## 5 Discussion
### Limitations and Future Research  
The HECOF system demonstrated promising results in enhancing students' learning outcomes by integrating AI-based adaptive learning; however, several limitations should be acknowledged in this initial innovative work.  
Firstly, the evaluation relied on a relatively small sample size of participants, which may affect the statistical significance of the results. Nevertheless, the students who participated in the evaluation process were carefully selected so that they represented a heterogeneous group in terms of gender (both males and females), academic performance (high and low grades), and prior familiarity with AI (none, moderate, and high familiarity). In the future, the implementation of HECOF will be expanded to a wider population of students with various characteristics, e.g., learning styles, disciplines, and institutions, to investigate the influence of these factors in the effectiveness of the adaptive learning system, as well as to test its scalability.  
Furthermore, while acknowledging that the sample size used was modest, it is worth noting that it was consistent with the scope and purpose of an exploratory pilot evaluation. The primary aim of this investigation was to assess the feasibility, usability, and pedagogical alignment of the system and its AI technologies in a real-word educational settings. The insights gained from this pilot will directly inform the design of another future evaluation with expanded cohorts and greater statistical power to validate and extend these preliminary results.  
In addition, another limitation of this study could be the fact that it did not include a longitudinal component to assess sustained learning or long-term retention. Therefore, the next aim of the work is to add longitudinal analyses to better understand user adaptation and the lasting impact of the system over time.  
Finally, in this initial study, there was no comparative control group using conventional learning methods, which limits the ability to determine whether the improvements are uniquely due to the HECOF system. However, the learner gains and self-reported outcomes suggest positive trends. Future research will include a comparative study with traditional teaching to explore the added value and effectiveness of AI-driven adaptive learning.  
The working group of the development of this system consisted of different disciplines. Co-designing the system with participation from both chemical engineering educators and information technology experts proved to be a significant strength of the proposed approach. It is no longer sufficient to simply know how to use machine learning tools, understanding the chemical processes that they are applied to is equally important. As a result, chemical engineering programs must incorporate deeper training in AI and statistical methods. Additionally, stronger collaboration between computer scientists and domain experts is essential. Without this, researchers may misuse tools, and data scientists may misinterpret results. Interdisciplinary research and close cooperation between both fields are crucial to avoid ineffective outcomes and disappointment [16].