Data-intensive education research uses information about student learning, including demographic information, academic performance, behavioral patterns, attendance, survey responses, and technology usage. Artificial intelligence can help researchers identify patterns, predict performance, identify students who may need support, and personalize learning. It can also produce ethical concerns, expose sensitive information, and make adverse decisions.
This project develops a practical framework for responsible AI use in education research, with particular attention to fairness, privacy, transparency, and stakeholder needs. The work is designed to connect ethical principles with tools, communication resources, and educational materials that support informed decisions about education data.
The project brings together three stakeholder perspectives:
The research program combines stakeholder studies, responsible AI methods, communication design, and educational support.
Surveys, interviews, and focus groups will examine stakeholder practices, concerns, requirements, and decision-making processes related to the use and sharing of education data.
The project will develop a user-oriented platform to help identify potential bias and privacy risks in datasets and models.
A demystifying toolkit will help education researchers communicate with data administrators and participants. It will translate technical concepts, model limitations, data requirements, fairness concerns, and privacy risks into accessible explanations and resources.
Educational materials will be tailored to stakeholder needs.
This project is supported by the U.S. National Science Foundation under the following awards: 2520496, 2520497, and 2520498.