Modeling the Impact of Digital Divide on Artificial Intelligence Literacy with Mediation of Computational Thinking and Cognitive Absorption among Student-Teachers

Document Type : Research Paper

Abstract

Introduction: The rapid advancement of artificial intelligence (AI) has introduced new dimensions to the digital divide, creating an AI divide that affects access, benefits, and opportunities across different regions and socioeconomic groups. Artificial intelligence (AI) literacy is increasingly vital in today's world as it becomes an integral part of daily life and the workforce. It involves understanding how AI works, its capabilities, limitations, and ethical implications. AI literacy empowers individuals to critically evaluate AI outputs, ensuring informed decision-making and responsible use of AI technologies. In education, AI literacy is crucial for educators and students to leverage AI for improved learning outcomes and educational management. It also opens up new job opportunities and enhances professional skills, making it a differentiating skill for future leaders. Moreover, with the AI Act mandating AI literacy for organizations using AI systems, it is essential for compliance and ethical AI deployment. By developing AI literacy, individuals can navigate an AI-infused world effectively, ensuring they remain informed consumers and critical thinkers. Overall, AI literacy is not just about technical knowledge but also about understanding AI's societal and ethical implications, making it indispensable for personal and professional growth in the digital age.  This study investigates the relationship between the digital divide and AI literacy, with a focus on the mediating roles of computational thinking and cognitive absorption among student-teachers. In today's technology-driven world, computational thinking (CT) and cognitive absorption are crucial skills that enhance an individual's ability to navigate complex digital environments effectively. Computational thinking involves problem-solving skills such as decomposition, pattern recognition, and algorithmic thinking, which are foundational for understanding and applying A) concepts. Cognitive absorption, on the other hand, refers to the degree to which an individual becomes engaged and immersed in a technological environment, which can significantly influence their learning and interaction with AI systems. CT skills are essential for breaking down complex problems into manageable parts, analyzing data, and developing solutions. This skillset is vital for educators and students alike, as it enhances their ability to integrate AI tools into educational settings. Studies have shown that computational thinking is a significant predictor of AI literacy, meaning that individuals with strong CT skills tend to have better understanding and application of AI concepts. In an era where AI and machine learning are rapidly evolving, CT skills enable individuals to adapt quickly to new technologies and innovations.
Method: This study employed a descriptive-correlational method, collecting data from a sample of 946 student-teachers at the Farhangian University of Zanjan using various scales (AI literacy skill, Computational Thinking, Cognitive absorption, and Information and Communication Technology Access Scale) and the census method. Data analysis was conducted using PLS-4 and SPSS-27 software.
Results: The study reveals that computational thinking is a significant factor in enhancing AI literacy. It indicated the pivotal role of CT in enhancing artificial intelligence AI literacy. Computational thinking, a problem-solving approach that involves decomposition, pattern recognition, and algorithmic thinking, is crucial for understanding and effectively using AI technologies. By fostering CT skills, individuals can better comprehend AI concepts, critically evaluate AI outputs, and apply AI solutions to real-world problems. It also shows that access to information and communication technologies (ICTs) facilitates better use of AI by individuals. Furthermore, having more motivation and skills to use AI technologies leads to a more positive experience. Notably, the findings indicate an indirect relationship between the digital divide and AI literacy, mediated by computational thinking and cognitive absorption. This suggests that these mediating factors play an additive role in bridging the gap between the digital divide and AI literacy. The results highlight the importance of promoting computational thinking and cognitive absorption to improve AI literacy among student-teachers. They also underscore the need for equitable access to ICTs to ensure that individuals can effectively engage with AI technologies. Ensuring equitable access to information and communication technologies (ICTs) is crucial for individuals to effectively engage with artificial intelligence (AI) technologies. This access is not merely a technological issue but also deeply intertwined with socio-economic factors, such as income and geographic location, which can exacerbate the digital divide.
Discussion and Conclusion: The digital divide refers to the unequal access to digital technologies, including computers, smartphones, and the internet, among different demographic groups and regions. This gap is not just about physical access but also encompasses differences in digital literacy and the ability to effectively use these technologies. The digital divide is multifaceted, involving disparities in access (first-level digital divide) and usage (second-level digital divide) of ICTs. It affects various socioeconomic groups within countries and between developed and developing nations, exacerbating existing social inequalities and creating a persistent information gap between those with access ("haves") and those without ("have-nots"). Factors contributing to the digital divide include affordability, availability of technology, geographic location, and education level. For instance, rural areas often have limited internet access compared to urban regions, while lower-income households may struggle to afford modern devices and internet services. Addressing the digital divide requires comprehensive strategies, such as investing in infrastructure, promoting digital literacy, and implementing policies to ensure equitable access to technology. Closing this gap is crucial for ensuring that all individuals can participate fully in the digital economy and benefit from its opportunities. This study contributes to understanding how educational interventions can address the AI divide by focusing on these mediating factors, ultimately enhancing digital equity and inclusivity in the educational sector. In conclusion, this study provides insights into how computational thinking and cognitive absorption mediate the digital divide and AI literacy relationship. It emphasizes the need for targeted educational strategies to enhance AI literacy and reduce the digital divide, ensuring that future educators are equipped to navigate and leverage AI technologies effectively. By addressing these challenges, educational institutions can foster a more inclusive and digitally literate community.
 

Keywords

Main Subjects

References

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
 
Studies in Learning & Instruction
Volume 16, Issue 2
September 2024
Pages 278-259

Files

History

  • Receive Date: 29 April 2024
  • Revise Date: 19 January 2025
  • Accept Date: 25 January 2025

Share

How to cite

Statistics