مدل‌سازی تاثیرشکاف دیجیتال، تفکر رایانشی (محاسباتی) و جذب شناختی بر سواد هوش مصنوعی در بین دانشجومعلمان

Acharya, N., Sassenberg, A. M., & Soar, J. (2023). Effects of cognitive absorption on continuous use intention of AI-driven recommender systems in ecommerce. Foresight, 25(2), 194-208. https://doi.org/10.1108/FS-10-2021-0200

Agarwal, R., & Karahanna, E. (2000). Time flies when you’re having fun: Cognitive absorption and beliefs about information technology usage. Manegement Information Systems (MIS) Quarterly, 24(4), 665-694. https://doi.org/10.2307/3250951

Arbatani Roshandel, T., Kazemi, H., & Haj Esmaeili, F. (2014). Determinants of the digital divide (case study: Rafsanjan citizen). Journal of Public Administration, 6(4), 681-703. Doi: 10.22059/jipa.2014.52035 [In Persian [

Ayatollahzadeh Shirazi, M., & Nargesian, A. (2013). Citizen relationship management: Study of its relationship with public participation, public accountability and transparency in 3’s region of city of Tehran. Journal of Public Administration, 5(2), 143-168. doi: 10.22059/jipa.2013.36747 ]In Persian[

Aydin, M. (2021). Does the digital divide matter? Factors and conditions that promote ICT literacy. Telematics Information, 58, 101536 https://doi.org/10.1016/j.tele.2020.101536.

Bolboli Qadikolaei, S., & Parsania, H. (2023). A systematic review of the ethical implications of using artificial intelligence in digital technologies and its relationship with the ethics of flourishing. Socio-Cultural Strategy, 12(3), 771-798. Doi: 10.22034/scs.2022.160772 [In Persian [

Canan Güngören, Ö., Gür Erdoğan, D., Kaya Uyanık, G., & Demirtaş Tolaman, T. (2022). The relationship between cognitive absorption and digital literacy skills among secondary school students. Participatory Educational Research, 9(6), 113-129. https://doi.org/10.17275/per.22.131.9.6

Celik, I. (2023). Towards intelligent-TPACK: An empirical study on teachers’ professional knowledge to ethically integrate artificial intelligence (AI)-based tools into education. Computers in Human Behaviors, 138, 107468 https://doi.org/10.1016/j.chb.2022.107468.

Celik, I., Dindar, M., Muukkonen, H.,. & Järvelä, S. (2022). The promises and challenges of artificial intelligence for teachers: A systematic review of research. Technology Trends, 66(4), 616–630. https://doi.org/10.1007/s11528-022-00715-y

Celik, I., & Muukkonen, H. (2023). Understanding and predicting teachers’ attention to use educational chatbots. In Proceedings of the 17th International Conference of the Learning Sciences-ICLS 2023. International Society of the Learning Sciences.

Chang, Y., Wong, S. F.,. & Park, M. C. (2016). A three-tier ICT access model for intention to participate online: a comparison of developed and developing countries. Information Development, 32(3), 226–242.

Chung, M., Ko, E., Joung, H.,. & Kim, S. J. (2020). Chatbot e-service and customer satisfaction regarding luxury brands. Journal of Bussiness Research, 117, 587–595. https://doi.org/10.1016/j.jbusres.2018.10.004.

Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd Ed.). New York: Routledge.

Cress, U., Rosé, C., Wise, A. F.,. & Oshima, J. (Eds.). (2021). International handbook of computer-supported collaborative learning. Springer Cham. https://doi.org/10.1007/978-3-030-65291-3

CSTA & ISTE. (2011). Operational definition of computational thinking for K-12 education. http://csta.acm.org/Curriculum/sub/CurrFiles/CompThinkingFlyer.pdf.

Davenport, T. H.,. & Ronanki, R. (2018). Artificial intelligence for the real world. Harvard Business Review, 96(1), 108–116.

DiMaggio, P., Hargittai, E., Celeste, C., & Shafer, S. (2004). Digital inequality: From unequal access to differentiated use. Social Inequality, 355-400.

Dohn, N. B., Kafai, Y., Mørch, A.,. & Ragni, M. (2022). Survey: Artificial intelligence, computational thinking and learning. KI-Künstliche Intelligenz, 36(1), 5–16. https://doi.org/10.1007/s13218-021-00751-5.

