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研究生: 林淑真
Lin, Joyce shucheng
論文名稱: 海外華語文教師應用ChatGPT於課程教學之研究
Acceptance of Using ChatGPT in Teaching Mandarin: The Perspective of Overseas Chinese Teachers
指導教授: 洪榮昭
Hong, Jon-Chao
口試委員: 洪榮昭
Hong, Jon-Chao
張瓅勻
Chang, Li-Yun
戴凱欣
Tai, Kai-Hsin
口試日期: 2024/07/08
學位類別: 碩士
Master
系所名稱: 華語文教學系海外華語師資數位碩士在職專班
Department of Chinese as a Second Language_Online Continuing Education Master's Program of Teaching Chinese as a Foreign Language
論文出版年: 2024
畢業學年度: 112
語文別: 中文
論文頁數: 157
中文關鍵詞: 人工智慧ChatGPT科技接受模式情境期望價值理論
英文關鍵詞: AI, ChatGPT, Technology acceptance model, Situated expectancy value theory
研究方法: 調查研究
DOI URL: http://doi.org/10.6345/NTNU202401438
論文種類: 學術論文
相關次數: 點閱:185下載:24
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  • ChatGPT的快速發展引發熱門話題,許多國內外學者已著手研究其在教育上之應用,本研究以科技接受模式及情境期望價值理論為理論框架,探討海外華語文教師在應用ChatGPT於華語文教學的情境下,教師使用ChatGPT的經驗是否影響其對ChatGPT的知覺有用性與易用性,並進一步探討這兩項知覺是否影響情境中的知覺使用價值,從而影響其持續使用意圖。
    研究方法採用問卷調查法,在華語文教學交流平台及群組上進行滾雪球取樣,以海外的華語文教師為研究對象,蒐集有效樣本共97份,以結構方程模式進行數據分析,結果顯示:一、ChatGPT使用經驗與有用性、易用性具有正相關。二、ChatGPT的知覺有用性、知覺易用性與知覺使用價值具有正相關。三、知覺使用價值與持續使用意圖具有正相關。四、使用經驗對知覺使用價值及持續使用意圖有間接正相關。五、知覺有用性及知覺易用性對持續使用意圖有間接正相關。六、每週使用ChatGPT超過7小時的教師在使用經驗、知覺有用性、知覺使用價值及持續使用意圖方面,皆高於每週使用不足7小時者。
    本研究填補了現有文獻中關於海外華語文教師應用ChatGPT於教學課程的研究空白,同時也為海外華語文教師在教學實務應用與未來研究提供建議。

    The rapid advancement of ChatGPT has sparked widespread discussions and many domestic and foreign scholars have begun to study its application in education. This study explores the application of ChatGPT in overseas Chinese language teaching based on the Technology Acceptance Model and Situated Expectancy Value Theory. The study aims to understand whether Chinese teachers' experiences with ChatGPT influence their perceived usefulness and ease of use of the tool, and further examines whether these perceptions affect their perceived task value, thereby enhancing their intention to continue using ChatGPT.
    This study employed a questionnaire survey method and utilized snowball sampling to target Chinese language teachers overseas. A total of 97 valid samples were collected and analyzed using Structural Equation Modeling.
    The results indicate that : (1) experience with ChatGPT positively correlates with its perceived usefulness and ease of use ; (2) perceived usefulness and ease of use positively correlate with perceived task value ; (3) perceived task value positively correlates with the intention to continue using ChatGPT ; (4) experience with ChatGPT has an indirect impact on perceived task value and the intention to continue using it ; (5) perceived usefulness and ease of use have an indirect impact on the intention to continue using ChatGPT ; (6) teachers who use ChatGPT for more than seven hours per week show higher levels of experience, perceived usefulness, perceived task value, and intention to continue using the tool compared to those who use it for less than seven hours per week.
    This study fills a gap in the existing literature regarding the application of ChatGPT by overseas Chinese language teachers in their instructional courses. It also provides practical suggestions for the application of ChatGPT in teaching practices and future educational research for overseas Chinese teachers.

