Harnessing Quality Education for Sustainable Development through Machine Learning Technology

Description

DOI: https://doi.org/10.5281/zenodo.23944816

Eric Blanco Niyitunga and James Otundo (School of Arts and Human Sciences, Institute of Policy and Development Studies, Daystar University, Kenya)

The study aimed to assess the prospects and potentials of Machine Learning in promoting quality and inclusive education to achieve sustainable development in Africa. The study found that Machine Learning technology used in education has a transformative power and can serve as a catalyst and cornerstone for achieving sustainable development goals in Africa. The technology used in education addresses barriers such as cost, inaccessibility, inequality, exclusion and socio-economic disparities. It reshapes educational practices, touching upon several key areas as it enables adaptive learning platforms that cater to diverse needs, including marginalized groups, thereby promoting equitable participation and lifelong learning. Machine Learning technology equips learners with innovation skills that promote healthy consumption and productivity, and promotes innovation skills that leaners can use to protection environment, foster global partnerships. The study found that quality inclusive education directly ensures the achievement because it reduces poverty and hunger, addresses inequality, enhances employability that leads to a decent life and economic growth, which in turn improves health and well-being. However, realizing Machine Learning’s full potential requires careful navigation of critical compliance issues, including protecting data privacy, ensuring algorithmic fairness and transparency, and upholding academic integrity amidst evolving challenges. To achieve the above assertions, the study adopted a qualitative research methodology with content analysis technique used to collect and interpreted collected data.