Lingo: An AI-Based Foreign Language Learning Platform

Abstract

This project presents Lingo, an AI-powered foreign language learning platform developed to assist non-native speakers, particularly Indian learners, in acquiring proficiency in English, German, and French. The application is designed as an interactive, mobile-based learning environment that combines structured lessons, quizzes, and real-time conversation with an AI tutor. The core technologies include Natural Language Processing (NLP) for understanding user inputs, speech recognition for voice interactions, and machine learning for adapting lessons based on user performance. The front-end is developed using Flutter to ensure cross-platform compatibility, while the backend is built with Node.js to manage user data, responses, and AI processing. A key feature of the platform is the 3D virtual tutor, which offers both spoken and text-based interaction, simulating a personalized learning experience. The application includes modules for grammar practice, vocabulary building, pronunciation correction, and progress tracking. This paper outlines the design methodology, implementation details, and expected educational impact of Lingo. The project aims to demonstrate how modern AI technologies can enhance language education and make personalized learning accessible to a wider audience through an affordable, mobile-first solution.

Introduction

The paper presents Lingo, an AI-driven, mobile-first language learning platform designed to meet the specific needs of Indian learners seeking proficiency in English, German, and French. It addresses critical challenges in India’s language education system, such as limited access to qualified instructors, high costs of quality tutoring, outdated teaching methods, and poor English proficiency levels.

Key Problems Identified:

• Existing platforms rely on static content and lack conversational depth, personalization, and cultural relevance.

• They often fail to accommodate regional accents, real-time feedback, and immersive learning experiences.

Objectives of the Research:

1. Develop a cross-platform mobile app using Flutter.

2. Provide modular, gamified learning content targeting grammar, vocabulary, listening, and speaking.

3. Integrate AI-driven conversational agents using NLP and speech technologies.

4. Use 3D animated avatars for engaging, human-like interaction.

5. Offer personalized progress tracking with visual performance metrics.

Technological Innovations:

• Utilizes AI and NLP (e.g., Google Gemini, OpenAI GPT) for natural conversation and grammar feedback.

• Real-time speech-to-text and pronunciation scoring, optimized for Indian accents.

• 3D avatar tutor created with Blender, offering nonverbal cues like lip-sync and expressions.

• Adaptive learning engine dynamically adjusts content based on learner performance.

System Architecture:

• Frontend: Flutter (with Dart)

• Backend: FastAPI, Node.js, MongoDB

• AI Services: Gemini, Dialogflow, Kokoro TTS, Google Speech

• 3D Design: Blender, Unity3D, flutter_gl

• Deployment: Firebase, Render

Methodology:

• Agile development process with iterative sprints, user feedback, and continuous testing.

• AI components designed from real learner data for contextual accuracy.

• A pilot study with 50 Indian undergraduates compared Lingo to a traditional static app over 4 weeks.

Pilot Study Results:

• Improved learning gains, pronunciation accuracy, and user engagement.

• Participants reported high satisfaction with the 3D tutor and adaptive feedback.

• Quantitative analysis showed statistically significant improvements in language skills.

Literature Review Insights:

• Current apps (like Duolingo and Babbel) lack deep speech interaction and immersive 3D interfaces.

• Research confirms that embodied AI (3D avatars), transformer-based models, and localized feedback enhance motivation and retention.

• Identified gaps include poor localization for Indian learners, lack of integrated speech/feedback systems, and reliance on high-end hardware.

Research Contributions:

1. A scalable system architecture optimized for mobile devices.

2. Real-time AI tutoring that adapts dynamically to learner needs.

3. Integration of speech, NLP, and 3D avatars in a single platform.

4. Empirical validation through user testing, proving the system’s effectiveness in enhancing language acquisition.

Conclusion

This paper has presented Lingo, an AI-based foreign language tutor tailored for Indian learners, and demonstrated its design, implementation, and evaluation. Through the integration of advanced AI technologies—Flutter’s speech-to-text plugin for on-device speech recognition [11], Google’s Gemini large language model (LLM) for context-aware dialogue and grammar correction [9], and the Kokoro TTS engine for natural and expressive text-to-speech synthesis [34]—within a cross-platform Flutter application, Lingo achieves a highly interactive and adaptive learning environment. A. Key Achievements 1) Enhanced Learning Outcomes: In a controlled four-week pilot study, Lingo users achieved a 15 % average gain in language proficiency—nearly double that of a static-content control group (p < 0.01) [19]. Retention measured two weeks post-intervention remained high (+12 % vs. +5 %). 2) Real-Time Interactive Feedback: The system maintained sub-3 second response times (2.1 s–2.4 s) across English, German, and French conversations, supporting seamless dialogue without perceptible lag [31]. Grammar correction accuracy reached 88 % on learner-generated errors[10]. 3) Immersive Engagement: The 3D tutor avatar rendered at 25–30 FPS with lip-sync delays under 150 ms, significantly boosting learner motivation and task persistence in line with research on embodied conversational agents [22]. 4) Scalability and Accessibility: The mobile-first design operated within a 250 MB memory footprint and consumed only 10–15 % battery per hour on mid-range devices, making it suitable for low-end hardware and intermittent connectivity scenarios [32]. B. Real-World Implications: Lingo addresses the acute shortage of personalized language instruction in rural and semi-urban India by automating real-time tutoring at minimal cost. Offline caching of lesson content and potential on-device inference of lightweight AI models promise to extend reach to learners with unreliable internet access [11]. By accommodating diverse regional accents and mother-tongue influences, Lingo reduces linguistic barriers and fosters confidence among non-native speakers. C. Limitations While Lingo excels in fundamental conversational practice and pronunciation feedback, its context retention currently spans only 4–5 dialogue turns before requiring a reset—a limitation inherent to token-window sizes in current LLMs. Additionally, full offline support for AI-driven features remains a future goal due to the computational demands of speech and language models. D. Future Work Building on this foundation, we propose: 1) Multilingual Expansion: Fine-tuning LLMs to support additional global and regional languages [25]. 2) Advanced Personalization: Incorporating reinforcement learning and Bayesian Knowledge Tracing for dynamically adaptive content sequencing [10]. 3) Augmented Reality Scenarios: Deploying AR modules to simulate real-world conversational contexts (e.g., marketplaces, interviews). 4) Explainable AI Feedback: Employing attention-based visualizations to clarify correction logic, enhancing learner trust. 5) Accessibility Enhancements: Adding WCAG-compliant UI elements and sign-language avatar support for learners with sensory impairments. In consequently, Lingo exemplifies the transformative potential of AI-enhanced, immersive language education. Its modular, scalable architecture and positive learning outcomes provide a blueprint for future educational technologies aimed at democratizing language acquisition across diverse, underserved populations.

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Copyright

Copyright © 2025 Sujeet Ghosh, Poorab Patel, Harsh Nair, Raghav Pandit, Baidyanath Ram, Dr. Goldi Soni. 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.