Abstract
Artificial intelligence has shifted the teaching-and-learning experience to a technology-embedded one, from planning to lesson execution. This educational refinement, however, raises emerging ethical concerns regarding fairness, responsibility, privacy, and transparency. This systematic literature review probed the ethical frameworks for artificial intelligence in education.
The systematic databases for peer-reviewed published articles, Scopus, Google Scholar, and Web of Science, screened 47 studies, which qualified for a systematic literature review. The narrative synthesis yielded four initial ethical frameworks and their dominant traits.
The systematic review coded four (4) primary themes of ethical frameworks: principle-based, governance-oriented, rights-based, and domain-specific. Among these frameworks, the review inquired into fairness through bias reduction, equal access, an inclusive approach, and just assessment. Accountability established a governance structure, stakeholder collaboration, administrative conformity, and audit approaches, while transparency and privacy were further addressed through informative AI, data-protective measures, informed consent, and privacy-preserving techniques.
Despite distinct evolutions, contrasts persist in empirical verification, real-world use, multicultural relevance, and ethical guidelines for emerging generative AI tools.
Keywords: AI Ethics, Artificial Intelligence, Education, Ethical Frameworks
DOI https://zenodo.org/records/22228636