隨著人工智慧技術的快速發展，AI 代理人（agent）已從個體層面的被動決策輔助工具，轉變為能主動參與團體討論的協作夥伴。透過語言互動，AI 代理人得以介入團體決策歷程，影響資訊分享、觀點形成與協作體驗。然而，儘管團體決策（group decision-making）理論上應具備整合多元觀點的優勢，實務上卻常受限於資訊共享偏誤（information sharing bias）等因素，使團隊傾向於討論既有共享資訊，而忽略關鍵的非共享資訊，進而對決策品質產生負面影響。因此，如何設計適當的 AI 代理人介入機制，使其能在不干擾團隊自然互動的前提下促進有效的資訊處理，成為當前人機協作決策研究的重要課題。

本研究旨在探討不同 AI 代理人介入機制對團隊決策品質、資訊揭露行為與主觀協作經驗之影響。實驗採用隱藏檔案任務（hidden profile task）模擬資訊不對稱的團隊決策情境，實驗於 LINE 即時通訊平台線上進行，每組由三位人類成員與一位 AI 代理人組成四人團隊，透過即時文字互動的方式共同完成決策任務。實驗採用 2（有╱無整合機制）× 2（有╱無探究機制）之組間實驗設計，比較混合型 AI、探究型 AI、整合型 AI 與無介入 AI 四種條件下，對團隊決策歷程的影響差異。

研究結果顯示，單一功能的 AI 代理人介入存在明顯侷限，無介入 AI 組在決策正確率與自主性上反而表現最佳，顯示當介入機制未能符合團隊需求時，AI 代理人的參與可能產生干擾。具體而言，探究機制雖能顯著促進非共享資訊揭露，但因缺乏後續的資訊整合，容易增加團隊認知負荷，進而降低決策自主性、滿意度與整體決策品質；相對地，整合機制能有效提升人機決策體驗與參與適切度，使團隊能感受到決策過程的幫助，但卻難以誘發資訊分享行為，而未能提高決策品質。然而，當探究與整合機制結合為混合型 AI 時，兩者之間呈現顯著的正向交互作用：整合機制帶來的體驗效益，得以部分緩衝探究機制所引入的介入代價，透過功能互補與體驗補償，能使團隊在一定程度上兼顧決策品質與協作體驗。

綜合以上結果，本研究指出 AI 代理人的設計不應被視為需求功能的疊加，而是高度仰賴情境脈絡的協作設計問題。團隊決策本質是一個動態的協作過程，唯有透過不同介入機制的互補與協調，使 AI 代理人能動態回應團隊於不同協作階段的實際需求，才能在維持自然互動結構的前提下，對決策歷程與協作體驗產生正向影響。本研究提供 AI 代理人作為團隊參與者的實證基礎，並為未來人機團隊（human-agent teams, HATs）協作決策之設計與實踐提出具體建議。

With the rapid advancement of artificial intelligence (AI), AI agents are evolving from passive decision-support tools for individuals into active collaborators in group decision-making. AI agents can intervene in decision-making processes, facilitating information sharing, opinion formation, and the overall collaboration experience. Although group decision-making can theoretically benefit from the integration of diverse perspectives, in practice it is often hindered by information sharing bias. Groups tend to focus on the information already shared among members while neglecting critical unshared information, ultimately compromising decision quality. Consequently, designing AI agents that facilitate effective information processing while preserving natural group dynamics remains a critical challenge in human-AI collaborative decision-making.

We investigated the impact of AI intervention on decision quality, information disclosure behavior, and collaboration experience using a “hidden profile” group decision making task. Each group consisted of three human participants and one AI agent, performing the task through real-time, text-based interaction on the LINE platform. The study employed a 2 (agent with or without integration capability) × 2 (agent with or without inquiry capability) between-subjects design, comparing four conditions: Hybrid AI, Inquiry-only AI, Integration-only AI, and a control condition in which the agent does not intervene with the discussion.

The results revealed significant limitations in single-function AI interventions. Teams in the control condition exhibited the highest decision accuracy and decision autonomy, suggesting that AI agents may be disruptive when misaligned with team needs. Specifically, while the inquiry mechanism significantly promoted the disclosure of unshared information, the lack of integration increased cognitive load, thereby reducing decision autonomy, satisfaction, and overall decision quality. In contrast, the integration mechanism was perceived as helpful and improved the human–agent interaction experience; however, it failed to stimulate sufficient information-sharing behavior to enhance decision quality. The Hybrid AI condition revealed a significant positive interaction effect, whereby the experiential benefits of integration mitigated the cognitive costs of inquiry. This functional complementarity and experiential compensation allowed teams to strike a balance between decision quality and collaborative satisfaction.

In conclusion, this study demonstrates that impacts of AI agent intervention is not a simple additive combination of its functions, but rather as a highly context-dependent collaborative challenge. Group decision-making is a dynamic process; only through the complementarity and coordination of multiple mechanisms—responsively aligned with the team's needs across different stages of collaboration—can AI agents positively impact decision outcomes while maintaining natural patterns of group interaction. Our findings provide empirical support for conceptualizing AI agents as team participants and offer concrete implications for the design of future human–AI teams.

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