Exploring Effective Models of Leadership in the Age of AI within Private Higher Education Institutions in England

Artificial intelligence (AI) has gained significant interest and application in various fields and the impacts are evident in teaching, learning, and assessment. On one hand, AI can bring the much-needed revolution in higher education by assisting leaders and management to offer personalized teaching approaches that meet specific student needs, it can also enhance efficient feedback and the automation of administrative functions including assessment and grading. These capabilities make AI an important instrument in promoting efficiency in higher education and creating room for institutions to redirect their focus on other equally important aspects such as curriculum development. However, despite these advantages, AI comes with significant setbacks that adversely impact the quality of education. Particularly, various platforms allow students to do their assignments automatically thus negating their responsibility to research and learn. As such, institutions must put in place effective models of leadership and approaches that will ensure the use of AI in an ethical manner without compromising the quality of education.

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Journal

Title: ScienceOpen Posters

Publisher: ScienceOpen

Publication date (Electronic preprint): 16 February 2024

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[1 ] SK HUB LTD;

Author notes

[* ]Email: Sarwar.khawaja@ 123456oxfordbusinesscollege.ac.uk .

Author information

Sarwar Khawaja https://orcid.org/0009-0004-7792-4794

Article

DOI: 10.14293/P2199-8442.1.SOP-.PDAXJM.v2

SO-VID: 850d4276-2718-4341-806a-bca06022c90f

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This work has been published open access under Creative Commons Attribution License CC BY 4.0 , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Conditions, terms of use and publishing policy can be found at www.scienceopen.com .

History

Date received : 16 February 2024

References

Baker Ryan, Siemens George. Educational Data Mining and Learning AnalyticsThe Cambridge Handbook of the Learning Sciences. p. 253–272. 2014. Cambridge University Press. [Cross Ref]
International Handbook of Information Technology in Primary and Secondary Education. 2008. Springer US. [Cross Ref]
Gordon Faith. Virginia Eubanks (2018) Automating Inequality: How High-Tech Tools Profile, Police, and Punish the Poor. New York: Picador, St Martin’s Press. Law, Technology and Humans. 162–164. 2019. Queensland University of Technology. [Cross Ref]

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[1] Baker Ryan, Siemens George. Educational Data Mining and Learning AnalyticsThe Cambridge Handbook of the Learning Sciences. p. 253–272. 2014. Cambridge University Press. [Cross Ref]

[2] International Handbook of Information Technology in Primary and Secondary Education. 2008. Springer US. [Cross Ref]

[3] Gordon Faith. Virginia Eubanks (2018) Automating Inequality: How High-Tech Tools Profile, Police, and Punish the Poor. New York: Picador, St Martin’s Press. Law, Technology and Humans. 162–164. 2019. Queensland University of Technology. [Cross Ref]