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研究生: 郭富凱
Kuo, Fu-Kai
論文名稱: 基於生成式 AI 方法開發的行動學習與導覽系統
Developing a Mobile Learning and Tour-guiding System based on Generative AI Approach
指導教授: 陳牧言
Chen, Mu-Yen
學位類別: 碩士
Master
系所名稱: 工學院 - 工程科學系碩士在職專班
Department of Engineering Science (on the job class)
論文出版年: 2024
畢業學年度: 112
語文別: 中文
論文頁數: 104
中文關鍵詞: 嵌入式AI多模態大型語言模型物件辨識行動學習導覽系統GAI使用意圖
外文關鍵詞: Embedded AI, Multimodal Large Language Model, Object Recognition, Mobile Learning, Tour-guiding System, Generative AI Usage Intention
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  • 摘要 i 誌謝 v 目錄 vii 表目錄 xi 圖目錄 xiii 第一章、 緒論 1 1.1 研究背景與動機 1 1.2 研究目的 3 1.3 研究範圍 4 1.4 論文架構 5 第二章、 文獻探討 6 2.1 物聯網與通用人工智慧 6 2.1.1 物聯網、雲端與邊緣運算 6 2.1.2 邊緣人工智慧 7 2.1.3 嵌入式人工智慧與微型機器學習 7 2.1.4 邁向通用人工智慧 8 2.2 新時代的學習與導覽 17 2.2.1 行動學習 17 2.2.2 數位導覽 18 2.2.3 生成式人工智慧的應用 19 第三章、 系統設計與建置 20 3.1 系統設計 20 3.2 實驗地點 21 3.2.1 實驗地點介紹 21 3.2.2 實驗資料集 23 3.3 實驗準備 24 3.3.1 模型訓練與資料預處理 24 3.3.2 實驗設備 27 3.4 系統開發 30 3.4.1 系統啟動流程 31 3.4.2 使用者可執行的指令 32 3.5 實驗準備結果 33 3.5.1 物件辨識模型訓練 33 3.5.2 向量資料庫預處理 38 3.5.3 小節整理 38 第四章、 使用者學習成效與GAIUA模型探討 39 4.1 實驗流程 39 4.1.1 實驗對象 39 4.1.2 實驗講解 40 4.1.3 前測 40 4.1.4 導覽 41 4.1.5 後測 46 4.1.6 量表 47 4.2 研究模型 47 4.2.1 理性行為理論 47 4.2.2 生成式人工智慧的使用意圖研究模型 48 4.2.3 構面 49 4.2.4 量表題項 53 第五章、 統計分析與研究假說結果 55 5.1 受測者族群分析 55 5.1.1 對照組 55 5.1.2 實驗組 56 5.2 學習成效 57 5.2.1 實驗組 57 5.2.2 對照組 57 5.3 量表 58 5.3.1 構面信度與效度 58 5.3.2 區別效度 59 5.3.3 因素負荷量與共線性 60 5.4 路徑假說 64 5.4.1 解決問題對感知人性有正面影響(H1) 64 5.4.2 感知人性對滿意度有正面影響(H2) 65 5.4.3 回應內容對類人互動有正面影響(H3) 65 5.4.4 類人互動對滿意度有正面影響(H4) 66 5.4.5 滿意度對使用意圖有正面影響(H5) 67 5.5 路徑係數 68 5.5.1 實驗指標 68 5.5.2 直接效果 69 5.5.3 間接效果 70 5.5.4 總和效果 72 第六章、 研究貢獻與未來展望 75 6.1 研究貢獻 75 6.2 實驗與研究意涵 76 6.2.1 實驗意涵 76 6.2.2 研究意涵 76 6.3 研究限制 77 6.3.1 系統面 77 6.3.2 實驗面 78 6.4 未來展望 79 參考文獻 80

    [1] N. Fei et al., "Towards artificial general intelligence via a multimodal foundation model," Nature Communications, vol. 13, no. 1, p. 3094, 2022.
