The creation and verification of a detection model for mild cognitive impairment by employing eye-tracking and gait metrics.

Artificial Intelligence (AI) has rapidly emerged as a transformative wave in education, holding promise for enhancing teaching and learning at every educational stage. In recent years, the rapid growth of AI-based educational technologies (AI EdTech), ranging from adaptive learning systems to generative AI tools like ChatGPT, has gained significant attention. Studies suggest that AI can personalize instruction, offer real-time feedback, automate routine tasks such as automated essay scoring, and assist teachers in allocating resources more effectively. For instance, recent surveys reveal that teachers acknowledge a range of AI benefits, including improved curriculum planning, better insights into student learning, the delivery of instant feedback through automated tools, and stronger assessment methods. Furthermore, systematic reviews highlight that AI-driven support can improve students’ learning conditions by enabling more efficient planning and data-informed interventions. Despite these potential benefits, the actual classroom adoption of AI remains limited. The empirical understanding of AI’s impact on teaching and learning is still nascent with a lack of widespread application of AI EdTech. This gap between the potential of AI and its practical implementation can be partly attributed to teachers’ readiness and attitudes (Hazzan-Bishara et al.). Teachers are pivotal to educational innovation, and their perspectives on AI will significantly shape how these tools are integrated into pedagogical approaches. The introduction of any new technology can create a sense of vulnerability, that teachers may worry it might not work as expected or could introduce new challenges in their classrooms. The willingness to embrace this vulnerability depends fundamentally on trust in technology. In essence, a teacher’s decision to adopt AI involves weighing expected benefits against perceived risks. Indeed, individuals are less likely to experiment with new technology when they perceive substantial risk, underscoring how anxiety and trust are key precursors to technology use. Teachers face multiple distinct challenges when considering AI adoption, spanning technical, pedagogical, and psychological dimensions. A frequently cited issue is the lack of AI literacy and training, as many teachers report unfamiliarity with how AI algorithms function or how to effectively integrate AI tools into their lesson plans. This gap can diminish confidence and willingness to experiment. Furthermore, inadequate technological infrastructure and support present significant barriers. In many educational settings, schools may lack the necessary reliable AI software, robust IT support, or clear institutional guidance to implement AI effectively. Even when tools are available, teachers often report insufficient technical assistance or support structures, leading to feelings of being technologically intimidated, as noted in qualitative studies.