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研究生: 孔令文
Kung, Ling-Wen
論文名稱: 技術型高中學生人工智慧素養學習內涵建構與實證分析之研究
The Construction and Empirical Analysis of Artificial Intelligence Literacy Learning Connotation for Technical High School Students
指導教授: 戴建耘
Dai, Chien-Yun
林騰蛟
Lin, Teng-Chiao
口試委員: 張明文
Chang, Ming-Wen
宋修德
Sung, Hsiu-Te
李懿芳
Lee, Yi-Fang
戴建耘
Dai, Chien-Yun
林騰蛟
Lin, Teng-Chiao
口試日期: 2023/04/25
學位類別: 博士
Doctor
系所名稱: 工業教育學系
Department of Industrial Education
論文出版年: 2023
畢業學年度: 111
語文別: 中文
論文頁數: 138
中文關鍵詞: 技術型高中電機與電子群人工智慧素養AIL國際認證
英文關鍵詞: Technical High School, Electrical Engineering and Electronic Engineering Group, Artificial Intelligence Literacy, AIL international certification
研究方法: 調查研究
DOI URL: http://doi.org/10.6345/NTNU202300682
論文種類: 學術論文
相關次數: 點閱:181下載:45
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  • 本研究旨在探討技術型高中學生人工智慧素養學習內涵與學習成效實證評量分析,透過相關文獻探討、專家會議與修正式Delphi調查法,發展「技術型高中學生人工智慧素養學習內涵」,建構規劃分成「人工智慧概念知識」、「人工智慧基礎知識」、「人工智慧基礎核心技術」、「人工智慧進階核心技術」、「人工智慧跨域應用」、「人工智慧社會發展」、「人工智慧倫理規範」等7個構面、24類別及60項學習內涵。
    學習成效實證評量分析部分,分成前測評量表現、後測評量表現、參與人工智慧素養AIL國際認證測驗以及課程結束後實施課後問卷,研究問卷各構面之組合信度(CR)介於0.795至0.964,Cronbach´s α值介於 0.655至0.957符合內部一致性標準,平均變異數萃取量(AVE)值介於 0.497至 0.859之間,具有收斂效度及區別效度。研究對象為新北市公立技術型高中電機與電子群-電子科、資訊科學生,採用便利抽樣共530人參與,有效樣本數為471人,有效回收率為88.87%。
    研究發現技術型高中學生人工智慧課程前測評量與後測評量,兩者Pearson相關係數為.402,具顯著中等相關;另後測評量與GLAD AIL國際認證測驗Pearson相關係數為.299,顯著趨近中等相關。此外,在結構路徑分析研究發現:課程規劃設計顯著正向影響與通過人工智慧素養AIL國際認證,後測評量表現顯著正向影響與通過人工智慧素養AIL國際認證,通過人工智慧素養AIL國際認證顯著正向影響對個人影響,以及對個人影響顯著正向影響與後續對他人的影響。
    另本研究有關教師訪談調查部分,記錄學校實施人工智慧素養課程現況與具體建議,彙整作為教育主管機關與學校推動人工智慧教育之參考。

    The purpose of this study is to investigate the connotation of AI literacy learning and the empirical evaluation of learning effectiveness of technical high school students. The plan is divided into 7 sections, 24 categories, and 60 learning contents, including "Artificial Intelligence Conceptual Knowledge", "Artificial Intelligence Basic Knowledge", "Artificial Intelligence Basic Core Technology", "Artificial Intelligence Advanced Core Technology", "Artificial Intelligence Cross-Domain Applications", "Artificial Intelligence Social Development", and "Artificial Intelligence Ethical Standards".
    The empirical analysis of learning effectiveness was divided into pre-test performance, post-test performance, participation in the AIL international certification test, and post-course questionnaire after the course. The AVE values ranged from 0.497 to 0.859, with convergent validity and discriminant validity. A total of 530 students participated in the study by convenience sampling, with a valid sample size of 471 and a valid recall rate of 88.87%.