    [2] T. Wu et al., "A brief overview of ChatGPT: The history, status quo and potential future development," IEEE/CAA Journal of Automatica Sinica, vol. 10, no. 5, pp. 1122-1136, 2023.
    [3] I. UNESCO, "Supporting teachers in back-to-school efforts. Guidance for policy-makers. International task force on teachers for education 2030; 2020," ed, 2021.
    [4] K. Masters, "Ethical use of artificial intelligence in health professions education: AMEE Guide No. 158," Medical Teacher, vol. 45, no. 6, pp. 574-584, 2023.
    [5] S. Grassini, "Shaping the future of education: exploring the potential and consequences of AI and ChatGPT in educational settings," Education Sciences, vol. 13, no. 7, p. 692, 2023.
    [6] J. Achiam et al., "Gpt-4 technical report," arXiv preprint arXiv:2303.08774, 2023.
    [7] S. Yin et al., "A survey on multimodal large language models," arXiv preprint arXiv:2306.13549, 2023.
    [8] J. Li, D. Li, S. Savarese, and S. Hoi, "Blip-2: Bootstrapping language-image pre-training with frozen image encoders and large language models," in International conference on machine learning, 2023: PMLR, pp. 19730-19742.
    [9] H. Liu, C. Li, Q. Wu, and Y. J. Lee, "Visual instruction tuning," Advances in neural information processing systems, vol. 36, 2024.
    [10] S. Huang et al., "Language is not all you need: Aligning perception with language models," Advances in Neural Information Processing Systems, vol. 36, 2024.
    [11] D. Zhu, J. Chen, X. Shen, X. Li, and M. Elhoseiny, "Minigpt-4: Enhancing vision-language understanding with advanced large language models," arXiv preprint arXiv:2304.10592, 2023.
    [12] G. Luo, Y. Zhou, T. Ren, S. Chen, X. Sun, and R. Ji, "Cheap and quick: Efficient vision-language instruction tuning for large language models," Advances in Neural Information Processing Systems, vol. 36, 2024.
    [13] Y.-C. Wang, J. Xue, C. Wei, and C.-C. J. Kuo, "An overview on generative ai at scale with edge-cloud computing," IEEE Open Journal of the Communications Society, 2023.
    [14] J. Wu, W. Gan, Z. Chen, S. Wan, and H. Lin, "Ai-generated content (aigc): A survey," arXiv preprint arXiv:2304.06632, 2023.
    [15] T. Brown et al., "Language models are few-shot learners," Advances in neural information processing systems, vol. 33, pp. 1877-1901, 2020.
    [16] H. Fassold, "Porting Large Language Models to Mobile Devices for Question Answering," arXiv preprint arXiv:2404.15851, 2024.
    [17] S. Mittal, "A survey on optimized implementation of deep learning models on the nvidia jetson platform," Journal of Systems Architecture, vol. 97, pp. 428-442, 2019.
    [18] S. Alyamkin et al., "2018 low-power image recognition challenge," arXiv preprint arXiv:1810.01732, 2018.
    [19] Y. Shi, K. Yang, Z. Yang, and Y. Zhou, "Mobile edge artificial intelligence: Opportunities and challenges," 2021.
    [20] G. Team et al., "Gemma: Open models based on gemini research and technology," arXiv preprint arXiv:2403.08295, 2024.
    [21] E. Almazrouei et al., "The falcon series of open language models," arXiv preprint arXiv:2311.16867, 2023.
    [22] T. Dettmers, A. Pagnoni, A. Holtzman, and L. Zettlemoyer, "Qlora: Efficient finetuning of quantized llms," Advances in Neural Information Processing Systems, vol. 36, 2024.
    [23] S. Ma et al., "The Era of 1-bit LLMs: All Large Language Models are in 1.58 Bits," arXiv preprint arXiv:2402.17764, 2024.
    [24] M. Fullan, C. Azorín, A. Harris, and M. Jones, "Artificial intelligence and school leadership: challenges, opportunities and implications," School Leadership & Management, pp. 1-8, 2023.
