## 1 Introduction  
A conventional chemical engineering curriculum is built on fundamental concepts such as mass and energy balances, transport phenomena, thermodynamics, reaction engineering, separations, and process control. Today, these thematic areas have been oriented toward subjects that support a more sustainable society, focusing on clean energy, climate change, well-being, and digital intelligence. Furthermore, shifting from traditional, static teaching evaluation methods to a dynamic, iterative approach in curriculum development is essential [1]. Furthermore, curriculum reconstructions are now designed to prepare students to navigate the challenges and opportunities presented by the new digital era revolution. The transition from traditional educational practices and tools into a digital format has become an unavoidable challenge for teaching methods within the field of chemical engineering. However, there are open challenges regarding how this transition will stay on track, what the legal framework will be, and who is going to have control of it. The digital age of education is here, bringing artificial intelligence (AI), collective intelligence, machine learning (ML), deep learning, and the concept of the extended mind. However, it is crucial to ensure that these new tools serve as a means of reinforcement rather than a substitute for the human mind. In the future, we must reconsider the definitions and use of terms like'mind' and 'intelligence' and explore whether intelligence can truly be replicated through technology [2]. Most chemical engineering processes aim to \"simulate\" physical phenomena, develop mathematical models for better understanding, test different scenarios, make predictions, and serve educational purposes. The use of digital tools for such simulations has been practiced in chemical engineering education for decades. Characteristic examples include older programming languages like Fortran, as well as software platforms such as MATLAB and Python, representing the latest iteration of this trend [3]. As demonstrated by numerous studies and reviews available in the literature, the current trend is the application of AI to model the behavior of chemical systems, process scheduling, control, and more [4]. The possibilities that AI offers to education are enormous, especially for tutoring, assessment, and the personalization of education, and most of these are yet to be discovered [5]. Furthermore, many authors support the creation of a more specialized AI course within the chemical engineering curriculum, with higher relevance to chemical applications or the chemical industry [6]. The application of AI in chemical engineering is not a recent development--it has been an active area of research and practice for over 40 years, marked by several notable achievements. The application of AI in Process Systems Engineering (PSE) dates back to the late 1960s and early 1970s, when pioneering researchers began exploring its potential in the field [7]. Currently, four different stages of the evolution of AI can be found [8]:  
-1 st Stage: Expert Systems (early 1980s to mid-1990s)  
This phase marked the first widespread effort to apply AI in chemical engineering. Research focused primarily on rule-based expert systems, aiming to encode human expertise into decision-making tools. For education purposes, programs like CHEMEX were used, which was an expert system developed to assist students in understanding chemical process synthesis and design [9]. Also, several flowsheet simulators were being developed, including PACER, CHES, and FLOWTRAN [10]. -2 nd Stage II: Neural Networks (~1990 to ~2008)  
During this period, interest shifted toward data-driven approaches, particularly artificial neural networks. These models were applied to tasks such as process modeling, control, and fault detection, benefiting from their ability to learn complex nonlinear relationships from data [11].