## 1 Introduction
One example involves combining expert systems and neural networks in a process called hybridization, used both in practice and to teach students about the complexities of process fault detection and the advantages of combining different AI approaches [12]. Another example is a feed-forward neural network for knowledge acquisition and storage, and subsequent use was developed for a chemical reactor selection expert system [13]. For the first two periods, the main problems and limitations were [7] as follows:  
Insufficient data availability, restricted data access, limited computational resources, and underdeveloped programming environments and paradigms. The presence of competing, successful, emerging technologies in chemical engineering, especially mechanistic modeling, optimization, and model predictive control. -3 rd Stage: Deep Learning Data Science (\(\sim\)2005 to present)  
This phase is defined by the rise of deep learning and the integration of data science into chemical engineering. Improved computing power, data availability, and algorithms have enabled accurate, data-driven models for process design and optimization. AI now captures both numerical and symbolic knowledge, bridging data- and knowledge-based methods [8]. Machine learning techniques have become increasingly popular in chemistry and chemical engineering for uncovering patterns in data that often elude human researchers [14; 15]. However, one of the primary limitations of these approaches lies in their \"black-box\" nature--while they can produce outputs from given inputs, the underlying decision-making processes remain opaque [16]. A representative example of hands-on activities introduced in an undergraduate chemical engineering course to teach machine learning was developed using Google Colaboratory, a free cloud platform for AI and ML. These activities can also be executed in a local Python environment and use public datasets from sources such as Kaggle and the UCI repository [17]. And what comes next? -4 th Stage: Hybrid and Generative AI Era (\(\sim\)2023-Future)  
In this emerging phase, AI is transitioning from a tool for prediction and optimization to a creative collaborator in chemical engineering. It now plays an active role in generating novel chemical pathways, proposing innovative process designs, and autonomously planning experiments. Foundation models, such as GPT and graph-based models, are fine-tuned with domain-specific data to serve as intelligent copilots for engineers and researchers. As generative AI becomes increasingly embedded in professional chemical engineering roles, it is essential that students learn to use these tools ethically, responsibly, and effectively. An example is the research that adopt the IDEE framework (Identify desired outcomes, Determine level of automation, Ensure ethics, Evaluate effectiveness), to develop a chemical engineering lab session which is augmented by large language models (LLMs) [18]. One example of this shift is a problem-based learning (PBL) initiative using ChatGPT 4.0 in a unit operations course, where students designed an industrial dryer for socially and economically relevant applications in Brazil. The project supported key undergraduate outcomes such as solving open-ended problems, gathering diverse data, and applying mathematical and simulation tools [19]. Another early example comes from the Technical University of Denmark (DTU), where a Good Manufacturing Practice (GMP) course is exploring \"ChatGMP,\" a digital audit tool. This tool simulates a fictional company, allowing students to conduct audit exercises by questioning the system about documentation and practices, potentially replacing instructors in the simulation [20]. Table 1 below summarizes these four stages along with representative examples of AI applications in chemical engineering. Furthermore, virtual reality (VR) was first introduced in the 1960s in chemical engineering education in the Department of Chemical Engineering at the University of Michigan in Ann Arbor [21].