Item Response Theory

Item Response Theory (IRT) is a statistical framework used to analyze test responses and estimate the latent abilities of individuals or the difficulty of items, aiming for more accurate and nuanced assessments than traditional methods. Current research focuses on improving model fit and efficiency through techniques like autoencoders and automated machine learning, as well as extending IRT's applications beyond educational testing to diverse fields such as language learning, computer vision, and even algorithm evaluation. This versatility makes IRT a powerful tool for creating more reliable and interpretable assessments across various domains, leading to improved decision-making in education, AI development, and other areas.