This study investigates the effectiveness of AI-tutored learning environments in implementing evidence-based learning techniques among undergraduate students. Drawing from cognitive science principles, particularly those outlined in Willingham’s (2023) work, we developed an innovative intervention utilizing AI tutors to simulate personalized learning environments focused on three key areas:...
The rapid advancement of artificial intelligence (AI), particularly large language models (LLMs), has introduced powerful tools for various research domains, including psychological scale development. This study presents a fully automated method to efficiently generate and select high-quality, non-redundant items for psychological assessments using LLMs and network psychometrics. Our approach...
Recent advances in large language models (LLMs) present opportunities for developing performance-based items in educational and psychological assessment. We introduce P-AI-GENIE (Performance-based Automatic Item Generation and Network-Integrated Evaluation), an extension of AI-GENIE that focuses on generating and validating performance items. The talk will cover how items can be developed and...
We propose a novel approach for modeling and understanding the dynamics of emotion facial expression recognition (FER) scores. Recent advancements in deep learning and transformer-based neural network architectures enable the time series analysis of FER scores extracted from images and videos. This type of data can be important for psychological research of affective dynamics and emotion...
