Enhancing English Language Communication Skills of Learners through AI-Driven Instructional Models
P, Murugan and Ganesa Murthy, A and A A, Jayashree Prabhakar and M, Nagalakshmi and S, Esakkiammal and Mary, Sugantha Ezhil (2026) Enhancing English Language Communication Skills of Learners through AI-Driven Instructional Models. In: 2025 International Conference on Emerging Engineering Technologies and Applications (IC-EETA), 06-11-2025 to 08-11-2025, Indore, Madhya Pradesh, India.
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Abstract
Using artificial intelligence in the classroom, especially in English as a Second Language (ESL) classrooms, has transformed how students learn new languages. Students may find it challenging to get better at speaking, listening, and using language in context when they use traditional approaches. This is because traditional methods don't let students get feedback or customize their learning in real time. A lot of English Language Learners (ELLs) have issues learning in places that don't change, hearing speech that sounds like that of native speakers, and getting feedback promptly. Most systems still don't understand context or have adaptive learning routes, even with all the new technology. This paper talks about an AI powered communication training model that has speech recognition, natural language processing (NLP), and chatbots. Adaptive feedback, suggestions for grammar, and corrections for speech are all supplied by the model in real time. AI chatbots and voice-based agents let students practice talking in a way that is like real life. We assess their answers on a lot of things, such as how well they pronounce words, how well they use grammar, how well they use vocabulary, how well they make sense, and how well they speak. In terms of communication capability, the A/B test indicated that the experimental group that employed the AI based model obtained much better outcomes than the control group that utilized traditional learning methodologies. This was proven to be true throughout the course of twelve weeks. The average score for communication skills went up by 24% because more students were involved, which was linked to the improvement. The AI model did very well in a number of areas, such as giving personalized feedback, keeping students engaged in real time, and letting students learn at their own pace. It worked better than regular approaches like static audio lectures, language apps for phones and tablets, and programs that only used human trainers.
| Item Type: | Conference or Workshop Item (Paper) |
|---|---|
| Subjects: | Computer Applications > Artificial Intelligence English > English |
| Domains: | Computer Applications |
| Depositing User: | Mr IR Admin |
| Date Deposited: | 10 Jun 2026 13:29 |
| Last Modified: | 10 Jun 2026 13:34 |
| URI: | https://ir.vistas.ac.in/id/eprint/21099 |
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