Machine Learning in Education: Innovations, Impacts, and Ethical Considerations

Machine Learning (ML) has recently emerged as a powerful tool with significant potential torevolutionize education, bringing about fundamental changes in pedagogy and research. Its applicationsspan various domains within academia, including administration, instructional method enhancement, andgrade prediction. This study illustrates how ML can enhance the effectiveness of research, instruction,and study strategies by adapting to student needs and leveraging new communication tools within virtuallearning environments. The literature review encompasses various empirical studies outlining theintegration of Artificial Intelligence (AI) and ML within the educational sector. This research paper mapsout how educators are leveraging these capabilities while documenting the associated risks and learningoutcomes across various educational tiers. You can also refer to the original context in the file named'Suhani Research Paper OG.pdf' for a comprehensive view. We advocate that current educationalpractices in engineering and computing with GenAI can indicate to us a roadmap of its potentialities,uses, and risks from the standpoint of both teachers and students. Our purpose with the outcomes andconclusions of this scoping review is to support educators, faculty members, and other stakeholders inengineering and computing education to co-create educational methodologies that articulate GenAI withcurricula, AI literacy, and prompt engineering encompassing students’ learning domains such ascognitive, affective, or behavioral. This sample paper is mapped against the IJREK standard studenttemplate format and expands on the empirical findings