Dowling, M.,. & Lucey, B. (2023). ChatGPT for (finance) research: The Bananarama conjecture. Financ Research Letters, 53, 103662 https://doi.org/10.1016/j.frl.2023.103662.

Faruqe, F., Watkins, R.,. & Medsker, L. (2021). Competency model approach to AI literacy: Research-based path from initial framework to model. Advances in Artificial Intelligence and Machine Learning; Research, 2(4), 580-578.

Feher, K.,. & Katona, A.I. (2021). Fifteen shadows of socio-cultural AI: A systematic review and future perspectives. Futures, 132, 102817. https://doi.org/10.1016/j.futures.2021.102817.

Gedrimiene, E., Celik, I., Mäkitalo, K., & Muukkonen, H. (2023). Transparency and trustworthiness in user intentions to follow career recommendations from a learning analytics tool. Journal of Learning Analytics, 10(1), 54-70. 10.18608/jla.2023.7791.

Ghallab, M. (2019). Responsible AI: Requirements and challenges. AI Perspectives, 1(1), 1–7. https://doi.org/10.1186/s42467-019-0003-z

Ghasemi, V., & Adlipour, S. (2014). Sociological explanation of digital division situation among Isfahan University students. Sociological Cultural Studies, 4(4), 139-163. ]In Persian [

Griffin, P., & Care, E. (Eds.). (2014). Assessment and teaching of 21st century skills:  Methods and approach. Springer.

Grover, S., & Pea, R. (2013). Computational thinking in K–12: A review of the state of the field. Educational Researcher, 42(1), 38–43.

Guha, A., Grewal, D., Kopalle, P. K., Haenlein, M., Schneider, M. J., Jung, H., Moustafa, R., Hegde, D. R., & Hawkins, G. (2021). How artificial intelligence will affect the future of retailing. Journal of  Retailing, 97(1), 28–41.

Henseler, J., Dijkstra, T. K., Sarstedt, M., Ringle, C. M., Diamantopoulos, A., Straub, D. W., Ketchen, D. J., Hair, J. F., Hult, G. T. M., & Calantone, R. J. (2014). Common beliefs and reality about PLS. Organizational Research Methods, 17(2), 182-209.

Hermann, E. (2022). Artificial intelligence and mass personalization of communication content—An ethical and literacy perspective. New Media and  Society, 24(5), 1258–1277. https://doi.org/10.1177/14614448211022702.

Hooshyar, D. (2022). Effects of technology-enhanced learning approaches on learners with different prior learning attitudes and knowledge in computational thinking. Computer Applications in Engineering Education, 30(1), 64–76. https://doi.org/10.1002/cae.22442.

Hou, A. C., Shiau, W. L., & Shang, R. A. (2019). The involvement paradox: The role of cognitive absorption in mobile instant messaging user satisfaction. Industrial Management & Data Systems, 119(4), 881– 901. https://doi.org/10.1108/IMDS-06-2018-0245

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. Doi:10.1080/10705519909540118

Huang, X. (2021). Aims for cultivating students’ key competencies based on artificial intelligence education in China. Education and Information Technologies, 26(5), 5127–5147. https:// doi.org/10.1007/s10639-021-10530-2.

ISTE. (2015). Computational thinking in K–12 education leadership toolkit. https://www.iste.org/explore/computational-thinking/computational-thinking-all.

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, Article 100026. https://doi.org/10.1016/j.caeai.2021.100026

Korkmaz, Ő., Çakir, R., & Őzden, M. Y. (2017). A validity and reliability study of the Computational Thinking Scales (CTS). Computers in Human Behavior, 72, 558–569.

Kuhail, M. A., Alturki, N., Alramlawi, S., & Alhejori, K. (2023). Interacting with educational chatbots: A systematic review. Education and Information Technologies, 28(1), 973–1018. https://doi.org/10.1007/s10639-022-11177-3.

Lee, I., Ali, S., Zhang, H., DiPaola, D., & Breazeal, C. (2021). Developing middle school students’ AI literacy. Proceedings of the 52nd ACM Technical Symposium on computer science education, pp. 191–197.