    謝 誌 i 摘 要 iii Abstract iv 目 次 vi 表 次 viii 圖 次 x 第一章 緒論 1 第一節 研究背景與動機 1 第二節 研究目的與問題 6 第三節 名詞解釋 7 第四節 研究範圍與限制 9 第二章 文獻探討 13 第一節 AI的發展與教學應用 13 第二節 科技接受模式 32 第三節 情境期望價值理論 41 第三章 研究方法 49 第一節 研究架構與假設 49 第二節 研究對象 52 第三節 研究工具 52 第四節 研究程序 59 第五節 資料處理與分析 62 第四章 研究結果 65 第一節 背景變項之次數分配 65 第二節 敘述統計分析 69 第三節 驗證性分析 74 第四節 信度與效度分析 85 第五節 路徑分析 88 第六節 間接效應分析 91 第七節 差異性分析 92 第八節 研究討論 107 第五章 結論與建議 119 第一節 研究結論 119 第二節 研究貢獻 122 第三節 研究限制與建議 123 參考文獻 127  中文部分 127  外文部分 131  中文網站 148  英文網站 150 附錄、問卷 153

    丁磊 (2023)。生成式人工智慧:AIGC的邏輯與應用。 金尉。
    亨利·季辛吉 Henry A. Kissinger、艾力克·施密特 Eric Schmidt、丹尼爾·哈騰洛赫 Daniel Huttenlocher ;葉妍伶譯。Kissinger, H., Schmidt, E., & Huttenlocher, D. (2022) . AI世代與我們的未來: 人工智慧如何改變生活, 甚至是世界? = The age of AI : and our human future (葉妍伶, 翻譯) 。聯經出版事業股份有限公司。
    李昀霖、鄭宇庭、蘇志雄(2020)。個人雲商業模式可行性之探討。Journal of Data Analysis,15(5),45-72。https://0-doi-org.opac.lib.ntnu.edu.tw/10.6338/JDA.202010_15(5).0003
    吳佳芬(2023)。科技接受模式的發展與未來趨勢分析。科學與人文研究,11(3),58-69。https://doi.org/10.6535/JSH.202311_11(3).0003
    吳宗遠(2023)。如何利用數位學伴-Chat GPT輔助學習化學。化學,81(2) 。 https://doi.org/10.6623/chem.202306_81(2).006
    林薰苑、陳仲詠、古惟中、楊仲捷(2023)。ChatGPT的衝擊與因應之道。電工通訊季刊,(4),91-98。https://doi.org/10.6328/CIEE.202312_(4).0009
    洪淑華、黃靖文(2022)。基於科技接受模式探討國小學生因材網使用意圖之研究。學校行政,(139),114-133。https://doi.org/10.6423/HHHC.202205_(139).0006
    孫鈺喬、陳俐文、陳棟樑(2021)。 運用科技接受模式探討使用Microsoft Teams進行遠距教學之學生學習滿意度。兩岸職業教育論叢, 5(1) 。https://doi.org/10.6685/ASVEJ.202110_5(1).0004
    袁齊笙(2023)。ChatGPT與人工智慧對我國高中學生權利的可能影響:國際教育當局因應對策的啟示。學生事務與輔導,61(4),9-16。https://doi.org/10.6506/SAGC.202303_61(4).0002
    高文忠 (2023)。AI與ChatGPT對教育的影響與因應之道。臺灣教育評論月刊, 12(7), 68-71。
    陳怡如(2024)。以人為本加速AI技術落地。工業技術與資訊月刊382, 18-19。
    陳英正、陳英豪(2017)。以科技接受模式探討國小資源班教師實施資訊科技融入教學之意願。人文社會科學研究:教育類,11(2),17-37。https://doi.org/10.6618/HSSRP.2017.11(2)2
    陳冠名(2023)。以ChatGPT協助學術論文寫作之初探。實踐博雅學報,(34),85-99。https://www.airitilibrary.com/Article/Detail?DocID=1810133x-N202307220001-00005
    陳郁仁(2022)。我國華語文教育產業國際發展策略之研究。僑教與海外華人研究學報,11,89–105。https://doi.org/10.6325/oce.202212_(11).0005
    陳明志、劉明洲(2023)。疫情時代下使用延伸科技接受模式探討因材網之使用意向。科學教育月刊,(462),18-36。https://www.airitilibrary.com/Article/Detail?DocID=a0000036-N202312300007-00002
    陳棟樑、陳俐文、邱怡瑄(2019)。高中生Instagram持續使用意圖之研究。 管理資訊計算, 8。https://doi.org/10.6285/MIC.201908/SP_01_8.0009
    陳逸姍、范斯淳(2022)。以科技接受模式探討國小藝術才能專長線上教學實施之現況-以舞蹈班為例。工業科技教育學刊,(15),102-120。https://doi.org/10.6306/JITE.202211_(15).0006
    許麗玲、何晉滄、黃文楷(2008)。探討Blog使用者持續採用行為之研究-以期望確認理論為基礎。資訊管理學報,15(4),1-26。https://doi.org/10.6382/JIM.200810.0001
    許麗玲、陳至柔、陳澔輝(2013)。雲端ERP系統服務品質與持續使用意圖之研究。電子商務學報,15(2),195-233。https://doi.org/10.6188/JEB.2013.15(2).02
    曾儀蓉、歐陽誾(2019)。以科技接受模式探討國小教師對於使用FB網路直播系統進行專業成長之使用意願。教育傳播與科技研究,(120),55-74。https://doi.org/10.6137/RECT.201906_120.0004
    湯家偉、莊俊儒、張其祿、洪雅琪(2020)。臺灣華語師資在新南向國家之競爭優劣勢:菲律賓華校觀點。教育科學研究期刊,65(3),55-79。https://doi.org/10.6209/JORIES.202009_65(3).0003
    彭思遠、蕭郁妮(2024)。ChatGPT帶來的影響。臺灣經濟研究月刊,47(1),74-80。https://doi.org/10.29656/TERM.202401_47(1).0011
    葉惠婷(2023)。AI生成文章對國中寫作教學可能的影響:以ChatGPT為例。臺灣教育評論月刊,12(4),111-115。https://www.airitilibrary.com/Article/Detail?DocID=P20130114001-N202304010020-00018
    詹勲育、陳薏如(2023)。技術型高中與五專工業群科一年級學生之轉出與升學意圖──脈絡化期望價值理論之向度比較觀點。中等教育,74(1) 。 https://doi.org/10.6249/SE.202303_74(1).0004
    趙楊靜、莫美仙、寧百慧、王連超、王軻冉、楊唐梅(2023)。AI教育在鄉村基礎教育公平中的應用研究。創新教育研究, 11(05) ,1106–1114。 https://doi.org/10.12677/CES.2023.115171
    歐宜佩、陳信宏(2018)。近期數位轉型發展趨勢之觀察。經濟前瞻,(178),94-99。https://www.airitilibrary.com/Article/Detail?DocID=10190376-201807-201808140012-201808140012-94-99
    顏榮泉 (2024) 。從認知處理觀點評論生成式 AI 對學習的影響。臺灣教育評論月刊,13(3) ,144-153。
    羅蕾(2023)。人工智慧時代傳統教育反思與應對——以ChatGPT為例。 教育進展,13(05) ,2358–2364。https://doi.org/10.12677/AE.2023.135371
    樓永堅、曾威智(2016)。以後設分析法探討科技接受模式之研究。科技管理學刊,21(2),1-28。https://www.airitilibrary.com/Article/Detail?DocID=10287353-201606-201703270037-201703270037-1-28 
    Agarwal, R., & Prasad, J. (1999). Are individual differences germane to the acceptance of new information technologies? Decision Sciences, 30(2), 361-391.
