Designing a Draft for a Metaheuristic Curriculum Evaluation Model (MCEM) Based on the Examination of Various Metaheuristic Artificial Intelligence Optimization Applications - Volkan Duran; Gülay Ekici (Uluslararası Türk Eğitim Bilimleri Dergisi)
This paper explores the integration of metaheuristic artificial intelligence (AI) optimization algorithms into the process of curriculum evaluation, proposing a novel approach that could enhance educational outcomes. A meta-synthesis of the existing literature on the application of AI optimization techniques—such as tabu search, simulated annealing, genetic algorithms, and ant colony optimization—in educational contexts was conducted. This study revealed a scarcity of direct applications of these algorithms in curriculum evaluation, thus identifying a gap in research and an opportunity for exploration. We proposed detailed models for adapting various metaheuristic optimization algorithms to assess curriculum components, including objectives, content, teaching methodologies, and assessment strategies. Our paper synthesizes insights from the literature review and suggests avenues for experimental studies to assess the effectiveness of AI optimization algorithms across diverse educational levels and curricula. Furthermore, we introduce a draft of the Metaheuristic Curriculum Evaluation Model (MCEM), synthesized from the reviewed optimization models and curriculum evaluation processes. This exploration into the integration of metaheuristic AI optimization algorithms within curriculum evaluation highlights a promising frontier in educational research. By detailing potential applications, addressing methodological rigor, and considering context-specific nuances, this paper lays the groundwork for future studies that could evaluate how curricula are developed, evaluated, and optimized from a different perspective.