The ability to make sense of data visualizations is a core component of data literacy and an essential skill in data science and analytics education. Yet, it remains unclear how this skill can be deliberately taught and practiced. To address this challenge, we designed a classroom activity called data story reading, in which students generate a title and narrative in response to a given visualization. Narratives are distinguished as either Show—surface-level descriptions of what is displayed—or Tell—deeper interpretations that uncover trends, patterns, or anomalies. Building on this distinction, we developed an AI-supported feedback tool that automatically classifies student narratives at the sentence level as Show or Tell, presents the results to students, and invites them to critique and reflect on the AI’s feedback. We piloted the tool in an introductory data science course in Spring 2025 enrolling students from diverse backgrounds. Data for analysis were drawn from system logs capturing sentence-level classifications, student critiques, and reflective comments. Findings suggest that AI feedback helped students recognize the difference between descriptive and interpretive sense-making, while also surfacing tensions in providing nuanced feedback. We discuss implications for AI-supported learning, limitations of the current tool, and directions for future refinement.