Keyword

AI, Higher Education, innovation, policy, assessment, ethics

Abstract

Artificial Intelligence (AI) is transforming the UK's higher education sector, impacting decision-making, policy development, knowledge exchange, operational processes, student recruitment, satisfaction, and notably, student assessment. This study explores AI's contributions in these domains, emphasizing its potential to foster innovation, enhance creativity, and elevate institutional reputations. Employing a mixed-methods approach, the research combines qualitative analyses of institutional case studies with published surveys of administrators, faculty, and students. Preliminary findings indicate that AI enhances decision-making efficiency by providing data-driven insights, streamlining policy formulation and strategic planning. In student assessment, AI-driven tools offer personalized feedback and adaptive testing environments, leading to improved performance and higher success rates. However, challenges persist, including ethical considerations related to data privacy, potential biases in AI algorithms, and resistance to technological adoption among staff and students. To address these challenges, the study recommends strategies for mitigating risks of AI implementation such as the development of comprehensive AI governance frameworks prioritizing ethical standards and data security. By embracing AI, UK higher education institutions can improve operational efficiency and educational outcomes, thereby bolstering their reputations as pioneers in academic innovation


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References
  • Aleven, V., McLaughlin, E. A., Glenn, R. A. and Koedinger, K. R. (2016) ‘Intelligent tutoring systems’, International Journal of Artificial Intelligence in Education, 26(1), pp. 1–28.
  • Altbach, P. G. and Knight, J. (2007) ‘The internationalization of higher education: Motivations and realities’, Journal of Studies in International Education, 11(3–4), pp. 290–305
  • Altbach, P. G., Reisberg, L. and Rumbley, L. E. (2019) Trends in Global Higher Education: Tracking an Academic Revolution. Rotterdam: Sense Publishers
  • Ammar, W., Groeneveld, D., Bhagavatula, C. and Beltagy, I. (2018) ‘Construction of the literature graph in semantic scholar’, in Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 3 (Industry Papers), pp. 84–91.
  • Aoun, J. (2017) Robot-Proof: Higher Education in the Age of Artificial Intelligence. Cambridge, MA: MIT Press.
  • Baker, P. and Smith, L. (2021) ‘AI in Higher Education: Ethical Considerations and Best Practices’, Journal of Educational Technology, 18(4), pp. 55–71.
  • Baker, R. S. and Hawn, A. (2021) ‘Algorithmic bias in education’, International Journal of Artificial Intelligence in Education, 31(3), pp. 523–563.
  • Beel, J., Gipp, B., Langer, S. and Breitinger, C. (2016) ‘Research-paper recommender systems: A literature survey’, International Journal on Digital Libraries, 17(4), pp. 305–338.
  • Biggs, J. and Tang, C. (2011) Teaching for Quality Learning at University: What the Student Does. 4th edn. Maidenhead: Open University Press.
  • Binns, R. (2018) ‘Fairness in machine learning: Lessons from higher education’, Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency.
  • Black, P. and Wiliam, D. (1998) ‘Assessment and classroom learning’, Assessment in Education: Principles, Policy & Practice, 5(1), pp. 7–74.
  • Boden, M. A. (2016) AI and Creativity: The Role of Artificial Intelligence in Art and Science. Oxford: Oxford University Press.
  • Boud, D. (2013) Enhancing Learning through Self-Assessment. London: Routledge.
  • Boud, D. and Molloy, E. (2013) Feedback in Higher and Professional Education: Understanding It and Doing It Well. London: Routledge.
  • Boud, D. and Soler, R. (2016) ‘Sustainable assessment revisited’, Assessment & Evaluation in Higher Education, 41(3), pp. 400–413.
  • Bowen, G. A. (2009) ‘Document analysis as a qualitative research method’, Qualitative Research Journal, 9(2), pp. 27–40.
  • Bresnick, J. (2019) ‘How AI is transforming creativity and innovation in higher education’, AI & Society, 34(4), pp. 109–125.
  • Brookhart, S. M. (2013) How to Create and Use Rubrics for Formative Assessment and Grading. Alexandria, VA: ASCD.
  • Brown, G. (2015) Assessment in Higher Education: A Scholarly Approach. London: Routledge.
  • Brown, R. and Carasso, H. (2013) Everything for Sale? The Marketisation of UK Higher Education. London: Routledge.
  • Bryman, A. (2012) Social Research Methods. 4th edn. Oxford: Oxford University Press.
  • Burstein, J., Chodorow, M. and Leacock, C. (2020) ‘Automated writing evaluation: Uses and limitations’, Journal of Writing Research, 12(1), pp. 45–67.
  • Christensen, C. M. and Eyring, H. J. (2011) The Innovative University: Changing the DNA of Higher Education from the Inside Out. San Francisco, CA: Jossey-Bass.
  • Clark, B. R. (2004) Sustaining Change in Universities: Continuities in Case Studies and Concepts. Berkshire: Open University Press.
  • Collini, S. (2012) What Are Universities For? London: Penguin.
  • Creswell, J. W. (2013) Qualitative Inquiry and Research Design: Choosing Among Five Approaches. 3rd edn. Thousand Oaks, CA: Sage.
  • Dweck, C. S. (2006) Mindset: The New Psychology of Success. New York: Random House.
  •     European Commission (2018) General Data Protection Regulation (GDPR). Available at: https://gdpr-info.eu/.
  • Field, A. (2018) Discovering Statistics Using IBM SPSS Statistics. 5th edn. London: Sage.
  • Floridi, L. and Cowls, J. (2019) ‘Governing AI in higher education: Ethical challenges and solutions’, AI & Society, 34(2), pp. 207–215.
  • Gough, D., Oliver, S. and Thomas, J. (2017) An Introduction to Systematic Reviews. 2nd edn. London: Sage.
  •     Jisc (2022) The Future of AI in Higher Education: Governance and Ethical Considerations. Bristol: Jisc Publications.
  • Nguyen, T., Rienties, B. and Richardson, J. (2020) ‘AI in learning analytics: Trends and future directions’, Higher Education Research & Development, 39(5), pp. 889–904.
  • Yin, R. K. (2018) Case Study Research and Applications: Design and Methods. 6th edn. Thousand Oaks, CA: Sage.
  • Zawacki-Richter, O., Marín, V. I., Bond, M. and Gouverneur, F. (2019) ‘Systematic review of research on AI in higher education: The state of play and future prospects’, International Journal of Educational Technology in Higher Education, 16(39)