Harnessing generative AI for next-gen education: a cognitive load and knowledge-based view approach to fuel innovation

Purpose This study aims to examine how students’ generative artificial intelligence (GenAI) knowledge acquisition and knowledge application are associated with their innovation ability and information literacy (IL) skills. It further explores how these factors are associated with their level of motivation and behavioral intentions (BI). Design/methodology/approach The study collected responses from 250 students using GenAI for their educational purposes. Based on the knowledge-based view (KBV) and cognitive load theories (CLT), the study developed an empirical model assessed using the partial least squares structural equation modeling (PLS-SEM) method. Findings The findings reveal that GenAI knowledge acquisition significantly enhances students’ innovation ability, while GenAI knowledge application exhibits a negative but significant relationship with IL. Students’ innovation abilities, backed by GenAI, positively influence their level of motivation and BI. However, IL negatively impacts students’ level of motivation and shows no significant effect on their BI. Originality/value The study results support KBV and CLT theories that effective utilization of knowledge resources fosters students’ innovation ability, while excessive cognitive load can hinder their learning processes. Current research contributes to both theory and practice and offers insights for educators and policymakers.