The Scope of Artificial Intelligence in English Language Teaching in India

Abstract

The integration of Artificial Intelligence (AI) into English Language Teaching (ELT) in India marks a transformative shift in pedagogical practices and learner engagement. AI technologies—ranging from intelligent tutoring systems and speech recognition software to chatbots and adaptive learning platforms—have begun to personalize learning experiences, address regional and linguistic challenges, and support large-scale language acquisition efforts. In the Indian context, where English serves as both a second language and a gateway to academic and professional advancement, AI holds significant promise for bridging educational divides. Despite infrastructural and digital literacy challenges, the growing accessibility of AI tools offers opportunities to enhance classroom teaching, provide real-time feedback, and foster autonomous learning. This paper explores the potential, current applications, and challenges of implementing AI-driven solutions in English language classrooms across India, with an emphasis on scalable and inclusive education.

Introduction

I. Overview

AI is transforming education globally, with English Language Teaching (ELT) emerging as a key area of innovation in India. Given English's role as a second language and a tool for upward mobility, improving English proficiency is crucial. However, challenges such as teacher shortages, uneven educational access, and linguistic diversity persist.

AI offers personalized, scalable, and adaptive solutions, aligning with the National Education Policy (NEP) 2020, which encourages the use of technology to improve learning outcomes, particularly in under-resourced regions.

II. Scope of AI in ELT

A. Personalized Learning Platforms

• Tools like Hello English, Duolingo, and Mindspark English adapt content in real-time based on learner performance.

• Features include grammar drills, reading comprehension, and assessments tailored to individual progress and language background.

B. NLP-Based Writing Support

• Tools like Grammarly, WriteToLearn, and Enguru offer real-time grammar corrections, coherence suggestions, and feedback.

• Widely used for improving writing, especially in professional and academic contexts.

C. Speech Recognition for Pronunciation

• ELSA Speak, Google Read Along, EnglishBolo provide feedback on pronunciation and fluency.

• These tools are particularly effective for learners with strong regional accents or limited access to English-speaking environments.

D. Conversational AI and Chatbots

• Buddy.ai, Xiaoice simulate interactive conversations for real-life scenarios.

• Enhance fluency and confidence through non-judgmental, repetitive practice.

E. Automated Assessment Systems

• Platforms like Pearson’s Essay Assessor and Next Education’s English Labs offer objective evaluation and personalized feedback.

• Track learning progress and reduce teacher workload through auto-grading and feedback loops.

F. Teacher Support and Data Analytics

• AI platforms such as Toppr and LEAD School OS assist teachers with student analytics, lesson planning, and instructional refinement.

• Help identify at-risk learners and optimize teaching strategies.

III. Key Challenges in Implementing AI in ELT

A. Digital Divide

• Infrastructural gaps in rural India hinder access to AI tools.

• Many students lack reliable internet, electricity, and devices.

B. Teacher Training Gaps

• Many educators lack the digital literacy to effectively use AI tools.

• Limited professional development programs exacerbate this gap.

C. Multilingual and Cultural Complexity

• India’s diverse accents and code-switching practices challenge NLP accuracy.

• Existing AI tools often fail to accommodate regional language influences.

D. Cost and Accessibility

• Premium versions of platforms are expensive and out of reach for low-income learners.

• Device availability remains a major barrier.

E. Data Privacy and Ethics

• Concerns over student data security, especially with minors.

• Regulatory frameworks like the Personal Data Protection Bill are still being implemented.

IV. Future Prospects and Recommendations

A. Integration into National Policy

• NEP 2020 and NDEAR (National Digital Education Architecture) offer a strategic framework for AI adoption.

• Emphasis on teacher training and public-private partnerships is vital.

B. Development of Indigenous AI Tools

• Tools tailored to Indian English, regional accents, and multilingual learners are needed.

• Projects like AI4Bharat aim to localize AI applications.

C. AI-Assisted Teacher Training

• Use AI to train educators via simulations and personalized feedback.

• Helps bridge the digital skills gap in resource-limited settings.

