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| Title: | 整合混合實境與人工智慧於博物館之應用：內向型與外向型AI NPC及使用者體驗之研究
Integrating Mixed Reality and Artificial Intelligence in Museums: A Study on Introverted and Extroverted AI NPCs and Visitor Experiences |
| Authors: | 余采嬙
Yu, Tsai-Chiang |
| Contributors: | 簡士鎰
Chien, Shih-Yi
余采嬙
Yu, Tsai-Chiang |
| Keywords: | 混合實境
人工智慧
博物館
個性特徵
人機互動
Mixed Reality
Artificial Intelligence
Museum
Personality traits
HCI |
| Date: | 2025 |
| Issue Date: | 2025-09-01 15:03:59 (UTC+8) |
| Abstract: | 博物館正逐漸採用混合實境 (Mixed Reality, MR) 與人工智慧 (Artificial Intelligence, AI)來改變訪客的觀展體驗。這些技術的引入不僅提升展覽互動性，還讓訪客能透過全新的數位方式與展覽內容進行學習。例如，透過 MR技術，訪客能沉浸式地觀賞重建的歷史場景或體驗藝術作品的創作過程，另一方面，AI增強了個性化互動，讓訪客享有即時且客製化的解說。然而，儘管這些創新技術在博物館的應用漸增，AI 和 MR 在博物館中結合應用的相關研究與文獻卻仍然有限，尤其是這些技術整合後的實際效益及其對訪客體驗的長期影響方面，仍有許多未知之處。因此，本研究希望能填補現有文獻中的空白，進一步揭示 AIMR 技術在博物館使用情境中如何創造更加豐富且有意義的文化體驗。MR 和AI的結合不僅是對技術上的創新發展，更可能對博物館觀展者的行為模式產生深遠影響。MR環境裡創建虛擬角色 (Non-Player Character, NPC) 作為展覽主題的延伸，不僅能提升使用者與展覽的互動性，亦能促進導覽系統的有趣性。本研究旨在探討，透過不同個性特質的 AI NPCs 來回答訪客觀展時提出之問題，是否會改變社會臨場感、視覺注意力與未來行為意圖。基於基於自然語言處理 (Natural Language Processing, NLP)、語音合成與深度學習的成熟發展，加上過往諸多機器人個性研究，本文透過大型語言模型 (Large Language Model, LLM) 和文本轉語音 (Text-to-Speech, TTS)，設計了內向與外向型 AI NPCs，內向型AI提供對話風格較正式、語氣嚴肅之展品解說，而外向型 NPC則更加熱情、活躍，且以較積極和正面情緒回覆問題。結果發現，外向 AI 提升了受測者的社會臨場感和使用意圖，且吸引力法則對外向受測者較為強烈; 內向 AI 則讓受測者每次注視停留時間增長、更多注意於展品上。未來博物館更可為不同性格的訪客提供多元化的導覽服務。透過這些問題探討，本研究為未來文化教育機構的 AIMR 技術應用提供實證依據和設計指南參考。技術的快速進步固然帶來了無限可能，如何透過更具彈性的客製化或人性化設計將這些科技創新有效融入博物館訪客體驗，仍然是一項需要持續研究與實踐的課題。
With the advancement of immersive technologies and AI, museums increasingly adopt Mixed Reality (MR) and AI to enhance exhibit interactivity and enable novel cultural engagement. MR immerses visitors in reconstructed historical scenes, while AI offers personalized, real-time explanations. However, research on their combined application in museums remains limited. This study explores how AIMR can enrich cultural experiences. Creating virtual characters (Non-Player Characters, NPCs) within MR environments as extensions of exhibition themes can enhance interactivity and make guided experiences more engaging. This study explores whether AI-driven NPCs with distinct personality traits, used to respond to visitors’ questions during exhibitions, can influence perceptions of social presence, visual attention, and behavioral intentions. Building upon the maturity of Natural Language Processing (NLP), speech synthesis, and deep learning technologies—as well as insights from prior research on robotic personalities—this study employs Large Language Models (LLMs) and Text-to-Speech (TTS) systems to design introverted and extroverted AI NPCs. The introverted AI provides formal, concise, and serious-toned explanations, while the extroverted counterpart responds with enthusiasm, liveliness, and positive emotional expressions. Experimental results indicate that extroverted AI enhance users’ sense of social presence and intention to use, with the attraction effect being stronger for extroverted participants. Conversely, introverted AI led to longer gaze durations per instance and increased visual attention toward exhibits. These findings suggest that future museums may offer diverse guidance experiences tailored to visitors’ individual personalities or needs. By delving into these questions, this study provides empirical evidence and design guidelines for the implementation of AIMR technologies in future museum strategies. While rapid technological progress presents vast opportunities, integrating these innovations into meaningful and human-centered visitor experiences remains an ongoing challenge requiring continuous research and thoughtful design. |
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| Description: | 碩士
國立政治大學
資訊管理學系
112356012 |
| Source URI: | http://thesis.lib.nccu.edu.tw/record/#G0112356012 |
| Data Type: | thesis |
| Appears in Collections: | [資訊管理學系] 學位論文
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