Bridging the Digital Divide: Socioeconomic Equity in AI-Assisted Hybrid Learning
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Evaluating structural equation models with unobservable variables and measurement error
Fornell, C., & Larcker, D. F. (1981) · Journal of Marketing Research · doi:10.1177/002224378101800104 · Cited by 109,190 · see citing works
A new criterion for assessing discriminant validity in variance-based structural equation modeling
Henseler, J., Ringle, C. M., & Sarstedt, M. (2015) · Journal of the Academy of Marketing Science · doi:10.1007/s11747-014-0403-8 · Cited by 38,459 · see citing works
Statistical power analyses using G∗Power 3.1: tests for correlation and regression analyses
Faul, F., Erdfelder, E., Buchner, A., & Lang, A.-G. (2009) · Behavior Research Methods · doi:10.3758/brm.41.4.1149 · Cited by 37,615 · see citing works
The "what" and "why" of goal pursuits: Human needs and the self-determination of behavior
Deci, E. L., & Ryan, R. M. (2000) · Psychological Inquiry · doi:10.1207/s15327965pli1104_01 · Cited by 34,210 · cited 1× · see citing works
“Rural students are in effect excluded from AI’s promise of personalization, suffering algorithmic bias when the system’s machine learning algorithms cannot collect or interpret high-quality user data from a low-band-width home environment [ 7 , 8].”
Zawacki-Richter, O., Marín, V. I., Bond, M., & Gouverneur, F. (2019) · International Journal of Educational Technology in Higher Education · doi:10.1186/s41239-019-0171-0 · Cited by 7,242 · see citing works
Artificial Intelligence in Education: A Review
Chen, L., Chen, P., & Lin, Z. (2020) · IEEE Access · doi:10.1109/access.2020.2988510 · Cited by 4,133 · cited 1× · see citing works
“In a recent study, the [5] reported that, of 14.8 million households, 98.8% had internet access in urban areas and 90.3% in rural areas [ 6].”
InfluentialHow to Specify, Estimate, and Validate Higher-Order Constructs in PLS-SEM
Sarstedt, M., Hair, J. F., Cheah, J., Becker, J.-M., & Ringle, C. M. (2019) · Australasian Marketing Journal (AMJ) · doi:10.1016/j.ausmj.2019.05.003 · Cited by 3,676 · see citing works
Exploring the impact of artificial intelligence on teaching and learning in higher education
Popenici, S. A. D., & Kerr, S. (2017) · Research and Practice in Technology Enhanced Learning · doi:10.1186/s41039-017-0062-8 · Cited by 2,019 · see citing works
Self-Directed Learning: Toward a Comprehensive Model
Garrison, D. R. (1997) · Adult Education Quarterly · doi:10.1177/074171369704800103 · Cited by 1,578 · see citing works
AI and education: guidance for policy-makers
UNESCO (2021) · doi:10.54675/pcsp7350 · Cited by 929 · see citing works
Manual avanzado de Partial Least Squares Structural Equation Modeling (PLS-SEM)
Hair, J. F., Ringle, C. M., Gudergan, S. P., Castillo‐Apraiz, J., UPV/EHU, U. D. P. V. /. E. H. U., Cepeda‐Carrión, G., de Sevilla, U., Roldán, J. L., & de Sevilla, U. (2021) · doi:10.3926/oss.407 · Cited by 523 · see citing works
Exploring the potential of artificial intelligence tools in educational measurement and assessment
Owan, V. J., Abang, K. B., Idika, D. O., Etta, E. O., & Bassey, B. A. (2023) · Eurasia Journal of Mathematics, Science and Technology Education · doi:10.29333/ejmste/13428 · Cited by 448 · see citing works
Almasri, F. (2024) · Research in Science Education · doi:10.1007/s11165-024-10176-3 · Cited by 361 · see citing works
Alam, A. (2022) · Lecture notes in electrical engineering · doi:10.1007/978-981-19-2980-9_32 · Cited by 302 · see citing works
The digital divide in online education: Inequality in digital readiness of students and schools
van de Werfhorst, H. G., Kessenich, E., & Geven, S. (2022) · Computers and Education Open · doi:10.1016/j.caeo.2022.100100 · Cited by 243 · see citing works
Sharma, S., Mittal, P., Kumar, M., & Bhardwaj, V. (2025) · Discover Sustainability · doi:10.1007/s43621-025-01094-z · Cited by 165 · see citing works
Wu, D., Zhang, S., Ma, Z., Yue, G. X.-G., & Dong, R. K. (2024) · Systems · doi:10.3390/systems12090332 · Cited by 127 · see citing works
Li, B., Bonk, C. J., Wang, C., & Kou, X. (2024) · IEEE Transactions on Learning Technologies · doi:10.1109/tlt.2024.3386098 · Cited by 100 · see citing works
Contemporary gaps in research on digital divide in education: a literature review
Mirazchiyski, P. (2024) · Universal Access in the Information Society · doi:10.1007/s10209-024-01166-3 · Cited by 39 · see citing works
Digital divide among B40 students in Malaysian higher education institutions
Devisakti, A., Muftahu, M., & Xiaoling, H. (2023) · Education and Information Technologies · doi:10.1007/s10639-023-11847-w · Cited by 37 · see citing works
Yusoff, S. M., Marzaini, A. F. M., Hao, L., Zainuddin, Z., & Basal, M. H. (2025) · Discover Computing · doi:10.1007/s10791-025-09567-5 · Cited by 29 · see citing works
Adarkwah, M. A. (2025) · International Journal of Educational Research · doi:10.1016/j.ijer.2025.102636 · Cited by 23 · see citing works
Generative AI in Self-Directed Learning: A Scoping ReviewPreprint
Roe, J., & Perkins, M. (2024) · arXiv (Cornell University) · doi:10.48550/arxiv.2411.07677 · Cited by 9 · see citing works
Hu, Y., Donald, C., & Giacaman, N. (2022) · International Journal of Artificial Intelligence in Education · doi:10.1007/s40593-022-00310-5 · Cited by 7 · see citing works
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Research works printed but not linked: 3
- Chin WW. The partial least squares approach to structural equation modeling. In: Marcoulides GA, editor. Modern methods for business research. Hillsdale: Lawrence Erlbaum Associates; 1998. p. 295–336.
- Cohen J. Statistical power analysis for the behavioral sciences. 2nd ed. Hillsdale: Lawrence Erlbaum Associates; 1988.
- Sen A. Development as freedom. Oxford: Oxford University Press; 1999.
Cited by
Still being checked.
- Authors
- Poh, R., Tan, B. W., Lee, H. W., & Tan, K. L.
- Affiliations
- Wawasan Open University (MY)
- Universiti Sains Malaysia (MY)
- Corresponding author
- Poh, Racheal
- Venue
- SN Computer Science
- Published
- 2025-12-26
- Volume
- 7
- Issue
- 1
- ISSN
- 2661-8907 (ISSN-L)
- OpenAlex
- W7117319193
- Semantic Scholar
- 3502f07349449a23c34b80c0ce9718c7a8642ef2
- Cited by
- 1
- References
- 24
- Citations by year
- 2026: 1
- FWCI
- 1.05
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- SN Computer Science (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Information Systems › Artificial Intelligence in Education
- Physical Sciences › Computer Science › Information Systems › Educational Innovations and Technology
- Social Sciences › Education › Online and Blended Learning
- Keywords
- digital divide, socioeconomic status, generative AI in education, self-directed learning, hybrid learning, open and distance learning, self-management, self-monitoring, student motivation, educational inequality, AI literacy, access to technology
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