Consultation-oriented teacher support and student innovation literacy: a structural model - Gao, Wenqian; Wei, Baojian; Xiong, Yan (Frontiers in Psychology, 2025)
Amid the accelerated transformation of higher education, university students face intensifying cognitive demands that challenge both learning and innovation. Drawing upon cognitive load theory and self-determination theory, this study investigates the psychological pathways through which consultation-oriented teacher or counselor support (TS) enhances students’ innovation literacy. A chain-mediation structural equation model (SEM) was developed to examine the sequential relationships among TS, psychological safety (PS), intrinsic motivation (IM), cognitive load (CL), and innovation literacy, while perceived innovation climate was introduced as a contextual moderator. Data were collected from 300 undergraduates at three universities located in Eastern, Central, and Western China using validated questionnaires assessing TS (emotional encouragement, exploratory guidance, feedback support), PS, IM, subjective CL (task complexity and effort), and innovation literacy (cognitive, motivational, and behavioral dimensions). Results indicated that TS significantly improved PS, which in turn enhanced IM and reduced perceived CL. These psychological mechanisms jointly promoted students’ innovation literacy, particularly its cognitive and motivational components, whereas the pathway from IM to observable innovation behavior remained comparatively weak, reflecting a motivational–behavioral gap. Furthermore, perceived innovation climate moderated several key paths, indicating that supportive institutional environments amplify the positive effects of consultation-oriented support on psychological resources and innovative outcomes. Overall, the study establishes an integrated consultation-oriented framework for understanding support-driven innovation in higher education and offers practical implications for consultant training, teacher coaching, and institutional policy aimed at optimizing task design, balancing cognitive demands, and transforming motivation into sustainable innovation behavior.