Fuzzy Inference Systems (FIS) has often been used to evaluate performance using few input variables as a result of fear for rules explosion. This problem is solved using Hierarchical Fuzzy Inference System (HFIS); a divide-andconquer approach that drastically reduce the number of rules at the same time preserved the fuzzy logic reasoning. As a result, this study explore the potential of this tool in details by applying it to evaluate students' exam records. The proposed model is compared to classical one and results show that HFIS is more promising from the perspective of simplicity and precision. However, for optimum results, the study suggests training FIS with neural networks and emerging optimization algorithms.

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