近年來，基於生大型語言模型之AI 代理人快速發展，並已逐漸應用於教育、諮詢與決策支援等情境。然而，AI 代理人在提供建議時，仍可能面臨資訊來源不透明、回應依據不明確、推理過程難以理解，以及生成錯誤資訊等問題，進而影響使用者對於 AI 代理人之信任感。留學諮詢涉及學校選擇、科系規劃、申請條件、學費成本與未來職涯發展等重要決策，因此使用者對於資訊正確性、推薦依據與系統可信度具有較高的需求。因此，本研究旨在探討使用具/不具「可解釋AI回應機制」之「AI留學諮詢代理人」系統輔以進行留學諮詢，對使用者 AI 信任感、可解釋 AI 體驗品質、聊天機器人優使性，以及科技接受度的影響。
本研究所建置之「AI留學諮詢代理人系統」係整合大型語言模型與檢索增強生成(Retrieval-Augmented Generation, RAG)技術發展而成，讓使用者可透過自然語言進行英國留學資訊之需求釐清、現況瞭解，以及獲得合適學校推薦等諮詢，來幫助自己進行留學規劃與決策。實驗組使用具「可解釋AI回應機制」之「AI留學諮詢代理人系統」輔以進行留學諮詢，而控制組則使用不具「可解釋AI回應機制」之「AI留學諮詢代理人系統」輔以進行留學諮詢。「可解釋AI回應機制」依互動階段逐步提供「資料來源說明」、「隱私與安全說明」、「系統能力限制聲明」、「RAG資料來源說明」、「推薦學校之適配度說明」、「揭露RAG演算法原理」，以及「推薦學校之適配度推算原理」等說明資訊，使使用者能理解系統回應內容之資料依據、推薦理由與推理流程，以逐步建立使用者對於「AI留學諮詢代理人系統」的信任感。
本研究以臺北市某高中二年級兩個班級學生為研究對象，並以班級為單位，將其隨機分派為採用具/不具「可解釋AI回應機制」之「AI留學諮詢代理人系統」輔以進行留學諮詢的實驗組與控制組。實驗開始前，受試者先填寫前測問卷，以了解其初始 AI 信任感與相關背景資料；其後依組別使用具/不具「可解釋AI回應機制」之「AI留學諮詢代理人系統」輔以進行留學諮詢。完成留學諮詢活動後，兩組研究對象填寫 AI 信任感量表、可解釋 AI 使用體驗品質量表、聊天機器人優使性量表，以及科技接受度問卷，以評估使用具/不具「可解釋AI回應機制」之「AI留學諮詢代理人系統」的AI 信任感、可解釋 AI 使用體驗品質、聊天機器人優使性，以及科技接受度感受。此外，本研究於實驗結束後隨機抽樣兩組部分受試者進行半結構式訪談，以了解受試者對於系統之實際體驗、感受與建議。
研究結果顯示，具「可解釋AI回應機制」之「AI留學諮詢代理人系統」有助於提升受試者之 AI 信任感。實驗組於整體 AI 信任感及能力、善意、信任等 信任感構面之表現顯著優於控制組，顯示當受試者逐步接觸資料來源、推薦依據、適配度說明，以及推理流程等「可解釋AI回應機制」後，更能理解 AI 回應內容之形成方式，進而提升其對於系統專業能力、協助意圖與整體可信度之正向評價。研究結果亦顯示，AI 信任感並非於首次接觸系統時即形成，而是會隨著互動歷程推進、資訊揭露增加與使用者理解深化而逐步累積；至於誠信構面則未因互動階段之不同而產生明顯變化。
此外，可解釋 AI 回應機制對於不同背景受試者之影響具有顯著差異。對 AI 整體呈正面態度之實驗組受試者的整體 AI 信任感、能力及善意構面之表現顯著優於對於 AI 整體呈正面態度之控制組受試者；對 AI 整體呈負面態度實之實驗組受試者的整體 AI 信任感與能力構面顯著優於對 AI 整體呈負面態度之控制組受試者。至於不同初始 AI 信任感方面，則「可解釋AI回應機制」較能強化已有一定AI信任基礎者之AI 信任感。
在可解釋 AI 體驗品質、聊天機器人優使性與科技接受度方面，實驗組整體表現亦顯著優於控制組。多數受試者認為，資料來源說明、推薦學校之適配度說明與系統運作流程，有助於理解 AI 如何蒐集資訊、分析需求與產生推薦結果，並降低使用過程中的不確定感。受試者亦肯定系統能快速整理留學資訊、提供學校推薦，並透過提問協助釐清個人需求，使留學規劃更具方向性。惟部分受試者期待系統未來能提供更完整的學校比較資訊、更多元的推薦選項，以及更符合個人需求之客製化建議。
綜合而言，本研究證實，使用具/不具「可解釋AI回應機制」之「AI留學諮詢代理人」系統輔以進行留學諮詢，會影響受試者對於 AI 系統之信任感、可解釋 AI 體驗品質、聊天機器人優使性與科技接受度。具「可解釋AI回應機制」之「AI留學諮詢代理人系統」能透過揭露資料來源、推薦依據、系統能力限制與推理流程，使使用者更清楚理解 AI 回應內容與推薦結果之形成方式，進而提升其對於系統能力、協助意圖與整體價值之正向評價。本研究結果可作為未來發展教育諮詢型 AI 代理人、可解釋 AI 介面設計，以及 AI 輔助留學決策系統之參考。

In recent years, AI agents based on large language models have developed rapidly and have gradually been applied in education, consultation, and decision-support contexts. However, when AI agents provide recommendations, they may still face problems such as opaque information sources, unclear response rationales, difficult-to-understand reasoning processes, and the generation of inaccurate information, thereby affecting users’ trust in AI agents. Study abroad consultation involves important decisions such as school selection, program planning, application requirements, tuition costs, and future career development. Therefore, users have higher expectations regarding information accuracy, recommendation rationales, and system trustworthiness. Accordingly, this study aims to investigate the effects of using an AI Study Abroad Advisory Agent system with or without “Explainable Response Mechanisms” to support study abroad consultation on users’ AI trust, explainable AI experience quality, chatbot usability, and technology acceptance.
