Artificial intelligence in e-learning: Promoting women's education in virtual universities

Authors

  • Shakiba Ahmadi Tourism Department, Bamyan University, Bamyan, Afghanistan
  • Tamanna Quraishi Computer Science Faculty, University of the People, USA
  • Atifa Fayazi Food Technology Department, Kabul University, Kabul, Afghanistan
  • Wajiha Ahmadi Economics Faculty, Kunduz University, Kunduz, Afghanistan
  • Behnaz Rahimi Medical Faculty, Zan Online University, Afghanistan

DOI:

https://doi.org/10.62568/tel.v1i1.177

Keywords:

Artificial intelligence, e-learning, online education, female students, educational technology

Abstract

The integration of artificial intelligence (AI) in e-learning has the potential to transform educational experiences, particularly in virtual university settings. This study explores the effectiveness and impact of AI-driven e-learning tools on female students at an online university. The primary purpose is to evaluate how these tools enhance educational outcomes, engagement, and inclusivity, while addressing challenges specific to female learners. A mixed-methods approach was employed, involving a structured questionnaire distributed to 95 female students across four faculties: Medical, Computer Science, Business Administration, and Education. The study utilized both quantitative and qualitative data collection techniques to assess students' perceptions of AI tools. Statistical analysis of survey responses provided insights into the effectiveness of AI tools in improving access to resources, supporting personalized learning, and enhancing overall engagement. The results indicate a generally positive perception of AI-driven e-learning tools among participants. Most students reported that AI tools effectively enhance educational outcomes and engagement, with notable improvements in accessibility and inclusivity. However, there are mixed opinions on the extent of these benefits, highlighting areas for further development. In conclusion, AI-driven e-learning tools show significant promise in transforming online education for female students by offering personalized, accessible, and supportive learning experiences. The study underscores the importance of continued refinement of AI applications to address the evolving needs of learners and enhance their educational experiences.

References

Akrami, K., Akrami, M., Akrami, F., & Hakimi, M. (2024). Investigating the Integration of Big Data Technologies in Higher Education Settings. Indonesian Journal of Multidisciplinary on Social and Technology, 2(2), 1–12. https://doi.org/10.31004/ijmst.v2i2.296

Al-Emran, M., Elsherif, H. M., & Shaalan, K. (2016). Investigating attitudes towards the use of mobile learning in higher education. Computers in Human behavior, 56, 93-102. https://doi.org/10.1016/j.chb.2015.11.033

Almaiah, M. A., Alfaisal, R., Salloum, S. A., Hajjej, F., Thabit, S., El-Qirem, F. A., ... & Al-Maroof, R. S. (2022). Examining the impact of artificial intelligence and social and computer anxiety in e-learning settings: students’ perceptions at the university level. Electronics, 11(22), 3662. https://doi.org/10.3390/electronics11223662

Fischer, C., Pardos, Z. A., Baker, R. S., Williams, J. J., Smyth, P., Yu, R., ... & Warschauer, M. (2020). Mining big data in education: Affordances and challenges. Review of Research in Education, 44(1), 130-160.https://doi.org/10.3102/0091732X20903304

Gazzawe, F., Mayouf, M., Lock, R., & Alturki, R. (2022). [Retracted] The Role of Machine Learning in E‐Learning Using the Web and AI‐Enabled Mobile Applications. Mobile Information Systems, 2022(1), 3696140. https://doi.org/10.1155/2022/3696140

Geng, S., Law, K. M., & Niu, B. (2019). Investigating self-directed learning and technology readiness in blending learning environment. International Journal of Educational Technology in Higher Education, 16(1), 1-22. https://link.springer.com/article/10.1186/s41239-019-0147-0

Hakimi, M., & Shahidzay, A. K. (2024). Transforming Education with Artificial Intelligence: Potential and Obstacles in Developing Countries. https://doi.org/10.20944/preprints202407.2542.v1

