Psychological Mechanisms and Heterogeneity in Art Design Students' Willingness to Use AI: An Empirical Analysis Based on an Integrated Framework

Hypotheses
This study tested whether psychological factors (perceived usefulness, ease of use, AI anxiety, creative self-efficacy, attitude toward AI itself, and attitude toward using AI) predict students' willingness to use AI, and whether students show distinct subgroups with different AI adoption patterns across design stages.

Data Collection
630 Chinese art design students completed an online survey using a novel "task allocation method": indicating what percentage of work they would prefer AI to complete across five stages (creative ideation, material collection, visual design, copywriting, final optimization).

Key Findings
Students' fundamental beliefs about AI's role in art were the strongest predictors of willingness, not perceptions of usefulness or ease of use. When these deeper attitudes were considered, classic technology acceptance variables became non-significant—suggesting identity and values matter more than functional considerations in creative domains.

Three distinct profiles emerged: a pragmatic majority using AI for auxiliary tasks while maintaining human control over core creativity; a low-reliance group resistant across all stages; and a high-acceptance group viewing AI as creative partner across the entire process, including ideation.

Upper-level students were more likely high-acceptance, suggesting experience fosters openness. No differences across majors indicated patterns transcend disciplinary boundaries.

Implications
Effective AI integration requires more than technical training—educators must help students develop nuanced understandings of AI's role in creativity. Different students need different approaches: low-reliance students require foundational experience, pragmatic students benefit from gradual expansion into creative stages, and highly accepting students need guidance maintaining critical perspectives. AI willingness is task-dependent, so interventions should be stage-specific rather than promoting uniform adoption.

研究假设
本研究旨在检验两大核心问题：其一，心理因素（感知有用性（perceived usefulness）、感知易用性（perceived ease of use）、AI焦虑、创造性自我效能感（creative self-efficacy）、对AI本身的态度以及对使用AI的态度）能否有效预测学生的AI使用意愿；其二，学生在各设计阶段是否存在具有差异化AI采用模式的独特子群体。

数据收集
630名中国艺术设计专业学生参与了一项采用新颖“任务分配法”的在线调查：参与者需表明，在创意构思（creative ideation）、素材收集（material collection）、视觉设计（visual design）、文案撰写（copywriting）、最终优化（final optimization）这五个设计阶段中，更希望由AI完成的工作占比。

核心研究发现
学生对AI在艺术创作中所扮演角色的核心信念，是其AI使用意愿的最强预测因子，而非感知有用性或感知易用性。当考量这些深层态度时，经典技术接受变量（technology acceptance variables）便不再具有统计学显著性——这表明在创意领域，身份认同与价值观相较于功能性考量更为关键。

本研究识别出三类截然不同的群体画像：一是务实型多数群体，他们将AI用于辅助任务，同时保留人类对核心创意的控制权；二是低依赖型群体，在所有设计阶段均对AI持抵触态度；三是高接受型群体，将AI视为创意伙伴，将其应用于涵盖创意构思在内的全流程创作。

高年级学生更大概率属于高接受型群体，这表明学习经历有助于提升学生对AI的开放包容度。不同专业背景的学生之间未发现显著差异，说明此类AI采用模式超越了学科边界。

研究启示
有效整合AI教学与实践不应仅局限于技术培训——教育者需帮助学生构建对AI在创意领域中角色的精细化认知。不同学生群体需要差异化的引导策略：低依赖型群体需获得基础实践经验，务实型群体可通过逐步拓展AI在创意阶段的应用场景获益，而高接受型群体则需要引导其保持批判性审视视角。此外，学生的AI使用意愿因任务阶段而异，因此相关干预措施应针对具体设计阶段制定，而非推行统一的AI采用方案。