Artificial intelligence excels in predicting scientific properties, but real scientific discovery is physical and iterative. This paper introduces 'embodied science,' a paradigm and PLAD framework that tightly couples agentic reasoning with physical execution to achieve autonomous, long-horizon scientific discovery.

This study empirically investigates how school-level Artificial Intelligence (AI) readiness influences student AI literacy in vocational education through the mediating role of aggregated teacher capabilities. Using a large national dataset, it establishes that institutional preparedness, particularly when translated into teacher competence, significantly impacts student learning outcomes across diverse regional contexts.

This paper introduces AgentDS, a new benchmark and competition designed to evaluate the performance of AI agents and human-AI collaboration in domain-specific data science tasks across six diverse industries. The findings indicate that while current AI agents struggle with domain-specific reasoning, the most effective solutions emerge from human-AI collaboration, highlighting the enduring value of human expertise.

This paper introduces a novel measurement framework to evaluate human-AI decision-making, shifting focus from mere model accuracy to the readiness of human-AI teams for safe and effective collaboration. It proposes a taxonomy of metrics and connects them to the Understand–Control–Improve lifecycle to assess calibration, error recovery, and governance in real-world deployments.

This paper reviews 24 EU AI regulatory documents from 2024-2025 to clarify definitions and provisions related to security, privacy, and agentic AI. It aims to resolve ambiguities and align regulatory obligations with the evolving capabilities of AI, particularly autonomous agents.