## 4 Results  
This section presents the results based on the post-survey (i.e., in terms of perceived improvement in subject understanding, perceived effectiveness of AI-based adaptive learning in achieving learning goals, engagement level, and overall perceived experience), as well as in terms of learning analytics through objective metrics computed through logs of the XAPI statements. The participants assessed how much their understanding of the subject improved after using the HECOF system. Half of the respondents (50%) reported a significant improvement (4--very much), while 40% indicated a moderate improvement (3--moderately). A smaller portion (10%) felt their understanding was extremely improved (5). Notably, no participants rated their improvement as slight (2) or nonexistent (1) (see Figure 8). These findings indicate that the HECOF system contributed positively to students' learning, though the extent of improvement varied among individuals. The participants evaluated how well the AI-based adaptive learning features helped them to achieve their learning goals. The majority (70%) reported that the features were considerably helpful (4), while 10% found them fully effective (5), a smaller portion (20%) felt that they were only somewhat helpful (3), and no participants rated them as slightly helpful (2) or not helpful at all (1) (see Figure 9). These results indicate that most students found AI-based adaptivity beneficial for their learning or completely effective. Figure 8: Post-survey—Percceived improvement in subject understanding after using HECOF—NTUA. The participants rated how the different HECOF learning experiences contributed to their understanding and retention of material (see Figure 10). The VR learning experience received the highest ratings, with a majority marking it as extremely helpful (5). The AI tutor (chatbot) was also well received, with many participants rating it very helpful. Guided mastery, reinforcement (revision) and think-pair-share activities were generally rated moderately to very helpful, with only a few students finding them slightly helpful. Good learning gain can also be seen through learning analytics. Students acquire learning through learning experiences structured as \"learning loops.\" During each instance of a \"learning loop,\" students interact with a structured sequence of instructional items (activities) and assessment items (questions) aligned as per the learning design above. Each loop targets one or more active concepts, defined as the knowledge components explicitly engaged and assessed during that session. The reinforcement loop was employed periodically to consolidate prior knowledge before introducing new material. Learner ability was estimated by the system's AI Engine, which continuously updates an estimation per concept based on response correctness and system-inferred previous estimation using Item Response Theory and Bayesian Networks as underlying approaches. Learning gain per session was computed as the difference in estimated ability for each active concept from the start to the end of the session. Figure 11 presents a boxplot of learning gains across sessions and concepts. The median learning gain was 9 points, indicating a moderate improvement in concept mastery per session. While most sessions showed positive learning progress, some yielded negative gains, which may reflect knowledge decay, distractions, or incomplete engagement with activities. Sessions with low or negative gains were more likely to involve either review material perceived as easy, leading to disengagement, or complex concepts introduced without prior scaffolding. Overall, the gain estimates provide a quantitative basis for evaluating adaptivity and session effectiveness within the HECOF system. Figure 10: Post-survey—Engagement levels across HECOF learning experiences. Figure 9: Post-survey—Effectiveness of AI-based adaptive learning in achieving learning goals.