人工智慧（AI）已成為電子競技生態系統中的遊戲玩法優化工具與觀眾參與催化劑，然而現有研究仍將「以玩家為中心」和「以觀眾為中心」的 AI 視為相互分離的領域。本論文提出了一個「雙向 AI 架構」（Bidirectional AI Framework），旨在透過建立競技表現與賽事轉播體驗之間的閉環關係來彌合這一鴻溝。本研究對 20 名玩家、實況主及核心觀眾進行了深度訪談，並採用演繹主題分析法（deductive thematic analysis）進行分析，探討在 MOBA（多人在線戰術競技遊戲）語境下，使用者對 AI 的認知、信任校準以及接受障礙。研究結果揭示了一個核心矛盾：專家用戶更看重自主權與競技公平性，而初學者則將 AI 視為學習支架（learning scaffold）；與此同時，觀眾普遍追求具備情境感與敘事驅動的視覺化呈現，以在不破壞情感高潮的前提下豐富觀賽體驗。該架構將這些發現轉化為以可解釋性 AI（XAI）、人類在環（HITL）控制以及情境感知個性化為核心的設計建議，為致力於在競技遊戲中發展「人機協同自適應系統」的開發商與轉播商提供了策略藍圖。

Artificial Intelligence (AI) has emerged as both a gameplay optimization tool and a spectator engagement catalyst within the Esports ecosystem, yet existing research treats player-centric and viewer-centric AI as disconnected domains. This thesis proposes a Bidirectional AI Framework that bridges this divide by creating a closed-loop relationship between competitive performance and broadcast experience. Drawing on 20 in-depth interviews with players, streamers, and dedicated viewers, analyzed deductive thematic analysis, the study examines user perceptions, trust calibration, and barriers to AI acceptance across the MOBA context. Findings reveal a core tension between expert users, who prioritize autonomy and competitive integrity, and beginners, who embrace AI as a learning scaffold; while viewers consistently seek contextual, narrative-driven visualizations that enrich without spoiling emotional climax. The framework translates these findings into design recommendations centered on explainability (XAI), Human-in-the-Loop (HITL) control, and context-aware personalization, offering a strategic roadmap for developers and broadcasters pursuing human–AI co-adaptive systems in competitive gaming.

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