Show and Tell: Exploring Large Language Model’s Potential inFormative Educational Assessment of Data Stories

Abstract

Crafting accurate and insightful narratives from data visualization is essential in data storytelling. Like creative writing, where one reads to write a story, data professionals must effectively ``read" visualizations to create compelling data stories. In education, helping students develop these skills can be achieved through exercises that ask them to create narratives from data plots, demonstrating both ``show" (describing the plot) and ``tell" (interpreting the plot). Providing formative feedback on these exercises is crucial but challenging in large-scale educational settings with limited resources. This study explores using GPT-4o, a multimodal LLM, to generate and evaluate narratives from data plots. The LLM was tested in zero-shot, one-shot, and two-shot scenarios, generating narratives and self-evaluating their depth. Human experts also assessed the LLM's outputs. Additionally, the study developed machine learning and LLM-based models to assess student-generated narratives using LLM-generated data. Human experts validated a subset of these machine assessments. The findings highlight the potential of LLMs to support scalable formative assessment in teaching data storytelling skills, which has important implications for AI-supported educational interventions.