    [25] G. Cooper, "Examining science education in ChatGPT: An exploratory study of generative artificial intelligence," Journal of Science Education and Technology, vol. 32, no. 3, pp. 444-452, 2023.
    [26] Y. Yang, J. Luo, and T. Lan, "An empirical assessment of a modified artificially intelligent device use acceptance model—From the task-oriented perspective," Frontiers in Psychology, vol. 13, p. 975307, 2022.
    [27] A. Lommatzsch, "A next generation chatbot-framework for the public administration," in Innovations for Community Services: 18th International Conference, I4CS 2018, Žilina, Slovakia, June 18-20, 2018, Proceedings, 2018: Springer, pp. 127-141.
    [28] N. Castelo, M. W. Bos, and D. R. Lehmann, "Task-dependent algorithm aversion," Journal of Marketing Research, vol. 56, no. 5, pp. 809-825, 2019.
    [29] B. J. Dietvorst, J. P. Simmons, and C. Massey, "Overcoming algorithm aversion: People will use imperfect algorithms if they can (even slightly) modify them," Management science, vol. 64, no. 3, pp. 1155-1170, 2018.
    [30] I. P. Tussyadiah, F. J. Zach, and J. Wang, "Do travelers trust intelligent service robots?," Annals of Tourism Research, vol. 81, p. 102886, 2020.
    [31] D. Gursoy, O. H. Chi, L. Lu, and R. Nunkoo, "Consumers acceptance of artificially intelligent (AI) device use in service delivery," International Journal of Information Management, vol. 49, pp. 157-169, 2019.
    [32] M. l. Yao, "Re‐imagining the ‘Taiwanese’nation in the interpretation of the Chinese‐oriented heritage," Nations and Nationalism, vol. 28, no. 4, pp. 1230-1248, 2022.
    [33] H. Hua, Y. Li, T. Wang, N. Dong, W. Li, and J. Cao, "Edge computing with artificial intelligence: A machine learning perspective," ACM Computing Surveys, vol. 55, no. 9, pp. 1-35, 2023.
    [34] W. Shi and S. Dustdar, "The promise of edge computing," Computer, vol. 49, no. 5, pp. 78-81, 2016.
    [35] M. Sajid and Z. Raza, "Cloud computing: Issues & challenges," in International conference on cloud, big data and trust, 2013, vol. 20, no. 13: sn, pp. 13-15.
    [36] Z. Xu and Y. Tian, "The development history and application of cloud computing," Inform Recording Mater, vol. 19, no. 8, pp. 66-67, 2018.
    [37] H. Cai, B. Xu, L. Jiang, and A. V. Vasilakos, "IoT-based big data storage systems in cloud computing: perspectives and challenges," IEEE Internet of Things Journal, vol. 4, no. 1, pp. 75-87, 2016.
    [38] S. Wan, S. Ding, and C. Chen, "Edge computing enabled video segmentation for real-time traffic monitoring in internet of vehicles," Pattern Recognition, vol. 121, p. 108146, 2022.
    [39] M. Satyanarayanan, "The emergence of edge computing," Computer, vol. 50, no. 1, pp. 30-39, 2017.
    [40] A. M. Ghosh and K. Grolinger, "Edge-cloud computing for Internet of Things data analytics: Embedding intelligence in the edge with deep learning," IEEE Transactions on Industrial Informatics, vol. 17, no. 3, pp. 2191-2200, 2020.
    [41] Y. Shi, K. Yang, T. Jiang, J. Zhang, and K. B. Letaief, "Communication-efficient edge AI: Algorithms and systems," IEEE Communications Surveys & Tutorials, vol. 22, no. 4, pp. 2167-2191, 2020.
    [42] W. Li, H. Hacid, E. Almazrouei, and M. Debbah, "A comprehensive review and a taxonomy of edge machine learning: Requirements, paradigms, and techniques," AI, vol. 4, no. 3, pp. 729-786, 2023.
    [43] I. C. Society, "IEEE Computer Society’s Top 12 Technology Trends for 2020," ed: IEEE Computer Society Washington, DC, USA, 2019.