    The findings of the study on the implementation of artificial intelligence literacy curriculum in Technical High Schools are as follows:
    1.The study found that the Pearson correlation coefficient between the pre-test and the post-test of the artificial intelligence course for Technical High School students was .402, with a significant moderate correlation; and the Pearson correlation coefficient between the post-test and the GLAD AIL international certification test was .299, with a significant moderate correlation.
    2.In the structural path analysis, it was found that the curriculum design had a significant positive impact on passing the AIL, the posttest measures had a significant positive impact on passing the AIL, the AIL had a significant positive impact on the individual, and the AIL had a significant positive impact on the individual and the subsequent impact on others.
    3.In addition, the interview survey of teachers in this study recorded the current status and specific suggestions for the implementation of AI literacy curriculum in schools, which will be compiled as a reference for educational authorities and schools to promote AI education.

    第一章 緒論 1 第一節 研究背景與動機 1 第二節 研究目的與待答問題 4 第三節 名詞釋義 5 第四節 研究範圍與限制 6 第二章 文獻探討 9 第一節 人工智慧起源與發展 9 第二節 人工智慧的意義與內涵 11 第三節 人工智慧素養學習內涵相關研究 24 第四節 學習成效實證相關研究 36 第五節 小結 37 第三章 研究設計與實施 39 第一節 研究架構 39 第二節 研究流程 41 第三節 研究對象 44 第四節 研究方法 47 第五節 研究工具 54 第六節 資料處理方法 62 第七節 學術倫理 63 第四章 研究分析與討論 65 第一節 人工智慧素養學習內涵建構分析 65 第二節 實證評量分析 76 第三節 教師訪談調查分析 97 第五章 結論與建議 107 第一節 研究結論 107 第二節 建議 111 參考文獻 113 附錄 127

    一、中文部分
    文崇一、楊國樞(2000)。訪問調查法。社會及行為科學研究法下冊。台北市:東華圖書。
    王文中、呂金燮、吳毓營、張郁雯、張淑慧(1999)。教育測驗與評量。臺北市:五南圖書。
    王文君(2020)。人工智慧素養問卷開發與驗證(未出版之碩士論文)。中原大學教育研究所,桃園市。
    王文科(2001)。教育研究法(六版)。臺北市:五南圖書。
    王文科(2007)。課程與教學論。臺北市:五南圖書。
    王文科、王智弘(2012)。教育研究法。臺北市:五南圖書。
    古明地正俊、長谷佳明、林仁惠(譯)(2018)。AI人工智慧的現在‧未來進行式。臺北市:遠流出版社。
    吳清山、林天祐(2005)。教育新辭書。臺北市:高等教育。
    李開復(2017)。AI時代,文科更有意思了。天下雜誌。取自https://www.cw.com.tw/article/5082928
    李隆盛(1998)。MST 取向的國中生活科技教材教法。 生活科技教育, 31(10),2-8。
    杜素豪(2004)。投票意向問題不同類型項目無反應之分析: 以 2000 年總統大選為例。 選舉研究, 11(2),111-131。
    周文欽、歐滄和、許擇基、盧欽銘、金樹人、范德鑫(1995)。心理與教育測驗。臺北市:心理測驗出版社。
    林育慈、吳正己(2016)。運算思維與中小學資訊科技課程。教育脈動, (6), 5-20。
    林義男(1985)。大學生對大學教學的滿意程度與學習成就的關係。國立臺灣教育學院輔導學系輔導學報,8,1-18。
    邱淑芬、蔡欣玲(1996)。德爾菲預測術-一種專家預測的護理研究方法。護理研究, 4(1),92-98。
    邱皓政(2011)。結構方程模式:LISREL的理論、技術與應用(二版)。臺北市:雙葉書廊。
