Schools and other educational instances (such as: elementary, primary and secondary schools, schools for applied sciences and universities) are making use of new innovations and technologies to help teachers and students. After Smart Boards, computers, and tablets, a new field of research arises: can robots in any way help in teaching? During this research an answer to the question "What is the effect of elaborated feedback versus minimal feedback given by a humanoid robot (NAO), on a primary school student solving basic math problems?". Also, two hypothesis are tested: "Feedback provided by NAO will improve the student math test results." and "Feedback improved the affection of the student towards NAO.". We find that lacing and split and add are the most used calculation strategies, and NAO also aims to identify commonly made mistakes such as switching units in bigger number or confusion about symbols or strategies. An important element of this research is feedback, which concerns information about how we perform in efforts to reach goals. It requires information about the goal, the followed track and the next steps. Feedback seems to be most effective when it is self-regulated and intended for process. Children need tangible and timely feedback, not too early (to prevent overwhelming the student) and not too late. Giving feedback on the result as well as on the track, challenging students and defining a clear road to success are crucial in helping the student succeed. In this research, NAO will provide this feedback and thereby should help the student succeed in solving math problems. Two scenarios are designed: an introductory class, to let students touch, feel and experiment with NAO, and a math tutoring session, which will be used during the experiment. We conduct an experiment, testing a feedback group versus a control group, where the feedback group is able to get feedback twice (elaborated feedback), and the control group only hears whether the answer given was wrong or correct. The elaborated feedback consists of detecting a mistake made by the student, and adapt the feedback according to the given answer. After a second try and incorrect answer, the process to the correct answer is explained to the student. Students had to fill in a PANAS form in order to measure affection towards NAO during the sessions. No significant results were found in both the math test and the PANAS scores. We did however find that students learned to work with NAO very quickly, interaction was robust, although speech recognition needs some improvement. Interesting to see was that we found contradictions when we compared the qualitative analysis with the quantitative analysis. Statistical analysis showed that there was no significant growth in the skills of a student, but all students mentioned that they felt that they did learn from the session with NAO. Future work could involve conducting the experiment for a longer period of time, to see a better lasting effect of the feedback given. Other work could be done in distinguishing groups according to their level of math, to see if students with a low grade in math have a higher profit from NAO's influence. Also, combining mistakes could lead to more mistakes that can be detected, and thus feedback could be more efficient.