An AI-Powered Personalized Adaptive Learning Coach for English language Learners
- DOI
- 10.2991/978-94-6463-948-3_51How to use a DOI?
- Keywords
- Adaptive learning; NLP; Speech recognition; Emotion detection; English coaching; Translation
- Abstract
English language learners face difficulties in pronunciation, grammar, vocabulary, and fluency. Existing digital tools offer general assistance but fail to adapt to individual learner needs. To address this gap, we propose an AI-powered adaptive learning coach that integrates automatic speech recognition, natural language processing, emotion detection, and knowledge tracing. Speech-to-text conversion is supported by Whisper and VOSK, while transformer-based models enable grammar correction and conversational practice. The system dynamically adjusts lesson difficulty, ensuring personalized learning pathways and higher engagement through gamification. Preliminary results show improved fluency, accuracy, and confidence compared to existing tools. The framework demonstrates the potential of combining AI techniques with adaptive learning for scalable, multilingual English education
- Copyright
- © 2025 The Author(s)
- Open Access
- Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.
Cite this article
TY - CONF AU - Om Mahadeokar AU - Ruchir Adnaik AU - Devavrat Tapare AU - Manish Chiwadshetty AU - Leeyan Shaikh PY - 2026 DA - 2026/01/06 TI - An AI-Powered Personalized Adaptive Learning Coach for English language Learners BT - Proceedings of the International Conference on Sustainable Innovation with Artificial Intelligence and Machine Learning 2025 (ICSIAIML 2025) PB - Atlantis Press SP - 732 EP - 743 SN - 1951-6851 UR - https://doi.org/10.2991/978-94-6463-948-3_51 DO - 10.2991/978-94-6463-948-3_51 ID - Mahadeokar2026 ER -