    Agarwal, R., Mehrotra, A., Pant, M. K., Alzeiby, E. A., & Vishnoi, S. K. (2024). Digital photo hoarding in online retail context: An in-depth qualitative investigation of retail consumers. Journal of Retailing and Consumer Services, 78, 103729. https://doi.org/10.1016/j.jretconser.2024.103729
    Ahmad, N., Omar, A., & Ramayah, T. (2010). Consumer lifestyles and online shopping continuance intention. Business Strategy Series, 11(4), 227-243.
    Ahmad, S., Zulkurnain, N. N. A., & Khairushalimi, F. I. (2016). Assessing the fitness of a measurement model using Confirmatory Factor Analysis (CFA). International Journal of Innovation and Applied Studies, 17(1), 159-168.
    Ahrari Khalaf, A., Hassan Abdalla Hashim, A., & Olowolayemo, A. (2024). Mutual character dialogue generation with semi-supervised multitask learners and awareness. International Journal of Information Technology, 16, 1357–1363. https://doi.org/10.1007/s41870-023-01720-x
    Alam, M., & Hasan, M. (2024). Applications and future prospects of artificial intelligence in education. International Journal of Humanities & Social Science Studies (IJHSSS), 10(1), 197-206. Doi:10.29032/ijhsss.v10.i1.2024.197-206
    Alsyouf, A., Lutfi, A., Alsubahi, N., Alhazmi, F. N., Al-Mugheed, K., Anshasi, R. J., Alharbi, N. I., & Albugami, M. (2023). The use of a technology acceptance model (TAM) to predict patients’ usage of a personal health record system: The role of security, privacy, and usability. International Journal of Environmental Research and Public Health, 20(2), 1347. https://doi.org/10.3390/ijerph20021347
    Amiel, D., & Blitz, C. (2024). ChatGPT in education – An educator-driven understanding of promise and the path forward. INTED2024 Proceedings, 2012–2012.https://doi.org/10.21125/inted.2024.0565
    Amisha, P. M., Pathania, M., & Rathaur, V. K. (2019). Overview of artificial intelligence in medicine. Journal of Family Medicine and Primary Care, 8(7), 2328.
    Atkinson, J. W. (1957). Motivational determinants of risk-taking behavior. Psychological Review, 64(6, Pt.1), 359–372. https://doi.org/10.1037/h0043445
    Atkinson, J. W. (1964). An Introduction to Motivation. Van Nostrand.
    Banks, S. (2011). A historical analysis of attitudes toward the use of calculators in junior high and high school math classrooms in the United States since 1975 Master of Education Research Theses No. 31. Cedarville University https://doi.org/10.15385/tmed.2011.1
    Bérešová, J. (2024). The impact of AI on improving written performances. INTED2024 Proceedings, 4625–4629. https://doi.org/10.21125/inted.2024.1196
    Bergey, B. W., Parrila, R. K., & Deacon, S. H. (2018). Understanding the academic motivations of students with a history of reading difficulty: An expectancy-value-cost approach. Learning and Individual Differences, 67, 41–52. https://doi.org/10.1016/j.lindif.2018.06.008
    Beymer, P. N., Benden, D. K., & Sachisthal, M. S. M. (2022). Exploring the dynamics of situated expectancy-value theory: A panel network analysis. Learning and Individual Differences, 100, 102233. https://doi.org/10.1016/j.lindif.2022.102233
    Bhattacherjee, A. (2001). Understanding information systems continuance: An expectation-confirmation model. MIS Quarterly, 25(3), 351-370. https://doi.org/10.2307/3250921
    Bhattacherjee, A., Perols, J., & Sanford, C. (2008). Information technology continuance: A theoretic extension and empirical test. Journal of Computer Information Systems, 49(1), 17-26.
    Biswas, S. S. (2023a). Potential use of chat gpt in global warming. Annals of Biomedical Engineering, 51(6), 1126–1127. https://doi.org/10.1007/s10439-023-03171-8
    Biswas, S. S. (2023b). Role of chat gpt in public health. Annals of Biomedical Engineering, 51(5), 868–869. DOI: 10.1007/s10439-023-03172-7
    Bong, M. (2001). Role of self-efficacy and task-value in predicting college students’ course performance and future enrolment intentions. Contemporary Educational Psychology, 26, 553-570. https://doi.org/10.1006/ceps.2000.1048
    Brown, T. B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D. M., Wu, J., Winter, C., … Amodei, D. (2020). Language Models are Few-Shot Learners. https://doi.org/10.48550/ARXIV.2005.14165
    Bubeck, S., Chandrasekaran, V., Eldan, R., Gehrke, J., Horvitz, E., Kamar, E., Lee, P., Lee, Y. T., Li, Y., Lundberg, S., Nori, H., Palangi, H., Ribeiro, M. T., & Zhang, Y. (2023). Sparks of artificial general intelligence: Early experiments with GPT-4. Cornell University https://doi.org/10.48550/arXiv.2303.12712
    Bubeck, S., Chandrasekaran, V., Eldan, R., Gehrke, J., Horvitz, E., Kamar, E., Lee, P., & others. (2023). Sparks of artificial general intelligence: Early experiments with GPT-4. https://doi.org/10.48550/arXiv.2303.12712
    Cao, Y., Li, S., Liu, Y., Yan, Z., Dai, Y., Yu, P. S., & Sun, L. (2023). A comprehensive survey of AI-generated content (AIGC): A history of generative AI from GAN to ChatGPT. https://doi.org/10.48550/arXiv.2303.04226
    Chen H.-L., & Chen H.-H. (2023). Have You Chatted Today? - Medical Education Surfing with Artificial Intelligence. Journal of Medical Education, 27(1), 1–4. https://doi.org/10.6145/jme.202303_27(1).0005
    Chen, X., Xie, H., Zou, D., & Hwang, G.-J. (2020). Application and theory gaps during the rise of Artificial Intelligence in Education. Computers and Education: Artificial Intelligence, 1, 100002. https://doi.org/10.1016/j.caeai.2020.100002
    Chuttur, M. (2009). Overview of the technology acceptance model: Origins, developments and future directions. Sprouts: Working Papers on Information Systems, 9(37).
    Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed.). Routledge. https://doi.org/10.4324/9780203771587
    Cohen, J., Cohen, P., West, S. G., & Aiken, L. S. (2002). Applied multiple regression/correlation analysis for the behavioral sciences (3rd ed.). Routledge. https://doi.org/10.4324/9780203774441
    Crevier, D. (1993). AI: The tumultuous search for artificial intelligence. Bulletin of Science, Technology & Society, 14(4), 224-224. https://doi.org/10.1177/027046769401400414
    Crompton, H., & Burke, D. (2023). Artificial intelligence in higher education: The state of the field. International Journal of Educational Technology in Higher Education, 20(1), 22. https://doi.org/10.1186/s41239-023-00392-8
    Davis, F. D. (1987). User acceptance of information systems: The technology acceptance model (TAM). University of Michigan. https://quod.lib.umich.edu/b/busadwp/images/b/1/4/b1409190.0001.001.pdf
    Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319–340. https://doi.org/10.2307/249008
    Eccles, J. S., & Wigfield, A. (2002). Motivational beliefs, values, and goals. Annual Review of Psychology, 53(1), 109–132. https://doi.org/10.1146/annurev.psych.53.100901.135153
    Eccles, J. (2009). Who am I and what am I going to do with my life? Personal and collective identities as motivators of action. Educational Psychologist, 44(2), 78–89. https://doi.org/10.1080/00461520902832368
    Eccles, J. S., & Wigfield, A. (2020). From expectancy-value theory to situated expectancy-value theory: A developmental, social cognitive, and sociocultural perspective on motivation. Contemporary Educational Psychology, 61, 101859. https://doi.org/10.1016/j.cedpsych.2020.101859
    Eccles, J. S., & Wigfield, A. (2023). Expectancy-value theory to situated expectancy-value theory: Reflections on the legacy of 40+ years of working together. Motivation Science, 9(1), 1–12. https://doi.org/10.1037/mot0000275
    Eccles, J. S., Adler, T. F., Futterman, R., Goff, S. B., Kaczala, C. M., Meece, J. L., & Midgley, C. (1983). Expectancies, values, and academic behaviors. In J. T. Spence (Ed.), Achievement and achievement motivation (pp. 75-146). W. H. Freeman.
    Epstein, Z., Hertzmann, A., the Investigators of Human Creativity, Akten, M., Farid, H., Fjeld, J., Frank, M. R., Groh, M., Herman, L., Leach, N., Mahari, R., Pentland, A. “Sandy”, Russakovsky, O., Schroeder, H., & Smith, A. (2023). Art and the science of generative AI. Science, 380(6650), 1110–1111. https://doi.org/10.1126/science.adh4451
    Felix, C. V. (2020). The role of the teacher and AI in education. In International perspectives on the role of technology in humanizing higher education, 33, 1-12. Emerald Publishing Limited. DOI:10.1108/S2055-364120200000033003
    Felten, E. W., Raj, M., & Seamans, R. (2023). How will Language Modelers like ChatGPT Affect Occupations and Industries? SSRN Electronic Journal. https://doi.org/10.2139/ssrn.4375268
    Flake, J. K., Barron, K. E., Hulleman, C., McCoach, B. D., & Welsh, M. E. (2015). Measuring cost: The forgotten component of expectancy-value theory. Contemporary Educational Psychology, 41, 232-244. https://doi.org/10.1016/j.cedpsych.2015.03.002
    Górriz, J. M., Ramírez, J., Ortíz, A., Martínez-Murcia, F. J., Segovia, F., Suckling, J., Leming, M., Zhang, Y. D., Álvarez-Sánchez, J. R., Bologna, G., Bonomini, P., Casado, F. E., Charte, D., Charte, F., Contreras, R., Cuesta-Infante, A., Duro, R. J., Fernández-Caballero, A., Fernández-Jover, E., … Ferrández, J. M. (2020). Artificial intelligence within the interplay between natural and artificial computation: Advances in data science, trends and applications. Neurocomputing, 410, 237–270. https://doi.org/10.1016/j.neucom.2020.05.078
    Granić, A. (2023). Technology acceptance and adoption in education. In O. Zawacki-Richter & I. Jung (Eds.), Handbook of open, distance and digital education (pp. 183–197). Springer Nature. https://doi.org/10.1007/978-981-19-2080-6_11
    Granić, A., & Marangunić, N. (2019). Technology acceptance model in educational context: A systematic literature review. British Journal of Educational Technology, 50(5), 2572–2593. https://doi.org/10.1111/bjet.12864
    Guilherme, A. (2019). AI and education: The importance of teacher and student relations. AI & Society, 34(1), 47–54. https://doi.org/10.1007/s00146-017-0693-8
    Hair, J. F., Risher, J. J., Sarstedt, M., & Ringle, C. M. (2019). When to use and how to report the results of PLS-SEM. European Business Review, 31(1), 2-24. https://doi.org/10.1108/EBR-11-2018-0203
    Hair, J., & Alamer, A. (2022). Partial least squares structural equation modeling (PLS-SEM) in second language and education research: ECECCF using an applied example. Research Methods in Applied Linguistics, 1(3), 100027. https://doi.org/10.1016/j.rmal.2022.100027
    Hill-Yardin, E. L., Hutchinson, M. R., Laycock, R., & Spencer, S. J. (2023). A Chat (GPT) about the future of scientific publishing. Brain, Behavior, and Immunity, 110, 152–154. https://doi.org/10.1016/j.bbi.2023.02.022
    Hill-Yardin, E. L., Hutchinson, M. R., Laycock, R., & Spencer, S. J. (2023). A chat (GPT) about the future of scientific publishing. Brain, Behavior, and Immunity, 110, 152–154. https://doi.org/10.1016/j.bbi.2023.02.022
    Holdack, E., Lurie-Stoyanov, K., & Fromme, H. F. (2022). The role of perceived enjoyment and perceived informativeness in assessing the acceptance of AR wearables. Journal of Retailing and Consumer Services, 65, 1-11
