Personalized AI Tutoring Through Learner Profiling and Adaptive Learning Path Optimization
Year: 2026
Type: conference-paper
Abstract: Personalized language tutoring improves learning efficiency through adaptive instructional strategies that reflect individual linguistic abilities. Hybrid clustering-based proficiency profiling analyzes learners’ performance patterns to group them into categories that represent distinct language competency levels. Existing language tutoring systems rely on static evaluation models that generate uniform learning paths across diverse learner populations. Limited capability for dynamic learner grouping reduces the accuracy of proficiency identification across grammar usage, vocabulary range, reading comprehension and pronunciation quality. The personalized AI Tutoring with Adaptive Proficiency Clustering Framework (APCF) introduces hybrid clustering mechanisms that construct detailed learner proficiency profiles from multidimensional linguistic features. The framework evaluates lexical diversity, syntactic accuracy, semantic relevance and response latency. Hybrid clustering operations organize learners into proficiency clusters that represent similar linguistic characteristics. Continuous learner interaction data updates cluster assignments, thereby maintaining an accurate representation of proficiency. APCF supports personalized tutoring through cluster-specific instructional strategies that generate adaptive exercises, targeted feedback and progressive difficulty levels. The tutoring platform delivers learning materials aligned with the linguistic profile of each learner group. Performance evaluation demonstrates improved learner classification accuracy, stronger cluster stability and enhanced proficiency differentiation across learner populations. The framework increases tutoring precision, learning engagement and adaptive capability in intelligent language education environments.
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
SDG: Quality education
Open Access status: hybrid
OpenAlex ID: W7223200460