The field of human-AI collaboration is moving towards a deeper understanding of the psychological factors that influence human decision-making and AI-assisted decision systems. Researchers are investigating how human self-confidence calibration, need for cognition, and actively open-minded thinking impact decision accuracy and metacognitive perceptions. Additionally, there is a growing interest in developing digital tools to support self-determination for vulnerable populations, such as people with intellectual disabilities and autism spectrum disorder. In the area of anomaly detection, large language models (LLMs) are being explored for their potential to detect contextual anomalies in text-attributed graphs. The development of benchmark datasets, such as TAG-AD and TAGFN, is facilitating the evaluation and improvement of graph anomaly detection methods. Noteworthy papers include: Beyond Awareness: Investigating How AI and Psychological Factors Shape Human Self-Confidence Calibration, which presents strategies to identify well-calibrated users and proposes design recommendations for AI-assisted decision systems. LLM-Powered Text-Attributed Graph Anomaly Detection via Retrieval-Augmented Reasoning, which introduces a retrieval-augmented generation framework for zero-shot anomaly detection and achieves performance comparable to human-designed prompts. TAGFN: A Text-Attributed Graph Dataset for Fake News Detection in the Age of LLMs, which provides a large-scale, real-world text-attributed graph dataset for outlier detection and facilitates the development of misinformation detection capabilities in LLMs.