Using Generative AI Personas to Study Consent in Educational Data Use: Views of Parents and Elementary School Students

Abstract: Digital transformation in education (Educational DX) is expected to improve learning outcomes and school management. To achieve this, various stakeholders need to utilize learning data. These include health and life data (such as health records, fitness history, health insurance cards), learning activity data (attendance, study histories, online logs, test answers, portfolios, videos), survey and evaluation data (named or anonymous questionnaires, psychological tests, peer reviews), and personal or family information (parental occupation, home situation, scholarship forms). Because such data often includes personal information, permission is required. If the data are about children, parents also need to agree. Even when people understand that the data are helpful, they may hesitate when it involves themselves or their children.We explored this issue with a persona-based simulation using generative AI. With the GPT-4.0 model, we created virtual parents and elementary school students with diverse backgrounds. These personas responded to applications for data use. Applications varied in purpose, length of data retention, and whether data would be shared with others. We considered five types of applicants: (1) school administrators and boards of education, (2) researchers, (3) teachers, (4) parents of students, and (5) others.This abstract presents the results for school administrators and boards of education, focusing on this group because their role is essential for promoting Educational DX and responding to social expectations.Parents gave clear responses. For attendance records, 45 of 50 gave conditional approval. Most of these (38 cases) required anonymization, and only one parent refused. For study histories, 43 parents gave conditional approval. Their main conditions were retention and deletion rules (16 cases) and anonymization (17 cases). For named surveys, 39 parents gave conditional approval, but the reasons were different: educational usefulness (12 cases) and clarity of purpose (six cases). Anonymization was not requested, since names were essential. Refusals were rare and usually due to unclear purpose or little educational value.Students also showed strong patterns. For attendance records, 45 of 50 gave conditional approval, mentioning anonymization (14 cases) or educational usefulness (11 cases). None refused. For named surveys, 37 gave conditional approval, often saying they hoped the results would improve their school. Anonymization was not relevant here. By contrast, health insurance cards led to 28 refusals, with only five approvals and 17 conditional approvals. Students said the data felt “too personal” or they did not know how it would be used. For parents’ occupational information, 21 refused, two approved, and 27 gave conditional approval, often saying it was not connected to their own learning.In summary, parents focused on anonymization, retention rules, and a clear purpose. Students were more willing to help with school improvement, but strongly rejected data that felt private or unrelated. Conditions for approval differed by data type. Anonymization was central for attendance and study histories, while educational usefulness was decisive for named surveys.This study shows that conditional approval was the most common response. While this abstract reports only on school administrators and boards of education, results for other applicant types will be presented in the full paper.

Keywords: Generative AI, Persona simulation, Consent formation, Educational data use, Parents and students, Privacy Protection

DOI: 10.54941/ahfe1006931

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