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This systematic review examines those challenges in light of data privacy, algorithmic bias, ethical implications, technological hurdles, and acceptance of AI by educators and students. First, data privacy should be a primary concern, as AI systems require extensive data, bringing up the potential for breach and misuse. Secondly, there must be a robust mechanism concerning data protection and against the application of GDPR. Another critical point is algorithm bias: biased training data sets may lead to discriminative decisions that will increase inequalities in education. It talks about AI's impact on teachers and classroom dynamics because the takeover of responsibilities may lower the intensity of necessary human contact. From a technical perspective, there is so much infrastructure and expertise required that too many educational institutions lack, especially in developing countries. In addition, educators themselves may feel that the change resists and fears job loss and therefore acts as a deterrent to AI integration. The review underscores the imperative for extensive training of teachers to support enabling the integration of AI. It now demands a collaborative effort on the part of all stakeholders to maximize the gains and reduce the drawbacks of AI in educational aspects. Continuous research in, policy-making for, and ethical guidelines on AI are required to benefit all aspects of education equitably and effectively.
Farooqi et al. (2024) studied this question.