Grammatical error correction (GEC) is a key task in natural language processing (NLP), widely applied in education, news, and publishing. Traditional methods mainly rely on sequence-to-sequence (Seq2Seq) and sequence-to-edit (Seq2Edit) models, while large language models (LLMs) have recently shown strong performance in this area.

Researchers have developed a novel generative AI model, called Collaborative Competitive Agents (CCA), that significantly improves the ability to handle complex image editing tasks. This new approach utilizes multiple Large Language Model (LLM)-based agents that work both collaboratively and competitively, resulting in a more robust and accurate editing process compared to existing methods. This breakthrough allows for a more transparent and iterative approach to image manipulation, enabling a level of precision previously unattainable. The findings were published on 15 November 2025 in Frontiers of Computer Science, co-published by Higher Education Press and Springer Nature.

Researchers from Tianjin University have introduced the Emergency Medical Procedures 3D Dataset (EMP3D), a pioneering resource that captures the intricate movements of medical professionals during life-saving interventions with unprecedented precision. Published on 15 November 2025 in Frontiers of Computer Science co-published by Higher Education Press and Springer Nature, this dataset leverages synchronized multi-camera systems, advanced AI algorithms, and rigorous human validation to create the first 3D digital blueprint of emergency medical workflows. The innovation holds the potential to fundamentally transform emergency medical training and enhance robotic support in healthcare settings.

A record-setting $55 million commitment from a Binghamton University, State University of New York alumnus and New York state will establish the Center for AI Responsibility and Research, the first-ever independent AI research center at a public university in the United States. Research conducted via the new center will build upon Binghamton research that advances AI for the public good. 

This feature explores how European education systems negotiate tensions between collective ideals and growing competition. Drawing on studies from Denmark, Sweden, Norway, and Belarus, it examines shadow education, policy debates over equity, culturally grounded early childhood learning, and enduring post-Soviet public institutions. Together, these perspectives reveal education as a social mirror, continuously balancing public good, cultural identity, historical legacy, and individual ambition across diverse European contexts and shared societal values.