Path analysis of AI-based assessment use in higher education: The roles of self-efficacy, digital literacy, and perceived academic performance
TL;DR
The findings suggest that students’ psychological readiness plays a more important role than technology use alone in explaining perceived academic performance within AI-supported assessment environments.
Abstract
Purpose: The growing adoption of artificial intelligence (AI)-based assessment in higher education has created new opportunities to enhance learning evaluation. However, empirical evidence explaining how students’ digital literacy and self-efficacy relate to AI-based assessment use and perceived academic performance remains limited, particularly in developing higher education contexts. This study examined the structural relationships among digital literacy, self-efficacy, AI-based assessment use, and perceived academic performance among university students.Method: A quantitative cross-sectional survey was conducted using purposive sampling involving 100 undergraduate students from five teacher education programs at Universitas Nahdlatul Ulama Lampung, Indonesia. Data were collected through a structured questionnaire comprising multi-item measures of digital literacy, self-efficacy, AI-based assessment use, and perceived academic performance. The proposed structural relationships were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM).Findings: The results indicated that digital literacy was positively associated with self-efficacy and AI-based assessment use. Self-efficacy showed a significant positive relationship with perceived academic performance and emerged as the strongest predictor of this construct. In contrast, AI-based assessment use and digital literacy were not directly associated with perceived academic performance. These findings suggest that students’ psychological readiness plays a more important role than technology use alone in explaining perceived academic performance within AI-supported assessment environments.Significance: Unlike previous studies that primarily emphasize technology acceptance or technological effectiveness, this study integrates digital literacy and self-efficacy within a structural model of AI-based assessment use to explain students’ perceived academic performance in an Indonesian higher education context. The findings provide practical implications for higher education institutions seeking to strengthen students’ digital competencies and self-efficacy to support the effective implementation of AI-based assessment.