StepMIND: A Visual Framework for Stepwise, Multimodal, and Bidirectional Explanations of AI-Generated Data Analysis Pipeline

Artificial intelligence (AI) enables users to generate data visualizations from natural language descriptions, lowering the barrier to data exploration. However, AI-generated visualizations often present only the final output, lacking transparency and limiting users’ ability to verify, interpret, or refine the results. To address this, we introduce StepMIND, a generalizable visual framework that enhances explainability and interactivity in AI-generated data analysis pipelines. StepMIND integrates four dimensions: (1) Stepwise Refinement, allowing users to engage in the AI decision process; (2) Multimodal Explanations, combining natural language, structured notation, direct manipulation, and content visualization for accessible interpretation; (3) Bidirectional Editing, enabling seamless updates across modalities; and (4) Familiar Interaction Models, such as code editor and spreadsheet-based manipulations, to support both technical and non-technical users. To demonstrate its utility, we apply StepMIND in STAGE, a case study system for AI-assisted data visualization. A within-subject user study (N=20) shows that STAGE significantly improves user confidence and trust, reduces cognitive load, and facilitates both exploratory and corrective refinements. Our findings further suggest that StepMIND can generalize to broader AI-assisted workflows, offering a visible and interactive approach to explainable AI.