Competency-based education(CBE) enables flexible, skill-centered learning by allowing students to progress based on mastery rather than fixed schedules. Atlas already utilizes machine learning techniques to support educators; nevertheless, further improvements are required to accelerate adoption and improve quality.
This thesis proposes an interactive AI-powered agent that assists instructors in creating, refining, and maintaining competency networks through natural language. Rather than relying on static, one-shot inputs, the agent engages in a dynamic conversation—asking clarifying questions, gathering relevant context, and presenting interactive proposals. This interaction model is designed to reduce redundant AI calls, avoid naive prompting, and ensure that the resulting competency networks align closely with course objectives.