Primary educational institutions aim to prepare children for further education and successful integration into the workforce. To consistently achieve this, they must meet the demands and expectations imposed by the constantly changing economic and social environments. Research shows that applying a constructive pedagogical approach is the most successful path, best fitting the expectations above. An essential and integral part of the educational process is the pedagogical supervision and evaluation procedure, aimed to assess performance and improve and support the learning process. A sad feature of the current system is that it needs to support individual learning paths sufficiently. Additionally, communication issues arising from the generational gap between teachers and students jeopardize the validity of the assessment tools. Educational institutions' advancement and continuous development can only be realized if the pedagogical supervision and evaluation system progresses and evolves to meet new challenges. Our research aims to develop such a method. We are working on creating a complex evaluation method supported by artificial intelligence (WTCAi – When The Child Ask with AI), which suggests appropriate items for assessment and evaluation to subject teachers. To achieve our main objective, we have planned to collect additional social background data from our students. Through analyzing these data, we hope to uncover numerous correlations that significantly influence the obtained data and results, thereby steering the development of the supervision-evaluation system in the most appropriate direction.