    Holden, R. J., & Karsh, B.-T. (2010). The technology acceptance model: Its past and its future in health care. Journal of Biomedical Informatics, 43(1), 159-172. https://doi.org/10.1016/j.jbi.2009.07.002
    Hong, J. C., Lin, C. H., & Juh, C. C. (2023). Using a chatbot to learn English via charades: The correlates between social presence, hedonic value, perceived value, and learning outcome. Interactive Learning Environments, 1-17. https://doi.org/10.1080/10494820.2023.2273485
    Hong, J. C., & Tai, T. Y. (2024). Exploring the role of internet self-efficacy, perceived enjoyment, and anxiety in intelligent personal assistant-based EFL learning. Innovation in Language Learning and Teaching. https://doi.org/10.1080/17501229.2024.2347884
    Hou, C. K. (2016). Understanding business intelligence system continuance intention: An empirical study of Taiwan’s electronics industry. Information Development, 32(5), 1359-1371.
    Hulleman, C. S., Barron, K. E., Kosovich, J. J., & Lazowski, R. A. (2016). Student motivation: Current theories, constructs, and interventions within an expectancy-value framework. In A. A. Lipnevich, F. Preckel, & R. D. Roberts (Eds.), Psychosocial skills and school systems in the 21st century: Theory, research, and practice (pp. 241–278). Springer Nature. https://doi.org/10.1007/978-3-319-28606-8_10
    Hwang, G. J., Xie, H., Wah, B. W., & Gašević, D. (2020). Vision, challenges, roles and research issues of artificial intelligence in education. Computers and Education: Artificial Intelligence, 1, 100001.https://doi.org/10.1016/j.caeai.2020.100001
    Islam, I., & Islam, M. N. (2024). Exploring the opportunities and challenges of ChatGPT in academia. Discover Education, 3(1), 31. https://doi.org/10.1007/s44217-024-00114-w
    Jacobs, J. E., & Eccles, J. S. (2000). Parents, task values, and real-life achievement-related choices. In C. Sansone & J. M. Harackiewicz (Eds.), Intrinsic Motivation (pp. 405–439). Academic Press. https://doi.org/10.1016/B978-012619070-0/50036-2
    Jukiewicz, M. (2024). The future of grading programming assignments in education: The role of ChatGPT in automating the assessment and feedback process. Thinking Skills and Creativity, 52, 101522. https://doi.org/10.1016/j.tsc.2024.101522
    Kadian, S., Kumari, P., Shukla, S., & Narayan, R. (2023). Recent advancements in machine learning enabled portable and wearable biosensors. Talanta Open, 8, Article 100267. https://doi.org/10.1016/j.talo.2023.100267
    Kamalov, F., Santandreu Calonge, D., & Gurrib, I. (2023). New era of artificial intelligence in education: Towards a sustainable multifaceted revolution. Sustainability, 15(16), 12451. https://doi.org/10.3390/su151612451
    Keuning, H., Heeren, B., & Jeuring, J. (2019). How teachers would help students to improve their code. In Proceedings of the 2019 ACM conference on innovation and technology in computer science education (pp. 1-6). https://doi.org/10.1145/3304221.3319780
    King, M. R., & ChatGPT. (2023). A conversation on artificial intelligence, chatbots, and plagiarism in higher education. Cellular and Molecular Bioengineering, 16(1), 1-2. https://doi.org/10.1007/s12195-022-00754-8
    Knekta, E., & Eklöf, H. (2015). Modeling the test-taking motivation construct through investigation of psychometric properties of an expectancy-value based questionnaire. Journal of Psychoeducational Assessment, 33, 662–673. https://doi.org/10.1177/0734282914551956
    Kohnke, L., Moorhouse, B. L., & Zou, D. (2023). ChatGPT for language teaching and learning. RELC Journal. https://doi.org/10.1177/00336882231162868
    Kumar, P., Hollebeek, L. D., Kar, A. K., & Kukk, J. (2023). Charting the intellectual structure of customer experience research. Marketing Intelligence & Planning, 41(1), 31–47. DOI:10.1108/MIP-05-2022-0185
    Kusumadewi, A. N., Lubis, N. A., Prastiyo, R., & Tamara, D. (2021). Technology Acceptance Model (TAM) in the use of online learning applications during the Covid-19 pandemic for parents of elementary school students. Estudios DemográficosyUrbanos,2(1),272–292. https://doi.org/10.51276/EDU.V2I1.120
    Law, L. (2024). Application of generative artificial intelligence (GenAI) in language teaching and learning: A scoping literature review. Computers and Education Open, 6, 100174. https://doi.org/10.1016/j.caeo.2024.100174
    LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436-444. https://doi.org/10.1038/nature14539
    Lee, S. J., & Kwon, K. (2024). A systematic review of AI education in K-12 classrooms from 2018 to 2023: Topics, strategies, and learning outcomes. Computers and Education: Artificial Intelligence, 6, 100211. https://doi.org/10.1016/j.caeai.2024.100211
    Legg, S., & Hutter, M. (2007). A collection of definitions of intelligence. Frontiers in Artificial Intelligence and Applications, 157, 17-24. https://doi.org/10.48550/arXiv.0706.3639
    Legris, P., Ingham, J., & Collerette, P. (2003). Why do people use information technology? A critical review of the technology acceptance model. Information & Management, 40(3), 191-204. https://doi.org/10.1016/S0378-7206(01)00143-4
    Li, Y., Xia, G., Wang, S., & Li, Y. (2023). A deep multimodal autoencoder-decoder framework for customer churn prediction incorporating chat-GPT. Multimedia Tools and Applications. https://doi.org/10.1007/s11042-023-17715-6
    Macedonia, M. (2003). The GPU enters computing's mainstream. Computer, 36(10), 106-108. DOI:10.1109/MC.2003.1236476
    MacKinnon, D. P. (2008). Introduction to statistical mediation analysis (1st ed.). Routledge. https://doi.org/10.4324/9780203809556
    Masrom, M. (2007). Technology acceptance model and e-learning. Technology, 21(24), 81.