    [44] Y. Wang, C. Liu, and Y.-F. Tu, "Factors affecting the adoption of AI-based applications in higher education," Educational Technology & Society, vol. 24, no. 3, pp. 116-129, 2021.
    [45] Z. Zhang and J. Li, "A review of artificial intelligence in embedded systems," Micromachines, vol. 14, no. 5, p. 897, 2023.
    [46] L. Dutta and S. Bharali, "Tinyml meets iot: A comprehensive survey," Internet of Things, vol. 16, p. 100461, 2021.
    [47] H. A. Imran, U. Mujahid, S. Wazir, U. Latif, and K. Mehmood, "Embedded development boards for edge-AI: A comprehensive report," arXiv preprint arXiv:2009.00803, 2020.
    [48] A. Garcia-Perez, R. Miñón, A. I. Torre-Bastida, and E. Zulueta-Guerrero, "Analysing Edge Computing Devices for the Deployment of Embedded AI," Sensors, vol. 23, no. 23, p. 9495, 2023.
    [49] S. Mittal and J. S. Vetter, "A survey of CPU-GPU heterogeneous computing techniques," ACM Computing Surveys (CSUR), vol. 47, no. 4, pp. 1-35, 2015.
    [50] F. Dou et al., "Towards artificial general intelligence (agi) in the internet of things (iot): Opportunities and challenges," arXiv preprint arXiv:2309.07438, 2023.
    [51] C. Zhang et al., "One small step for generative ai, one giant leap for agi: A complete survey on chatgpt in aigc era," arXiv preprint arXiv:2304.06488, 2023.
    [52] R. Bommasani et al., "On the opportunities and risks of foundation models," arXiv preprint arXiv:2108.07258, 2021.
    [53] C.-J. Hsu, C.-L. Liu, F.-T. Liao, P.-C. Hsu, Y.-C. Chen, and D.-S. Shiu, "Breeze-7B Technical Report," arXiv preprint arXiv:2403.02712, 2024.
    [54] L. Ouyang et al., "Training language models to follow instructions with human feedback," Advances in neural information processing systems, vol. 35, pp. 27730-27744, 2022.
    [55] M.-J. Tsai and S.-L. Peng, "QR code beautification by instance segmentation (IS-QR)," Digital Signal Processing, vol. 133, p. 103887, 2023.
    [56] A. Wang et al., "YOLOv10: Real-Time End-to-End Object Detection," arXiv preprint arXiv:2405.14458, 2024.
    [57] J. Terven and D. Cordova-Esparza, "A comprehensive review of YOLO: From YOLOv1 to YOLOv8 and beyond," arXiv preprint arXiv:2304.00501, 2023.
    [58] P. Hu, Y. Gong, Y. Lu, and A. W. Ding, "Speaking vs. listening? Balance conversation attributes of voice assistants for better voice marketing," International Journal of Research in Marketing, vol. 40, no. 1, pp. 109-127, 2023.
    [59] C. S. Oh, J. N. Bailenson, and G. F. Welch, "A systematic review of social presence: Definition, antecedents, and implications," Frontiers in Robotics and AI, vol. 5, p. 409295, 2018.
    [60] D. Grewal, S. M. Noble, A. L. Roggeveen, and J. Nordfalt, "The future of in-store technology," Journal of the Academy of Marketing Science, vol. 48, pp. 96-113, 2020.
    [61] A. Radford, J. W. Kim, T. Xu, G. Brockman, C. McLeavey, and I. Sutskever, "Robust speech recognition via large-scale weak supervision," in International Conference on Machine Learning, 2023: PMLR, pp. 28492-28518.
    [62] A. S. Luccioni, Y. Jernite, and E. Strubell, "Power hungry processing: Watts driving the cost of ai deployment?," arXiv preprint arXiv:2311.16863, 2023.
    [63] B. Rouhani et al., "A Microsoft custom data type for efficient inference," 2020.