    洪榮昭(2001)。PBL教學策略。技術及職業教育雙月刊,61,10-12。
    美國全球學習與發展中心GLAD(2018)。人工智慧素養AIL模組架構。取自http://www2.gladworld.net/gladworld/CHT_AIL.php
    范信賢(2016)。核心素養與十二年國民基本教育課程綱要:導讀《 國民核心素養: 十二年國教課程改革的 DNA》。教育脈動,5,1-7。
    馬傳鎮(2005)。我國社會科學院之發展趨勢。高教簡訊,177,13-14。
    國科會(2017)。人工智慧推動策略。取自https://www.most.gov.tw/folksonomy/detail?subSite=&l=ch&article_uid=a3f5e1d4-8206-4bd9-b456-1d7e2b084558&menu_id=d3c30297-bb63-44c5-ad30-38a65b203288&content_type=P&view_mode=listView。
    國科會(2019)。人工智慧科研發展指引。取自 http://bit.ly/39lzUAc
    國家教育研究院(2013)。運算思維與中小學資訊科技課程。新北市:國家教育研究院。
    張偉豪(2011)。論文寫作-SEM不求人。臺北:鼎茂。
    教育部(2018)。十二年國民基本教育課程綱要國民中學暨普通型高級中等學校-科技領域。臺北市:教育部。
    教育部(2019)。AI教育X教育AI-人工智慧教育及數位先進個人化、適性化學習時代來臨!。取自https://www.edu.tw/News_Content.aspx?n=9E7AC85F1954DDA8&s=D4C4CD32CAE3FF5D
    教育部(2019)。和AI做朋友-人工智慧中小學教學示範例。取自https://market.cloud.edu.tw/list/ai.jsp
    盛杏湲、周應龍(2008)。選樣偏誤模型在調查研究中項目無反應問題的應用。臺灣政治學刊, 12(1),147-183。
    郭生玉(1980)。心理與教育測驗。臺北市:精華書局。
    陳李綢(1983)。大專男女生自我統整程度與職業選擇、學習漏意度及交母養育方式之比較研究。教育心理學報,16,89-98。
    陳柏熹、王文中(1999)。生活品質量表的發展。中國測驗學會測驗年刊, 46(1),57-74。
    陳清檳,黃文喜,張文宗(2010)。高職電機電子群學生父母管教方式、學習態度與學習成效知覺之研究。教育與多元文化研究期刊,2,223-260。
    陳德禹(1985)。公務人員的人格特質與組織態度—臺北市政府的個案研究。第四次社會科學研討會發表之論文,427-457,臺北市。
    陳璽宇(2020)。人工智慧素養測驗發展及其與科技素養之相關研究(未出版之碩士論文)。國立臺灣師範大學,臺北市。
    黃仁暐、涂益郎(2020)。和AI做朋友-相知篇:從0開始學AI(教材)。取自https://market.cloud.edu.tw/resources/web/1798168
    黃東益、施佳良、謝忠安(2011)。台灣文官調查研究資料蒐集方法與調查品質:[訪員遞送與受訪者自填問卷]方法之探討。調查研究-方法與應用,25,141-179。
    黃姵嫙(2018)。綠色招募活動對組織人才吸引力之影響:以個人組織配適知覺為中介變數與個人環保態度為干擾變數(未出版之碩士論文)。東海大學,台中市。
    黃瑞琴(1991)。質性教育研究。臺北市:心理。
    黃曉東(2021)。旨在培養基於我國人工智能教育的學生核心素養。教育和信息技術,26 , 5127–5147。
    葉乃靜(2012)。詮釋學。圖書館學與資訊科學大辭典。取自http://terms.naer.edu.tw/detail/1678710/?index=9
    葉國良、李懿芳(2013)。失業者職前訓練成效評估之探討–以托育人員為例。中國工業職業教育學會102年度學術論文輯(83-104)。台北:國立臺灣師範大學。
    葉國良、蔡逸舟(2019)。非資訊領域學生運算思維與程式設計教學實踐之學習診斷及成效分析成果報告。教育部委託之108年度大專校院教學實踐研究計畫成果報告(PGE1080396)。臺北市:教育部。
    靳知勤(2009)。中等科學師資培育機構評鑑指標之發展研究。科學教育學刊, 17(4),275-292。
    劉協成(2006)。德懷術之理論與實務初探。教師之友, 47(4),91-99。
    劉松、謝逍遙(2021)。師範生AI素質培養與應用能力培養。2021年第七屆計算機網絡與信息系統國際會議(ICNISC)發表之論文,中國貴陽。
    戴建耘(2020)。最新人工智慧概論。新北市:臺科大圖書。
    二、外文部分
    ACM, IEEE-CS.(2020). Draft Report of the Computing Curricula 2020 Project, Retrieved from https://www.cc2020.net
    Agrawal, A., Gans, J. S., & Goldfarb, A. (2019). Exploring the impact of artificial intelligence: Prediction versus judgment. Information Economics and Policy, 47, 1-6.
