Intelligent Recommendation of Adaptive Learning Platforms Based on Knowledge Graph in English Vocabulary Teaching

This paper presents the approach of knowledge graphs in increasing the adaptable learning platforms in English vocabulary learning because they facilitate adaptable instruction on specific information in an individual context. Taking the qualitative comparative case study research approach, the paper will review and synthesize secondary data sources and use a thematic assessment of two popular applications- Vocabulary.com and Duolingo. It examines five main dimensions: recommendation logic, knowledge graph structure, feedback mechanisms, multilingual support, and the level of personalization. The findings show that Duolingo uses real-time NLP-driven recommendation engines and hybrid graph networks that are used to provide adaptive and multilingual learning environments. Conversely, Vocabulary.com has structured semantic relationships that lack flexibility and customization by users. Even though the two systems have shown pedagogical worth, Duolingo one has outperformed based on responsive behavior and engagement. Other issues mentioned in the study incorporate data privacy, algorithmic complexity and infrastructure scalability. The results indicated that knowledge graph-based systems have the potential to revolutionize vocabulary learning, given that the ethical and technical concerns are fulfilled. The researchers in the future should investigate sustainable models and the outcomes of learners in the long term.