## 3 Methods
### Study Design and Data Collection  
This study was conducted during the academic year in a Greek lower secondary school with the participation of 20 ninth-grade students (12 girls and 8 boys, aged 14-15). The intervention consisted of two 45-min lessons focusing on artificial intelligence (AI) and machine learning (ML), incorporating the use of two physical robots and web-based machine learning platforms. The two sessions were separated by one week and delivered by a guest instructor supported by the school's computer science teacher.  
Figure 10: SelfLearn logic diagram.  
The first session was held in the classroom and included demonstrations, videos, and discussions introducing the concepts of AI, machine learning, and robotics. The second session took place in the school's computer lab, where students were divided into pairs per computer station. Each station was equipped with an Arduino Mini Manipulator robot, a USB cable, and access to JavaScript scripts and the Arduino IDE ([[https://docs.arduino.cc/software/ide/](https://docs.arduino.cc/software/ide/)]([https://docs.arduino.cc/software/ide/](https://docs.arduino.cc/software/ide/)) (accessed on 23 November 2024)). Students trained their own machine learning models using the Teachable Machine platform and integrated them with the robot.  
Data were collected through both quantitative and qualitative means. At the end of the second lesson, students completed a five-item questionnaire using a 5-point Likert scale (1 = not at all, 5 = very much), designed to assess their interest in the lectures and topic as well as their perceived understanding of core concepts such as the difference between preprogrammed behavior and machine learning, the use of the Teachable Machine platform, and reinforcement learning.  
In addition to the questionnaire, qualitative data were gathered through systematic classroom observation. During both sessions, the classroom teacher and an additional computer science instructor documented behavioral indicators of engagement and motivation. These included attentiveness, participation, willingness to collaborate, and the frequency of unsolicited questions. Particular emphasis was placed on contrasting these behaviors with those recorded during a previous implementation of the same content delivered without the use of robots. In that earlier lesson, 36 students (21 boys and 15 girls) were taught AI/ML concepts using software-only tools such as Scratch [29] and ML4 Kids [28]. The same questionnaire was administered and similar observational data were collected for comparative purposes.  
Analysis of the data involved descriptive statistics for the questionnaire responses (means and standard deviations), while open-ended student responses and teacher notes were examined thematically to identify trends in engagement, understanding, and attitudes toward the use of robotics in AI education.  
The implementation did not require students to have prior programming or advanced mathematical knowledge. All software tools and robot control scripts were open-source and accompanied by detailed documentation. Ethical approval for the study was obtained through the school administration, and all activities were carried out in alignment with educational ethical standards. No personal data were collected.