M31899 Essentials of Marketing Essay 1 Sample

M31899 Essentials of Marketing Essay 1

What you need to do

In order to develop your academic skills of researching, critical thinking, and writing, you are required to respond to the essay title listed below.
Research and critically evaluate the impact of the use of artificial intelligence (AI) on higher education in the UK.

Solution

Introduction

AI is transforming several domains and education in UK higher education The advent of AI integration has changed the face of traditional practices in learning and teaching, research methods and university management processes (Dwivedi et al., 2021). In this essay, the advantages and challenges of using AI in higher education courses are critically analyzed about its future implications.

Benefits of AI in Higher Education

Personalized Learning: Machine learning systems that use artificial intelligence to process the results of students’ performance evaluate their level based on individually customised student needs, and develop tailored educational materials. For instance, such platforms as Century Tech provide personalized learning pathways via AI algorithms that lead to increased student engagement and better academic performance (Tapalova & Zhiyenbayeva, 2022). In a study conducted in the year 2022 at King’s College London, it was realized that AI-based tutoring systems provided powerful learning support for science learners.

Efficient Administrative Processes: AI simplifies administrative duties including admission, enrollment and services targeted at students. With the help of chatbots installed with natural language processing technology, inquiries are processed thus minimizing response time and administrative process (Mulligan & Bamberger, 2019). For instance, the chatbot of the University of Birmingham helps students get course information and academic support.

Enhanced Research Capabilities: Automating data analysis, literature reviews, and even hypothesis generation that are essential to research endeavours is one of the things AI does which expedites them (Wagner et al., 2022). Researchers rely on AI-driven tools such as IBM Watson together with Google Scholar in terms of processing large quantities and patterns for pushing the frontiers by way of discoveries, and inventions unimaginable.

Accessible Education: AIso, makes allowance for the development of inclusive educational tools that are accessible to learners with disabilities (Zhang et al., 2020). Text voices allow turning any kind of words into sounds representing thoughts within a process accompanied by narration, whereby listeners have a chance to be equal participants along with female visual subjects from secondary schools but the paragraph goes on helping them increase their Chatbots or virtual assistants provide non-stop support and answer student questions covering administration, study materials including personal concerns.

Challenges of AI in Higher Education

Ethical Concerns: When using AI, the privacy of data is an ethical issue that needs to be put into consideration (Nassar & Kamal, 2021). For higher institutions of learning to take care of their students’ privacy, they have to guarantee that AI deployment is transparent and responsibilities must be identified with deferential implications.

Quality of Education: Although there may be a large number of advantages one cannot but disagree with the possible negativity related to the quality and relevance level of education provided by AI deliverable systems (Magrabi et al., 2019). The critics claim that no AI can be able to create the complex interplay and personal touches of such a nature given by human instructors, thus altering diagnostic experience.

Workforce Displacement: As such, employees performing more routine and predictable tasks using AI will likely lose their jobs. Some examples of affected sectors include administrative staff roles that need entry-level qualifications or unskilled labour requirements in UK universities (Butler & Hammer, 2020). In the age of AI, qualified candidates can effectively complete job assignments with minimal intervention while demands regarding competency address today’s changing necessities, universities must also be precocious to retrain rather than replace their employees.

Future Implications and Recommendations

Ethical AI Governance: Clear and strong ethical guidelines as well as governance frameworks should be in place so that AI development is done by UK universities. Such activities include recurrent auditing to detect and address algorithmic biases as well as ensuring transparency in AI decision processes (Landers & Behrend, 2023).

Digital Inclusion Initiatives: The Digital divide can be overcome by universities introducing programs to promote digitization specifically targeting the disadvantaged areas in society (Leguina et al., 2021). This may involve subsidizing devices or loaning the learners someones own, providing training programs on use of technology and partnering with local organizations to aid fill in missing accessibility towards usage of AI-enabled study materials.

Hybrid Learning Models: Universities should not completely get away from traditional teaching methods, there have to be hybrid models that incorporate AI-powered technologies as well as live instruction (Bathla et al., 2022). This kind of solution can be used to optimise AI as responsible education is a keyword when learning through critical thinking, creative and social engagement.

Conclusion

Thus, after having considered the discussion provided above it may be stated that drawing a line under AI incorporation for management assignment expert into higher education institutions creates clear perspectives for better teaching performance about students’ engagement and practice scoring as well as simplified administrative management yet advanced research capabilities. With such challenges adequately addressed through ethical governance, inclusive initiatives allowing everyone to benefit from AI progress and strategic planning that ensures optimal use of the technology UK universities can unlock all benefits it may provide in creating a more just, broader and innovative base for their operations.

Reference List

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Butler, P., & Hammer, A. (2020). Pay progression in routinised service sector work: navigating the internal labour market in a fast food multinational company. Industrial Relations Journal, 51(4), 351-371. https://onlinelibrary.wiley.com/doi/pdfdirect/10.1111/irj.12302

Dwivedi, Y. K., Hughes, L., Ismagilova, E., Aarts, G., Coombs, C., Crick, T., ... & Williams, M. D. (2021). Artificial Intelligence (AI): Multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research, practice and policy. International Journal of Information Management, 57, 101994.https://uobrep.openrepository.com/bitstream/handle/10547/623613/1_s2.0_S026840121930917X_main.pdf?sequence=4

Landers, R. N., & Behrend, T. S. (2023). Auditing the AI auditors: A framework for evaluating fairness and bias in high stakes AI predictive models. American Psychologist, 78(1), 36. https://psycnet.apa.org/psycarticles/2022-30899-001.pdf

Leguina, A., Mihelj, S., & Downey, J. (2021). Public libraries as reserves of cultural and digital capital: Addressing inequality through digitalization. Library & Information Science Research, 43(3), 101103. https://www.researchgate.net/profile/Adrian-Leguina-2/publication/353029256_Public_libraries_as_reserves_of_cultural_and_digital_capital_Addressing_inequality

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Li, L. (2022). Reskilling and upskilling the future-ready workforce for industry 4.0 and beyond. Information Systems Frontiers, 1-16. https://link.springer.com/article/10.1007/s10796-022-10308-y

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Mulligan, D. K., & Bamberger, K. A. (2019). Procurement as policy: Administrative process for machine learning. Berkeley Tech. LJ, 34, 773.https://par.nsf.gov/servlets/purl/10214677

Nassar, A., & Kamal, M. (2021). Ethical dilemmas in AI-powered decision-making: a deep dive into big data-driven ethical considerations. International Journal of Responsible Artificial Intelligence, 11(8), 1-11.https://www.nuffieldfoundation.org/wp-content/uploads/2019/02/Ethical-and-Societal-Implications-of-Data-and-AI-report-Nuffield-Foundat.pdf

Tapalova, O., & Zhiyenbayeva, N. (2022). Artificial Intelligence in Education: AIEd for Personalised Learning Pathways. Electronic Journal of e-Learning, 20(5), 639-653.https://files.eric.ed.gov/fulltext/EJ1373006.pdf

Wagner, G., Lukyanenko, R., & Paré, G. (2022). Artificial intelligence and the conduct of literature reviews. Journal of Information Technology, 37(2), 209-226.https://journals.sagepub.com/doi/pdf/10.1177/02683962211048201

Zhang, X., Tlili, A., Nascimbeni, F., Burgos, D., Huang, R., Chang, T. W., ... & Khribi, M. K. (2020). Accessibility within open educational resources and practices for disabled learners: A systematic literature review. Smart Learning Environments, 7, 1-19.https://link.springer.com/article/10.1186/s40561-019-0113-2