Generative AI (GenAI) is rapidly transforming creative fields, raising critical questions about its impact on artistic identity and authorship, which also influence art education. While studies have explored GenAI adoption, the diversity of art students’ subjective perspectives on collaborating with these tools remains under-examined. This study uses Q methodology, a mixed-methods approach to investigating subjectivity, to explore the distinct perspectives art students hold regarding their creative identity when collaborating with GenAI. We conducted a Q study with 40 university students from various art-related disciplines (e.g., visual arts, design, music, animation) across different academic levels. Participants sorted 42 statements reflecting diverse viewpoints on GenAI collaboration, authorship, ethics, and creative control. By-person factor analysis revealed five distinct perspectives: (1) Human-centered directors, who treat GenAI as a tool under strong human intention; (2) Authorship guardians, who resist AI co-authorship and prioritise authenticity and ethical boundaries; (3) Conflicted co-creators, who use GenAI but struggle with ownership, disclosure, and earned pride; (4) Pragmatic integrators, who adopt GenAI for workflow efficiency while keeping craft expertise central; and (5) Enthusiastic explorers, who embrace AI’s creative possibilities, open to co-agency and novel expression. These findings offer a nuanced typology of student viewpoints, moving beyond simple acceptance or rejection. We further propose a two-dimensional framework that maps these perspectives according to students’ openness to GenAI and their sharing of creative agency with it. This research contributes empirical insights into how art students negotiate creative identity with GenAI, provides a valuable framework for educators to develop tailored pedagogical strategies addressing diverse student needs and concerns, and offers implications for designing more human-centered GenAI tools for creative practice.

This study investigates the role of quality of information (QI) in shaping students’ engagement with GenAI, specifically focusing on the Doubao chatbot in undergraduate English learning. Integrating the Technology Acceptance Model (TAM) with the Stimulus-Organism-Response (SOR) framework, the research examines how QI affects students’ perceived ease of use (PEU), perceived usefulness (PU), and ultimately their behavioral (BE), cognitive (CE), and emotional engagement (EE) with the tool. Data were collected from 110 undergraduate students. The findings revealed that QI significantly influences both PEU and PU, which subsequently affect students’ engagement. Notably, PEU exerted a stronger impact on engagement than PU. While PU drives behavioral engagement, it does not directly influence cognitive or emotional engagement. The results underscore the importance of high-quality, easy-to-use GenAI tools in enhancing student engagement. Developers and educators are encouraged to prioritize content quality and usability to foster deeper intrinsic motivation and long-term engagement.

Despite the increasing adoption of desktop virtual reality (VR) in higher education, the specific instructional efficacy of 3D interactive prompts remains inadequately understood. This study examines how such prompts—specifically dynamic spatial annotations and 3D animated demonstrations—influence learning outcomes within a desktop virtual learning environment (DVLE). Employing a quasi-experimental design integrated with eye-tracking and multimodal learning analytics, university students were assigned to either an experimental group (DVLE with 3D prompts) or a control group (basic DVLE) while completing physics tasks. Data collection encompassed eye-tracking metrics (fixation heatmaps, pupil diameter and dwell time), post-test performance (assessing knowledge comprehension and spatial problem-solving), and cognitive load ratings. Results indicated that the experimental group achieved significantly superior learning outcomes, particularly in spatial understanding and dynamic reasoning, alongside optimized visual attention patterns—characterized by shorter initial fixation latency and prolonged fixation on key 3D elements—and reduced cognitive load. Eye-tracking metrics were positively correlated with post-test scores, confirming that 3D prompts enhance learning by improving spatial attention guidance. These findings demonstrate that embedding 3D interactive prompts in DVLEs effectively directs visual attention, alleviates cognitive burden, and improves learning efficiency, offering valuable implications for the design of immersive educational settings.

Background: Anxiety is one of the most prevalent global mental health disorders, with its incidence having risen substantially since the COVID-19 pandemic and remaining elevated in the post-pandemic era. According to the process model of emotion regulation, mindfulness may serve as an early-stage cognitive change strategy that enhances emotional stability and ultimately reduces anxiety. While previous research has demonstrated robust interconnections among anxiety, emotional stability, and mindfulness, the majority of studies have relied on cross-sectional designs and aggregate scores, neglecting their longitudinal, item-level dynamic relationships. Method: This study collected data from 547 college students ( M _age ± SD = 20.11 ± 1.20) across three time points and utilized a cross-lagged panel network (CLPN) approach to analyze item-level temporal dynamics. The goal was to build network models of anxiety, emotional stability, and mindfulness, and to investigate how these constructs were temporally interrelated at the item level in an exploratory manner. Result: Mindfulness items had higher out-Expected influence (OEI), whereas anxiety symptoms had stronger in-Expected Influence (IEI), with anxiety and emotional stability having the strongest bridge effects. Specifically, mindfulness components “Thought Acceptance” (M7) and “Nonjudgmental Awareness” (M8) exerted significant effect on anxiety and emotional stability. Anxiety symptoms were highly responsive to other symptoms, which undermined emotional stability. The network analysis revealed complex temporal and predictive associations among anxiety, emotional stability, and mindfulness. Conclusion: This study is the first investigation to reveal the longitudinal dynamic connections between anxiety, emotional stability, and mindfulness at the item level, consequently provided longitudinal support consistent with the emotion regulation process model.