    Aoun, J. E. (2017). Robot-proof: higher education in the age of artificial intelligence. MIT press.
    Arsalan Soltani, A., Huang, H., Wu, J., Kulkarni, T. D., & Tenenbaum, J. B. (2017). Synthesizing 3d shapes via modeling multi-view depth maps and silhouettes with deep generative networks. In Proceedings of the IEEE conference on computer vision and pattern recognition ,1511-1519. Retrieved from https://doi.org/10.1109/CVPR.2017.269
    Bandura, A. (1986). Social foundations of thought and action: a social cognitive theory. Englewood Cliffs, NJ: Prentice Hall.
    Bawden, D. (2008). Origins and concepts of digital literacy. Digital literacies: Concepts, policies and practices, 30(2008), 17-32.
    Bell, S. (2010). Project-based learning for the 21st century: Skills for the future. The clearing house, 83(2), 39-43.
    Bentler, P. M. & Bonett, D. G. (1980). Significance tests and goodness-of –fit in the analysis of covariance structures. Psychological Bulletin, 88, 588-606.
    Betz, N. E. (1970). Prevalence, distribution, and correlates of math anxiety in college students. Journal of Counseling Psychology, 25, 441-448.
    Bjork, R. C. (2016). The Master Algorithm: How the Quest for the Ultimate Learning Machine Will Remake Our World. Perspectives on Science and Christian Faith, 68(3), 214-216.
    Bloom, B. S., & Committee of College and University Examiners. (1964). Taxonomy of educational objectives (Vol. 2). New York: Longmans, Green.
    Buchanan, B. G. (2005). A (very) brief history of artificial intelligence. Ai Magazine, 26(4), 53-53.
    Carrasquilla, J., & Melko, R. G. (2017). Machine learning phases of matter. Nature Physics, 13(5), 431-434.
    Chin, W. W. (1998). Commentary: Issues and Opinion on Structural Equation Modeling. Management Information Systems Quarterly, 22(1), 7-16
    Ching, T., Himmelstein, D. S., Beaulieu-Jones, B. K., Kalinin, A. A., Do, B. T., Way, G. P., ... & Greene, C. S. (2018). Opportunities and obstacles for deep learning in biology and medicine. Journal of The Royal Society Interface, 15(141), 20170387.
    Chou, H. W. (2001). Influences of cognitive style and training method on training effectiveness. Computer & Education, 37,11-25.
    Dalkey, N. C. (1969). An experimental study of group opinion. Futures, 1(5), 408-426. Retrieved from https://doi.org/10.1016/S0016-3287(69)80025-X
    D'Ignazio, C. (2017). Creative data literacy: Bridging the gap between the data-haves and data-have nots. Information Design Journal, 23(1), 6-18.
    Draskovic, D., Cvetanovic, M., & Nikolic, B. (2018). SAIL—Software system for learning AI algorithms. Computer Applications in Engineering Education, 26(5), 1195-1216.
    Druga, S., Vu, S. T., Likhith, E., & Qiu, T. (2019). Inclusive AI literacy for kids around the world. In Proceedings of FabLearn 2019 ,104-111. Retrieved from https://doi.org/10.1145/3311890.3311904
    Fernandes, M. A. (2016). Problem‐based learning applied to the artificial intelligence course. Computer Applications in Engineering Education, 24(3), 388-399.
    Fiske, A., Henningsen, P., & Buyx, A. (2019). Your robot therapist will see you now: ethical implications of embodied artificial intelligence in psychiatry, psychology, and psychotherapy. Journal of medical Internet research, 21(5), e13216.
    Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39-50.
    François-Lavet, V., Henderson, P., Islam, R., Bellemare, M. G., & Pineau, J. (2018). An introduction to deep reinforcement learning. Foundations and Trends® in Machine Learning, 11(3-4), 219-354.
    Furey, H., & Martin, F. (2019). AI education matters: a modular approach to AI ethics education. AI Matters, 4(4), 13-15.
    Gay, L. R. & Diehl, P. L. (1992). Research methods for business and management. New York: Macmillan.
    George, J. (1990). Personality, Affect, and Behavior in Groups. Journal of Applied Psychology, 75, 107-116.
    Glick, W., (1985). Conceptualizing and measuring organizational and psychological climate: Pitfalls in multilevel research. Academy of Management Review, 10(3), 126-137. Retrieved from https://doi.org/ 10.2307/258140
    Goldberg, Y. (2016). A primer on neural network models for natural language processing. Journal of Artificial Intelligence Research, 57, 345-420.
    Griffin, P., & Care, E. (Eds.). (2014). Assessment and teaching of 21st century skills: Methods and approach. Springer.
    Guzman, A. L. (2019). Voices in and of the machine: Source orientation toward mobile virtual assistants. Computers in Human Behavior, 90, 343-350.
    Haenlein, M., & Kaplan, A. (2019). A brief history of artificial intelligence: On the past, present, and future of artificial intelligence. California management review, 61(4), 5-14.
    Hair, J. F., Hult, G. T. M., Ringle, C. M., & Sarstedt, M. (2013). A primer on partial least squares structural equation modeling. Thousand Oaks, CA: Sage.
    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.
    Hakan, K. Ö. R., ERBAY, H., & ENGİN, M. Activity Suggestion Decision Support System Design In Online Learning Environment. Electronic Letters on Science and Engineering, 15(3), 8-22.
    Han, S. G. (2020). Digital Content to Improve Artificial Intelligence Literacy Ability. Journal of the Korea Society of Computer and Information, 25(12), 93-100.
    Hassabis, D., Kumaran, D., Summerfield, C., & Botvinick, M. (2017). Neuroscience-inspired artificial intelligence. Neuron, 95(2), 245-258.
    Hockly, N. (2011). The digital generation. ELT journal, 65(3), 322-325.
    Hu, L., & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling, 6(1), 1-55.
    Hu, L.-t., and Bentler, P. M. (1998). Fit Indices in Covariance Structure Modeling: Sensitivity to Underparameterized Model Misspecification, Psychological Methods, 3(4),424-453.
    Hutter, M. (2004). Universal artificial intelligence: Sequential decisions based on algorithmic probability. Springer Science & Business Media.
    Jackson, J. (2016). Myths of active learning: Edgar Dale and the cone of experience. Journal of the Human Anatomy and Physiology Society, 20(2), 51-53.
    Jakhar, D., & Kaur, I. (2019). Artificial intelligence, machine learning and deep learning: definitions and differences. Clinical and experimental dermatology, 45(1), 131-132.
    James, L. R. (1982). Aggregation bias in estimates of perceptual agreement. Journal of Applied Psychology, 67, 219-229. Retrieved from https://doi.org/10.1037/0021-9010.67.2.219
    James, L. R., Demaree, R. G., and Wolf, G. (1984). Estimating within-group interrater reliability with and without response bias. Journal of Applied Psychology, 69, 85-98.
    Jeon, I., & Song, K. (2020). Research on Artificial Intelligence Convergence Education Curriculum based on Teacher's Demand Analysis. The Journal of Korean Association of Computer Education, 23(5), 43-52.
    Jöreskog, K. G., & Sörbom, D. (1989). LISREL 7: A guide to the program and applications. Chicago: SPSS Inc.
    Juškevičienė, A., & DagienĖ, V. (2018). Computational thinking relationship with digital competence. Informatics in Education, 17(2), 265-284.
