The application of generative artificial intelligence in the field of education has been receiving increasing attention, with the performance of chatbot ChatGPT being particularly prominent. This study aims to explore in depth the performance impact on higher education students utilizing ChatGPT. To this end, we conducted a survey on 448 university students and employed the partial-least squares (PLS) method of structural equation modeling for data analysis. The results indicate that all eight hypothetical paths posited in this study were supported, and surprisingly, the hypothesis that technology characteristics have a direct effect on performance impact was supported. Moreover, the study found that overall quality is a crucial factor determining performance impact. Overall quality indirectly affects performance impact through task-technology fit, technology characteristics, and compatibility, among which the mediating effect of compatibility is most significant, followed by technology characteristics. This study offers practical recommendations for students on the proper use of ChatGPT during the learning process and assists developers in enhancing the services of the ChatGPT system.

Introduction: The development of virtual reality (VR) films requires novel editing strategies to optimize narrative cognition in immersive environments. While traditional film editing guides attention through controlled sequences of shots, the interactive nature of VR disrupts linear storytelling, challenging creators to balance emotional experience and spatial coherence. By combining eye-tracking technology with neuroscientific findings, this study aims to investigate how different editing techniques in virtual reality (VR) films affect viewers’ narrative cognition, focusing on visual attention, emotional experience and cognitive load, and to optimize VR film editing strategies through a neurocognitive lens.Methods: A controlled experiment with 42 participants was conducted using three versions of a VR movie: an unedited movie, a hard cut edited movie, and a dissolve-transition edited movie. Eye-tracking metrics were recorded using the HTC Vive Pro Eye headset, and emotional experiences were assessed using post-viewing questionnaires. Data were analyzed using SPSS and visualized using heat maps and trajectory maps.Results: The unedited movie (F1) elicited the highest visual attention (TDF: M = 18,953.83 vs. F2/F3, p < 0.001) and emotional immersion, with 75% of viewers rating it as “highly immersive.” It also showed sustained activation in areas related to emotional engagement. Edited movies, both hard cuts (F2) and dissolve-transitions (F3), reduced cognitive load (TSD: M = 16,632.83 for F1 vs. 15,953.18 for F3, p < 0.01) but resulted in fragmented attention. Dissolve-transitions (F3) decreased viewer enjoyment (APD: M = 0.397 vs. F1, p < 0.001). One-way ANOVA analysis revealed that seamless editing enhanced emotional coherence, while abrupt cuts disrupted spatial and temporal integration, leading to reduced emotional engagement.Discussion: Unedited VR films promote emotional coherence driven by the amygdala and maintain attention stability mediated by the prefrontal cortex, which enhances immersive narrative cognition. In contrast, editing techniques prioritize cognitive efficiency at the expense of emotional experience. To maintain immersion, filmmakers should focus on seamless transitions, while strategically using edits to direct attention in the complex 360° environment of VR. These findings contribute to neurocinematic theory by connecting the neural dynamics induced by editing with behavioral outcomes, offering practical insights for VR content creation.

As education undergoes digital transformation, ChatGPT-4 has emerged as one of the most visible tools of generative artificial intelligence. While widely discussed, its impact on student satisfaction and learning outcomes in higher education remains underexplored. This study investigates the factors that shape art and design students’ satisfaction when using ChatGPT to support coursework. Unlike previous research focusing on ChatGPT adoption behavior, this study extends the Information Systems Success Model (ISSM) to the context of art and design education. Drawing on 435 valid survey responses, we employed a mixed-methods approach. Partial Least Squares Structural Equation Modeling (PLS-SEM) was first applied to examine how system quality, compatibility, personal innovativeness, and perceived usefulness influence satisfaction directly and through mediating mechanisms. To complement this, fuzzy-set Qualitative Comparative Analysis (fsQCA) was used to identify multiple combinations of conditions that lead to high satisfaction. The findings show that compatibility, perceived usefulness, and personal innovativeness significantly enhance satisfaction, with path coefficients of 0.378, 0.342, and 0.155, respectively. Importance–Performance Map Analysis (IPMA) further highlights personal innovativeness and system quality as critical drivers. By providing both theoretical and practical insights, this study contributes to the growing body of research on generative AI in art and design education and informs the design of courses and digital learning tools.