Imagine an AI tutor helping a student with a tricky math problem. The student proudly presents their solution, but there’s a hidden flaw in their logic. Unfortunately, the AI misses the error and praises the student's work. This scenario, while frustrating, highlights a common challenge in developing AI tutors: accurately identifying student errors. A fascinating new research paper, "Stepwise Verification and Remediation of Student Reasoning Errors with Large Language Model Tutors," tackles this head-on. Researchers found that even cutting-edge large language models (LLMs) struggle to pinpoint the exact step where a student’s reasoning goes astray. Inspired by human teachers who carefully analyze a student’s work before offering feedback, the researchers propose a two-step approach. First, a “verifier” model scans the student’s solution, comparing it to the correct answer and identifying the first incorrect step. Then, a separate LLM tutor, armed with this precise error information, generates a targeted response. They tested different “verifier” approaches, including classifying solutions as correct or incorrect, providing detailed textual descriptions of errors, and aligning student steps with the correct solution. The results were promising. AI tutors using this two-step method provided more accurate, targeted, and actionable feedback. Instead of generic responses, they zeroed in on the student’s specific mistake. For example, instead of simply saying, “Your calculation is incorrect,” the AI tutor could say, “You added the two numbers before multiplying, remember the order of operations?” This research reveals a significant step toward creating truly effective and helpful AI tutors. By mimicking the diagnostic approach of human teachers, these systems can move beyond simply providing answers to guiding students toward genuine understanding.