Artificial Intelligence in Education: A Human-Centered Architecture for Personalization, Assessment, and Responsible Governance

Artificial intelligence (AI) is transforming education by intelligent tutoring, predictive analytics, intelligent sequencing of content, intelligent development of feedback and less famous but more recent generations of AI chatbots, article summarization, document writing, and expert simulators. However, the problem of educational deployment is not just a technical issue. It is a socio-technical design dilemma and entails pedagogy, teacher workload, equity, data control, scholarly integrity, accessibility and social trust. In this paper, a more advanced review-and-framework paper on the AI in education with a clear journal orientation is developed. Instead of purporting a live field trial, the article asserts a concept scoping synthesis of highly instrumental literature and policy reportages as well as a deployable reference architecture, algorithmic procedures, notebook-based prototyping commodiatives, and explicitly outlined illustrative analytics. There are four contributions of the manuscript. To begin with, it synthesizes the recent work around the areas of personalization, assessment, learning analytics, generative support, and governance. Second, it suggests a multi-level structure that links the learner information, instructional regulations, machine learning algorithms, retrieval enhanced generation, instructor control, and monitoring. Third, it demonstrates the adaptive recommendation and retrieval-grounded feedback generation pseudocode and provides evidence in the form of mock Jupyter notebooks, diagrams, and graphs with which institutions can examine such systems. Fourth, it is converting up-to-date ethical and policy discussions into an action plan in terms of a governance checklist and a roadmap of gradual deployment. The main point is that educational AI of high worth will be created not through the replacement of educators but through the systemate engineering of human-AI co-operation where the intent of instruction, transparency, and accountability can be seen throughout the system lifecycle.