本研究探討在職場溝通情境中，AI 使用揭露與發訊者語言身分（nativity）如何影響他人對其溫暖感、能力感與社會可接受性的評價。具體而言，本研究檢驗兩種可能的評價偏誤：其一，是否在揭露使用 AI 時會產生所謂的「AI 懲罰」；其二，是否存在以語言或文化接近性為基礎的「語言身分懲罰」，亦即母語者是否會對文化上較接近自己的發訊者給予較高評價，而對語言背景較疏遠者給予較低評價。
為探討上述動態，本研究採用 3 × 3 受試者間實驗設計，並透過 Prolific 招募美國受試者作為訊息接收者，請其根據實驗中所提供的文本評估對發訊者的看法。實驗文本以電子郵件為形式，並以兩個因素進行操弄：AI 使用揭露（明確揭露 AI、未揭露 AI、人類撰寫）與發訊者語言身分（美國母語者、歐洲英語使用者、東亞英語使用者），共形成九種不同的電子郵件刺激。
研究結果透過 ANCOVA 與 調節式迴歸（PROCESS）模型進行分析，並控制人口統計變項。整體結果顯示，確實存在 AI 懲罰效果，但該效果並非源自 AI 的實際使用本身，而是來自明確揭露訊息由 AI 撰寫這一事實；一旦揭露 AI 使用，訊息在溫暖感、能力感與社會可接受性上的評價皆顯著降低。相對地，未揭露使用的 AI 文本與人類撰寫之文本在評價上並無顯著差異。此外，個體對 AI 的態度會影響其在溫暖感與能力感判斷中對 AI 揭露的反應。相反地，語言身分懲罰並不存在，一旦 AI 使用被揭露，發訊者的語言身分並未影響評價結果。人口變項亦影響結果：性別差異特別體現在溫暖感與社會可接受性的評價上，而族群身分則與部分評價向度的基準評價傾向相關。該研究該研究發現，在職場溝通情境中，公開揭露 AI 使用本身即構成一項重要的社會訊號，而個體特質與人口背景亦在 AI 中介的訊息評價中扮演關鍵角色。

This study examines how AI disclosure and sender nativity shape perceptions of warmth, competence, and social acceptability in workplace communication. Specifically, it investigates whether disclosing AI use triggers an AI penalty, and whether a nativity-based penalty emerges, such that native speakers evaluate culturally closer senders more positively than those from more distant linguistic backgrounds. To investigate these dynamics, the study employed a 3 × 3 between-subjects experimental design, with U.S. participants serving as message receivers who evaluated their perceptions of the sender based on the stimuli. Email was used as the stimulus format and was designed to vary along two factors: AI disclosure and sender nativity, resulting in nine distinct email stimuli across conditions.
Overall, the results indicate the presence of an AI penalty; however, this effect is not driven by AI use itself, but rather by the explicit disclosure that a message was written by AI. Once AI use was disclosed, perceptions of warmth, competence, and social acceptability were significantly reduced. In contrast, AI-written messages without disclosure and human-written messages did not differ significantly in their evaluations. In addition, individuals’ attitudes toward AI moderated responses to AI disclosure, and more positive AI attitudes attenuated the AI penalty. Conversely, no nativity-based penalty was observed, as the sender’s nativity did not systematically influence evaluative judgments once AI disclosure was salient. Beyond these focal effects, demographic factors also shaped evaluations: gender differences emerged particularly for warmth and social acceptability, and ethnicity was associated with baseline evaluative tendencies across some dimensions.

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