Accurate and trustworthy AI rating applications are essential for evaluating complex skills such as effective communication and problem-solving. This session offers a comprehensive overview of developing, architecting, and applying multimodal AI rating systems, with a focus on best practices and successful cases to achieve high accuracy, explainability, and trustworthiness.

Join us to explore the latest advancements in multimodal AI rating applications. We will delve into the architecture of these systems, highlighting the keys to high accuracy and reliability. Learn how decisions related to feature extractors, test-specific large language models, and domain-specific fine-tuning contribute to the development of trustworthy AI ratings. We will also examine various modes of human-AI collaboration, illustrating how human oversight and expert alignment can enhance AI performance.

Our session will conclude with an examination of successful multimodal AI applications across different subjects and test formats. Through a mix of research, data, and real-world case studies, we will demonstrate the potential of human-AI collaboration in delivering accurate, explainable, and trustworthy ratings. See how these innovations can transform the rating process of your exam programs.