The rapid expansion of artificial intelligence in education creates critical barriers for humanities students, with traditional technical approaches failing to accommodate non-technical learning needs. This research addresses a significant gap by systemically identifying and quantifying AI education barriers for humanities students—an underexplored population in technology education research. A content analysis of educational programs from ten universities across four continents (six Russian and four international) was conducted using systematic coding criteria. The authors quantified barriers using a validated five-point scoring system with mathematical normalization. The results reveal substantial disparities between educational approaches. Russian universities demonstrate significantly higher cognitive barriers (68–85%) due to technical program orientation, with peak levels reaching 85%. International universities achieve considerably lower barriers. Stanford demonstrates minimal levels (18%) through ethics-focused approaches. UCL and the University of Amsterdam maintain moderate levels (50%) via humanistic case studies and adaptive methodologies. This research presents a theoretical framework for meta-digital competence and proposes MetaLearning, an evidence-based educational architecture that integrates technical accessibility, humanistic content, personalized pedagogy, and socio-psychological support. This scalable solution systemically addresses identified barriers while preserving humanistic values, offering practical implications for global educational policy in the digital transformation era.