## 5 Discussion  
The findings from this exploratory study suggest that the HECOF system, integrating AI, adaptive learning, and VR, can provide a meaningful and engaging learning experience in chemical engineering education. Students demonstrated measurable learning gains per session, particularly among those who started with lower initial ability levels, supporting the system's ability to personalize instruction and scaffold learning appropriately. These results are consistent with the system's design principles, which aim to align instructional content with each learner's current mastery level and dynamically adjust feedback and pacing through AI-driven adaptation. In addition to the system-generated performance data, the participants' subjective evaluations provided valuable insight into the perceived effectiveness, usability, and impact of the HECOF system. Most students (70%) rated the AI-based adaptive learning features as considerably helpful in achieving their learning goals, with 10% finding them fully effective. Although 20% found adaptivity only somewhat helpful, no participants reported it as unhelpful or ineffective, suggesting generally positive perceptions of the adaptive features (see Figure 9). Similarly, 90% of students rated their overall experience with the AI-based adaptivity as good or excellent (Figure 13), and 80% reported being satisfied with their learning experience (Figure 14). In terms of learning outcomes, 50% of participants reported a significant improvement in subject understanding, while 40% noted moderate gains, and 10% reported extreme improvement (Figure 8). Furthermore, 90% of respondents found easy or very easy to use the AI-based adaptive learning technology and its recommendations (Figure 18), indicating that the adaptive interface was largely accessible and well-integrated. Taking together, these results reflect a consistently positive user experience, with most students finding HECOF to be both supportive and effective, though with some variation in perceived learning impact. Importantly, the observed learning gains and engagement patterns reinforce the potential benefits of AI-based personalization in complex and hierarchical domains like chemical engineering. By leveraging learner modeling, learning loops, and a retrieval-augmented AI tutor, HECOF was able to respond to student performance in real time, providing differentiated support without sacrificing content depth. While these findings are promising, they should be interpreted within the scope of a pilot study, where the absence of a control group and the modest sample size limit the generalizability of the results. Nonetheless, the patterns observed provide a strong foundation for future research and suggest that HECOF may offer a scalable and effective model for delivering an adaptive, learner-centered classroom experience in the future of higher education. The use of AI-driven educational technologies like HECOF raises important ethical concerns, including transparency, data privacy, fairness, and learner autonomy. A recent systematic review of AI in education emphasizes that systems must be designed for explainability, allowing students to understand how and why the system generates recommendations or feedback. In HECOF, transparency is supported through visible exposure of the learner model (i.e., students can see the estimation updated in real-time through updates on the mastery meter and through the learner dashboard), content model (i.e., the content item difficulty is exposed to the students together with the content type), and curriculum model (both the hierarchy and the prerequisite networks are presented to the students). Furthermore, students are exposed to progress indicators (i.e., locally, in each learning experience and overall, across the curriculum) and to the justification of the transitions in the learning experiences within the learning loop. Additionally, learner autonomy must be preserved. While AI can guide, it should not override student agency.