Ethical and Cognitive Impacts of Machine Learning in Education: A Stakeholder-Centric Analysis
DOI:
https://doi.org/10.70680/sanskriti.v2i1.8921Keywords:
Machine, Learning, Ethics, Cognition, StakeholdersAbstract
This study explores machine learning (ML) in education, focusing on its effects on creativity, critical thinking, and a human-centered approach to learning. It addresses ethical issues and the risk of dehumanizing education, stressing the importance of balanced frameworks that maintain human interaction and essential pedagogical principles. The research utilizes a quantitative approach, examining online data analytics and stakeholder engagement metrics from platform X. The stakeholder analysis shows differing engagement levels: students displayed a high level of interest, while teachers showed mixed responses. Quantitative findings, such as the average views and standard deviation (882.5) for teachers, highlight the complex dynamics of stakeholder interest. By integrating these insights with an analysis of stakeholder discussions, the study uncovers significant gaps and opportunities in ML-enhanced learning environments. It underscores the necessity of equipping educators with the skills to effectively incorporate ML and promoting inclusive, reflective practices to enhance cognitive engagement. The results contribute to the ongoing conversation about the responsible and meaningful implementation of ML in education, providing strategies to tackle ethical, pedagogical, and stakeholder-related challenges. This study concludes on the ethical use of machine learning processes and recommends for more stakeholders and educational institutions active engagement.
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