Access, Engagement, and Equity Challenges in AI-Driven Educational Tools: A Socio-Technical Analysis of Higher Education Students in Quetta, Balochistan

Authors

  • Shumaila Dad University of Balochistan, Quetta
  • Bahram Nazeer University of Balochistan, Quetta
  • Nadia Ali University of Balochistan, Quetta

Keywords:

Access, Digital Divide, Student Engagement, Equity, Artificial Intelligence in Education, Multilingual Learning, Balochistan

Abstract

Artificial intelligence (AI) is frequently proposed as a solution to the geographic and infrastructural barriers that constrain higher education in Balochistan, Pakistan's largest and least densely served province. Yet the promise of AI-driven educational tools is conditioned on students' actual access to reliable connectivity, devices, and linguistically appropriate systems, and on whether access translates into sustained engagement rather than superficial novelty. This study examines student engagement with AI-driven educational tools in Quetta, Balochistan, and interprets the results against the socio-technical literature on access, the digital divide, and equity in AI-enabled education. A quantitative, cross-sectional survey was administered to students at the University of Balochistan and BUITEMS. Using Cochran's (1977) formula for finite populations, a minimum sample of 196 was required from a population of 400; 220 questionnaires were distributed via simple random sampling, yielding 199 valid responses. A five-point Likert-scale Engagement subscale (Items 21–30), reverse-coded for a negatively phrased item, achieved a Cronbach's alpha of 0.759. Descriptive results indicated a positive mean engagement level (M = 3.77, SD = 0.63), confirmed by a one-sample t-test against the neutral midpoint, t(197) = 17.03, p < .001. A one-way ANOVA found no statistically significant gender difference in engagement, F(1, 196) = 0.20, p = .66. Despite this generally positive picture, engagement scores contained noticeable pockets of neutrality, which, read against the literature on linguistic barriers, connectivity gaps, and gendered access constraints in Balochistan, suggest that sustained engagement is not guaranteed by access alone. The study concludes that engagement with AI-driven educational tools in Balochistan is shaped as much by infrastructural and linguistic equity as by individual motivation, and that localized, offline-capable, and multilingual AI systems are necessary to convert access into durable engagement.

References

American Psychological Association. (2020). Publication manual of the American Psychological Association (7th ed.).

Bangulzai, S., Waseem, M., & Naz, A. (2024). Barriers to technology integration in Balochistan's educational institutions: A quantitative analysis. International Journal of Educational Development, 105, Article 102874. https://doi.org/10.1016/j.ijedudev.2024.102874

Cochran, W. G. (1977). Sampling techniques (3rd ed.). Wiley.

Couldry, N., & Mejias, U. A. (2019). The costs of connection: How data is colonizing human life and appropriating it for capitalism. Stanford University Press.

Creswell, J. W., & Creswell, J. D. (2018). Research design: Qualitative, quantitative, and mixed methods approaches (5th ed.). SAGE Publications.

DeVellis, R. F. (2016). Scale development: Theory and applications (4th ed.). SAGE Publications.

Farid, S., Ahmed, R., & Khan, Z. (2023). Faculty readiness for AI integration in Pakistani universities. Pakistan Journal of Educational Research, 6(1), 78–98. https://doi.org/10.58622/pjer.v6i1.450

Field, A. (2018). Discovering statistics using IBM SPSS statistics (5th ed.). SAGE Publications.

Holmes, W., Bialik, M., & Fadel, C. (2022). Artificial intelligence in education: Promises and implications. Center for Curriculum Redesign.

Leedy, P. D., & Ormrod, J. E. (2021). Practical research: Planning and design (12th ed.). Pearson.

Nunnally, J. C., & Bernstein, I. H. (1994). Psychometric theory (3rd ed.). McGraw-Hill.

Polit, D. F., & Beck, C. T. (2018). Essentials of nursing research: Appraising evidence for nursing practice (9th ed.). Wolters Kluwer Health.

Sajja, R., Sermet, Y., Cikmaz, M., Cwiertny, D., & Demir, I. (2023). Artificial intelligence-enabled intelligent assistant for personalized and adaptive learning in higher education. arXiv. https://doi.org/10.48550/arXiv.2309.10892

UNESCO. (2022). Gender and ICT in South Asia. UNESCO.

United Nations. (2015). Transforming our world: The 2030 agenda for sustainable development. United Nations. https://doi.org/10.18356/3405d09f-en

Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 27(3), 425–478.

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), Article 39. https://doi.org/10.1186/s41239-019-0171-0

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Published

2026-07-01

How to Cite

Dad, S. ., Nazeer, B. ., & Ali, N. . (2026). Access, Engagement, and Equity Challenges in AI-Driven Educational Tools: A Socio-Technical Analysis of Higher Education Students in Quetta, Balochistan. Journal of Education And Humanities Research (JEHR), University of Balochistan, Quetta, 21(1), 42–50. Retrieved from http://journal.uob.edu.pk/journal/index.php/jehr/article/view/583

Issue

Vol. 21 No. 1 (2026): Journal of Education & Humanities Research (JEHR)

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Articles

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Copyright (c) 2026 Journal of Education And Humanities Research (JEHR), University of Balochistan, Quetta