Automated Teacher Behavior Inventory Management System with AI-Driven Recommendations

Abstract

To address limitations in traditional teacher evaluation, this developmental research introduced the Automated Teacher Behavior Inventory Management System with AI-Driven Recommendations. Leveraging Python for analytics and OpenAI for content generation, the system automates student surveys across six core teaching competencies to deliver personalized professional development advice. Results demonstrated that the tool effectively identifies performance gaps and generates meaningful, non-repetitive feedback—available both online and offline—that aligns closely with institutional standards. Ultimately, the study confirms the practical value of AI integration in fostering continuous professional growth, with future directions pointing toward enhanced scalability, deeper analytics, and Learning Management System