A study on intelligent identification and regulation methods for ethical risks in educational AI based on graph neural networks and fuzzy decision-making

With the widespread application of artificial intelligence in the educational field, potential ethical risks of AI systems have become increasingly prominent, demanding efficient and controllable technical solutions for their identification and regulation. This paper proposes an intelligent identification and regulation method for ethical risks in educational AI systems, integrating Graph Neural Networks (GNN) and fuzzy decision-making to address the complexity and dynamics of these risks. The method employs GNN for deep feature learning and relational model on multi-source heterogeneous data (such as user behaviour, system decision pathways, and environmental factors), thereby capturing hidden correlations and propagation patterns of ethical risks. A fuzzy decision-making mechanism is introduced to embed uncertainty and fuzzy logic into the process, enabling adaptive control over multi-dimensional and ambiguous risks. Experimental results demonstrate the method’s high accuracy and real-time performance in recognizing and regulating ethical risks under complex educational scenarios. This research offers technical support for developing safe and trustworthy educational AI systems.