How do system quality, information quality, and anthropomorphism influence AI learner guidance use intention?

Abstract

Generative AI systems have rapidly evolved in recent years in multiple different settings. Still, how different system features and characteristics influence AI system adoption and use is not entirely understood. Drawing from IS success model and literature on anthropomorphism, this paper examines the influence of system quality and information quality on intention to use, as well as how system quality and anthropomorphic features influence perceived information quality. Through an online experiment in higher education context (n=328), where participants utilize an AI assistant to guide their personal competence development, we examine how manipulating avatar design (playful vs. professional) may influence the relationships between the factors. Quantitative analysis through structural equation modeling reveals that system quality factors can positively influence use intention. Also, information quality can be influenced by system quality factors and anthropomorphism but can be negatively influenced by perceived overuse of playful features. Our research offers theoretical and practical contributions.