Sarcouncil Journal of Engineering and Computer Sciences

Abstract: The ever-increasing global spread of mobile learning has created a need for transparent, effective credit transfer between universities. Traditional ongoing 'course' awarding involves a less objective version of the facts of the work product, can be labor-intensive, can lead to inconsistencies in assessment criteria, and can delay graduation and result in a lack of ‘mobility options' for the student. In this study, an artificial intelligence (AI)-based system would be used to automate the evaluation of course equivalencies using natural language processing, including semantic similarity analysis, knowledge representation, and explainable artificial intelligence (XAI) methods. In this context, the course description, learning outcomes, credit structure, and assessment (credit giving and credit taking) will be evaluated, and sound arguments will be provided for every assessment equivalence, and vice versa. In addition, a thorough examination of mapping accuracy, processing speed, scalability, and consistency will be conducted across all institutions. AI will offer an opportunity to streamline the course equivalency evaluation rule, benefiting students, while the old, rule-based system of evaluating courses will be more accurate and transparent in the decision-making process. In addition, the proposed framework will enable evaluation of multilingual curricula and provide educational institutions with the key building blocks for a scalable, intelligent infrastructure that will continue to support international student mobility and foster academic cooperation and exchange worldwide in the digital age of credential recognition.