## 4 Results  
Throughout the robot demonstrations and laboratory activities, students consistently displayed a high level of interest and engagement. Compared to a similar implementation conducted in a previous academic year--where AI and ML concepts were introduced through lectures and a Scratch-based lab activity without the use of robots--the difference in student behavior was substantial. In the earlier implementation, 36 students (21 boys and 15 girls) completed a post-lesson questionnaire. When asked whether the lectures were interesting (on a 5-point Likert scale), 2 students answered \"a little\", 6 \"moderately\", 12 \"quite\", and 16 \"very much\", with a resulting mean score of 4.17 (SD = 0.91). Similarly, regarding their overall opinion of the topic, 1 student found it \"a little interesting\", 3 \"moderately\", 12 \"interesting\", and 20 \"very interesting\", yielding a mean score of 4.42 (SD = 0.77). Teacher observations during that session recorded noticeable disengagement: one in five students lost focus during the lectures, and in half of the lab groups (6 out of 12), at least one member disengaged from the activity, necessitating teacher intervention. In contrast, during the current lesson involving three physical robots, no student was observed losing interest at any point during either the lecture or the lab session. Some students seated at the back even expressed frustration over limited visibility, eager not to miss any aspect of the presentation. All students remained focused, including those typically disengaged. Enthusiasm was particularly evident during the hands-on portion, where students witnessed their code executed by the robots--eliciting applause in some cases. When teachers visited each group to gather impressions, student responses were unanimously positive. Notably, 12 out of 20 students requested to continue working with the robot during the break, indicating an exceptional level of motivation not observed in the previous implementation. A post-lesson questionnaire administered to the 20 students involved in the current intervention further confirmed these observations. The mean score for interest in the lecture was 4.85 (SD = 0.36), and for interest in the topic, 4.20 (SD = 0.77), both notably high. Regarding content comprehension, the mean scores were also encouraging: 4.10 (SD = 0.85) for understanding the distinction between preprogrammed motions and machine learning, 3.95 (SD = 0.75) for comprehension of the Teachable Machine platform, and 4.00 (SD = 0.80) for understanding the basics of reinforcement learning. These findings indicate strong engagement paired with meaningful conceptual understanding. This comparison effectively serves as an informal control study, as both implementations addressed similar learning objectives with comparable student demographics, differing only in the inclusion of educational robots. The observed increase in engagement and motivation, supported by both qualitative observation and quantitative student feedback, reinforces the added pedagogical value of the robotics-based methodology. The robot presentations and lab activities were completed within the allocated time; however, one teaching hour proved to be barely sufficient, leaving limited opportunity for student questions and interaction with the instructor. Nonetheless, student inquiries were recorded for future lessons. Many of these questions were ethical in nature, including whether humans could be replaced by intelligent machines, whether AI development should continue, and how much autonomy should be granted to intelligent systems. Two students asked, \"If we eventually succeed in making robots capable of replacing us in all jobs, what will humans do?\" Such questions underscore the importance of including ethical considerations in AI education, as emphasized by several researchers [40, 41, 42, 43, 44]. During the lab session, technical questions--such as the clarification of code elements or locating functions--were addressed promptly.