## 1 Introduction
\begin{table}
\begin{tabular}{p{142.3 pt} p{142.3 pt} p{142.3 pt} p{142.3 pt}} \hline \hline
**Time** & **Stage** & **Enabling Technology** & \begin{tabular}{c} **Education Chemical Engineering** \\ **Examples** \\ \end{tabular} \\ \hline \begin{tabular}{c} Early \\ 1980s–Mid-1990s \\ \end{tabular} & Expert Systems & Expert systems, rule-based inference & \begin{tabular}{c} CHEMEX for process synthesis and \\ design, \\ flowsheet simulators PACER, CHES, \\ FLOWTRAN \\ \end{tabular} \\ \(\sim\)1990 to \(\sim\)2008 & Neural Networks & \begin{tabular}{c} Feed-forward/recurrent \\ neural networks, hybrid \\ models \\ \end{tabular} & \begin{tabular}{c} CHEMEX for process synthesis and \\ design, \\ flowsheet simulators PACER, CHES, \\ FLOWTRAN \\ \end{tabular} \\ \(\sim\)2005 to present & \begin{tabular}{c} Deep Learning Data \\ Science \\ \end{tabular} & \begin{tabular}{c} Deep learning, GNNs, \\ transformers, knowledge \\ graphs \\ \end{tabular} & \begin{tabular}{c} Chemical reactor selection expert system \\ Revealing patterns in data \\ \end{tabular} \\ \(\sim\)2023–Future & Hybrid/Generative AI & \
\begin{tabular}{c} Foundation models, \\ generative AI (e.g., GPT, \\ GNN+LLM), causal AI, \\ neuro-symbolic systems \\ \end{tabular} &
\begin{tabular}{c} Unit operations course, industrial drier \\ fictional company \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Description of AI phases in chemical engineering education over the years. system is designed to address this by combining AI-driven personalization, generative tools, and immersive VR environments into a cohesive platform tailored for engineering education. Based on the increasing demand for effective digital tools in engineering education, this study investigates the potential of a unified, AI-driven learning environment that integrates AI-based adaptive learning, VR, and generative AI. While prior research has explored these technologies in isolation, few systems have examined their combined impact in a single framework. To address this gap, this study poses the following research question:  
To what extent does integrating AI, adaptive learning, and VR enhance student engagement, conceptual understanding, and perceived instructional effectiveness in chemical engineering education? This question guides the system's design rationale and informs the pilot evaluation, focusing on both cognitive and affective outcomes of student interaction with the platform. Furthermore, this study aims to explore the effectiveness of HECOF, a system that integrates artificial-intelligence-driven adaptivity, a retrieval-augmented virtual tutor, and immersive virtual reality modules, in the context of chemical engineering education. The purpose is to assess whether this integrated system can enhance student engagement, support personalized learning experiences, and contribute to a conceptual understanding in a complex, process-oriented domain. Given the exploratory nature of this work, the following additional research question guides this study:  
To what extent does the HECOF system support learner engagement, personalized progress, and perceived learning improvement in higher education chemical engineering courses? Based on prior research in adaptive learning and virtual tutoring systems, we propose the following expectations:  
**H1:**: _Students using the HECOF system will report high levels of engagement, satisfaction, and perceived learning improvement with the AI-based adaptive features._
**H2:**: _Students will demonstrate measurable learning gains across sessions, as estimated by the system's internal knowledge model._  
These hypotheses are evaluated through a combination of system-generated metrics and student self-reports in a pilot implementation. In this work, the Higher Education Classroom of the Future (HECOF) project is presented.