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Fostering Ethical AI Adoption for Education and Development

Authors: Dr. S. B. Kishor;

Fostering Ethical AI Adoption for Education and Development

Abstract

The integration of Artificial Intelligence (AI) in education holds immense promise for transforming learning experiences and improving educational outcomes. However, this convergence of technology and learning comes with significant ethical considerations that must not be overlooked. As educational institutions and developers embrace AI-driven solutions, they must prioritize privacy and data security to safeguard sensitive student information. Addressing bias and promoting fairness through diverse and representative datasets is vital to ensure equal opportunities for all learners. Maintaining a balance between AI-driven personalized learning and human interaction is essential to foster emotional support and critical thinking development. Transparency and explain ability of AI decisions build trust and facilitate collaborative problem-solving between educators and AI systems. By investing in robust cybersecurity measures, educational institutions can protect against potential cyber threats. Furthermore, addressing the digital divide and ensuring equitable access to AI-powered educational resources help bridge the gap between different student populations. Continuous evaluation and improvement of AI systems, along with clear accountability and liability, are necessary for responsible AI use in education. In navigating the ethical landscape of AI and education, stakeholders must remember that technology should serve as a supportive tool to enhance learning and development, not a replacement for essential human interactions. With a commitment to ethical practices, AI can truly revolutionize education, empowering learners, educators, and society at large with the tools needed to thrive in a rapidly evolving world.

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selected citations
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This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
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