14.5510

Issue

Vol. 14 No. 1 (2026)

Section

Research articles

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