One of the main aims of educational institutions, is to prepare children for further education and successful integration into the workforce. To achieve this, as much as possible, it is necessary to meet the requirements and expectations set by dynamically changing economic and social environments. Research has shown that learner-centered knowledge transfer and a constructive pedagogical approach have proven more effective in achieving all possible learning outcomes than cognitivist learning theory (teacher-centered education). Therefore, pedagogical monitoring and evaluation procedures are necessary and integral parts, of the educational process, with the goal not only being performance evaluation but also improving and supporting the learning process. Then again, a sad characteristic of the current system is that, on the one hand, it needs to support individual learning paths adequately and on the other hand, the validity of measuring tools suffers, due to the generation gap between teachers and children. Therefore, the development of pedagogical monitoring and evaluation processes, should be an integral part of the constant improvement of educational institutions. Consequently, the question arises: How can the pedagogical monitoring and evaluation system be developed, so that individual learning paths are supported and the validity of measurement tools is optimally ensured? In seeking the answer to this question, our research aims to develop a complex evaluation system, supported by artificial intelligence (WTCAi-When The Child Ask with AI), that suggests suitable actions for monitoring and evaluation, for subject teachers.