Table 6 List of challenges in Al Technological ‘his section discussed an empirical study on emerging architecture for AIED systems and the drivers hat influence the creation of AIED systems. Those factors that drive the AIED system and the AIED tructure. First, it was observed that the principal factors influencing AIED research are Educational leeds, which as a result generates AI theories, tools, and techniques. Scholars discussed the four ore elements of AIED: a model of the learner, domain expertise, teaching expertise, and interfaces. The goal of the AIED/ITS system is geared towards the individual learner. However, in recent years. idvancement in a broader learning context is encouraged with the inclusion of other parties such as eachers and parents. Moreover, data is needed in creating a group learning environment but data haring poses a serious challenge which gives rise to machine learning and educational data mining ommunities (Kay, 2012). The study Pierce and Hathaway (2018) state that data is important in Treating an effective AI system. The study further stressed that inaccurate or incomplete data in Al echnology may affect decisions or outcomes made by the system; since decisions made depend largely m the knowledge domain of the systems. Another challenge is the use of the wrong technology in he design of adaptive learning systems. Vendors must be probed enough to ensure that their systems ise machine learning technology and not any other alternate technology. (Pierce & Hathaway, 2018). AI technology raises some privacy concerns with the focus on information ownership. The use yf AI in education requires the collection of sensitive information like student academic records and