    McCorduck, P. (2004). Machines who think (2nd ed.). A. K. Peters, Ltd. P434–435 ISBN 1-56881-205-1
    Menon, D., & Shilpa, K. (2023). “Hey, Alexa” “Hey, Siri”, “OK Google” ….” exploring teenagers’ interaction with artificial intelligence (AI)-enabled voice assistants during the COVID-19 pandemic. International Journal of Child-Computer Interaction, 38, 100622. https://doi.org/10.1016/j.ijcci.2023.100622
    Niven, M. (2022). A thesis submitted to the Faculty of Education in conformity with the requirements for the degree of Master of Education. Queen's University. http://hdl.handle.net/1974/30141
    Norvig, P., & Russell, S. (2003). Artificial intelligence: A modern approach (4nd ed.). Prentice Hall.
    Pantano, E., Pedeliento, G., & Christodoulides, G. (2022). A strategic framework for technological innovations in support of the customer experience: A focus on luxury retailers. Journal of Retailing and Consumer Services, 66, Article 102959.
    Pavlik, J. V. (2023). Collaborating with ChatGPT: Considering the implications of generative artificial intelligence for journalism and media education. Journalism & Mass Communication Educator. https://doi.org/10.1177/10776958221149577
    Pfeifer, R., & Scheier, C. (2001). Understanding intelligence. MIT Press.
    Pekrun, R. (2006). The control-value theory of achievement emotions: Assumptions, corollaries, and implications for educational research and practice. Educational Psychology Review, 18(4), 315–341. https://doi.org/10.1007/s10648-006-9029-9
    Popenici, S. A. D., & Kerr, S. (2017). Exploring the impact of artificial intelligence on teaching and learning in higher education. Research and Practice in Technology Enhanced Learning, 12(22), 1–13. https://doi.org/10.1186/s41039-017-0062-8
    Rosenzweig, E. Q. & Wigfield, A. (2016). STEM motivation interventions for adolescents: A promising start, but further to go. Educational Psychologist, 51(2), 146-163. https:// doi.org/10.1080/00461520.2016.1154792
    Rosenzweig, E. Q., Wigfield, A., & Eccles, J. S. (2019). Expectancy-value theory and its relevance for student motivation and learning. In K. A. Renninger & S. E. Hidi (Eds.), The Cambridge handbook of motivation and learning (1st ed., Vol. 3, pp. 617-644). Cambridge University Press. https://doi.org/10.1017/9781316823279.026
    Rosenzweig, E. Q., Wigfield, A., & Hulleman, C. S. (2020). More useful or not so bad? Examining the effects of utility value and cost reduction interventions in college physics. Journal of Educational Psychology, 112(1), 166-182. https://doi.org/10.1037/edu0000370
    Rubach, C., Dicke, A.-L., Safavian, N., & Eccles, J. S. (2023). Classroom transmission processes between teacher support, interest value and negative affect: An investigation guided by situated expectancy-value theory and control-value theory. Motivation and Emotion, 47(4), 575–594. https://doi.org/10.1007/s11031-023-10013-6
    Saadé, R., & Bahli, B. (2005). The impact of cognitive absorption on perceived usefulness and perceived ease of use in on-line learning: An extension of the technology acceptance model. Information & Management, 42(2), 317-327. https://doi.org/10.1016/j.im.2003.12.013
    Saif, N., Khan, S. U., Shaheen, I., ALotaibi, F. A., Alnfiai, M. M., & Arif, M. (2024). Chat-GPT; validating Technology Acceptance Model (TAM) in education sector via ubiquitous learning mechanism. Computers in Human Behavior, 154, 108097. https://doi.org/10.1016/j.chb.2023.108097
    Salkind, N. J., & Green, S. B. (2005). Using SPSS for Windows and Macintosh: Analyzing and understanding data. Pearson Prentice Hall.
