Comuniqa : Exploring Large Language Models for improving speaking skills
Project Overview
The document explores the use of generative AI in education, focusing on the Comuniqa application, which leverages a Large Language Model (LLM) to enhance English speaking skills among non-native speakers in India. It critiques traditional language learning methods and underscores the benefits of AI in delivering scalable, accessible, and personalized educational experiences. Through a study comparing the effectiveness of the Comuniqa app with feedback from human experts, the findings indicate that while AI can provide precise assessments, it falls short in delivering the empathy and cognitive engagement characteristic of human instructors. Consequently, the study advocates for a blended learning approach that combines the strengths of both AI and human feedback to achieve the best educational outcomes. This highlights a significant shift in educational practices, illustrating how generative AI can complement traditional teaching methods to foster improved language acquisition and learning efficiency.
Key Applications
Comuniqa
Context: Improving English speaking skills for non-native speakers in India
Implementation: The application was implemented as a mobile app allowing users to practice speaking and receive instant feedback using LLMs.
Outcomes: Provided scalable and accessible feedback on speaking skills, enhancing learning experiences for those without access to human experts.
Challenges: Lacks human-level cognitive capabilities and empathy, which may impact user trust and engagement.
Implementation Barriers
Technological Limitations
AI systems, including LLMs, lack the emotional intelligence and empathy that human instructors provide.
Proposed Solutions: Integrating human feedback into the AI learning process to enhance user experience and outcomes.
Accessibility
Not all learners may have the same access to technology or the internet, limiting the reach of AI tools.
Proposed Solutions: Developing offline versions of the app or providing access through community resources.
User Trust
Users may doubt the accuracy of AI-generated assessments, affecting their willingness to rely on the technology.
Proposed Solutions: Enhancing transparency in how scores are calculated and providing users with more context on AI's capabilities.
Project Team
Manas Mhasakar
Researcher
Shikhar Sharma
Researcher
Apurv Mehra
Researcher
Utkarsh Venaik
Researcher
Ujjwal Singhal
Researcher
Dhruv Kumar
Researcher
Kashish Mittal
Researcher
Contact Information
For information about the paper, please contact the authors.
Authors: Manas Mhasakar, Shikhar Sharma, Apurv Mehra, Utkarsh Venaik, Ujjwal Singhal, Dhruv Kumar, Kashish Mittal
Source Publication: View Original PaperLink opens in a new window
Project Contact: Dr. Jianhua Yang
LLM Model Version: gpt-4o-mini-2024-07-18
Analysis Provider: Openai