## 3 Research Design
### Methodology  
The approach for HECOF evaluation employs a mixed-methods approach, combining quantitative metrics from system performance and learning analytics with qualitative feedback from users. This study employed a quasi-experimental pilot design to evaluate the HECOF system's impact on student learning and engagement in a higher education chemical engineering course. The intervention was conducted over an 8-week period, during which students interacted with the HECOF platform, including adaptive learning content, VR modules, and an AI-powered virtual tutor. The participants were undergraduate students enrolled in a senior-level chemical engineering course at NTUA, recruited through class invitation. Participation in this study was voluntary, and all participants provided informed consent. The inclusion criteria required that the students had basic knowledge of online learning environments and chemical process concepts. Students who did not complete both the pre- and post-surveys were excluded from the final analysis. A total number of N=10 students filled in both the pre- and post-surveys. Data were collected through multiple instruments to assess both cognitive and affective learning outcomes, mainly through usage logs and pre- and post-surveys. The HECOF system automatically recorded interaction data using the XAPI standard, including evidence of each interactivity (i.e., an XAPI for each question, each instructional) and VR module interactions. The students completed a structured questionnaire containing Likert-scale pre- and post-use surveys, given the presented evaluation framework. The surveys also included open-ended questions to gather qualitative feedback on the learning experience.  
The methodology integrates current AI-adaptive learning and VR training research to assess the system's impact [66; 67; 68]. The evaluation dimensions include the pedagogical impact--learning improvements and effectiveness, satisfaction, and perceived usefulness--student experience, and usability evaluation--learning experience in learning loops and overall system usability.  
The pedagogical impact of the HECOF AI experience was measured through key metrics that track learning improvements and overall effectiveness. One of the primary indicators of learning effectiveness was the learning gain per session per concept, which measures how much students improve in their understanding of specific concepts after each learning session. This was closely monitored through learning analytics, which provide insights into student progress and mastery of concepts, allowing us to track incremental  
Figure 7: Example of student interface—topic view—using Adaptemy course player, highlighting recommendations.  
improvements over time. Additionally, the students' perceived improvement was evaluated through surveys.  
The satisfaction and perceived usefulness of the HECOF system are essential for ensuring both students and teachers have a positive experience with the system. This dimension evaluates the perceived usefulness, where students and teachers evaluate how beneficial the AI-driven personalization and exercises were in achieving their learning and teaching objectives. Lastly, the evaluation focused on overall user satisfaction, evaluating how satisfied the students were with the platform's ability to enhance their learning and experiences without overwhelming them with complexity or technical difficulties. The usability evaluation of the HECOF system focused on how effectively it supports seamless learning experiences through adaptive learning loops, the integration of VR, and overall system usability. In the context of learning loops, the goal is for students to progress through these loops without disruptions.