Evaluating the effectiveness of artificial intelligence-generated content and user-generated content in health learning: the roles of cognitive complexity and health information literacy
Xiaoning Sun, Shuang Zhang, Siqi LiuPurpose
Grounded in cognitive load theory and dual process theory, this study aims to investigate how cognitive complexity and health information literacy (HIL) influence users’ cognitive load, perceived learning and cognitive system utilization when acquiring health-related knowledge through artificial intelligence-generated content (AIGC) and user-generated content (UGC). It aims to reveal cognitive mechanisms underlying these processes and inform improvements in the health information environment.
Design/methodology/approach
A 2 (learning tools: AIGC vs UGC) × 2 (cognitive complexity: high vs low) × 2 (HIL: high vs low) experimental design was adopted. Data were collected through a questionnaire and evaluated using analysis of variance.
Findings
Users experienced significant differences in intrinsic and extraneous cognitive load and across all dimensions of perceived learning when using AIGC compared with UGC; Task complexity caused significant differences across all types of cognitive load; HIL levels significantly affected intrinsic and germane cognitive load. Regarding cognitive system utilization, no significant differences emerged between AIGC and UGC. However, higher task complexity increased System 2 engagement universally, and users with higher HIL relied more on System 2 (analytical processing).
Originality/value
This study develops an integrated framework for examining how learning tools, cognitive complexity and HIL jointly shape learning effectiveness in digital environments. It advances understanding of cognitive processing in digital learning contexts and provides actionable insights for designing cognitively adaptive AIGC and UGC systems.