The AI Study Abroad Advisory Agent System developed in this study integrates a large language model with Retrieval-Augmented Generation (RAG) technology. Through natural language interaction, the system assists users in clarifying their needs, understanding relevant information about studying in the United Kingdom, and obtaining suitable school recommendations, thereby supporting their study abroad planning and decision-making. The experimental group used the AI Study Abroad Advisory Agent System with “Explainable Response Mechanisms” to support study abroad consultation, whereas the control group used the AI Study Abroad Advisory Agent System without “Explainable Response Mechanisms.” The “Explainable Response Mechanisms” progressively provided explanatory information across different interaction stages, including “Data Source Explanation,” “Privacy and Security Explanation,” “System Capability Limitation Statement,” “RAG Data Source Explanation,” “Suitability Explanation of Recommended Schools,” “Disclosure of the RAG Algorithm Principle,” and “Suitability Calculation Principle of Recommended Schools.” These explanations enabled users to understand the data basis, recommendation rationales, and reasoning process behind the system’s responses, thereby gradually building users’ trust in the AI Study Abroad Advisory Agent System.
This study recruited two classes of eleventh-grade students from a senior high school in Taipei City as research participants. The classes were randomly assigned as the experimental group and the control group, which used the AI Study Abroad Advisory Agent System with or without “Explainable Response Mechanisms,” respectively, to support study abroad consultation. Before the experiment, participants completed a pre-test questionnaire to collect information on their initial AI trust and related background variables. They then used the AI Study Abroad Advisory Agent System with or without “Explainable Response Mechanisms” according to their assigned group. After completing the study abroad consultation activity, both groups completed the AI Trust Scale, Explainable AI Experience Quality Scale, Chatbot Usability Scale, and Technology Acceptance Questionnaire to evaluate their AI trust, explainable AI experience quality, chatbot usability, and technology acceptance when using the system with or without “Explainable Response Mechanisms.” In addition, after the experiment, this study randomly selected several participants from both groups for semi-structured interviews to understand their actual experiences, perceptions, and suggestions regarding the system.
The results showed that the AI Study Abroad Advisory Agent System with “Explainable Response Mechanisms” helped enhance participants’ AI trust. The experimental group performed significantly better than the control group in overall AI trust and in the trust dimensions of ability, benevolence, and trust. This indicates that when participants gradually encountered “Explainable Response Mechanisms,” such as data sources, recommendation rationales, suitability explanations, and reasoning processes, they were better able to understand how AI responses were generated, thereby improving their positive evaluations of the system’s professional ability, supportive intention, and overall trustworthiness. The results also showed that AI trust was not formed immediately upon first contact with the system, but gradually accumulated as the interaction progressed, information disclosure increased, and users’ understanding deepened. As for the integrity dimension, no obvious change was observed across different interaction stages.
In addition, the effects of Explainable Response Mechanisms differed significantly among participants with different backgrounds. Among participants with an overall positive attitude toward AI, those in the experimental group performed significantly better than those in the control group in overall AI trust and in the dimensions of ability and benevolence. Among participants with an overall negative attitude toward AI, those in the experimental group performed significantly better than those in the control group in overall AI trust and the ability dimension. Regarding different levels of initial AI trust, the “Explainable Response Mechanisms” were more effective in strengthening AI trust among participants who already had a certain foundation of trust in AI.
In terms of explainable AI experience quality, chatbot usability, and technology acceptance, the experimental group also performed better overall than the control group. Most participants indicated that the Data Source Explanation, Suitability Explanation of Recommended Schools, and system operation process helped them understand how AI collected information, analyzed needs, and generated recommendation results, while also reducing uncertainty during use. Participants also acknowledged that the system could quickly organize study abroad information, provide school recommendations, and help clarify personal needs through questions, making study abroad planning more directional. However, some participants expected the system to provide more complete school comparison information, more diverse recommendation options, and more personalized suggestions in the future.
In summary, this study confirms that using an AI Study Abroad Advisory Agent system with or without “Explainable Response Mechanisms” to support study abroad consultation affects participants’ trust in AI systems, explainable AI experience quality, chatbot usability, and technology acceptance. The AI Study Abroad Advisory Agent System with “Explainable Response Mechanisms” enables users to better understand how AI responses and recommendation results are generated by disclosing data sources, recommendation rationales, system capability limitations, and reasoning processes. This further enhances users’ positive evaluations of the system’s ability, supportive intention, and overall value. The findings of this study may serve as a reference for the future development of educational consultation-oriented AI agents, explainable AI interface design, and AI-assisted study abroad decision-support systems.

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