Decoding Dispositions: Attitudes of Indian Engineering Students Towards AI in the ESL Classroom and its Correlation with Academic Performance

Authors

  • Inzamul Sarkar VIT-AP School of Social Sciences and Humanities, VIT-AP University, Amaravati
  • Anand Pal Singh GLA University, Mathura
  • Zuhair Ahmad Maulana Azad National Urdu University, Hyderabad
  • Nasrin Banu Khan University of Gour Banga, Malda

DOI:

https://doi.org/10.16920/jeet/2026/v39is2/26050

Keywords:

Academic performance; AI-integrated English language learning; Engineering education; ESL classroom; Pedagogical integration; Student attitudes.

Abstract

This paper investigates the complex relationship between engineering students’ attitudes toward AI-integrated English language learning and their academic outcomes. As AI tools increasingly reshape educational paradigms, understanding student dispositions is crucial for effective pedagogical integration. Conducted in the Indian higher education context, the research employs a sequential mixed-methods design, beginning with a quantitative survey of 250 engineering students, followed by semi-structured interviews with 25 students and 15 English language educators. Quantitative findings reveal a significant paradox. While students report high perceived utility of AI and strongly value human instruction, a moderate negative correlation (r=−0.42, p<.001) exists between an attitude of over-reliance and academic performance. Furthermore, regression analysis shows that students’ confidence in detecting AI errors is more strongly predicted by their technical discipline than their demonstrated language proficiency, indicating a critical overconfidence dilemma. The qualitative data substantiates these findings, with educators expressing alarm over skill atrophy and the uncritical acceptance of AI-generated “hallucinations”, a tendency students confirmed. The study argues that for engineering students, this overconfidence, born from their technical identity, creates a significant blind spot, leading them to outsource foundational critical thinking and writing skills. While AI offers powerful tools, its unmanaged integration risks promoting intellectual passivity and undermining long-term communicative competence. The study concludes by recommending the integration of AI within structured pedagogical frameworks that balance technological support with active, teacher-led development of critical evaluation skills. For engineering educators, the findings highlight the need to embed structured AI literacy, critical evaluation of machine-generated text, and teacher-guided verification practices into ESL coursework to prevent skill atrophy and promote long-term communicative competence.

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Published

2026-02-17

How to Cite

Sarkar, I., Singh, A. P., Ahmad, Z., & Khan, N. B. (2026). Decoding Dispositions: Attitudes of Indian Engineering Students Towards AI in the ESL Classroom and its Correlation with Academic Performance. Journal of Engineering Education Transformations, 39(Special Issue 2), 416–423. https://doi.org/10.16920/jeet/2026/v39is2/26050

Issue

Volume 39, Special Issue 2, January 2026

Section

Articles
Crossref
Scopus
Google Scholar
Europe PMC

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