在假訊息氾濫的時代，借助 AI 代理人協助事實查核逐漸成為國際趨勢，此做法有望分擔人工查核的負擔。然而，這項技術要發揮實際效益，前提是使用者必須具備判斷力以採用AI代理人的結果，而非盲目依賴。過往研究假設人會理性地與AI代理人合作、或是探討擬人化設計如何提高信任，皆忽略了人智互動過程中，AI技術理解門檻高，一般使用者難以理性評估其判斷品質，容易衍生出情感信任的負面影響。人們信任AI代理人的程度決定了依賴的程度，理想的人智協作建立在適當依賴之上，即人們賦予AI代理人適當信任，正確時採用其建議、出錯時也能判別其的錯誤，並相信人們原本正確的判斷。若人們對能力尚未成熟的AI代理人產生過度信任，放棄自己原本的查核判斷，如此錯誤地與AI代理人協作，將使人智協作結果比任一方單獨作業更差。
本研究從捷思—系統式訊息處理觀點（Heuristic-Systematic Model, HSM）出發，將使用者對代理人的信任區分為認知信任與情感信任，並引用情感捷思（affective heuristic）與偏誤假設（bias hypothesis），主張即便是傾向系統式處理、理應審慎的高涉入者，在面對具備一定複雜度的查核任務時，也可能以對 AI代理人的整體好感取代理性分析，形成情感信任，進而增加過度依賴的可能性。
本研究以人們與模擬的 AI 事實查核聊天機器人協作判別假訊息為情境，採準實驗設計，考量涉入程度作為內在心理狀態，視為受試者變項（Subject Variables），搭配高或低情感捷思作為主變項，測量參與者的認知信任、情感信任，以及依賴行為。研究結果顯示，情感捷思設計可以引發情感信任提升，證實偏誤假設所說，即使是高涉入者，同樣也會受到捷思線索的影響。其次，情感信任能顯著預測過度依賴的傾向，這代表當AI代理人提供錯誤判斷時，產生較高情感信任者更傾向跟隨AI代理人犯錯。此外，我們做探索性分析發現，情感捷思對過度依賴的傾向具有直接效果，指出情感設計本身就是驅動過度依賴之傾向的來源。
最後，本研究指出，設計者在開發事實查核代理人解釋介面時，應審慎看待可能誘發情感捷思的設計元素，避免使用者錯置信任；同時也為 XAI 與人智信任研究，補上情感信任路徑。

Using AI to support fact-checking is a growing trend that eases manual workloads. For this technology to develop smoothly, however, users need to possess good judgment to evaluate AI’s outcomes, instead of over-relying on it. Prior studies have either assumed that people interact with AI agents in a rational way, or focused on how anthropomorphic design increases trust. Both overlook the negative effect that emotional trust can have during human–AI interaction, such as overreliance. The degree to which people trust an AI agent determines how much they rely on it. Ideal human–AI interaction is built on appropriate reliance: users adopt the agent's suggestions when correct, but recognize its mistakes and stand by their own accurate judgments when wrong. If people place overtrust in an immature AI agent, they will consequently abandon their own correct judgment, resulting in collaborative outcomes worse than what either could achieve alone.
Our research is based on the Heuristic-Systematic Model and discusses human trust in terms of emotional and cognitive trust. Drawing on the concepts of the affect heuristic and the bias hypothesis, we argue that even individuals with high outcome relevance involvement can also be influenced by affective heuristic. These affective cues foster emotional trust, ultimately leading to overreliance on the AI agent.
To investigate this, we conducted a quasi-experimental design. Considering involvement as a mental state, we treated it as a subject variable, and crossed it with high or low affective heuristic conditions; both were our main variables. Through this design, we aimed to examine how these main variables influence participants' cognitive trust, emotional trust, and reliance behavior. Our results confirmed the bias hypothesis that even high-involvement individuals can be affected by heuristic cues. Second, emotional trust positively predicted overreliance tendency, meaning that participants who reported higher levels of emotional trust were more likely to follow incorrect decisions given by the AI agent. Finally, our exploratory analysis demonstrated that affective heuristic has a direct effect on overreliance tendency, which means affective design itself can induce overreliance tendency.
To prevent users from misplacing their trust in AI agents, our findings suggest that designers of AI agent interfaces must exercise caution when incorporating affective cues. Our research elucidates the affective mechanisms underlying human-AI interaction.

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