Beheiry, SalwaObaid, Basel2019-12-152019-12-152019-0735.232-2019.40http://hdl.handle.net/11073/16548A Master of Science thesis in Engineering Systems Management by Basel Obaid entitled, “The Use of Artificial Intelligence and Big Data in the Continuous Improvement Process of Engineering Curricula”, submitted in July 2019. Thesis advisor is Dr. Salwa Beheiry. Soft and hard copy available.Higher education institutions generate huge caches of data that can be pivotal in creating value for the next generation. The excellence of these institutions and the engineering programs they provide, their continuous improvement and, above all, the sustainability of engineering education can be ensured if big data, current and dynamic, heterogeneous and large in volume, is collected, analysed and evaluated accurately. Engineering Programs can satisfy the Accreditation Board for Engineering and Technology (ABET) criteria by leveraging the latest disruptive technologies, such as Artificial Intelligence and Big Data Mining, to achieve cost efficiency and to develop better processes for the continuous improvement of the accredited programs. Data analysis helps programs showcase their efforts and help ABET assess the institutions’ conformity to the set standards and provide feedback as well. Above all, the integration of AI in the ABET framework will help in reducing the human involvement and assess the student outcomes in relation to the course learning outcomes. Better decision-making, decision control, trend forecasting, and greater participation of the educational program constituents are some of the other advantages. The primary aim of this research was to develop a framework to integrate AI and Big Data techniques in the continuous improvement process. Subsequently, a metric entitled the Artificial Intelligence Engineering Curricula Index (AIECI) was developed to measure the adoption level of AI and Big Data in the ABET continuous improvement process. This thesis used the existing literature body to amalgamate different AI applications that can be solidly linked to the ABET continuous improvement process. Furthermore, experts from the educational sector were solicited to validate the importance of each AI tool and its link to the ABET continuous improvement process using the Relative Importance Index (RII). Finally, rank sum, reciprocal rank and rank exponent were used to specify weight for each tool based on the results obtained from RII. The results show that learning analytics and gap analysis can be referred to as the most important application for AI with RII of 0.92. Lastly, Performance Prediction was ranked last, with an RII of 0.640.en-USArtificial IntelligenceBig DataEngineering CurriculaABETContinuous Improvement ProcessAccreditation Board for Engineering and Technology (ABET)Thesis