AI-Enabled Virtual Assistants for Hydrogen Energy Systems Education
Abstract
The global transition toward hydrogen-based energy systems has heightened the need for specialized education that equips students and professionals with both technical knowledge and digital communication skills. Traditional teaching methods, relying on textbooks and instructor-led lectures, often lack interactivity and adaptability, limiting engagement and comprehension in complex subjects such as hydrogen production, storage, and fuel cell technologies. The problem lies in the absence of intelligent educational tools capable of delivering personalized, digital learning experiences that address diverse learner needs while ensuring conceptual clarity. To address this gap, the paper propose the AI-Enabled Virtual Assistant for Digital Hydrogen Energy Systems Education (AIVA-BHESE) framework, which integrates natural language processing, machine translation, and adaptive dialogue management to provide interactive guidance, realtime feedback, and digital explanations in English and a target second language. AIVA-BHESE supports contextualized learning, answers domain-specific queries, and adapts its instructional approach based on learner responses. Experimental evaluation conducted with hydrogen energy educational modules demonstrated that AIVABHESE improved learner engagement by 32 %, enhanced knowledge retention by 27 %, and reduced misunderstanding rates in digital content by 24 %, compared to conventional instruction. These results suggest that AI-enabled virtual assistants can significantly augment hydrogen energy education by providing adaptive, interactive, and digital support. In conclusion, the AIVA-BHESE framework offers a scalable, intelligent solution for enhancing accessibility, comprehension, and engagement in digital hydrogen energy systems education, bridging the gap between advanced energy technologies and diverse learner populations.