DagFlow AI: Agentic Synthetic Data Generation for Personalized Statistics Learning
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
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BibTeX
@misc{arthur2026dagflow,
title = {{DagFlow AI: Agentic Synthetic Data Generation for Personalized Statistics Learning}},
author = {David Arthur and Ranjini Grove and Zaher Kmail and Curtis Atkisson},
howpublished = {arXiv},
year = {2026},
month = oct,
doi = {10.48550/arxiv.2610.30580},
eprint = {2610.30580},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2610.30580},
note = {CC BY-NC-ND 4.0},
}
RIS
TY - UNPB
TI - DagFlow AI: Agentic Synthetic Data Generation for Personalized Statistics Learning
AU - David Arthur
AU - Ranjini Grove
AU - Zaher Kmail
AU - Curtis Atkisson
PY - 2026
DA - 2026/10/22
PB - arXiv
DO - 10.48550/arxiv.2610.30580
UR - https://arxiv.org/abs/2610.30580
AB - 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
ER -