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Artificial Intelligence and the Future of Higher Education: Towards Inclusive, Ethical, and Employability-Driven Learning Ecosystems

Authors

  • Sandra Chinyeaka Nwokocha PhD , Faculty of Business & Tourism Management, Canterbury Christ Church University, GBS Partnership, Birmingham, United Kingdom; and PENKUP Research Institute, Birmingham, United Kingdom
  • Kennedy Oberhiri Obohwemu, PhD , Faculty of Health, Wellbeing & Social Care, Oxford Brookes University, GBS Partnership, Birmingham, United Kingdom; and PENKUP Research Institute, Birmingham, United Kingdom
  • Gordon Mabengban Yakpir PhD , Faculty of Health, Wellbeing & Social Care, Oxford Brookes University, GBS Partnership, Birmingham, United Kingdom; and PENKUP Research Institute, Birmingham, United Kingdom
  • Fidelis Evwiekpamare Olori PhD , Faculty of Business Management, Oxford Brookes University, GBS Partnership, Birmingham, United Kingdom; and PENKUP Research Institute, Birmingham, United Kingdom
  • Christian Atabong Nchindia PhD , Faculty of Business Management, University of Suffolk, GBS Partnership, Manchester, United Kingdom; and PENKUP Research Institute, Birmingham, United Kingdom
  • Charles Leyman Kachitsa PhD , Faculty of Business Management and Enterprise, Leeds Trinity University, GBS Partnership, Manchester, United Kingdom; and PENKUP Research Institute, Birmingham, United Kingdom
  • Olusunmola Osinubi PhD , Faculty of Health, Wellbeing & Social Care, Oxford Brookes University, GBS Partnership, Birmingham, United Kingdom; and PENKUP Research Institute, Birmingham, United Kingdom
  • Bartholomew Ituma Aleke PhD , Faculty of Health, Wellbeing & Social Care, Oxford Brookes University, GBS Partnership, Leeds, United Kingdom; and PENKUP Research Institute, Birmingham, United Kingdom
  • Aliyuda Ali PhD , School of Computing and Digital Technologies, Sheffield Hallam University, Sheffield, United Kingdom; and PENKUP Research Institute, Birmingham, United Kingdom
  • Ibrahim Olanrewaju Lawal PhD , Faculty of Business & Tourism Management, Canterbury Christ Church University, GBS Partnership, Birmingham, United Kingdom; and PENKUP Research Institute, Birmingham, United Kingdom
  • Iyevhobu Oshiokhayamhe Kenneth MPH , Department of Medical Microbiology, Faculty of Medical Laboratory Science, Ambrose Alli University, Ekpoma, Edo State, Nigeria
  • Oluwadamilola R. Tayo MPH , Faculty of Health, Wellbeing & Social Care, Oxford Brookes University, GBS Partnership, Leeds, United Kingdom; and PENKUP Research Institute, Birmingham, United Kingdom
  • Rupali Chauhan MPH , Faculty of Health, Wellbeing & Social Care, Oxford Brookes University, GBS Partnership, Manchester, United Kingdom
  • Shubham Sharma MDS , Independent Researcher, Manchester, United Kingdom
  • Divya Motupalli MPHGH , Faculty of Health, Wellbeing & Social Care, Oxford Brookes University, GBS Partnership, Manchester, United Kingdom
  • Aung Htet Sai Bo Bo MPH , Department of Health, Wellbeing & Social Care, Oxford Brookes University, GBS Partnership, Manchester, United Kingdom; and PENKUP Research Institute, Birmingham, United Kingdom
  • Samuel Oluwatosin Adejuyitan MSc , Department of Project Management, School of Computing, Engineering and Physical Sciences, University of the West of Scotland, United Kingdom; and PENKUP Research Institute, Birmingham, United Kingdom
  • Onomuighokpo Hillary Onome MBBS , Department of Orthopaedics & Trauma, Federal Medical Centre, Asaba, Delta State, Nigeria; and PENKUP Research Institute, Birmingham, United Kingdom

DOI:

https://doi.org/10.55640/corr-v03i02-04

Keywords:

Artificial Intelligence, Inclusive Learning, Employability Skills, Algorithmic Bias, Gender Equity

Abstract

Artificial Intelligence (AI) is revolutionising higher education by generating novel opportunities for inclusive learning, skill enhancement, and equal access to information. This chapter analyses the influence of AI on higher education, emphasising its contribution to fostering diversity, improving employability skills, and mitigating gender inequities. AI-driven solutions, including adaptive learning platforms, automated feedback systems, and natural language processing technologies, provide the capability to customise the learning experience, assist students with varied needs, and enhance academic English proficiency. Moreover, AI-powered career development solutions are transforming employability training through real-time feedback, workplace simulations, and focused skill enhancement interventions. Nonetheless, whereas AI presents opportunity to bridge educational disparities, it simultaneously engenders worries about algorithmic bias, ethical dilemmas, and data privacy vulnerabilities. While reviewing literature, this chapter examines AI's capacity to enhance inclusive education while critically evaluating the dangers of unequal access and algorithmic bias. It promotes a balanced approach that emphasises human-centred education, gender equality, and preparedness for employment. The findings enhance the overarching dialogue regarding AI in education, impacting policy deliberations and institutional approaches to fostering equal learning environments in the digital era.

References

Aoun, J. E. (2017). Robot-proof: Higher education in the age of artificial intelligence. MIT Press.

Baker, R., & Hawn, A. (2021). Algorithmic fairness in education: Biases, mitigation, and implications. Educational Researcher, 50(5), 297-307.

