Redefining Classroom Dynamics: AI Tools and the Future of English Language Pedagogy
Downloads
Published
DOI:
https://doi.org/10.58414/SCIENTIFICTEMPER.2025.16.11.13Keywords:
Artificial Intelligence, English Language Teaching, Language Acquisition, Pedagogical Innovation, Mixed-Methods Research, Digital Innovations, ChatGPT, Teachers' Perceptions, Technology in EducationDimensions Badge
Issue
Section
License
Copyright (c) 2025 The Scientific Temper

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
This study looks at the transformative impact of artificial intelligence (AI) on English language training, specifically how AI-powered tools and approaches improve both educational outcomes and teaching strategies. The major goal is to assess the impact of AI on language acquisition and develop personalised teaching strategies for instructors and learners. A mixed-methods strategy was used to achieve this goal. During the quantitative phase, students from various colleges were given questionnaires with Likert scales to judge the effectiveness of AI technology in instructional procedures. Additionally, semi-structured interviews were conducted with educators who have actively used AI tools in their practices, allowing for the identification of both advantages and emerging issues. The findings show significant gains in the efficiency of English language acquisition, particularly in grammar understanding and vocabulary enhancement, which can be attributed to the ability to tailor learning situations. However, there are still issues, such as not being able to access the data, teachers being unprepared, and concerns about data privacy and discrimination. This study presents a paradigm for incorporating AI into English Language Teaching (ELT), with an emphasis on access, teacher training, ethical norms, and blended learning approaches to maximise the benefits of AI. The study emphasises the importance of professional teacher training and ethical norms in improving AI’s effectiveness and sustainability in English Language Teaching (ELT). The methodology and review findings aim to enable educators, developers, and policymakers in creating an AI-enhanced learning environment that meets educational goals while resolving current limitations.Abstract
How to Cite
Downloads
Similar Articles
- Renuka Thapliyal, Can Shimla be fitted into the compact city model? , The Scientific Temper: Vol. 15 No. 02 (2024): The Scientific Temper
- Augustine Antony L, Mary Priya Dharsini A, Some fixed point theorems for contraction on b-multiplicative metric spaces , The Scientific Temper: Vol. 16 No. 05 (2025): The Scientific Temper
- Rajarajeswari M, Reena Ravi, Effectiveness of multicomponent intervention on smartphone addiction and leisure wellbeing among adolescents of selected PU college in Bangalore , The Scientific Temper: Vol. 16 No. 06 (2025): The Scientific Temper
- Ashwani Pandey, Sanjay Madan, Kumari Sandhiya, Ruchi Sharma, Akansha Raturi, Ashmita Bhatt, Naveen Gaurav, Comparison of Antioxidant, Phytochemical Profiling of Bacopa monnieri (Brahmi) , The Scientific Temper: Vol. 13 No. 02 (2022): The Scientific Temper
- S. Jerinrechal, I. Antonitte Vinoline, A vendor-constrained economic production quantity model integrating scrap recovery under sustainability , The Scientific Temper: Vol. 16 No. 08 (2025): The Scientific Temper
- Nupur Dogra, Shaveta Sharma, Impact of social networking sites on adolescent alienation and depression with special reference to Facebook usage , The Scientific Temper: Vol. 14 No. 03 (2023): The Scientific Temper
- Khairunnisa, Dr. D. I. George Amalarethinam, STDO: Siberian Tiger and Devil Optimization — A Novel Hybrid Metaheuristic Algorithm for Energy-Efficient Task Scheduling in Cloud Computing , The Scientific Temper: Vol. 17 No. 03 (2026): The Scientific Temper
- Deena Merit C K , Haridass M, Analysis of multiple sleeps and N-policy on a M/G/1/K user request queue in 5g networks base station , The Scientific Temper: Vol. 14 No. 02 (2023): The Scientific Temper
- R. Kalaiselvi, P. Meenakshi Sundaram, Unified framework for sybil attack detection in mobile ad hoc networks using machine learning approach , The Scientific Temper: Vol. 16 No. 02 (2025): The Scientific Temper
- M. Monika, J. Merline Vinotha, A resilient supply chain model integrating demand variability and carbon emissions in imperfect production systems , The Scientific Temper: Vol. 16 No. 08 (2025): The Scientific Temper
<< < 57 58 59 60 61 62 63 64 65 66 > >>
You may also start an advanced similarity search for this article.