    Kandlhofer, M., Steinbauer, G., Hirschmugl-Gaisch, S., & Huber, P. (2016, October). Artificial intelligence and computer science in education: From kindergarten to university. In 2016 IEEE Frontiers in Education Conference (FIE) ,1-9. Retrieved from https://doi.org/10.1109/FIE.2016.7757570
    Kirkpatrick, D. L. (1994). Evaluating training programs: The four levels. San Francisco, CA: Berrett-Koehler.
    Kok, J. N., Boers, E. J., Kosters, W. A., Van der Putten, P., & Poel, M. (2009). Artificial intelligence: definition, trends, techniques, and cases. Artificial intelligence, 1, 270-299.
    Kong, S. C., Cheung, W. M. Y., & Zhang, G. (2021). Evaluation of an artificial intelligence literacy course for university students with diverse study backgrounds. Computers and Education: Artificial Intelligence, 2, 100026.
    Kumar, D., & Meeden, L. (1998). A robot laboratory for teaching artificial intelligence. ACM SIGCSE Bulletin, 30(1), 341-344.
    Lake, B. M., Ullman, T. D., Tenenbaum, J. B., & Gershman, S. J. (2017). Building machines that learn and think like people. Behavioral and brain sciences, 40. Retrieved from https://doi.org/10.1017/S0140525X1500062X
    Larson, L. C., & Miller, T. N. (2011). 21st century skills: Prepare students for the future. Kappa Delta Pi Record, 47(3), 121-123.
    Lawshe, C. H. (1975). A quantitative approach to content validity. Personnel psychology, 28(4), 563-575.
    Lee, E. (2020). A comparative analysis of contents related to artificial intelligence in national and international K-12 curriculum. The Journal of Korean association of computer education, 23(1), 37-44.
    Li, K., Kim, D. J., Lang, K. R., Kauffman, R. J., & Naldi, M. (2020). How should we understand the digital economy in Asia? Critical assessment and research agenda. Electronic commerce research and applications, 44, 101004.
    Long, D., & Magerko, B. (2020, April). What is AI literacy? Competencies and design considerations. In Proceedings of the 2020 CHI conference on human factors in computing systems,1-16. Retrieved from https://doi.org/10.1145/3313831.3376727
    Ludwig, B. (1997). Predicting the future: Have you considered using the Delphi methodology? Journal of Extension, 35(5), 1-4.
    Luger, G. F. (2005). Artificial intelligence: structures and strategies for complex problem solving. Pearson education.
    Marr, D. (1977). Artificial intelligence—a personal view. Artificial Intelligence, 9(1), 37-48.
    Martin Kandlhofer & Gerald Steinbauer, Sabine Hirschmugl-Gaisch, Petra Huber.(2016 October 12-15).Artificial Intelligence and Computer Science in Education: From Kindergarten to University[Conference presentation], 2016 IEEE Frontiers in Education Conference (FIE),Erie, Pennsylvania, U.S.A.
    McCarthy, J. (1988). Mathematical logic in artificial intelligence. Daedalus, 297-311.
    McGregor, A., Hall, M., Lorier, P., & Brunskill, J. (2004). Flow clustering using machine learning techniques. In International workshop on passive and active network measurement, 205-214. Springer, Berlin, Heidelberg. Retrieved from https://doi.org/10.1007/978-3-540-24668-8_21
    McKernan, L. C., Clayton, E. W., & Walsh, C. G. (2018). Protecting life while preserving liberty: ethical recommendations for suicide prevention with artificial intelligence. Frontiers in psychiatry, 9, 650.
    Ministry of Education Singapore ( n.d.). 21st century competencies. Retrieved from https://www.moe.gov.sg/education-in-sg/21st-century-competencies.
    Minsky, M. (1986). The society of mind. New York: Simon & Schuster.
    Moreno, A., & Redondo, T. (2016). Text analytics: the convergence of big data and artificial intelligence. IJIMAI, 3(6), 57-64.
    Murry, J. W., & Hammoms, J. O. (1995). Delphi: A versatile methodology for conducting qualitative research. The Review of Higher Education, 18(4), 423-436.
    Neumann, M. (2019). AI education matters: a first introduction to modeling and learning using the data science workflow. AI Matters, 5(3), 21-24.