Hakimi, N., Hakimi, M., Hejran, M., Quraishi, T., Qasemi, P., Ahmadi, L., ... & Ulusi, H. (2024). Challenges and Opportunities of E-Learning for Women's Education in Developing Countries: Insights from Women Online University. EDUTREND: Journal of Emerging Issues and Trends in Education, 1(1), 57-69. https://doi.org/10.59110/edutrend.310

Hirashima, T., Supianto, A. A., & Hayashi, Y. (2017). Model-based approach for educational big data analysis of learners thinking with process data. In 2017 International Workshop on Big Data and Information Security (IWBIS) (pp. 11-16). San Diego: IEEE. https://doi.org/10.1177/0165551518789880

Holland, A. A. (2019). Effective principles of informal online learning design: A theory-building metasynthesis of qualitative research. Computers & Education, 128, 214–226. https://doi.org/10.1016/j.compedu.2018.09.026

Jia, K., Wang, P., Li, Y., Chen, Z., Jiang, X., Lin, C. L., & Chin, T. (2022). Research landscape of artificial intelligence and e-learning: a bibliometric research. Frontiers in psychology, 13, 795039. https://doi.org/10.3389/fpsyg.2022.795039

Kalaian, S. A., Kasim, R. M., & Kasim, N. R. (2019). Descriptive and predictive analytical methods for big data. In Web Services: Concepts, Methodologies, Tools, and Applications (pp. 314–331). USA: IGI global. https://doi.org/10.4018/978-1-5225-7501-6.ch018

Kashive, N., Powale, L., & Kashive, K. (2020). Understanding user perception toward artificial intelligence (AI) enabled e-learning. The International Journal of Information and Learning Technology, 38(1), 1-19. https://doi.org/10.1108/IJILT-05-2020-0090

Martin, F., Polly, D., Coles, S., & Wang, C. (2020). Examining higher education faculty use of current digital technologies: Importance, competence, and motivation. International Journal of Teaching and Learning in Higher Education, 32(1), 73-86.

Quraishi, T., Bayani, B. M., Yashar, S., Daudzai, M., Salamzada, R., Amini, M., & Hakimi, M. (2024). Integration of mobile learning technologies in Afghanistan universities: Opportunities and challenges. Education Specialist, 2(1), 1–14. https://doi.org/10.59535/es.v2i1.227

Rahimi, B., Halimi, Y., Ghiasi, Z., Afifi, S., Quraishi, T., & Hakimi, M. (2024). Enhancing Medical Education Through Online Technologies: Investigating Adoption, Impact, and Optimization Strategies. Educative: Jurnal Ilmiah Pendidikan, 2(2), 69-79. https://doi.org/10.37985/educative.v2i2.420

Sanusi, S., Kadir, F. K. A., Larson, E. M., Kundra, S., Hakimi, M., Fajri, I., & Suhendar, A. (2024). Exploring the digital crossroads: The influence of social media on cultural engagement in Aceh. Global Arts, Humanities and Culture Review, 1(1), 40-56.

Tang, K. Y., Chang, C. Y., & Hwang, G. J. (2023). Trends in artificial intelligence-supported e-learning: A systematic review and co-citation network analysis (1998–2019). Interactive Learning Environments, 31(4), 2134-2152. https://doi.org/10.1080/10494820.2021.1875001

Downloads

Submitted

2024-08-24

Published

2024-08-23

How to Cite

Ahmadi, S., Quraishi, T., Fayazi, A., Ahmadi, W., & Rahimi, B. (2024). Artificial intelligence in e-learning: Promoting women’s education in virtual universities. Tech in Learning, 1(1), 10–20. https://doi.org/10.62568/tel.v1i1.177

Issue

Vol. 1 No. 1 (2024): August 2024 (In Progress)

Section

Articles

License

Copyright (c) 2024 Shakiba Ahmadi, Tamanna Quraishi, Atifa Fayazi, Wajiha Ahmadi, Behnaz Rahimi

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

Most read articles by the same author(s)