This paper presents the design and development of an AI-powered web application for personalized language

learning, with a focus on integrating native language support and intelligent interaction. The proposed system

addresses limitations of existing platforms, such as English-only interfaces, limited voice assistance, and fixed

learning paths. The platform incorporates a mother tongue interface, AI-driven pronunciation training, and an

intelligent chatbot mentor to provide real-time feedback and adaptive learning experiences. By combining speech

recognition, natural language processing, and cultural context integration, the system enhances learner

essential for personal, academic, and professional growth. Language learning platforms have emerged as

powerful tools to facilitate this process, offering learners access to structured courses, vocabulary exercises, and

interactive content. However, despite the proliferation of such platforms, significant challenges remain in

delivering truly personalized and accessible learning experiences. Most existing systems rely primarily on

English as the interface language, which creates barriers for learners from diverse linguistic backgrounds.

Furthermore, these platforms often provide limited voice assistance, fixed learning paths, and minimal real-time

feedback, resulting in reduced learner engagement and suboptimal outcomes.

To address these limitations, this study proposes an AI-powered web application for personalized language

The architecture of the proposed platform is implemented using modern web technologies, with backend support

through Python-based APIs to handle AI functionalities such as speech processing, text analysis, and learner

personalization. The system’s adaptive algorithms track learner progress, identify weak areas, and adjust content

delivery accordingly, ensuring that learning remains dynamic and tailored to individual performance. This

approach promotes active engagement, retention, and confidence in learners, while providing educators and

administrators with analytics to monitor progress and optimize learning paths.

By bridging technological advancements with educational theory, this work contributes to the development of a

scalable, user-friendly, and socially impactful language learning solution. It seeks to overcome the barriers of

conventional platforms by offering a system that is inclusive, adaptable, and responsive to the unique needs of

learners from different linguistic and cultural backgrounds. The proposed solution represents a significant step

learning path. While effective for basic vocabulary acquisition, these systems often lack personalization and fail

to adapt to individual learner needs, resulting in decreased engagement over time.

Recent research has emphasized the integration of artificial intelligence (AI) to create adaptive learning

environments. AI-driven platforms leverage natural language processing (NLP) and machine learning algorithms

to provide personalized feedback, automatically adjust difficulty levels, and track learner progress. For example,

intelligent tutoring systems (ITS) have demonstrated improved learning outcomes by tailoring exercises and

providing corrective feedback in real time. However, most of these systems are English-centric, limiting

accessibility for learners whose native language differs from English.

Voice-based learning tools have also gained attention, particularly for pronunciation training. Speech recognition

technologies allow learners to receive immediate feedback on pronunciation accuracy, enabling active speaking

reducing flexibility and the naturalness of interaction.

In summary, prior studies highlight the importance of personalization, adaptive learning, voice integration, and

conversational practice. Despite these advancements, there remains a gap in providing a scalable, native-

language-supported, AI-driven platform that combines speech recognition, intelligent tutoring, and cultural

context awareness. The proposed system in this paper aims to bridge this gap by integrating these key features

into a comprehensive, user-friendly, and culturally inclusive language learning environment.

The methodology adopted for the proposed AI-powered language learning platform is based on a modular,

layered architecture that integrates user interaction, AI-driven processing, and data management. This

architecture ensures scalability, adaptability, and seamless interaction between learners and intelligent tutoring

The Application Layer is responsible for managing communication between the user interface and backend

services. It is implemented using React, HTML5, CSS3, and JavaScript to ensure responsive, user-friendly

design. A REST API facilitates secure communication with the backend, developed using Python-based

frameworks (Flask/Django). This layer handles user authentication, session management, and content delivery,

ensuring smooth learner experiences across devices.

At the core of the platform is the AI/ML Layer, which powers personalization, speech processing, and intelligent

tutoring:

maintains authentication details and learner profiles. The Lesson Content Database provides structured materials,

while the Progress Database tracks learner performance, enabling adaptive feedback and assessment analytics.

To extend functionality, the system integrates with third-party services such as external AI APIs, cloud storage,

and authentication services. These services support advanced NLP, speech synthesis, and secure user

management. Additionally, cloud storage enables efficient handling of multimedia files such as voice recordings

and lesson media.

The workflow begins when a learner logs in and selects a lesson. Speech or text inputs are captured via the User

Interface Layer and transmitted to the Application Layer. The backend communicates with the AI/ML Layer for

The proposed system is designed as an AI-powered web application that integrates multiple modules to provide

an adaptive, personalized, and culturally inclusive language learning experience. Each module addresses specific

limitations identified in existing platforms while collectively forming a comprehensive learning environment.

The system adopts a structured six-level learning path that gradually progresses from basic literacy skills to

advanced communication:

1. Letters and Sounds: Focuses on phonetics, alphabet recognition, and sound association with AI-driven

speech feedback.

2. Vocabulary Building: Introduces words with visual aids, cultural examples, and voice-enabled drills for

retention.

This module employs Automatic Speech Recognition (ASR) to capture learner speech and provide real-time

feedback on accuracy, fluency, and accent. By integrating multilingual ASR engines such as Whisper and Google

Speech-to-Text, the system ensures support for diverse linguistic backgrounds. Learners receive instant

corrections and adaptive practice sessions that reinforce proper articulation.

The Intelligent Chatbot Mentor functions as a conversational partner and adaptive tutor. Powered by Large

Language Models (LLMs), the chatbot supports multi-turn dialogues, grammar correction, and vocabulary

expansion. It simulates real-world conversations and integrates emotion-aware responses, providing learners

with both guidance and motivational feedback.

Figure 3: Homepage – Interactive User Interface

When compared with conventional CALL systems and popular commercial apps, the proposed system

demonstrated:

to broaden inclusivity. Second, offline and edge AI capabilities should be explored through lightweight, on-

Additional implementation details, datasets, and evaluation results can be provided upon request for further

research and validation purposes.

The authors would like to thank the Department of Artificial Intelligence and Data Science, SRM Valliammai

Engineering College, for their support and guidance throughout this research work. Special acknowledgment is

extended to Mr. S. Srinivasan for his valuable mentorship.

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