Adaptive Knowledge Assessment Technologies in Power Grid Planning: A Semantic Parsing and Ontology Mapping Approach

This paper presents an innovative approach to automated knowledge assessment for power grid planning by leveraging advanced artificial intelligence (AI) techniques. The system integrates semantic parsing, ontology mapping, reinforcement learning (RL), and Graph Neural Networks (GNNs) to automatically generate diverse question types, including multiple-choice, fill-in-the-blank, and true/false questions. Key contributions include significant improvements in question generation efficiency, accuracy, and complexity. GNN-based processing, in particular, allows for multi-step reasoning and enhanced question relevance by capturing structural dependencies between concepts in the power grid domain. The system demonstrates an 81% improvement in reasoning depth, a 35% increase in dependency capture rate, and a 150% enhancement in the diversity of question types. This adaptable and scalable system is well-suited for large-scale deployments, making it a valuable tool for real-world applications in training and certification programs for power grid engineers and technicians.