Copyright © 2025 Authors retain the copyright of this article. This article 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.

@article{183312,
        author = {SANIDP NIRANJAN VENDE and DINESH PRAKASH BAVISKAR and Miss. Ruchita Chandrakant Shirsath},
        title = {Computer Vision, Text, and Audio Feedback with Sentiment Analysis for Scaling Value Education and Enhancing Universal Human Values},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {3},
        pages = {1284-1293},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=183312},
        abstract = {Background: Traditional value education systems struggle to provide personalized, scalable assessment of student social behaviors and character development in diverse educational environments.
Objective: This study presents a comprehensive AI-driven framework for automated social behavior indexing of students through multimodal analysis, integrating computer vision, natural language processing, and audio sentiment analysis to enhance value education delivery and assess universal human values development.
Methodology: We developed a multi-layered architecture combining convolutional neural networks (CNNs) for facial expression recognition, transformer-based models for text sentiment analysis, and mel-frequency cepstral coefficients (MFCCs) with deep learning for audio emotion detection. The system processes real-time classroom interactions, peer communications, and behavioral patterns to generate comprehensive social behavior indices across five core value dimensions: empathy, integrity, respect, responsibility, and collaboration.
Results: Validation on a dataset of 2,847 students across 15 educational institutions demonstrated 92.3% accuracy in behavior classification, 89.7% precision in sentiment analysis, and 87.4% correlation with human expert assessments. The system successfully identified behavioral patterns with 94.1% sensitivity for positive value demonstration and 91.6% specificity for concerning behaviors.
Conclusion: The proposed framework offers a scalable, objective approach to value education assessment, enabling personalized interventions and systematic tracking of character development. This technology bridges the gap between traditional moral education and modern AI-driven pedagogical tools.},
        keywords = {artificial intelligence, behavior analysis, computer vision, sentiment analysis, value education},
        month = {August},
        }