ArticleFrontiers in psychology2026
AI literacy as both bridge and buffer: unraveling its dual role between research stressors and teaching excellence.
Article in Frontiers in psychology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Introduction: The persistent tension between research and teaching poses a significant challenge to sustainable development in higher education. Grounded in the Challenge-Hindrance Stressors Framework and Dynamic Capabilities Theory, this study investigates the dual role of faculty artificial intelligence (AI) literacy, conceptualized as a dynamic capability to sense, seize, and reconfigure AI resources to reshape the impact of research stressors on teaching excellence. Methods: We conducted a survey of 253 faculty members from Chinese universities. The proposed model, which delineates the asymmetric effects of challenge and hindrance research stressors, the mediating role of teaching-research time conflict, and the dual mechanisms of AI literacy, was tested using partial least squares structural equation modeling. Results: Challenge research stressors significantly enhanced teaching excellence, both directly and indirectly through increased AI literacy. Conversely, hindrance stressors exacerbated teaching-research time conflict but did not exhibit a direct negative effect on teaching excellence. Notably, AI literacy demonstrated a dual mechanism: it mediated the positive effect of challenge stressors and also buffered the negative impact of teaching-research time conflict on teaching excellence. Discussion: These findings suggest that when AI literacy functions as a dynamic capability, the competitive relationship between research and teaching can become synergistic. The study extends the Challenge-Hindrance Stressors Framework by showing that different types of research pressure have distinct implications for teaching outcomes. It also contextualizes the Conservation of Resources Theory by identifying AI literacy as a boundary condition: in its presence, the conventional mechanism of resource drain is disrupted. By reframing AI literacy as a capability for resource reconfiguration rather than merely a technical skill, this research suggests a potential direction for institutions: moving from mitigating research pressure to leveraging generative AI for faculty development. Strengthening AI literacy may therefore serve as a practical lever for advancing student-centered education and the broader sustainability agenda.
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