In this poster, we introduce Anonymized, an AI-Driven classroom assistant designed to promote active learning in introductory programming (CS1) courses through scalable, personalized, and explainable feedback. The assistant combines the generative capabilities of large language models (LLMs) with instructor-in-the-loop authoring and an explainable AI engine to ensure pedagogically aligned support. Instructors can co-design problems with LLM assistance, provide exemplar solutions, define common student errors, and author targeted feedback. The AI engine analyzes student code submissions, identifies misconceptions, and maps them to instructor-verified feedback in real time. This system is designed to ensure that key educational concepts and common misconceptions are explicitly addressed by instructors, while also leveraging LLMs to provide reasonable feedback for novel or edge-case solutions that instructors may not have anticipated. By combining instructor expertise with the flexibility of generative AI, the assistant helps close feedback gaps and ensures more comprehensive coverage of student learning needs—especially in large or diverse classrooms where instructional resources are limited.