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| Title: | AI虛擬導覽員促進知識圖譜於元宇宙數位策展應用成效的影響研究
A Study on the Effects of AI Virtual Guides on Enhancing the Application Effectiveness of Knowledge Graph for Digital Curation in the Metaverse |
| Authors: | 戴婉娟
Dai, Wan-Juan |
| Contributors: | 陳志銘
Chen, Chih-Ming
戴婉娟
Dai, Wan-Juan |
| Keywords: | AI虛擬導覽員
知識圖譜
元宇宙數位策展
數位人文
圖文認知風格
AI Virtual Guide
Knowledge Graph
Metaverse Digital Curation
Digital Humanities
Visual-Verbal Cognitive Style |
| Date: | 2025 |
| Issue Date: | 2025-08-04 14:02:46 (UTC+8) |
| Abstract: | 隨著沉浸式科技與生成式人工智慧的快速發展，傳統實體策展正逐漸轉型為數位策展。特別是在元宇宙數位策展中，沉浸式虛擬展示空間不僅可突破時空限制，更能夠提供高度互動與臨場感的學習體驗，促進觀展學習者對於策展內容的理解與情感連結。然而，目前多數元宇宙數位策展仍以圖文與影音為主，呈現方式仍偏向於單向資訊傳遞，缺乏即時互動與引導之觀展機制。加上元宇宙中的非線性資訊結構雖具彈性與多樣性，卻可能導致觀展學習者難以掌握完整的知識脈絡。而「知識圖譜」（Knowledge Graph）可透過資訊的視覺化與結構化呈現，協助學習者釐清概念關係並辨識資訊節點之間的連結，有助於提升觀展者對於策展內容的理解。然而，觀展者在使用「知識圖譜」輔以觀展時，仍需透過人工近讀（close reading）以補足節點之間所缺乏的語意脈絡，如此將增加操作上的負擔。而結合大型語言模型（Large Language Model, LLM）與檢索增強生成（Retrieval-Augmented Generation, RAG）技術所建構的「AI虛擬導覽員」，能即時回應自然語言提問，補充知識圖譜中的資訊缺口，進一步提升觀展時的互動體驗與學習效率。
因此，本研究旨在探討結合「AI虛擬導覽員」與「知識圖譜」於元宇宙數位策展之輔助觀展模式，是否有助於提升觀展學習成效、學習動機與認知投入，並降低其認知負荷。本研究以《馬來西亞興化群賢錄》為策展主題，採用真實驗研究法，招募41位馬來西亞華人參與研究，並依是否使用「AI虛擬導覽員」輔以「知識圖譜」之觀展將研究對象隨機分派為使用結合AI虛擬導覽員與知識圖譜輔助觀展的實驗組（21人），以及僅使用知識圖譜輔助觀展的控制組（20人），以評估兩組觀展學習者在觀展學習成效、學習動機、認知投入，以及認知負荷是否具有顯著的差異，並納入圖文認知風格作為背景變項，探討不同認知風格觀展學習者在這兩種輔助觀展模式下的觀展學習成效、學習動機、認知投入，以及認知負荷是否具有顯著的差異。另外，透過半結構式訪談深入了解兩組觀展學習者對於這兩種不同輔助觀展模式的觀展歷程、感受與建議。
研究結果顯示，實驗組的觀展學習成效顯著優於控制組觀展學習者，對於文字型觀展學習者在觀展學習成效之知識理解、學習愉悅、啟發與創造力等面向上的效益尤為顯著。此外，實驗組在學習動機之興趣、樂趣、感知能力與感知價值上亦顯著優於控制組，對於文字型觀展學習者在學習動機之興趣與樂趣上的效益尤為顯著。在認知投入方面，實驗組在精緻化與批判性思考能力上顯著優於控制組，對於圖像型觀展學習者在批判性思考方面的效益尤為顯著。在認知負荷方面，實驗組在外在認知負荷上顯著低於控制組，對於文字型觀展學習者的效益尤為顯著。訪談結果進一步指出，AI虛擬導覽員的互動問答機制有助於釐清學習脈絡、補足知識圖譜中的資訊缺口，並強化問題意識、激發學習動機與深化認知投入，同時減少操作多重工具所帶來的負擔，進而提升整體觀展學習成效。此外，其語言導向與個人化互動特性對文字型觀展學習者的學習歷程產生正面影響。
綜合而言，本研究證實結合「AI虛擬導覽員」與「知識圖譜」的輔助觀展模式在元宇宙數位策展環境中具備提升互動性、導引性與個人化學習體驗的潛力，對未來數位人文、文化展示與教育推廣的智慧導覽設計與知識詮釋策略提供實證基礎與發展方向。未來可進一步強化AI虛擬導覽員的多模態感知能力，以滿足多元需求，並建議將此模式擴展應用至其他主題領域與沉浸式互動環境中，驗證其應用的廣泛性與可行性。此外，亦可深化AI虛擬導覽員與知識圖譜間的結構性整合，強化其語意推理與知識組織能力，建構具備邏輯一致性與知識可驗證性的主動支援式導覽系統。
As immersive technology and generative artificial intelligence rapidly advance, traditional physical curation is increasingly shifting to digital curation. In particular, metaverse digital curation leverages immersive virtual exhibition environments that not only transcend the constraints of time and space but also provide highly interactive and realistic learning experiences that enhance visitors’ understanding of curatorial content and foster emotional engagement. However, most current metaverse digital curation still primarily relies on textual and audiovisual content, with presentation modes largely centered on unidirectional information delivery and lacking real-time interaction and guiding mechanisms. Furthermore, while the nonlinear information structure of the metaverse offers flexibility and diversity, it may also hinder learners’ ability to comprehend a coherent knowledge framework.
