Real-Time AI-Powered Text Evaluation for EV Charging Standards and Safety Education

The rapid growth of electric vehicle (EV) adoption demands high-quality education and compliance with evolving charging standards and safety protocols. Real-time AI-powered text evaluation can ensure accurate and standardized content delivery in EV training and documentation. However, existing methods rely heavily on manual review and static rule-based systems, which are time-consuming, inconsistent, and often fail to adapt to dynamic changes in global standards. These limitations hinder the effectiveness of safety training and regulatory compliance. To address these issues, this study proposes a framework utilizing Natural Language Processing with Bidirectional Encoder Representations from Transformers Models (NLP-BERT). These models semantically analyze EV-related text and compare it against a structured corpus of regulatory documents, enabling intelligent, real-time evaluation. The proposed method is applied in a smart training platform, where it provides instant feedback on technical accuracy, identifies missing compliance elements, and enhances clarity of safety-related content. Results show that the model improves content reliability and reduces human review time by up to 60 %, ensuring consistent adherence to EV charging standards and boosting overall educational quality.

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