Bender, E. M., Gebru, T., McMillan-Major, A., & Shmitchell, S. (2021). On the dangers of stochastic parrots: Can language models be too big? Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency, 610-623

Bennett, D., Butler, P., & Green, J. (2022). AI-driven medical simulators: Improving clinical reasoning and reducing errors in medical trainees. Journal of Medical Education and Training, 45(3), 123-134. https://doi.org/10.1007/jmet.2022.6789

Bessen, J. (2019). AI and jobs: The role of demand. NBER Working Paper Series, 24235. https://doi.org/10.3386/w24235

Binns, R. (2018). Fairness in machine learning: Lessons from political philosophy. Proceedings of the 2018 Conference on Fairness, Accountability, and Transparency, 149-159.

Brynjolfsson, E., & McAfee, A. (2017). Machine, platform, crowd: Harnessing our digital future. W. W. Norton & Company.

Buchanan, T. (2021). The limitations of AI in student assessment: Balancing automation with human oversight. Journal of Educational Technology & Society, 24(4), pp.115-129. Available at: https://doi.org/10.2307/41505691 [Accessed 5 March 2025].

Buolamwini, J., & Gebru, T. (2018). Gender shades: Intersectional accuracy disparities in commercial gender classification. Conference on Fairness, Accountability, and Transparency, 77-91.

Burrus, J., Jackson, T., Xi, N., & Steinberg, J. (2013). Identifying the most important 21st-century workforce competencies: An analysis of the Occupational Information Network (O*NET). ETS Research Report Series, 2013(2), 1-55.

Crawford, K. (2021). Atlas of AI: Power, Politics, and the Planetary Costs of Artificial Intelligence. Yale University Press.

Dede, C., Richards, J., & Saxena, A. (2021). The 60-year curriculum: New models for lifelong learning in the digital economy. Routledge.

Huang, M. H., & Rust, R. T. (2018). Artificial intelligence in service. Journal of Service Research, 21(2), 155-172.

Holmes, L., McElwee, G., & Palmer, M. (2021). Developing employability skills through AI: Enhancing career preparedness in a digital world. Journal of Higher Education and Employment, 34(1), 67-80. https://doi.org/10.1093/jhee.2021.0567

Holmes, W., Bialik, M. and Fadel, C., 2022. Artificial intelligence in education: The importance of human oversight. AI and Education Journal, 33(1), pp.9-25. Available at: https://doi.org/10.1007/s10826-022-01697-2 [Accessed 5 March 2025].

Jordan, K., 2020. The role of artificial intelligence in student assessment and feedback. International Journal of Educational Technology, 9(2), pp.112-127. Available at: https://doi.org/10.1080/20424775.2020.1760781 [Accessed 5 March 2025].

Johnson, W. L., and Valente, A. (2021). AI-driven virtual humans for soft skills training. Educational Technology Research and Development, 69(4), 927-948.

Knox, J. (2020). Artificial intelligence and education: A critical view through the lens of human learning. Learning, Media and Technology, 45(3), 257-270.

Lazarus, A., Stern, S., and Tilly, J. (2020). Soft skills and the future of work. Brookings Institution.

Luckin, R., Motiwalla, L., & Bradshaw, J. (2021). AI-based feedback systems in higher education: Transforming student learning and employability. Educational Technology Review, 42(5), 234-249. https://doi.org/10.1016/j.etr.2021.0234

Makridakis, S. (2017). The forthcoming AI revolution: Its impact on society and firms. Futures, 90, 46-60.

Molnar, A., and Wiliam, D. (2023). AI-driven simulations for developing employability skills: A transformative approach to learning. Educational Technology & Society, 26(4), 124-137. https://doi.org/10.1111/ets.2023.0479

Popenici, S. A., and Kerr, S. (2017). Exploring the impact of AI on higher education. Research and Practice in Technology Enhanced Learning, 12(1), 1-13.

Selwyn, N. (2019). Should robots replace teachers? AI and the future of education. Polity Press.

Shermis, M.D. and Burstein, J., 2018. Automated essay scoring: A cross-disciplinary perspective. Springer. Available at: https://doi.org/10.1007/978-3-319-98635-4 [Accessed 5 March 2025].

Williamson, B., & Eynon, R. (2020). Datafication and education: A critical sociological perspective on emerging trends and impacts. Learning, Media and Technology, 45(2), 117-131.

UNESCO. (2020). Artificial intelligence and gender equality: Key findings and recommendations. Retrieved from https://unesdoc.unesco.org

Verma, S. (2022). AI-driven career guidance and gender stereotypes: A critical review. International Journal of Educational Technology in Higher Education, 19(1), 45-63.

West, D., Johnson, K., & Smith, L. (2019). AI-driven career advising systems: Navigating the future of work through technology. Career Development Quarterly, 60(2), 79-92. https://doi.org/10.1002/cdq.2020.1078

Zhai, C., Xu, Q. and Liu, L., 2022. AI-powered feedback in programming education: Advancements and challenges. Computers in Education, 153, pp.76-93. Available at: https://doi.org/10.1016/j.compedu.2020.103920 [Accessed 5 March 2025].

Zhang, H., & Lu, C. (2021). AI-driven mentorship programs and female participation in STEM: A systematic review. Journal of Educational Research & Practice, 11(3), 78-95.

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Published

2025-04-15

How to Cite

Artificial Intelligence and the Future of Higher Education: Towards Inclusive, Ethical, and Employability-Driven Learning Ecosystems. (2025). Critique Open Research & Review, 3(02), 18-29. https://doi.org/10.55640/corr-v03i02-04

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How to Cite

Artificial Intelligence and the Future of Higher Education: Towards Inclusive, Ethical, and Employability-Driven Learning Ecosystems. (2025). Critique Open Research & Review, 3(02), 18-29. https://doi.org/10.55640/corr-v03i02-04

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