AI ‐Enhanced E‐Learning for Intellectual Property Education: Cultivating International Talents Through Accessible and Personalised Digital Training
Year: 2026
Type: article
Abstract: ABSTRACT The rapid advancement of online learning and artificial intelligence (AI) offers new opportunities to strengthen intellectual property (IP) education, an area still underdeveloped in most educational contexts. This study introduces an AI‐driven e‐learning system designed to deliver adaptive, differentiated, and globally accessible IP education. By incorporating machine learning, intelligent tutoring, adaptive testing, and gamification, the system creates personalised learning pathways that address diverse learner needs. To evaluate feasibility, synthetic student datasets representing international learners were employed, alongside surveys and interaction logs capturing engagement, satisfaction, usability, and accessibility. Findings reveal that personalization significantly improved engagement, with ratings of 4.3/5 for tailored pathways and 4.5/5 for overall engagement. Multilingual support (4.0) and cultural sensitivity (4.3) were positively perceived, though regional adaptability scored lower (3.2), suggesting a need for localised content. Usability results indicated intuitive navigation and interface design, while technical support emerged as the main area for improvement. Predictive analytics was the most valued AI function (4.2), emphasising its contribution to learner achievement. Overall, the study highlights both theoretical and practical relevance, demonstrating scalable approaches for embedding AI in specialised education to promote inclusivity, global talent development, and cross‐cultural collaboration.
Source: European Journal of Education
DOI: 10.1111/ejed.70480
Authors: Peizhao Li, Jieming Liu, Jun Yao, Lin Luo
Institution: Wuhan University of Technology
Language: English
Subfield: Information Systems
Field: Computer Science
Domain: Physical Sciences
Open Access status: bronze
OpenAlex ID: W7126263292