stdClass Object
(
    [id] => 15561
    [paper_index] => 202504-01-020929
    [title] => ETHICAL CHALLENGES IN AI USE IN SCHOOLS: A STUDY OF DATA PRIVACY, SURVEILLANCE, AND BIAS
    [description] => 
    [author] => Harsh Shukla, Kshama Pandey, Neeraj Kumar
    [googlescholar] => 
    [doi] => https://doi.org/10.36713/epra20929
    [year] => 2025
    [month] => April
    [volume] => 11
    [issue] => 4
    [file] => fm/jpanel/upload/2025/April/202504-01-020929.pdf
    [abstract] => The rapid integration of Artificial Intelligence (AI) in school education has transformed teaching methods and administrative processes, offered personalized learning experiences, and streamlined management. However, this adoption has raised significant ethical concerns, particularly in the areas of data privacy, surveillance, and algorithmic bias. This study explores these ethical challenges, drawing insights from a comprehensive literature review. The findings highlight the potential risks associated with AI-powered educational tools, including threats to student privacy, the psychological impact of continuous surveillance, and the perpetuation of existing societal inequities. The study emphasizes the urgent need for robust ethical governance frameworks to ensure responsible and equitable AI deployment in schools. Key implications for educators, policymakers, and developers are discussed, focusing on the development of AI literacy, the establishment of clear guidelines and regulations, and the adoption of ethical-by-design approaches. The study concludes by calling for further research into the long-term impacts of AI in education, increased stakeholder awareness, and comprehensive policy reform to create a regulatory environment that promotes innovation while safeguarding ethical principles. Addressing these challenges is crucial for harnessing the potential of AI to enhance education while mitigating risks and ensuring the best interests of all learners.
    [keywords] => Artificial Intelligence (AI), Data Privacy, Surveillance, Student Privacy, Algorithmic Bias, Personalized Learning, Algorithmic Bias
    [doj] => 2025-04-09
    [hit] => 
    [status] => 
    [award_status] => P
    [orderr] => 36
    [journal_id] => 1
    [googlesearch_link] => 
    [edit_on] => 
    [is_status] => 1
    [journalname] => EPRA International Journal of Multidisciplinary Research (IJMR)
    [short_code] => IJMR
    [eissn] =>  2455-3662 (Online)
    [pissn] => - --
    [home_page_wrapper] => images/products_image/11.IJMR.png
)
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