Automated Group Discussion Feedback and Scoring with AI

Abstract

The proposed AI-driven group discussion platform addresses several key challenges identified in 15 studies, such as dominance by certain participants, insufficient feedback, lack of structure, and unethical analysis difficulties. It also tackles issues like network variability, ML implementation complexity, security concerns, data biases, low student engagement, and privacy issues. The platform provides solutions by leveraging AI and machine learning for real-time monitoring and personalized feedback, ensuring balanced and productive discussions. Emotion recognition algorithms analyze micro-expressions, while multi-language support and gamification elements enhance accessibility and motivation. Integration with Learning Management Systems (LMS) streamlines resource access and performance tracking, and an advanced analytics dashboard offers insights into group dynamics. Efficient video delivery and scalable multipoint videoconferencing mitigate network issues. Robust security measures protect user data, and continuous assessment features enable personalized feedback. Future enhancements like virtual and augmented reality aim to create immersive discussion experiences and facilitate teamwork, addressing the limitations of existing systems and fostering an inclusive, engaging, and productive environment for all participants.