隨著生成式人工智慧技術的快速發展，AI繪圖工具逐漸成為數位內容創作的重要媒介。相較於傳統圖像設計需具備專業技能，AI繪圖工具具有操作門檻低、即時生成與高互動性的特性，使一般使用者亦能參與圖像創作。然而，過往資訊系統研究多以功能導向系統為主要研究對象，對於以創作體驗與娛樂性為特徵的AI繪圖工具，其使用行為與心理機制仍缺乏系統性探討。因此，本研究以科技接受模型為理論基礎，結合享樂動機與自我效能等心理變數，探討使用者對AI繪圖工具之使用態度與使用意圖的影響機制。

本研究以曾經使用過AI繪圖工具之使用者為研究對象，以網路問卷進行資料蒐集，共回收有效樣本215份。研究模型包含感知有用、感知易用、享樂動機與自我效能為自變數，使用態度為中介變數，使用意圖為依變數，並進一步探討不同設計熟悉度使用者之差異。

研究結果顯示，感知有用、感知易用與享樂動機對使用態度具有顯著正向影響，而使用態度亦對使用意圖具有顯著正向影響，顯示使用者對AI繪圖工具的整體評價會進一步影響其後續使用意願。此外，使用態度在部分變數與使用意圖之間具有中介效果。研究結果亦發現，不同設計熟悉度的使用者在AI繪圖工具使用意圖上存在差異性。

With the rapid advancement of generative artificial intelligence technologies, AI image generation tools have gradually become an important medium for digital content creation. Compared with traditional graphic design that requires professional skills, AI image generation tools feature a low operational threshold, real time generation, and high interactivity, allowing general users to participate in image creation. However, previous research in the field of information systems has primarily focused on function-oriented systems, while the user behavior and psychological mechanisms associated with AI image generation tools, which emphasize creative experience and entertainment value, remain relatively underexplored. Therefore, this research adopts the Technology Acceptance Model as the theoretical foundation and incorporates psychological variables including hedonic motivation and self-efficacy to investigate the mechanisms influencing users’ attitudes and behavioral intentions toward AI image generation tools.

This research targeted users who have previously used AI image generation tools. Data were collected through an online questionnaire survey, resulting in 215 valid responses. The research model includes perceived usefulness, perceived ease of use, hedonic motivation, and self-efficacy as independent variables, user attitude as the mediating variable, and behavioral intention as the dependent variable. In addition, differences among users with different levels of design familiarity were further examined.

The results indicate that perceived usefulness, perceived ease of use, and hedonic motivation have significant positive effects on user attitude, while user attitude also has a significant positive effect on behavioral intention. These findings suggest that users’ overall evaluations of AI image generation tools can further influence their intention to use such tools. Furthermore, user attitude was found to partially mediate the relationship between certain variables and behavioral intention. The results also reveal differences in behavioral intention toward AI image generation tools among users with different levels of design familiarity.

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