    [64] S. Roy, "Understanding the Impact of Post-Training Quantization on Large-scale Language Models," arXiv preprint arXiv:2309.05210, 2023.
    [65] J. Lin, J. Tang, H. Tang, S. Yang, X. Dang, and S. Han, "Awq: Activation-aware weight quantization for llm compression and acceleration," arXiv preprint arXiv:2306.00978, 2023.
    [66] E. Frantar, S. Ashkboos, T. Hoefler, and D. Alistarh, "Gptq: Accurate post-training quantization for generative pre-trained transformers," arXiv preprint arXiv:2210.17323, 2022.
    [67] K. VM, H. Warrier, and Y. Gupta, "Fine Tuning LLM for Enterprise: Practical Guidelines and Recommendations," arXiv preprint arXiv:2404.10779, 2024.
    [68] Y. Zhang et al., "Siren's song in the AI ocean: a survey on hallucination in large language models," arXiv preprint arXiv:2309.01219, 2023.
    [69] A. Gupta et al., "RAG vs Fine-tuning: Pipelines, Tradeoffs, and a Case Study on Agriculture," arXiv preprint arXiv:2401.08406, 2024.
    [70] M. A. Habib, S. Amin, M. Oqba, S. Jaipal, M. J. Khan, and A. Samad, "TaxTajweez: A Large Language Model-based Chatbot for Income Tax Information In Pakistan Using Retrieval Augmented Generation (RAG)," in The International FLAIRS Conference Proceedings, 2024, vol. 37.
    [71] F. Cuconasu et al., "The power of noise: Redefining retrieval for rag systems," arXiv preprint arXiv:2401.14887, 2024.
    [72] C. Wang, X. Chen, T. Yu, Y. Liu, and Y. Jing, "Education reform and change driven by digital technology: a bibliometric study from a global perspective," Humanities and Social Sciences Communications, vol. 11, no. 1, pp. 1-17, 2024.
    [73] A. Bryan et al., "NMC Horizon Report: 2019 Higher Education Edition. louisville," CO: EDUCAUSE, 2019.
    [74] D. Keegan, "The incorporation of mobile learning into mainstream education and training," in World Conference on Mobile Learning, Cape Town, 2005, vol. 11, pp. 1-17.
    [75] H. Crompton, "The benefits and challenges of mobile learning," Learning and leading with technology, vol. 41, 2013.
    [76] I. Goksu, "Bibliometric mapping of mobile learning," Telematics and Informatics, vol. 56, p. 101491, 2021.
    [77] A. Bhati and I. Song, "New methods for collaborative experiential learning to provide personalised formative assessment," International Journal of Emerging Technologies in Learning, vol. 14, pp. 179-195, 2019.
    [78] M. Al-Emran, V. Mezhuyev, and A. Kamaludin, "Towards a conceptual model for examining the impact of knowledge management factors on mobile learning acceptance," Technology in Society, vol. 61, p. 101247, 2020.
    [79] M. J. Timms, "Letting artificial intelligence in education out of the box: educational cobots and smart classrooms," International Journal of Artificial Intelligence in Education, vol. 26, pp. 701-712, 2016.
    [80] I. Roll and R. Wylie, "Evolution and revolution in artificial intelligence in education," International Journal of Artificial Intelligence in Education, vol. 26, pp. 582-599, 2016.
    [81] S. Huang, B. Weiler, and G. Assaker, "Effects of interpretive guiding outcomes on tourist satisfaction and behavioral intention," Journal of Travel Research, vol. 54, no. 3, pp. 344-358, 2015.
    [82] Y. Reisinger and C. Steiner, "Reconceptualising interpretation: The role of tour guides in authentic tourism," Current issues in tourism, vol. 9, no. 6, pp. 481-498, 2006.
    [83] F. Umam, F. Adiputra, A. Dafid, and S. Wahyuni, "Autonomous museum tour guide robot with object detection using tensorflow learning machine," in 2022 IEEE 8th Information Technology International Seminar (ITIS), 2022: IEEE, pp. 274-281.
