Journal of Emerging Trends in Artificial Intelligence

ABSTRACT

Artificial intelligence (AI) enhances teaching in higher education. The accuracy and dependability of material produced by AI are questioned. Faculty opinions about AI's accuracy and susceptibility to hallucinations are examined in this study. The effectiveness of training is affected by these ideas. These opinions are mediated by trust in AI. The research utilized a cross-sectional quantitative design. Data was collected using a structured questionnaire from 75 faculty members in management institutions. Partial least squares structural equation modeling (PLS-SEM) was employed to test the hypothesized relationships among constructs. AI accuracy positively impacts faculty trust and teaching effectiveness. Faculty trust in AI is the most significant predictor of teaching effectiveness. Faculty trust significantly mediates the relationship between AI accuracy and teaching outcomes. AI hallucination risk does not have a significant direct or indirect effect. The study transitions from AI adoption to AI reliability, highlighting trust as a vital mediating factor in AI-driven teaching environments. Findings stress the importance of dependable AI systems and the necessity of faculty training for assessing AI-generated content for effective teaching. The study presents an innovative empirical model that connects AI accuracy, hallucination risk, trust, and teaching effectiveness from the faculty's perspective.

Keywords: Artificial Intelligence; Teaching Effectiveness; AI Accuracy; Hallucination Risk; Faculty Trust; PLS-SEM

JEL Classification Codes

I23, O33, C12, C38, D83