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[
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.
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.
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.
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.
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.
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.
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.
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
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.
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
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.
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.
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
)