Integrating the idea of AI efficient individualized learning into educational mechanisms has multiple advantages which at the same time is accompanied by a list of certain difficulties. The first one is a capability of AI to individually address the requirements and learning preferences and rates of each learner. This customization can improve student interest as the acquisition is done based on the essentials that are neither too boring nor too challenging for the students. With the use of AI, students can be quickly corrected and directed on what knowledge areas they need to supplement immediately based on the results of the quiz. It, therefore, make sure that students progression is as result of their understanding of the contents of the course and not a mechanized one.

In addition, the application of AI personalization can help teachers by easing the burden of some of the analytic work, examining the student records containing data, finding trends and problems, and providing recommendations of teaching methods. This can enable the teachers to spend more time on high IR and/or VAL teaching activities, and create a more supportive and responsive classroom environment. AI in learning can support students with disabilities or the ones who need special attention through individualization of learning tasks and the use of learning technologies.

These perusing benefits are not without some pitfalls following which they are characterized. Such downside is related to data privacy and security since the generation of large amounts of data necessarily exposes it to risks of hacking, theft, and leakage. Very large amounts of student data are being collected and analyzed, and methods for the security of this data must be established to avoid hackings and other misuse. Another is too much reliance on AI resulting to the negation of sociological aspects of learning including psychological support, interpersonal skills, which can never be offered by AI. There is also the problem of AI investment to several infrastructures as well as training which presents a challenge for underfunded schools. Last but not the least, the question of how and to what extent algorithmic bias is a problem that has to be solved is presented to promote fairness and equality, in which aspect, AI learning systems shall allow students from different regions to learn as much as possible.