    Scherer, R., Siddiq, F., & Tondeur, J. (2019). The technology acceptance model (TAM): A meta-analytic structural equation modeling approach to explaining teachers’ adoption of digital technology in education. Computers & Education, 128, 13–35. https://doi.org/10.1016/j.compedu.2018.09.009
    Scherer, R., Siddiq, F., & Tondeur, J. (2019). The technology acceptance model (TAM): A meta-analytic structural equation modeling approach to explaining teachers' adoption of digital technology in education. Computers & Education, 128, 13-35. https://doi.org/10.1016/j.compedu.2018.09.009
    Seo, K., Tang, J., Roll, I., Fels, S., & Yoon, D. (2021). The impact of artificial intelligence on learner–instructor interaction in online learning. International Journal of Educational Technology in Higher Education, 18(1), 54. https://doi.org/10.1186/s41239-021-00292-9
    Sharp, J. H. (2007). Development, extension, and application: A review of the Technology Acceptance Model. Information Systems Education Journal, 5(9), 1-11. http://isedj.org/5/9/.
    Shrestha, N. (2021). Factor analysis as a tool for survey analysis. American Journal of Applied Mathematics and Statistics, 9(1), 4-11. https://doi.org/10.12691/ajams-9-1-2
    Simon, H. A. (1965). The shape of automation for men and management. Harper & Row.
    Sparrow, B., Liu, J., & Wegner, D. M. (2011). Google effects on memory: Cognitive consequences of having information at our fingertips. Science, 333(6043), 776–778. https://doi.org/10.1126/science.1207745
    Sun, Y., Hong, J.-C., Ye, J.-H., & Ye, J.-N. (2023). Satisfaction with online study abroad predicted by motivation and self-efficacy: A perspective based on the situated expectancy–value theory during the COVID-19 epidemic. Sustainability, 15(5), 4070. https://doi.org/10.3390/su15054070
    Tai, T.-Y. (2024). Comparing the effects of intelligent personal assistant-human and human-human interactions on EFL learners’ willingness to communicate beyond the classroom. Computers & Education, 210, 104965. https://doi.org/10.1016/j.compedu.2023.104965
    Tang, X., Lee, H. R., Wan, S., Gaspard, H., & Salmela-Aro, K. (2022). Situating expectancies and subjective task values across grade levels, domains, and countries: A network approach. AERA Open, 8, 233285842211171. https://doi.org/10.1177/23328584221117168
    Teo, T. (2010). An Empirical Study to Validate the Technology Acceptance Model (TAM) in Explaining the Intention to Use Technology among Educational Users: International Journal of Information and Communication Technology Education, 6(4), 1–12. https://doi.org/10.4018/jicte.2010100101
    To, A. T., & Trinh, T. H. M. (2021). Understanding behavioral intention to use mobile wallets in Vietnam: Extending the TAM model with trust and enjoyment. Cogent Business & Management, 8(1), 1-14. https://doi.org/10.1080/23311975.2021.1891661
    Turing, A. M. (1950). Computing machinery and intelligence. Mind, 59, 433-460.
    Venkatesh, V., & Davis, F. D. (2000). A theoretical extension of the Technology Acceptance Model: Four longitudinal field studies. Management Science, 46(2), 186-204.
    Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. Management Information Systems Quarterly, 27(3), 425-478.
    Verdú, E., Regueras, L. M., Gal, E., et al. (2017). Integration of an intelligent tutoring system in a course of computer network design. Educational Technology Research and Development, 65, 653–677. https://doi.org/10.1007/s11423-016-9503-0
    Wang, Q., & Xue, M. (2022). The implications of expectancy-value theory of motivation in language education. Frontiers in Psychology, 13, 992372. https://doi.org/10.3389/fpsyg.2022.992372
    Wang, X., Li, L., Tan, S. C., Yang, L., & Lei, J. (2023). Preparing for AI-enhanced education: Conceptualizing and empirically examining teachers’ AI readiness. Computers in Human Behavior, 146, 107798. https://doi.org/10.1016/j.chb.2023.107798
    Wigfield, A. (1994). Expectancy-value theory of achievement motivation: A developmental perspective. Educational Psychology Review, 6(1), 49–78. https://doi.org/10.1007/BF02209024
    Wigfield, A., & Cambria, J. (2010). Students’ achievement values, goal orientations, and interest: Definitions, development, and relations to achievement outcomes. Developmental Review, 30, 1–35. https://doi.org/10.1016/j.dr.2009.12.001
    Wigfield, A., & Eccles, J. S. (2000). Expectancy-value theory of achievement motivation. Contemporary Educational Psychology, 25, 68–81. https://doi.org/10.1006/ceps.1999.1015
    Wigfield, A., & Eccles, J. S. (2002). Development of Achievement Motivation. Academic Press. https://doi.org/10.1016/B978-0-12-750053-9.X5000-1
    Xu, S. (2022). Construct-oriented or goal-motivated? Interpreting test preparation of a high-stakes writing test from the perspective of expectancy-value theory. Frontiers in Psychology, 13, 846413. https://doi.org/10.3389/fpsyg.2022.846413
    Yan, M., Filieri, R., & Gorton, M. (2021). Continuance intention of online technologies: A systematic literature review. International Journal of Information Management, 58, 102315. https://doi.org/10.1016/j.ijinfomgt.2021.102315
    Yıldız, A. T. (2023). The impact of ChatGPT on language learners’ motivation. Journal of Teacher Education and Lifelong Learning, 5(2), 582-597. https://doi.org/10.51535/tell.1314355