D. Hybrid Learning Models

• Combine classroom instruction with AI-driven, self-paced learning.

• Already effective in rural settings via platforms like Mindspark and Pratham initiatives.

E. Equitable Access and Open-Source Platforms

• Promote free or low-cost, open-source AI tools to bridge socio-economic gaps.

• Collaborate with NGOs and tech companies to equip public schools.

Conclusion

Artificial Intelligence (AI) has significant potential to transform English Language Teaching (ELT) in India by making education more personalized, accessible, and effective. AI-powered tools such as adaptive learning platforms, speech recognition software, and automated feedback systems can help address key challenges in ELT, such as teacher shortages, learner diversity, and performance gaps. These technologies enable personalized learning, providing tailored experiences based on individual needs and real-time feedback, which is particularly crucial in India’s diverse educational landscape. AI can also automate administrative tasks, allowing educators to focus on individualized instruction.However, realizing this potential requires overcoming several challenges. The digital divide, especially in rural and low-income areas, limits access to AI tools due to insufficient infrastructure, including internet connectivity and devices. Additionally, there is a need for comprehensive teacher training to equip educators with the skills necessary to use AI effectively. AI tools must also be developed to address India’s multilingual context to ensure accuracy in language learning. Furthermore, data privacy and ethical concerns surrounding the collection of student data must be addressed through proper regulations to ensure safe and responsible use of AI.To make AI a viable tool for all students, efforts must be made to ensure equitable access, particularly through affordable AI platforms, public-private partnerships, and government funding. With these investments, AI can help bridge educational gaps, enhance learning outcomes, and contribute to a more inclusive and future-ready education system in India.

References

[1] ASER Centre. (2021). Annual Status of Education Report (Rural) 2021. https://www.asercentre.org [2] British Council India. (2021). Partnering with Schoolnet to Improve English Proficiency. https://www.britishcouncil.in/programmes/english-partnerships [3] Burstein, J., Chodorow, M., & Leacock, C. (2003). Criterion Online Essay Evaluation: An Application for Automated Evaluation of Student Essays. In M. D. Shermis& J. Burstein (Eds.), Automated Essay Scoring: A Cross-disciplinary Perspective (pp. 27–45). Lawrence Erlbaum Associates. [4] FICCI. (2020). Reimagining India’s Education Sector with Technology. Federation of Indian Chambers of Commerce and Industry. [5] Kumar, R. (2018). Hello English: How This Indian Startup Became One of the Most Popular Learning Apps in Asia. YourStory. https://yourstory.com [6] Ministry of Education. (2020). National Education Policy 2020. Government of India. [7] NCTE. (2020). Digital Readiness among Indian Teachers: Report on ICT Training. National Council for Teacher Education. [8] NITI Aayog. (2021). Responsible AI for All: Strategy for India. https://www.niti.gov.in [9] Pratham. (2020). AI and Education: A New Approach for Rural Learners. Pratham Education Foundation. [10] Kukulska-Hulme, A. (2020). Will mobile learning change language learning? ReCALL, 32(2), 162–178. https://doi.org/10.1017/S0958344020000055 [11] Li, Z., &Hegelheimer, V. (2013). Mobile-assisted grammar exercises: Effects on self-editing in L2 writing. Language Learning & Technology, 17(3), 135–156. https://www.lltjournal.org/item/3080 [12] Nagy, P., &Habók, A. (2021). Digital literacy, language learning strategies and attitudes of students in English as a foreign language context. Sustainability, 13(23), 12941. https://doi.org/10.3390/su132312941 [13] Shaikh, Z. A., & Khoja, S. A. (2011). Role of ICT in shaping the future of Pakistani higher education system. Turkish Online Journal of Educational Technology, 10(1), 149–161. [14] Kaur, P., & Singh, R. (2021). Artificial Intelligence in English language teaching: A futuristic approach in Indian education. Journal of Education and Practice, 12(4), 85–92.

Copyright

Copyright © 2025 Niranjana Santhosh, Keerthana Santhosh. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.