Personality Factors Relating to Dishonest AI Use in Higher Education

Project Abstract

As artificial intelligence (AI) and large language models become more advanced, students in higher education are increasing their use of these tools for academic purposes (Marshik et al., 2025). There are many personal attributes that might influence whether and how students make use of AI, especially for academically dishonest purposes.

Academic dishonesty has been positively correlated with fear of failure (Mih & Mih, 2016; Ifeagwazi, 2019). A meta-analysis by Krou, Fong, and Hoff (2021) found a significant negative correlation between academic dishonesty and a mastery goal orientation. Students with low need for cognition are more likely to disengage from difficult classes (Lavrijsen et al., 2025), which may lead them to use AI to complete assignments they deem too difficult or unimportant. Self-efficacy, or students’ belief in their own abilities, strongly affects academic performance. Students with higher self-efficacy are more motivated to put in effort, and are more likely to achieve better grades (Wood et al., 1987). Further, having higher self-efficacy has been found to be negatively associated with academic dishonesty (Krou et al., 2021).

While much research has looked at these variables as they relate to traditional methods of academic dishonesty, little research has examined how they relate specifically to dishonest use of AI tools. The current study aims to provide a foundation for understanding how and why students in higher education utilize AI for their coursework, with the goal that this knowledge can be used for further development of strategies to guide students to use AI in legitimate ways.

Data collection for this project is currently ongoing. Students complete a questionnaire regarding both AI-driven and traditional forms of academic dishonesty, attitudes towards AI, goal orientation, need for cognition, self-efficacy, and fear of failure using adapted, well-cited scales. Correlational analyses will be conducted to determine if there are any significant relationships between students’ academic AI use and the traits measured. It is expected that academic AI use will be positively related with fear of failure, and negatively related to self-efficacy, need for cognition, and mastery orientation. Outcomes and implications will be discussed, particularly in regard to where relationships between AI-driven and more traditional dishonesty demonstrate different relationships with the personality variables.

Conference

Midwestern Psychological Association (midwesternpsych.org)

April 17-19

Funding Type

Travel Grant

Academic College

College of Humanities and Fine Arts

Area/Major/Minor

Psychology

Degree

Masters

Classification

Graduate

Name

Dr. Patrick Cushen

Academic College

College of Humanities and Fine Arts

This document is currently not available here.

Share

COinS