| Summary: | Imparting quality higher education is one of the main tasks of the higher education institutes (HEIs). With advancements in teaching, learning, and assessment, new methods have evolved. In engineering education, the Washington Accord is considered a benchmark. Pakistan, through the Pakistan Engineering Council (PEC), became a full-time member in 2017. Outcome-based education (OBE) is the pivot around which this system revolves. This is a student-centric system. Student evaluation in OBE is a complicated task that involves multiple factors and evaluation mechanisms. In this research work, we have used the artificial intelligence (AI) technique of fuzzy logic for monitoring students’ academic performance. From the literature, three factors, including direct assessment, indirect assessment, and stress, have been identified. The third factor stress has been added as an additional factor to gain more insight into student monitoring. Fuzzy inferencing systems using both Mamdani and Sugeno inferencing methods have been designed. The output of the system shows the comfort zone, which is satisfied evaluation; the average zone, which shows medium or acceptable evaluation; and the highly stressed zone, reflecting areas of concern where work is required to be performed to improve students’ evaluation. A prototype mobile application for the system has also been developed. Results have shown that the Mamdani system performed better than the Sugeno system. The results are promising and indicate that more work is required to be executed to develop fully automated intelligent systems for students’ performance monitoring and to help in achieving the United Nation’s sustainable development goal (SDG) No. 4, which is quality education.
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