    Newell, A., & Simon, H. A. (2007). Computer science as empirical inquiry: Symbols and search. In ACM Turing award lectures: the first twenty years (1966-1985). Association for Computing Machinery, New York: United States.
    Ng, D. T. K., & Chu, S. K. W. (2021). Motivating Students to Learn AI through Social Networking Sites: A Case Study in Hong Kong. Online Learning, 25(1), 195-208.
    Ng, D. T. K., Leung, J. K. L., Chu, K. W. S., & Qiao, M. S. (2021). AI Literacy: Definition, Teaching, Evaluation and Ethical Issues. Proceedings of the Association for Information Science and Technology, 58(1), 504-509.
    Ng, D. T. K., Leung, J. K. L., Chu, S. K. W., & Qiao, M. S. (2021). Conceptualizing AI literacy: An exploratory review. Computers and Education: Artificial Intelligence, 2, 100041.
    OECD, D. (2005). Definition and selection of key competencies-executive summary. Retrieved from http://www. deseco. admin. ch/bfs/deseco/en/index/02.html.
    Organisation for Economic Co-operation and Development (OECD). (2018). Future of education and skills 2030: Conceptual learning framework. OECD. Retrieved from https://www.oecd.org/education/2030/Education-and-AI-preparing-for-the-future-AI-Attitudes-and-Va lues.pdf.
    Park, D., Ahn, J., Jang, J., Yu, W., Kim, W., Bae, Y., & Yoo, I. (2020). The development of software teaching-learning model based on machine learning platform. Journal of The Korean Association of Information Education, 24(1), 49-57.
    Poole, D. L., & Mackworth, A. K. (2017). Artificial Intelligence: foundations of computational agents (2nd Ed.). Cambridge University Press.
    Reeke, G. N., & Edelman, G. M. (1988). Real brains and artificial intelligence. Daedalus, 117(1), 143-173.
    Rhodes, A., Jasani, B., Barnes, D. M., Bobrow, L. G., & Miller, K. D. (2000).Reliability of immunohistochemical demonstration of oestrogen receptors inroutine practice: interlaboratory variance in the sensitivity of detection and evaluation of scoring systems. Journal of clinical pathology, 53(2), 125-130.
    Robinson, S. C. (2020). Trust, transparency, and openness: How inclusion of cultural values shapes Nordic national public policy strategies for artificial intelligence (AI). Technology in Society, 63, 101421.
    Rodríguez-García, J. D., Moreno-León, J., Román-González, M., & Robles, G. (2020). Introducing Artificial Intelligence Fundamentals with LearningML: Artificial Intelligence made easy. In Eighth International Conference on Technological Ecosystems for Enhancing Multiculturality, 18-20. Retrieved from https://doi.org/10.1145/3434780.3436705
    Russel, S., & Norvig, P. (2013). Artificial intelligence: a modern approach. London: Pearson Education Limited.
    Russell, S. J. (1997). Rationality and intelligence. Artificial intelligence, 94(1-2), 57-77.
    Russell, S., & Norvig, P. (2021). Artificial intelligence: A modern approach, global edition 4th. Foundations, 19, 23.
    Russelll, S. J., & Norvig, P. (2002). Artificial Intelligence-A modern Approach, 2nd edn. Indian Reprint. Upper Saddle River, New Jerscy.
    Sabuncuoglu, A. (2020). Designing one year curriculum to teach artificial intelligence for middle school. In Proceedings of the 2020 ACM Conference on Innovation and Technology in Computer Science Education,96-102. Retrieved from https://doi.org/10.1145/3341525.3387364
    Schmidhuber, J. (2015). Deep learning in neural networks: An overview. Neural networks, 61, 85-117.
    Schunk, D. H. (1981). Modeling and attributional effect on children´s achievement: a self-efficacy analysis. Journal of Educational Psychology, 73, 93-105.
    Shelton, S., & Alliger, G. M. (1993). Who’s afraid of level 4 evaluation? A practical approach. Training and Development Journal, 47, 43–46.
