Year: 2026
Type: preprint
Abstract: To provide personalized learning experiences in courses that teach data analysis skills, instructors often search for datasets that appeal to a student's interests, align with specific learning objectives, and are appropriate for the student's skill level. Datasets that match all three criteria can be difficult to find. DagFlow-AI is an agentic AI data generation tool that creates realistic, synthetic datasets tailored to these criteria. Rather than mimicking existing datasets or generating observations directly, DagFlow-AI uses the broad knowledge and reasoning capabilities of large language models (LLMs) to specify the components of transparent, reproducible data-generating mechanisms from which observations are generated programmatically. We illustrate how the tool can be used to construct a realistic, synthetic dataset and describe how it aligns with both the Guidelines for Assessment and Instruction of Statistics Education (GAISE) and the Problem, Plan, Data, Analysis, and Conclusion (PPDAC) framework for teaching data analysis
Source: arXiv (Cornell University)
Institutions: University of Washington Tacoma, University of Washington
Language: English
Subfield: Statistics and Probability
Field: Mathematics
Domain: Physical Sciences
SDG: Quality education
Open Access status: green
Funder: University of Washington
OpenAlex ID: W7223279912