ACE-TA: An Agentic Teaching Assistant for Grounded Q&A, Quiz Generation, and Code Tutoring
This addresses the need for scalable, personalized teaching assistants in programming education, though it appears incremental as it builds on existing LLM capabilities.
The paper tackles the problem of providing automated educational support for programming courses by introducing ACE-TA, a framework that autonomously routes queries to grounded Q&A, quiz generation, and code tutoring, resulting in a system that offers precise explanations, adaptive assessments, and stepwise guidance with sandboxed execution.
We introduce ACE-TA, the Agentic Coding and Explanations Teaching Assistant framework, that autonomously routes conceptual queries drawn from programming course material to grounded Q&A, stepwise coding guidance, and automated quiz generation using pre-trained Large Language Models (LLMs). ACE-TA consists of three coordinated modules: a retrieval grounded conceptual Q&A system that provides precise, context-aligned explanations; a quiz generator that constructs adaptive, multi-topic assessments targeting higher-order understanding; and an interactive code tutor that guides students through step-by-step reasoning with sandboxed execution and iterative feedback.