Abstracts
Abstract
This study examined how task characteristics (TC) and individual characteristics (IC) affect cognitive load (CL) and how artificial intelligence generated content (AIGC) moderates these effects in online learning. Participants included 435 undergraduate students (200 males and 235 females) enrolled in an introductory educational technology course. A structural model, conducted using Mplus software, was employed to test the relationships between each of TC and IC, and CL. Additional analyses explored the moderating role of AIGC on the relationship between TC and CL, the impact of AIGC on the relationship between IC and CL, as well as how these patterns differed by gender. Results revealed that TC positively affected CL, whereas IC exhibited a negative correlation. Moreover, AIGC negatively affected the relationship between TC and CL, but it enhanced the relationship between IC and CL. The moderating role of AIGC differed by gender. Specifically, AIGC positively influenced the connection between IC and CL among males but not females, and it weakened the relationship between TC and CL among females but not males. The implications and limitations are also discussed.
Keywords:
- task characteristics,
- individual characteristics,
- cognitive load,
- AIGC,
- structuring equation modeling,
- online learning
Appendices
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