    [84] S. Wang and H. I. Christensen, "Tritonbot: First lessons learned from deployment of a long-term autonomy tour guide robot," in 2018 27th IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN), 2018: IEEE, pp. 158-165.
    [85] A. D. Diallo, S. Gobee, and V. Durairajah, "Autonomous tour guide robot using embedded system control," Procedia Computer Science, vol. 76, pp. 126-133, 2015.
    [86] S. Rosa et al., "Tour guide robot: a 5G-enabled robot museum guide," Frontiers in Robotics and AI, vol. 10, p. 1323675, 2024.
    [87] I. A. Wong, Q. L. Lian, and D. Sun, "Autonomous travel decision-making: An early glimpse into ChatGPT and generative AI," Journal of Hospitality and Tourism Management, vol. 56, pp. 253-263, 2023.
    [88] I. Carvalho and S. Ivanov, "ChatGPT for tourism: applications, benefits and risks," Tourism Review, vol. 79, no. 2, pp. 290-303, 2024.
    [89] D. E. Salinas-Navarro, E. Vilalta-Perdomo, R. Michel-Villarreal, and L. Montesinos, "Using Generative Artificial Intelligence Tools to Explain and Enhance Experiential Learning for Authentic Assessment," Education Sciences, vol. 14, no. 1, p. 83, 2024.
    [90] R. P. d. Santos, "Enhancing chemistry learning with chatgpt and bing chat as agents to think with: A comparative case study," arXiv preprint arXiv:2305.11890, 2023.
    [91] V. Mazzia, A. Khaliq, F. Salvetti, and M. Chiaberge, "Real-time apple detection system using embedded systems with hardware accelerators: An edge AI application," IEEE Access, vol. 8, pp. 9102-9114, 2020.
    [92] Z. Zou, K. Chen, Z. Shi, Y. Guo, and J. Ye, "Object detection in 20 years: A survey," Proceedings of the IEEE, vol. 111, no. 3, pp. 257-276, 2023.
    [93] Y. Gao et al., "Retrieval-augmented generation for large language models: A survey," arXiv preprint arXiv:2312.10997, 2023.
    [94] A. Q. Jiang et al., "Mistral 7B," arXiv preprint arXiv:2310.06825, 2023.
    [95] Y.-C. Hsieh, K.-m. Lyu, and R.-Y. Lyu, "Taiwanese/Mandarin Speech Recognition using OpenAI’s Whisper Multilingual Speech Recognition Engine Based on Generative Pretrained Transformer Architecture," in Proceedings of the 35th Conference on Computational Linguistics and Speech Processing (ROCLING 2023), 2023, pp. 210-214.
    [96] Google, "Google earth homepage [online]," 2005.
    [97] B. Sekachev, M. Nikita, and Z. Andrey, "Computer vision annotation tool: a universal approach to data annotation," Intel [Internet], vol. 1, 2019.
    [98] YAHBOOM. "Jetson Orin NX SUB Board with 16GB RAM Superior Kit." https://category.yahboom.net/products/jetson-orin-nx?variant=45177043059004 (accessed June 4th, 2024).
    [99] I. Ajzen and M. Fishbein, "Attitude-behavior relations: A theoretical analysis and review of empirical research," Psychological bulletin, vol. 84, no. 5, p. 888, 1977.
    [100] P. B. Brandtzaeg and A. Følstad, "Why people use chatbots," in Internet Science: 4th International Conference, INSCI 2017, Thessaloniki, Greece, November 22-24, 2017, Proceedings 4, 2017: Springer, pp. 377-392.
    [101] K.-L. Hsiao and C.-C. Chen, "What drives continuance intention to use a food-ordering chatbot? An examination of trust and satisfaction," Library Hi Tech, vol. 40, no. 4, pp. 929-946, 2022.
    [102] P. Daugherty, H. Wilson, and K. Narain, "Generative AI will enhance—not erase—customer service jobs," Harvard business review. https://hbr. org/2023/03/gener ative-ai-will-enhan ce-not-erase-cust o mer-servi ce-jobs. Accessed, vol. 25, 2023.
