Transforming Educational Excellence through Artificial Intelligence: An Exploration of AI Applications Using a Mixed-Methods Approach

Abstract :

 Given the growing importance of adopting such innovative technologies in education, investment in AI-enabled educational initiatives has become a strategic priority for educational institutions. To ensure the effectiveness of these investments, decision-makers require a comprehensive understanding of the opportunities and applications of AI in order to align AI-driven initiatives with broader educational objectives. Despite the increasing volume of research in this field, the development of an integrated framework that systematically identifies, categorizes, and prioritizes AI applications in education has received limited attention.

Accordingly, the primary objective of this study is to address this theoretical gap through a mixed-methods approach. In the first phase, AI applications in education were identified and categorized through a comprehensive literature review and focus group discussions involving domain experts. In the second phase, the identified applications were prioritized using the Best-Worst Method (BWM). The findings reveal that the proposed framework comprises three primary layers: AI-based tools, application domains, and stakeholders. The identified application categories, ranked by priority, include personalized teaching and learning, communication and interaction, personalized educational planning, intelligent assessment and feedback, and educational management and process automation.

By integrating international evidence from the literature with the contextual knowledge of experts obtained through focus group discussions, this study develops a holistic framework for understanding AI applications in education. Furthermore, it prioritizes these applications, an aspect that has been largely overlooked in previous studies. .

References:

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