AI-enhanced learning on learning management system: A unified theory of acceptance and use of technology perspective

Abstract

Post-pandemic digital transformation has accelerated the adoption of Learning Management Systems (LMS) and hybrid learning models, shifting the key issue from technology availability to acceptance, use, and sustained engagement. The emergence of AI-enhanced LMS—integrating generative AI and deep learning analytics—introduces both opportunities and risks that affect user adoption. This study examines acceptance and use of NotebookLM integrated with Google Classroom at Universitas Negeri Semarang using the Unified Theory of Acceptance and Use of Technology (UTAUT) framework extended with AI Literacy as a moderating variable. A quantitative approach using Partial Least Squares Structural Equation Modelling (PLS-SEM) was applied to data collected from 83 students of the Civil Engineering Education study programme. Results show that six of seven hypotheses were supported: Performance Expectancy (β = 0.433), Effort Expectancy (β = 0.512), Social Influence (β= 0.590), and Facilitating Conditions (β = 0.441) significantly predicted Behavioral Intention; Facilitating Conditions (β = 0.526) and Behavioral Intention (β = 0.763) significantly predicted Use Behavior. Social Influence emerged as the strongest predictor of Behavioral Intention. AI Literacy did not moderate the Effort Expectancy–Behavioral Intention relationship but acted as an independent predictor. Findings suggest that institutional social climate and instructor support are critical drivers for AI-enhanced LMS adoption in vocational engineering education.