To address these challenges, the knowledge graph, through the visualization and structuring of information, helps learners clarify conceptual relationships and identify connections among information nodes, thereby enhancing their understanding of the curatorial content. Nonetheless, when engaging with knowledge graphs, learners often still need to do additional close reading to bridge the semantic gaps between nodes, an effort that adds extra cognitive load. The integration of large language models (LLMs) and retrieval-augmented generation (RAG) technologies into the design of AI Virtual Guides enables real-time responses to natural language queries, supplementing missing information in the Knowledge Graph and further enhancing interactive experiences and learning efficiency during exhibitions.
Accordingly, this study aims to explore whether integrating AI virtual guides and knowledge graphs as an assistive exhibition model in metaverse digital curation can improve exhibition learning outcomes, increase learning motivation and cognitive engagement, and reduce cognitive load. Taking The Biographies of Malaysian Henghua Personalities as the curatorial theme, the study adopted a true experimental research design, recruiting 41 Malaysian Chinese participants. Participants were randomly assigned to either an experimental group (n=21), which experienced exhibition learning through the combined use of AI virtual guides and knowledge graphs, or a control group (n=20), which used only knowledge graphs. The study examined whether there were significant differences between the two groups in terms of exhibition learning outcomes, learning motivation, cognitive engagement, and cognitive load. Additionally, visual-verbal cognitive style was included as a background variable to investigate how learners with different cognitive styles performed under the two assistive exhibition models. Semi-structured interviews were also conducted to gain deeper insights into participants' experiences, perceptions, and suggestions regarding the two assistive exhibition modes.
The results revealed that the experimental group outperformed the control group in terms of exhibition learning outcomes. This effect was particularly pronounced among verbal learners, who showed significantly greater gains in knowledge comprehension, enjoyment, inspiration, and creativity. In terms of learning motivation, the experimental group reported higher levels of interest, enjoyment, perceived competence, and perceived value, with verbal learners in the experimental group demonstrating significantly higher levels of interest and enjoyment compared to their counterparts. Regarding cognitive engagement, the experimental group exhibited significantly higher levels of elaborative and critical thinking, especially among visual learners, who outperformed those in the control group in critical thinking. In terms of cognitive load, the experimental group experienced significantly lower extraneous cognitive load, with verbal learners benefiting the most.
Interview data further revealed that the interactive Q&A functionality of the AI virtual guides helped clarify learning pathways, supplement information gaps in the knowledge graph, and cultivate problem awareness, which in turn stimulated learning motivation, promoted deeper cognitive engagement, and reduced the cognitive load associated with operating multiple tools, thereby improving overall exhibition learning outcomes. The language-based and personalized interaction of the AI virtual guides also had a particularly positive impact on the learning process of verbal learners.
In conclusion, this study demonstrates that integrating AI virtual guides with knowledge graphs as an assistive exhibition model within metaverse digital curation holds strong potential for enhancing interactivity, guidance, and personalized learning experiences. It provides an empirical foundation for the future development of intelligent guidance systems and knowledge interpretation strategies in digital humanities, cultural exhibitions, and educational outreach. Future research is encouraged to further strengthen the multimodal response capabilities of AI virtual guides, enabling them to meet diverse user needs. Additionally, expanding the application of this model to other subject domains and immersive interactive environments will validate its broader feasibility and applicability. Additionally, enhancing the structural integration between AI virtual guides and knowledge graphs can improve their semantic reasoning and knowledge organization, resulting in a proactive support-based guiding system with logical consistency and verifiable knowledge. |
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| Description: | 碩士
國立政治大學
圖書資訊與檔案學研究所
112155001 |
| Source URI: | http://thesis.lib.nccu.edu.tw/record/#G0112155001 |
| Data Type: | thesis |
| Appears in Collections: | [圖書資訊與檔案學研究所] 學位論文
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