Manuscript received March 8, 2024; revised June 7, 2024; accepted August 26, 2024; published October 22, 2024
Abstract—The integration of technology in education, particularly through the combination of adaptive learning and Artificial Intelligence (AI), has significantly transformed teaching and learning methodologies. This paper addresses the challenges associated with manually planning study paths and modules at universities, including the ordering of relationships between modules across various disciplines. It considers the prerequisites and competencies required for each module and highlights several limitations, such as the extensive time and resources required, subjectivity in decision-making, and the complexities introduced by the LMD system. To address these challenges, a decision support system utilizing genetic algorithms is proposed to increase the efficiency and objectivity of academic career planning. The system analyzes a dataset that includes information on study units, modules, competencies, and learning objectives. Genetic algorithms, which incorporate mutation, crossover, and selection processes, are used to generate coherent and suitable study paths. The system was tested using data from the Faculty of Science and Technology at Tangier, demonstrating its capacity to adapt existing academic pathways and propose enhanced alternatives with improved consistency. The results indicate the potential of the system to enhance the quality of academic career planning within the modular educational framework.
Keywords—artificial intelligence, genetic algorithms, academic career planning, education, decision support systems
Cite: Abderrahim El Yessefi, Loubna Cherrat, Mohammed Rida Ech-Charrat, and Mostafa Ezziyyani, "Implementing Genetic Algorithm-Based Expert Systems to Enhance Academic Career Planning," International Journal of Information and Education Technology vol. 14, no. 10, pp. 1443-1452, 2024.