Aim/Purpose
Generative artificial intelligence (GAI) tools based on Large Language Models (LLMs) are increasingly used to enrich knowledge, support decision-making, and generate new content. However, as beneficial as these tools may be, they also store vast amounts of information, which might jeopardize users’ privacy and data protection. Thus, users who wish to maximize the benefits of GAI tools must relinquish some of their privacy, a phenomenon known in the literature as the “privacy paradox.”

Background
This research aims to determine whether users experience privacy concerns when using GAI tools by examining the relationships among privacy concerns, trust in AI operators, and actual usage patterns among students. It considers different theoretical models (Privacy Calculus, TAM, and UTAUT) to deter-mine if perceived benefits outweigh the risks of information disclosure.

Methodology
To examine this issue, a quantitative pilot study was conducted using closed-ended questionnaires distributed to 121 students in Israel. Data were analyzed using Pearson correlations and a stepwise multivariate linear regression to identify predictors of GAI tool usage.

Contribution
This study contributes to the body of knowledge by being the first to examine GAI privacy perceptions within Israeli academia. It challenges the universal applicability of the privacy paradox by demonstrating that, in this specific context, trust and proficiency are more decisive than privacy concerns.

Findings
The findings indicate that AI-related privacy concerns have no significant correlation with the amount of GenAI tool usage. However, higher levels of trust in the operators of GenAI tools were found to positively affect the amount of GenAI tool use and Internet proficiency.

Recommendations for Practitioners
Operators of GAI tools should prioritize transparency regarding data collection and usage to build user trust, which directly influences adoption rates. Educational institutions should integrate AI literacy programs to increase students’ proficiency, thereby fostering more confident and effective use of AI tools.

Recommendation for Researchers
Researchers should move beyond simple correlational studies to explore causal links between emotional engagement and data disclosure. It is also recommend-ed to use validated, multidimensional scales to measure privacy concerns rather than broad self-reports.

Impact on Society
The findings suggest that as users feel safe and knowledgeable about digital risks, their intent to utilize GAI increases, potentially accelerating the integration of AI into work and study processes.

Future Research Future studies should employ larger, more diverse samples across multiple institutions to test the generalizability of these findings. Additionally, a mixed-methods approach, including qualitative interviews, could provide a deeper understanding of the privacy paradox.