Lo, C. K. (2023). What is the impact of ChatGPT on education? A rapid review of the literature. Education Sciences, 13(4), 1–15. https://doi.org/10.3390/educsci13040410.

Long, D., Blunt, T., & Magerko, B. (2021). Co-designing AI literacy exhibits for informal learning spaces. Proceedings of the ACM on Human-Computer Interaction, Volume 5, Issue CSCW2. Article No.: 293, Pages 1 – 35. https://doi.org/10.1145/3476034

Long, D., & Magerko, B. (2020). What is AI literacy? Competencies and design considerations. Proceedings of the 2020 CHI conference on human factors in computing systems (pp. 1–16). Honolulu, USA https://dl.acm.org/doi/fullHtml/10.11 45/3313831.3376727.

Lozano, I. A., Molina, J. M., & Gijon, C. (2021). Perception of artificial intelligence in Spain. Telematics and Informatics, 63, 101672 https://doi.org/10.1016/j.tele.2021.101672.

Luo, X., Tong, S., Fang, Z., & Qu, Z. (2019). Frontiers: Machines vs. humans: The impact of artificial intelligence chatbot disclosure on customer purchases. Marketing Science, 38(6), 937–947. https://doi.org/10.1287/mksc.2019.1192.

Mehra, B., Merkel, C., & Bishop, A. P. (2004). The internet for the empowerment of minority and marginalized users. New Media and Society, 6(6), 781–802. https://doi.org/ 10.1177/146144804047513.

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. https://doi.org/10.1016/j.caeai.2021.100041.

Obenza, B. N., Go, L. E., Francisco, J. A. M., Buit, E. E. T., Mariano, F. V. B., Cuizon Jr, H. L., Cagabhion, A. J. D., & Agbulos, K. A. J. L. (2024). The nexus between cognitive absorption and AI literacy of college students as moderated by sex. American Journal of Smart Technology and Solutions, 3(1), 32–39. https://doi.org/10.54536/ajsts.v3i1.2603

OECD (2001). Understanding the digital divide. Organization for Economic Co-operation and Development (OECD), Paris.  https://www.oecd.org

Pérez-Mercado, R., Balderas, A., Munoz, A., Cabrera, J. F., Palomo-Duarte, M., & Dodero, J. M. (2023). ChatbotSQL: Conversational agent to support relational database query language learning. SoftwareX 22(101346). https://doi.org/10.1016/j.softx.2023.101346

Ringle, C. M., Wende, S., & Becker, J. M. (2024). SmartPLS 4. https://www.smartpls.com

Roohi, M., Dehghani, M., & Khattat, M. (2023). Identifying the components of computational thinking involved in learning skills at the primary school level. Studies in Learning & Instrauction, 15(1), 16-45 .]In Persian [

Sadeghi, H., & Naser, M. (2021). Legal-ethical challenges of the EU regulation on artificial intelligence performance in health domian. Akhlāq-I zīstī i.e., Bioethics Journal, 10(35), 1–14. https://doi.org/10.22037/bioeth.v10i35.27500 [In Persian]

Salem, A. A., & Azizkhani, M. (2023). Factors affecting the digital divide in Iran's provinces. Macroeconomics Research Letter, 18(38), 7-34. Doi: 10.22080/iejm.2024.26400.2019 [In Persian]

Selby, C. C. (2015). Relationships: computational thinking, pedagogy of programming, and Bloom’s taxonomy. WiPSCE '15: Proceedings of the Workshop in Primary and Secondary Computing Education (pp. 80–87). https://doi.org/10.1145/2818314.2818315

Selby, C. C., & Woollard, J. (2013). Computational thinking: The developing definition. Paper presented at the 18th Annual Conference on Innovation and Technology in Computer Science Education, Canterbury.

Shute, V. J., Sun, C., & Asbell-Clarke, J. (2017). Demystifying computational thinking. Educational Research. Review, 22(1), 142–158. https://www.learntechlib.org/p/204418/.

