The Impact of AI-Powered Digital Assistants on Student Emotions, Engagement, and Academic Performance: A PLS-SEM Analysis in Higher Education
TL;DR
The results demonstrate that service experience is the most influential antecedent of both emotional and behavioural outcomes, whereas the effects of functional features and information quality vary across the examined relationships.
Abstract
This study investigates the impact of AI-powered digital assistants on students’ feelings, engagement, and academic success within higher education environments. The study aims to investigate post-adoption behaviour, emphasizing how service experiences, functional attributes, information quality, and ease of interaction influence emotional and behavioural results. A structured survey was used to gather data from 431 respondents in higher education at Exploits university, Malawi, and the study utilized a quantitative approach. Measurement scales were adapted from validated studies in the AI adoption and educational technology literature and contextualized for the higher education setting. Partial Least Squares Structural Equation Modelling (PLS-SEM) version 4.1.1.8 was utilized to examine the connections between variables. Common method bias was assessed using the full collinearity approach, and all VIF values were below the recommended threshold, indicating that common method bias was not a significant concern. The results indicate that Service experience leads to Positive emotions (β = 0.205, p = 0.002) and Student engagement (β = 0.242, p < 0.001), validating H1a and H1b. Quality of information → Positive feelings (β = 0.161, p = 0.005), backing H3a, whereas Functional characteristics → Student involvement (β = 0.409, p < 0.001), supporting H4b. Positive emotions → Student involvement (β = 0.190, p < 0.001) and Academic achievement (β = 0.460, p < 0.001), and Student involvement → Academic achievement (β = 0.310, p < 0.001), confirming H5–H7. Contextualization × Positive emotions → Academic performance was noteworthy (β = 0.063, p = 0.038), reinforcing H8a. Nonetheless, H2a, H2b, H3b, H4a, and H8b received no support (p > 0.05). The research advances theoretical understanding by broadening AI adoption literature to include emotional and behavioural effects, while also enhancing practical implications by highlighting service quality and system efficiency. Suggestions emphasize the importance of focusing on contextual, high-quality AI resources to enhance student engagement, emotional well-being, and educational achievement. The results demonstrate that service experience is the most influential antecedent of both emotional and behavioural outcomes, whereas the effects of functional features and information quality vary across the examined relationships.