    [103] C. Bartneck, D. Kulić, E. Croft, and S. Zoghbi, "Measurement instruments for the anthropomorphism, animacy, likeability, perceived intelligence, and perceived safety of robots," International journal of social robotics, vol. 1, pp. 71-81, 2009.
    [104] M. Mori, "The uncanny valley: the original essay by Masahiro Mori," Ieee Spectrum, vol. 6, pp. 1-6, 1970.
    [105] S. Li, A. M. Peluso, and J. Duan, "Why do we prefer humans to artificial intelligence in telemarketing? A mind perception explanation," Journal of Retailing and Consumer Services, vol. 70, p. 103139, 2023.
    [106] X. Ma and Y. Huo, "Are users willing to embrace ChatGPT? Exploring the factors on the acceptance of chatbots from the perspective of AIDUA framework," Technology in Society, vol. 75, p. 102362, 2023.
    [107] A. Rapp, L. Curti, and A. Boldi, "The human side of human-chatbot interaction: A systematic literature review of ten years of research on text-based chatbots," International Journal of Human-Computer Studies, vol. 151, p. 102630, 2021.
    [108] Y. Cao et al., "A comprehensive survey of ai-generated content (aigc): A history of generative ai from gan to chatgpt," arXiv preprint arXiv:2303.04226, 2023.
    [109] H. Zhang, Y. Li, F. Ma, J. Gao, and L. Su, "Texttruth: an unsupervised approach to discover trustworthy information from multi-sourced text data," in Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, 2018, pp. 2729-2737.
    [110] R. Rombach, A. Blattmann, D. Lorenz, P. Esser, and B. Ommer, "High-resolution image synthesis with latent diffusion models," in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, 2022, pp. 10684-10695.
    [111] S. Wang, D. Lo, and L. Jiang, "An empirical study on developer interactions in stackoverflow," in Proceedings of the 28th annual ACM symposium on applied computing, 2013, pp. 1019-1024.
    [112] J. White et al., "A prompt pattern catalog to enhance prompt engineering with chatgpt," arXiv preprint arXiv:2302.11382, 2023.
    [113] T. Araujo, "Living up to the chatbot hype: The influence of anthropomorphic design cues and communicative agency framing on conversational agent and company perceptions," Computers in human behavior, vol. 85, pp. 183-189, 2018.
    [114] J. Hill, W. R. Ford, and I. G. Farreras, "Real conversations with artificial intelligence: A comparison between human–human online conversations and human–chatbot conversations," Computers in human behavior, vol. 49, pp. 245-250, 2015.
    [115] Y. Mou and K. Xu, "The media inequality: Comparing the initial human-human and human-AI social interactions," Computers in Human Behavior, vol. 72, pp. 432-440, 2017.
    [116] L. Ciechanowski, A. Przegalinska, M. Magnuski, and P. Gloor, "In the shades of the uncanny valley: An experimental study of human–chatbot interaction," Future Generation Computer Systems, vol. 92, pp. 539-548, 2019.
    [117] J. Fan et al., "A robotic coach architecture for elder care (ROCARE) based on multi-user engagement models," IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 25, no. 8, pp. 1153-1163, 2016.
    [118] E. Go and S. S. Sundar, "Humanizing chatbots: The effects of visual, identity and conversational cues on humanness perceptions," Computers in human behavior, vol. 97, pp. 304-316, 2019.
    [119] R. M. Schuetzler, G. M. Grimes, and J. Scott Giboney, "The impact of chatbot conversational skill on engagement and perceived humanness," Journal of Management Information Systems, vol. 37, no. 3, pp. 875-900, 2020.
    [120] C. Pelau, D.-C. Dabija, and I. Ene, "What makes an AI device human-like? The role of interaction quality, empathy and perceived psychological anthropomorphic characteristics in the acceptance of artificial intelligence in the service industry," Computers in Human Behavior, vol. 122, p. 106855, 2021.
