Give a Little, Take a Little? A Factorial Survey Experiment on Students’ Willingness to Use an AI-based Advisory System and to Share Data

Abstract

Given its ability to handle a large amount of data, artificial intelligence (AI) has the potential to improve data-driven decisions under various situations. The present research identifies the necessary conditions for the implementation of an AI-based advisory system (AS) in higher education. Using a factorial survey design, we examine experimentally varied features of an AI-based AS to explore students’ willingness to use it and students’ willingness to share their data as a core challenge for successful implementation. Theoretically, we focus on the perceived costs and benefits to explain students’ intention, but we also highlight the role of trust and privacy concerns in regard to collecting data for the AS. In terms of benefits, information about the predictive power of the AS significantly increases students’ intention to use the tool and to share data and thus offers an incentive for students to share data. Moreover, a disproportionately long survey duration and survey topics that seem unrelated to the AS reduce students’ willingness to share data. With respect to trust and privacy concerns, our results indicate that providing transparent information about the AS has no effect on students’ willingness to share data, while aspects regarding who has access to the AS results and a long period of data storage reduce students’ intentions to share data. Based on these findings, we advise universities to communicate students’ expected benefits from a system to implement the AS, but we also recommend seriously considering students’ privacy concerns. Who has access to the data and the results of the AS should be transparent, as well as for what reason and how long. Otherwise, a substantial and probably selective part of the student body may not use the tool or share data due to privacy concerns.

Publication
In T. Wolbring, H. Leitgöb, & F. Faulbaum (Eds.), Sozialwissenschaftliche Datenerhebung im digitalen Zeitalter. Springer VS