Effects of AI on the Trust and Confidence of Faculty Members in Submitted Academic Works by Students

This study examined faculty trust, student confidence, and the perceived impact of artificial intelligence (AI) tools in academic assessment within higher education institutions. Specifically, it explored faculty perceptions regarding authenticity, authorial verifiability, and evaluative certainty; students’ confidence in submitting academic work; and the influence of AI-related practices such as algorithmic surveillance and linguistic flattening. Using a quantitative descriptive-correlational research design, data were gathered from faculty members and students through survey questionnaires and analyzed using weighted mean, Pearson correlation, and ANOVA. Findings revealed that faculty members generally remained confident in evaluating student submissions, with a grand mean interpreted as “Confident/Agree,” although concerns regarding authenticity and authorship verification persisted. Students likewise demonstrated confidence in submitting academic work, particularly in academic self-efficacy, but also reported anxiety regarding AI detection systems and moderate trust in institutional assessment practices. The study further found that AI tools significantly influenced student behavior, particularly through linguistic flattening, where students intentionally simplified writing styles to avoid detection. Correlation analysis showed no significant relationship between the frequency of AI detector use and faculty trust in student submissions, while ANOVA results revealed no significant differences in perceptions based on years of teaching experience. Overall, the study concluded that AI has substantially reshaped academic assessment practices, faculty perceptions, and student writing behavior, emphasizing the need for balanced institutional policies, ethical AI governance, improved AI literacy, and assessment frameworks that promote both academic integrity and responsible AI use in higher education.