Exploring Components of an AI-Based Content Recommendation Platform to Enhance University Instructors' Teaching Competencies

This study explores components of an AI-based content recommendation platform to enhance university instructors’ teaching competencies. An initial framework was developed through literature review and expert consultation, followed by a Delphi survey to validate its structure. The expert panel consisted of scholars in education, information and communication engineering, and digital content studies, as well as practitioners with over seven years of relevant experience. The results show that platform functions are divided into instructor and administrator roles, comprising 14 components across 5 domains. Instructor functions include diagnosing competencies (user profile analysis), engaging in personalized content-based learning (self-directed learning support), and interacting with peers (interaction support). To support these, the infrastructure was defined as 13 components across 5 domains, including a recommendation model, data analysis engine, and server system. Academically, the study shifts the focus of AI-based personalized education from students to instructors, applying self-regulated and adaptive learning theories to faculty development. Practically, it offers universities a blueprint for moving beyond uniform faculty support toward data-driven, personalized systems to strengthen teaching competencies.