    [121] D. M. Nguyen, Y.-T. H. Chiu, and H. D. Le, "Determinants of continuance intention towards banks’ chatbot services in Vietnam: A necessity for sustainable development," Sustainability, vol. 13, no. 14, p. 7625, 2021.
    [122] C. M. Ringle, S. Wende, and J.-M. Becker, "SmartPLS 4. Oststeinbek: SmartPLS," Retrieved March, vol. 13, p. 2023, 2022.
    [123] L. J. Cronbach, "Coefficient alpha and the internal structure of tests," psychometrika, vol. 16, no. 3, pp. 297-334, 1951.
    [124] C. Fornell and D. F. Larcker, "Evaluating structural equation models with unobservable variables and measurement error," Journal of marketing research, vol. 18, no. 1, pp. 39-50, 1981.
    [125] R. P. Bagozzi and Y. Yi, "On the evaluation of structural equation models," Journal of the academy of marketing science, vol. 16, pp. 74-94, 1988.
    [126] J. F. Hair, "Multivariate data analysis," 2009.
    [127] G. James, D. Witten, T. Hastie, and R. Tibshirani, An introduction to statistical learning. Springer, 2013.
    [128] J. G. Kemeny, "Man viewed as a machine," Scientific American, vol. 192, no. 4, pp. 58-67, 1955.
    [129] J. McCarthy, M. L. Minsky, N. Rochester, and C. E. Shannon, "A proposal for the dartmouth summer research project on artificial intelligence, august 31, 1955," AI magazine, vol. 27, no. 4, pp. 12-12, 2006.
    [130] S. Natale and A. Ballatore, "Imagining the thinking machine: Technological myths and the rise of artificial intelligence," Convergence, vol. 26, no. 1, pp. 3-18, 2020.
    [131] P. Bory, "Deep new: The shifting narratives of artificial intelligence from Deep Blue to AlphaGo," Convergence, vol. 25, no. 4, pp. 627-642, 2019.
    [132] L. I. D. Faruk, R. Rohan, U. Ninrutsirikun, and D. Pal, "University Students’ Acceptance and Usage of Generative AI (ChatGPT) from a Psycho-Technical Perspective," in Proceedings of the 13th International Conference on Advances in Information Technology, 2023, pp. 1-8.
    [133] M. Mazzone and A. Elgammal, "Art, creativity, and the potential of artificial intelligence," in Arts, 2019, vol. 8, no. 1: MDPI, p. 26.
    [134] D. Choi et al., "Unlock Life with a Chat (GPT): Integrating Conversational AI with Large Language Models into Everyday Lives of Autistic Individuals," in Proceedings of the CHI Conference on Human Factors in Computing Systems, 2024, pp. 1-17.
    [135] D. Wang et al., "From human-human collaboration to Human-AI collaboration: Designing AI systems that can work together with people," in Extended abstracts of the 2020 CHI conference on human factors in computing systems, 2020, pp. 1-6.
    [136] M. Cohn et al., "Believing Anthropomorphism: Examining the Role of Anthropomorphic Cues on Trust in Large Language Models," in Extended Abstracts of the CHI Conference on Human Factors in Computing Systems, 2024, pp. 1-15.
    [137] A. Waytz, J. Heafner, and N. Epley, "The mind in the machine: Anthropomorphism increases trust in an autonomous vehicle," Journal of experimental social psychology, vol. 52, pp. 113-117, 2014.
    [138] D. Jaiswal, V. Kaushal, A. Mohan, and P. Thaichon, "Mobile wallets adoption: pre-and post-adoption dynamics of mobile wallets usage," Marketing Intelligence & Planning, vol. 40, no. 5, pp. 573-588, 2022.
    [139] T. E. Mofokeng, "The impact of online shopping attributes on customer satisfaction and loyalty: Moderating effects of e-commerce experience," Cogent Business & Management, vol. 8, no. 1, p. 1968206, 2021.
    [140] W. Reim, J. Åström, and O. Eriksson, "Implementation of artificial intelligence (AI): a roadmap for business model innovation," Ai, vol. 1, no. 2, p. 11, 2020.

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