    Simon, H. A. (1957). Models of man; social and rational. Wiley.
    Simpson, E. J. (1972). The classification of educational objectives in the psychomotor domain (Vol. 3). Washington, DC: Gryphon House.
    Starcic, A. I. (2019). Human learning and learning analytics in the age of artificial intelligence. British journal of educational technology, 50(6), 2974-2976.
    Steinbauer, G., Kandlhofer, M., Chklovski, T., Heintz, F., & Koenig, S. (2021). A differentiated discussion about AI education K-12. KI-Künstliche Intelligenz, 35(2), 131-137.
    Stephenson Smith, S. (2003). The new international webster's comprehensive dictionary of the english language: deluxe encyclopedic edition. Trident Reference Pub.
    Thomas, D. (1990). Intra-household resource allocation: An inferential approach. Journal of human resources, 25(4), 635-664.
    Touretzky, D., Gardner-McCune, C., Martin, F., & Seehorn, D. (2019). Envisioning AI for k-12: What should every child know about AI?. In Proceedings of the AAAI conference on artificial intelligence, 33(1), 9795–9799. Retrieved from https://doi.org/10.1609/aaai.v33i01.33019795
    Ullman, J. B. (2001). Structural equation modeling. In: B. G. Tabachnick, & L. S. Fidell (Eds.), Using multivariate statistics. Boston, MA: Pearson Education.
    van Brummelen, E. M., Levchenko, E., Dómine, M., Fennell, D. A., Kindler, H. L., Viteri, S., ... & Trigo, J. (2020). A phase Ib study of GSK3052230, an FGF ligand trap in combination with pemetrexed and cisplatin in patients with malignant pleural mesothelioma. Investigational new drugs, 38(2), 457-467.
    Van Brummelen, J., Heng, T., & Tabunshchyk, V. (2021, May). Teaching tech to talk: K-12 conversational artificial intelligence literacy curriculum and development tools. In 2021 AAAI Symposium on Educational Advances in Artificial Intelligence (EAAI). Retrieved from https://doi.org/10.1609/aaai.v35i17.17844
    Waltz, C. F., Strickland, O. L., & Lenz, E. R. (1991). Measurement in Nursing Research (2nd Ed.). PA: A. Davis.
    Wan, X., Zhou, X., Ye, Z., Mortensen, C. K., & Bai, Z. (2020). SmileyCluster: supporting accessible machine learning in K-12 scientific discovery. In Proceedings of the Interaction Design and Children Conference, 23-35. Retrieved from https://doi.org/10.1145/3392063.3394440
    Wang, P. (2011). The assumptions on knowledge and resources in models of rationality. International Journal of Machine Consciousness, 3(1), 193-218.
    Wang, P. (2019). On Defining Artificial Intelligence. J. Artif. Gen. Intell., 10(2), 1-37.
    Wenger, E. (2014). Artificial intelligence and tutoring systems: computational and cognitive approaches to the communication of knowledge. Morgan Kaufmann.
    Williams, R., Park, H. W., & Breazeal, C. (2019). A is for artificial intelligence: the impact of artificial intelligence activities on young children's perceptions of robots. In Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems,447, 1-11. Retrieved from https://doi.org/10.1145/3290605.3300677
    Wing, J. M. (2006). Computational thinking. Communications of the ACM, 49(3), 33-35.
    Wright, B. D., & Stone, M. H. (1979). Best test design : Rasch Measurement. Chicago: MESA press.
    Zhou, H., Zhang, H., Zhou, Y., Wang, X., & Li, W. (2018, July). Botzone: an online multi-agent competitive platform for ai education. In Proceedings of the 23rd Annual ACM Conference on Innovation and Technology in Computer Science Education , 33-38. Retrieved from https://doi.org/ 10.1007/978-981-15-6747-6_7
    Zimmermann-Niefield, A., Polson, S., Moreno, C., & Shapiro, R. B. (2020, June). Youth making machine learning models for gesture-controlled interactive media. In Proceedings of the Interaction Design and Children Conference, 63-74. Retrieved from https://doi.org/10.1145/3392063.3394438

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