## 4 Discussion  
The present study investigated the feasibility of utilizing eye tracking for real-time gaze estimation as a supportive tool for educators. Through pragmatic observation of the advantages and challenges encountered in preparing the class and handling instructional equipment, we presented teachers' perspectives in an effort to obtain valuable insights into the potential application of eye tracking in educational settings. Previous research has indicated the potential use of eye-tracking technology in educational settings [6; 36; 39; 41]. However, this study takes a step further by suggesting that teachers could incorporate this technology with their students and share their perspectives on the practicality of using data from students' eye movements during classes. Teacher comments indicate specific instances in which eye tracking can be beneficial, such as immediately identifying when students are attentive or experiencing confusion during class. Eye tracking is becoming a widespread method and is expected to become even more prevalent in the future, given the advancements in technology [42]. Deep learning approaches are enabling the development of systems that can inform researchers about where and on what a user's gaze is focused at any given time [43]. This has been particularly useful in webcam-based scientific experiments and user research [44; 45]. By filtering out noise and addressing tracking losses, AI-enabled tools have the potential to enhance the quality of eye-tracking data, highlighting key gaze patterns relevant to analysis [45]. Previous studies have reported gaze accuracy between 3\({}^{\circ}\) and 4\({}^{\circ}\) using the most effective WebGazer model [46]. Additionally, similar methods have been implemented on mobile phones [47] and tablets [48], driven by advancements in selfie cameras and application-specific integrated circuits on these devices. Such advances in technology expand the possibility of broader applications in schools. One of the key benefits of using this technology in class is the immediate feedback it provides. This feedback can support the teacher, mainly when students cannot clearly communicate where or when they require further information to excel in the given task. Therefore, teachers could find value in using eye-tracking data to improve instruction during classes. In the context of instruction, valuable information can be imparted to educators to aid in explaining complex subject matter during instructional sessions. Among the pedagogical techniques frequently deliberated in education, scaffolding has emerged as a prominent strategy, given its propensity to foster a constructive mode of engagement [35; 40]. The scaffolding method is deeply associated with interactions, and recent empirical evidence has shown that scaffolding facilitates generative learning [49; 50], allowing instructors to provide support based on learners' immediate needs. Teacher-student interactive scaffolding refers to educators providing students with thought-provoking problems and offering targeted suggestions to enhance their ability to think critically, facilitate concept construction understanding, and enhance the learning experience [40; 51]. The scaffolding strategy encompasses a range of supportive behaviors to guide students to achieve their learning goals. However, the challenges become pronounced for teachers due to the limited teacher-student ratio [52], making it difficult to provide each student with the necessary individualized scaffolding support. Recent research has demonstrated the effectiveness of incorporating eye-tracking technology into educational scaffolding [53; 54]. This innovative approach could be valuable in the classroom, where educators must tailor their teaching to accommodate the diverse knowledge levels of individual students, providing them with customized instructional support. A recent study conducted by [PERSON] and [PERSON] (2019) demonstrated that using eye-tracking scaffolding positively impacted fostering learners' self-efficacy.