Artificial Intelligence-Integrated Educational Technology: Educators’ Cognitive Perceptions and Engineering Implications for Smart Education
AUTHORS
Ahmed Al Mansoori,Department of Computer Science, Khalifa University, Abu Dhabi, United Arab Emirates
Fatima Al Nuaimi,School of Digital Public Health and Department of Machine Learning, Mohamed bin Zayed University of Artificial Intelligence, Abu Dhabi, United Arab Emirates
ABSTRACT
The rapid advancement of Artificial Intelligence (AI) and Educational Technology (EdTech) is transforming educational systems worldwide, creating new opportunities and challenges for digitally driven learning environments. The increasing adoption of intelligent technologies in teaching and learning, including physical education and sports instruction, has accelerated the development of personalized, data-driven, and adaptive educational practices. However, successful implementation depends not only on technological readiness but also on educators' cognitive perceptions, acceptance, and capacity to utilize AI-enabled educational systems effectively. This study investigates the cognitive factors influencing educators' perceptions of AI-integrated EdTech and explores strategies for its effective implementation in educational settings. Drawing upon Bloom’s cognitive taxonomy, the study examines six dimensions of cognition: knowledge, comprehension, application, analysis, synthesis, and evaluation. A qualitative research design employing in-depth interviews was used to explore educators’ experiences with AI-supported instructional technologies. Furthermore, the ADDIE instructional design framework was adopted to develop a structured approach for integrating AI into educational practice. The findings indicate that educators generally perceive AI positively, particularly regarding its capacity to provide personalized feedback, facilitate data-driven decision-making, enhance learner engagement, and support individualized instruction. Nevertheless, concerns remain regarding data reliability, technological readiness, professional training, ethical considerations, and disparities in access to digital infrastructure. The study highlights several engineering challenges, including the development of reliable AI systems, interoperability among educational technologies, secure data management, and the design of user-centered AI services. It also identifies opportunities for advancing smart education through AI-enabled analytics, wearable technologies, immersive learning environments, and adaptive instructional systems. The findings contribute to the growing body of knowledge on AI-driven education and provide practical recommendations for policymakers, educators, and system developers seeking to foster sustainable educational innovation in rapidly digitizing educational environments.
KEYWORDS
Artificial intelligence, Educational technology, Smart education, Physical education; Cognitive factors, ADDIE model
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