    Yu, H., Fu, Y., Jiang, B., Fan, P., Shen, L., Gao, L., & Lu, H. (2023). Applications of GPT-4 for accurate diagnosis of retinal diseases through optical coherence tomography image recognition. https://doi.org/10.21203/rs.3.rs-3644163/v1
    Yun-Fang Tu. (2024). Roles and functionalities of ChatGPT for students with different growth mindsets: Findings of drawing analysis. Educational Technology & Society, 27(1). https://doi.org/10.30191/ETS.202401_27(1).TP01
    Zand, H., & Crowe, W. D. (1997). Novices entering mathematics 2: The graphic calculator and distance learners. Computers & Education, 29(1), 25-32. https://doi.org/10.1016/S0360-1315(97)00026-2
    Zawacki-Richter, O., Marín, V. I., Bond, M., & Gouverneur, F. (2019). Systematic review of research on artificial intelligence applications in higher education – where are the educators? International Journal of Educational Technology in Higher Education, 16(1), 39. https://doi.org/10.1186/s41239-019-0171-0
    Zhai, C., & Wibowo, S. (2023). A systematic review on artificial intelligence dialogue systems for enhancing English as foreign language students’ interactional competence in the university. Computers and Education: Artificial Intelligence, 4, 100134. https://doi.org/10.1016/j.caeai.2023.100134
    Zhang, C., Meng, Y., & Ma, X. (2024). Artificial intelligence in EFL speaking: Impact on enjoyment, anxiety, and willingness to communicate. System, 121, 103259. https://doi.org/10.1016/j.system.2024.103259
    Zhang, L., Anjum, M. A., & Wang, Y. (2023). The impact of trust-building mechanisms on purchase intention towards metaverse shopping: The moderating role of age. International Journal of Human-Computer Interaction, 1–19. https://doi.org/10.1080/10447318.2023.2184594
    Zohery, M. (2023). ChatGPT in academic writing and publishing: A comprehensive guide. Artificial Intelligence in Academia, Research and Science: ChatGPT as a Case Study (1st ed.). Ferct Publishing. https://doi.org/10.5281/zenodo.7803703
    Zollman, D., Sirnoorkar, A., & Laverty, J. T. (2023). Analyzing AI and student responses through the lens of sensemaking and mechanistic reasoning. In R. Jones, & R. Pawl (Eds.), Proceedings of the Physics Education Research Conference (PERC 2023). American Association of Physics Teachers. https://doi.org/10.1119/perc.2023.pr.Zollman
    李育杰(2023年03月30日)。「生成式 AI」和「分辨式 AI」有哪裡不一樣? 科技大觀園。https://scitechvista.nat.gov.tw/Article/C000003/detail?ID=c746ecd6-5e7d-4fc1-afe3-d91f2c06b992
    李珣瑛(2023年05月01日)。清大開第一槍!公布教學指引 明訂將培養學生AI素養。經濟日報 https://udn.com/news/story/6928/7134856
    國立臺灣師範大學(2023年)。生成式AI之學習應用及參考指引。國立臺灣師範大學教學發展中心。https:// ctld.ntnu.edu.tw/generative_ai
    國立臺灣大學(2023年)。臺大針對生成式 AI 工具之教學因應措施。國立臺灣大學教學發展中心及數位學習中心https://www.dlc.ntu.edu.tw/ai-tools/
    黃煒軒(2023年02月15日)。「他有點亞斯伯格症的味道...」ChatGPT父傳奇揭密:極度聰明,很像比爾蓋茲年輕的樣子。今周刊1365期。https://www.businesstoday.com.tw/article/category/183015/post/202302150003/?utm_source=businesstoday&utm_medium=search&utm_campaign=article
    微軟新聞(2023年6月15日)。人工智慧黃金時代開啟,微軟重構對未來和工作的想像。 微軟新聞。https://news.microsoft.com/zh-cn/人工智能黄金时代开启,微软重构对未来和工作的/
    熊治民(2023年07月19日)。生成式AI在製造領域應用展望。經濟部產業技術司。https://www.moea.gov.tw/MNS/doit/industrytech/IndustryTech.aspx?menu_id=13545&it_id=490
    數位時代(2021年04月23日)。【圖解】人工智慧發展80年,十大里程碑推動今日AI!未來發展命繫哪3支柱? AI與大數據。https://www.bnext.com.tw/article/76138/computing-algorithm-big-data-artificial-intelligence-bedrock?
    Hu, K. (2023, February 2). ChatGPT sets record for fastest-growing user base - analyst note. Thomson Reuters. https://www.reuters.com/technology/chatgpt-sets-record-fastest-growing-user-base-analyst-note-2023-02-01/
    Klein, A. (2024, February 29). Will AI use in schools increase next year? 56 percent of educators say yes. Education Week. https://www.edweek.org/technology/will-ai-use-in-schools-increase-next-year-56-percent-of-educators-say-yes/2024/02
    Langreo, L. (2024, January 8). Most teachers are not using AI. Here’s why. Education Week. https://www.edweek.org/technology/most-teachers-are-not-using-ai-heres-why/2024/01
    OECD. (2023, December 13). OECD Digital Education Outlook 2023: Towards an Effective Digital Education Ecosystem. Organisation for Economic Co-operation and Development. https://www.oecd-ilibrary.org/education/oecd-digital-education-outlook-2023_c74f03de-en
    OpenAI. (2024, May 15). Microsoft invests in and partners with OpenAI to support us building beneficial AGI. OpenAI. https://openai.com/index/microsoft-invests-in-and-partners-with-openai/
    OpenAI. (2024, May 13). Hello GPT-4. OpenAI. https://openai.com/index/hello-gpt-4o/
    OpenAI LP. (2019, March 11). OpenAI LP. OpenAI. https://openai.com/index/openai-lp/
    Sam Altman (2022, December 05) 「ChatGPT launched on wednesday. Today it crossed 1 million users!」 X (formerly Twitter). https://twitter.com/sama/status/1599668808285028353
    Roose, K. (2023, January 18). The brilliance and weirdness of ChatGPT. The New York Times. https://web.archive.org/web/20230118134332/https://www.nytimes.com/2022/12/05/technology/chatgpt-ai-twitter.html
    World Economic Forum. (2023, June 26). Top 10 emerging technologies of 2023. World Economic Forum.https://www.weforum.org/publications/top-10-emerging-technologies-of-2023/

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