臺灣現有逾 86 萬名外籍移工，正面臨語言隔閡、行政流程複雜、法律保障不足與心理適應困難等多重挑戰。現行管理作業高度依賴人力翻譯與紙本作業，效率低落且資訊不對稱，亟需科技輔助解決方案。
本研究依循設計導向研究（Design Science Research, DSR）方法論，透過對五位具備移工管理實務經驗之產業專家進行深度訪談，萃取 174 項原始管理困境陳述，採用層級分析法（Analytic Hierarchy Process, AHP）精煉出 5 大構面、18 項核心評估準則，並對 18 位專家施測問卷以量化各準則相對重要性。
AHP 分析結果顯示，「多語即時翻譯」（整體權重 0.2548）位居第一，「多語知識庫與專業詞彙校正」（0.1341）次之，「心理健康與社群支援」（0.1151）排名第三，「圖像化友善介面」（0.0649）排名第四，「生活資訊與行政支援」（0.0568）排名第五。敏感度分析確認前三名準則在五組情境下均維持前三名之內，整體排序具備良好之方法論穩健性。
依據權重排序，本研究以 Botpress 平台為工具，結合檢索增強生成（Retrieval-Augmented Generation, RAG）技術與差異化提示工程（Prompt Engineering），開發涵蓋多語即時翻譯（T4）、多語知識庫與專業詞彙校正（K2）、心理健康支援（M1）及生活行政支援（A3）四大模組之 AI代理人原型系統。四大模組對應 AHP 整體權重排名第一、第二、第三及第五之核心準則，排除第四名「圖像化友善介面」係因其屬介面設計層面，可整合於其他模組之呈現方式中，而非獨立功能模組。透過四維度驗證架構（D1 資訊精確與行動導引、D2 語言鏡像與語義保真、D3 誠實性與安全防禦、D4 跨文化同理與社群支持）及 52 組結構化測試案例進行多輪迭代優化，並輔以 5 名不同背景之移工使用者進行質性情境觀察測試。
研究結果顯示，本原型系統初步展現危機介入 SOP 觸發、官方法規術語精準提取、行政流程步驟導引，以及多語語義保真等核心能力，在一定程度上呼應 AHP 專家共識所界定之管理需求。本研究貢獻在於建立 AI代理人功能優先排序之可量化決策框架、提出適用於高責任多語境對話系統之四維度評估架構，以及提供可供後續研究複現之完整設計物文件。

Taiwan currently employs over 860,000 foreign migrant workers, facing compounding challenges including language barriers, complex administrative procedures, insufficient legal protection, and psychological adaptation difficulties. Existing management practices rely heavily on manual translation and paper-based processes, resulting in low operational efficiency and persistent information asymmetry that calls for technology-assisted solutions.
This study adopts the Analytic Hierarchy Process (AHP) combined with Design Science Research (DSR) methodology. Through in-depth interviews with five industry experts in migrant worker management, 174 raw management difficulty statements were extracted and consolidated into an AHP hierarchical framework comprising five dimensions and eighteen core evaluation criteria. Eighteen expert questionnaires were administered to quantify the relative importance of each criterion, and sensitivity analysis was conducted under five simulated scenarios to verify robustness.
AHP results indicate that “Multilingual Real-Time Translation” ranks first (weight: 0.2548), followed by “Multilingual Knowledge Base and Technical Terminology Correction” (0.1341), “Mental Health and Community Support” (0.1151), “Visually Friendly Interface” (0.0649), and “Life Information and Administrative Support” (0.0568). Sensitivity analysis confirmed that the top- three criteria remained within the top three across all five simulated scenarios, demonstrating strong methodological robustness.
Based on these rankings, an AI agent prototype was developed on the Botpress Cloud platform, integrating Retrieval-Augmented Generation (RAG) technology with differentiated prompt engineering. The four functional modules —T4 Multilingual Real-Time Translation, K2 Multilingual Terminology Correction, M1 Mental Health Support, and A3 Life and Administrative Support — correspond to the AHP criteria ranked first, second, third, and fifth respectively. “Visually Friendly Interface” (ranked fourth) was excluded as it represents an interface design consideration that can be integrated into the presentation layer of other modules, rather than constituting an independent functional module. The prototype was validated through a four-dimensional framework (D1–D4) and 52 structured test cases. Qualitative scenario-based testing with five migrant worker users further validated real-world usability and emotional support effectiveness.
Contributions include: an AHP-based decision framework for prioritizing AI agent functions in migrant worker management; a four-dimensional evaluation architecture for high-accountability multilingual AI systems; and complete design artifact documentation to support replication by future researchers.

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