Smith, M., Szongott, C., Henne, B., & Von Voigt, G. (2012). Big data privacy issues in public social media. 2012 6th IEEE International Conference on Digital Ecosystems and Technologies (DEST). IEEE, Campione d'Italia, Italy, 2012, pp. 1-6. Doi: 10.1109/DEST.2012.6227909

Sohn, K., & Kwon, O. (2020). Technology acceptance theories and factors influencing artificial Intelligence-based intelligent products. Telematics Inform, 47, 101324 https://doi.org/10.1016/j.tele.2019.101324

Soomro, K. A., Kale, U., Curtis, R., Akcaoglu, M., & Bernstein, M. (2018). Development of an instrument to measure faculty’s information and communication technology access (FICTA). Education and Information Technologies, 23, 253–269.

Steinbauer, G., Kandlhofer, M., Chklovski, T., Heintz, F., & Koenig, S. A. (2021). A differentiated discussion about AI education K-12. Künstliche Intelligenz, 35(2), 131–137. https://doi.org/10.1007/s13218-021-00724-8.

Usluel, Y. K., & Vural, F. K. (2009). Adaptation of cognitive absorption scale to Turkish. Ankara University Journal of Faculty of Educational Sciences (JFES), 42(2), 77–92.

van Deursen, A. J. A. M., van Dijk, J. A. G. M., & Peters, O. (2012). Proposing a survey instrument for measuring operational, formal, information, and strategic internet skills. International Journal of Human-Computer Interaction, 28(12), 827–837. https://doi.org/10.1080/10447318.2012.670086

van Dijk, J. (2020). The digital divide. John Wiley & Sons.

van Dijk, J. A. G. M.. (2005). The deepening divide: Inequality in the information society. Sage Publications, Thousand Oaks.

van Dijk, J. A. G. M. (2006). Digital divide research, achievements and shortcomings. Poetics 34 (4–5), 221–235. https://doi.org/10.1016/j.poetic.2006.05.004

Wang, B., Rau, P. L. P., & Yuan, T. (2022). Measuring user competence in using artificial intelligence: Validity and reliability of artificial intelligence literacy scale. Behavior & Information Technology, 1–14 https://doi.org/10.1080/0144929X.2022.2072768

Wang, W., & Siau, K. (2019). Artificial intelligence, machine learning, automation, robotics, future of work and future of humanity: A review and research agenda. Journal of Database Management, 30(1), 61–79. https://doi.org/10.4018/JDM.2019010104

Wendy Zhu, W., & Morosan, C. (2014). An empirical examination of guests’ adoption of interactive mobile technologies in hotels: Revisiting cognitive absorption, playfulness, and security. Journal of Hospitality and Tourism Technology, 5(1), 78-94. https://doi.org/10.1108/JHTT-09-2013-0029

Weng, J. (2004). Developmental robotics: Theory and experiments. International Journal of Humanoid Robotics, 1(2), 199-236. https://www.researchgate.net/publication/220065694

Weng, J. (2006). Autonomous robot intelligence through developmental learning. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics) , 36(4), 715-728

Westland, J. C. (2015). Structural equation models: Studies in systems decision and control. Springer Cham. https://doi.org/10.1007/978-3-030-12508-0

Wetzels, M., Odekerken-Schröder, G., & Van Oppen, C. (2009). Using PLS path modeling for assessing hierarchical construct models: Guidelines and empirical illustration. Management Information System Quarterly, 177-195.

Willinsky, J. (2017). The new literacy: Redefining reading and writing in the schools. Routledge.

Wing, J. M. (2006). Computational thinking. Communication of the ACM, 49(3), 33–35. https://doi.org/10.1145/1118178.1118215

Wu, S. Y., & Su, Y. S. (2021). Visual programming environments and computational thinking performance of fifth-and sixth-grade students. Journal of Educational Computing Research, 59(6), 1075–1092. https://doi.org/10.1177/0735633120988807

Yadav, A., Hong, H., & Stephenson, C. (2016). Computational thinking for all: Pedagogical approaches to embedding a 21st century problem solving in K-12 classrooms. Technology Trends, 60, 565–568. https://doi.org/10.1007/s11528-016-0087-7

Yuan, Y. H., Liu, C. H., & Kuang, S. S. (2021). An innovative and interactive teaching model for cultivating talent’s digital literacy in decision making, sustainability, and computational thinking. Sustainability, 13(9), 5117. https://doi.org/10.3390/su13095117

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), 1–27. https://doi.org/10.1186/s41239-019-0171-0