全部 标题 作者
关键词 摘要

OALib Journal期刊
ISSN: 2333-9721
费用:99美元

查看量下载量

相关文章

更多...

Navigating the Technology Divide: The Role of Educational Leadership in Generative AI Usage among Diverse Age Groups

DOI: 10.4236/ojl.2024.134027, PP. 515-531

Keywords: Generative AI, Age, Technology Adoption, Academic Performance, Ease of Use, Educational Leadership

Full-Text   Cite this paper   Add to My Lib

Abstract:

This study investigates how the ages of Yemyung Graduate University (YGU) students influence their perceptions and usage of generative AI tools, examining factors such as frequency of use, ease of use, and anticipated future interactions with these technologies. Utilizing a quantitative research design, the study surveyed a diverse sample of students, revealing significant differences in perceptions based on age. Findings indicate that younger students tend to view generative AI tools as essential for academic success, whereas older students often perceive them as less critical. This disparity suggests that educational leadership must prioritize targeted training and support initiatives tailored to the unique needs of older students to bridge the technology divide. By integrating generative AI tools into the curriculum and promoting peer mentorship programs, educational leaders can foster an inclusive learning environment that empowers all students to effectively utilize these technologies. The implications for academia emphasize the need for tailored support, while policy recommendations call for equitable access to resources that enhance digital literacy among diverse age groups. Furthermore, the study identifies avenues for future research to explore the long-term effects of training interventions and cultural influences on generative AI adoption. Ultimately, this research highlights the crucial role of educational leadership in addressing disparities in technology engagement, ensuring that all learners benefit from advancements in educational technology.

References

[1]  Caballero, A., Ramos, P. A., & Hattori, J. (2019). Student Engagement in the Context of AI Tools: A Review. Journal of Educational Technology, 35, 212-225.
[2]  Chen, X., Xie, H., & Hwang, G. (2021a). A Multi-Perspective Study on Artificial Intelligence in Education: Grants, Conferences, Journals, Software Tools, Institutions, and Researchers. Computers and Education: Artificial Intelligence, 1, Article 100005.
https://doi.org/10.1016/j.caeai.2020.100005
[3]  Chen, Y., Zhang, L., & Liu, J. (2021b). The Role of AI in Enhancing Student Learning Experiences in Higher Education. Educational Technology Research and Development, 69, 1281-1301.
[4]  Cochran, W. G. (1977). Sampling Techniques (3rd ed.). Wiley.
[5]  Cohen, J. (1988). Statistical Power Analysis for the Behavioral Sciences (2nd ed.). Lawrence Erlbaum Associates.
[6]  Czaja, S. J., & Sharit, J. (1998). Age Differences in Attitudes toward Computers. The Journals of Gerontology Series B: Psychological Sciences and Social Sciences, 53, 329-340.
https://doi.org/10.1093/geronb/53b.5.p329
[7]  Davis, F. D. (1989). Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Information Technology. MIS Quarterly, 13, 319-340.
https://doi.org/10.2307/249008
[8]  Field, A. (2013). Discovering Statistics Using IBM SPSS Statistics (4th ed.). SAGE Publications.
[9]  Hemsley-Brown, J., & Oplatka, I. (2015). University Choice: What Do We Know, What Don’t We Know and What Do We Still Need to Find Out? International Journal of Educational Management, 29, 254-274.
https://doi.org/10.1108/ijem-10-2013-0150
[10]  Huang, Y., & Liao, H. (2015a). Using Artificial Intelligence to Support Students’ Learning: A Review of Research. Computers in Human Behavior, 50, 619-631.
[11]  Huang, Y., & Liao, P. (2015b). Adult Learners’ Learning Styles and Attitudes towards Online Learning. Adult Education Quarterly, 65, 148-161.
https://doi.org/10.1177/0741713614564968
[12]  Igbaria, M., Parasuraman, S., & Baroudi, J. J. (1997). A Motivational Model of Microcomputer Usage. Journal of Management Information Systems, 13, 127-143.
https://doi.org/10.1080/07421222.1996.11518115
[13]  Koch, H., & Boudreau, M. C. (2020). Let’s Make It Personal: A Reflection on Technology Acceptance Research. Information Systems Journal, 30, 795-802.
https://doi.org/10.1111/isj.12266
[14]  Mariano, A., de Castro, F. F., & Scuderi, D. (2021). Technological Self-Efficacy and the Adoption of Digital Tools: Age Matters. Computers in Human Behavior, 121, Article 106805.
[15]  McGivney, V. (2004). Understanding Persistence in Adult Learning. Open Learning: The Journal of Open, Distance and e-Learning, 19, 33-46.
https://doi.org/10.1080/0268051042000177836
[16]  Prensky, M. (2001). Digital Natives, Digital Immigrants Part 1. On the Horizon, 9, 1-6.
https://doi.org/10.1108/10748120110424816
[17]  Santos, A. M., Soares, M., & Neves, M. (2020). AI Adoption in Education: A Comparison between Age Groups. International Journal of Technology in Education and Science, 4, 125-138.
[18]  Scherer, R., Siddiq, F., & Tondeur, J. (2019). The Role of Students’ Age, Gender, and Prior ICT Experience in the Acceptance of Digital Technologies in Higher Education. Computers in Human Behavior, 91, 17-23.
https://doi.org/10.1016/j.chb.2018.09.017
[19]  Sweller, J. (1988). Cognitive Load during Problem Solving: Effects on Learning. Cognitive Science, 12, 257-285.
https://doi.org/10.1207/s15516709cog1202_4
[20]  Van Dijk, J. (2017). Digital Divide: Impact of Access. In The International Encyclopedia of Media Effects (pp. 1-11). Wiley Online.
[21]  Van Dijk, J. A. G. M. (2020). The Digital Divide. Polity Press.
[22]  Venkatesh, V., & Bala, H. (2008). Technology Acceptance Model 3 and a Research Agenda on Interventions. Decision Sciences, 39, 273-315.
https://doi.org/10.1111/j.1540-5915.2008.00192.x
[23]  Venkatesh, V., & Morris, M. G. (2000). Why Don’t Men Ever Stop to Ask for Directions? Gender, Social Influence, and Their Role in Technology Acceptance and Usage Behavior. MIS Quarterly, 24, 115-139.
https://doi.org/10.2307/3250981
[24]  Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User Acceptance of Information Technology: Toward a Unified View. MIS Quarterly, 27, 425-478.
https://doi.org/10.2307/30036540
[25]  Wang, T., Lund, B. D., Marengo, A., Pagano, A., Mannuru, N. R., Teel, Z. A. et al. (2023). Exploring the Potential Impact of Artificial Intelligence (AI) on International Students in Higher Education: Generative AI, Chatbots, Analytics, and International Student Success. Applied Sciences, 13, Article 6716.
https://doi.org/10.3390/app13116716
[26]  Zastudil, C., Rogalska, M., Kapp, C., Vaughn, J., & MacNeil, S. (2023). Generative AI in Computing Education: Perspectives of Students and Instructors. In 2023 IEEE Frontiers in Education Conference (FIE) (pp. 1-9). IEEE.
https://doi.org/10.1109/fie58773.2023.10343467
[27]  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, Article No. 39.
https://doi.org/10.1186/s41239-019-0171-0
[28]  Zhou, M., & Wang, G. (2020). User-Centered Design in Artificial Intelligence: Enhancing Engagement through Intuitive Interfaces. Computers in Human Behavior, 106, Article 106191.
https://doi.org/10.1016/j.chb.2020.106191

Full-Text

Contact Us

service@oalib.com

QQ:3279437679

WhatsApp +8615387084133