Intellectual Property Rights in AI Training: A Royalty-Based Compensation Model for Artists

To overcome the challenges associated with traditional LADs, we designed an agentic AI–based LAD that deliv-ers real-time, explainable, and educator-centered decision support in live classroom settings. The main purpose of the proposed system is to con-stantly monitor and interpret student engagement in a way that is both technically efficient and pedagogically meaningful. The results demonstrate that computer vision–based behavioral sensing, combined with lightweight and interpretable machine learning models, can accurately identify engagement states while maintaining the low-latency performance required for real-world deployment. The high statistical correlation with the expert human judgment also helps to improve the reliability and educa-tional applicability of the engagement metrics proposed.

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