Trusting the Machine: How AI Assessment Feedback and AI Literacy Shape Students' Idea Implementation Skills

ABSTRACT Artificial intelligence (AI) has transformed higher education. The research shows that university students use AI and acquire academic ideas to develop innovative solutions to their problems. However, there is limited research on how AI feedback helps students improve their idea implementation. This study was rooted in the Technology Acceptance Model (TAM) and the Social Cognitive Theory (SCT). The objective was to examine how the AI assessment feedback (AIAF), trust in AI (TAI) and AI literacy (AIL) influence university students' idea implementation skills (IIS). A descriptive survey design was used to collect data from 486 university students. The analysis revealed that AIAF significantly contributes to students' IIS at the university level. TAI was found to partially mediate the relationship between university students' AIAF and IIS. AIL was found to contribute to students' IIS individually. However, the interaction effect (AIAF*AIL) was not found to be a significant contributor to students' IIS. We found that the interplay among the study's variables (AIAF, TAI and AIL) positively enhances university students' IIS in solving their academic problems. It was recommended that students should use self‐regulated learning in AI feedback to improve their achievements. In addition, the current study has several practical, research and policy implications.