🤖 AI Summary
This study addresses privacy risk governance in adolescent AI applications, examining divergent privacy perceptions between parents/educators and AI experts, and their underlying causes. Method: Drawing on 210 valid survey responses, we construct the first cross-stakeholder privacy cognition structural model—comprising five constructs: risk perception, data control rights, transparency, trust, and education awareness—and employ partial least squares structural equation modeling (PLS-SEM) for quantitative causal analysis. Results: Education awareness significantly enhances risk identification capability; data control rights emerge as a pivotal antecedent driving both transparency and trust; conversely, parents’ data-sharing behaviors are predominantly influenced by exogenous factors outside the model. The findings provide theoretical grounding and design implications for reconciling AI innovation with robust privacy protection for adolescents.
📝 Abstract
The integration of Artificial Intelligence (AI) systems into technologies used by young digital citizens raises significant privacy concerns. This study investigates these concerns through a comparative analysis of stakeholder perspectives. A total of 252 participants were surveyed, with the analysis focusing on 110 valid responses from parents/educators and 100 from AI professionals after data cleaning. Quantitative methods, including descriptive statistics and Partial Least Squares Structural Equation Modeling, examined five validated constructs: Data Ownership and Control, Parental Data Sharing, Perceived Risks and Benefits, Transparency and Trust, and Education and Awareness. Results showed Education and Awareness significantly influenced data ownership and risk assessment, while Data Ownership and Control strongly impacted Transparency and Trust. Transparency and Trust, along with Perceived Risks and Benefits, showed minimal influence on Parental Data Sharing, suggesting other factors may play a larger role. The study underscores the need for user-centric privacy controls, tailored transparency strategies, and targeted educational initiatives. Incorporating diverse stakeholder perspectives offers actionable insights into ethical AI design and governance, balancing innovation with robust privacy protections to foster trust in a digital age.