This study explores the integration of artificial intelligence, specifically ChatGPT, into higher education institutions in Mexico as a means of fostering educational innovation. The focus is on leveraging ChatGPT to enhance professor-student collaboration and improve the overall learning experience. The research delves into the implementation of ChatGPT in diverse academic settings to analyze its impact on student engagement, knowledge retention, and collaborative learning dynamics. The study employs a mixed-methods approach, combining quantitative data collection through pre- and post-implementation assessments, and qualitative insights gathered from interviews, surveys, and focus groups with both professors and students. By examining the perceptions and experiences of participants, the research aims to identify the strengths and challenges associated with integrating ChatGPT into the higher education program of studies in Mexico. The findings shed light on the effectiveness of ChatGPT in facilitating interactive and dynamic exchanges between professors and students. Additionally, the study investigates the role of ChatGPT in personalized learning experiences, addressing individual student needs and learning preferences. Ultimately, this study contributes to the discourse on educational innovation in Mexico by offering insights into the transformative potential of artificial intelligence, particularly ChatGPT, in higher education. Ultimately, this study contributes to the discourse on educational innovation in Mexico by offering insights into the transformative potential of artificial intelligence, particularly ChatGPT, in higher education. The outcomes of this research provide practical recommendations for institutions seeking to integrate AI technologies to active professor-student collaboration and improve the overall educational view.

Currently, emerging technologies such as digital twins, through the application of frontier techniques, have achieved physics-based simulations that reduce time and costs. Hence, its application is of the utmost importance in the industry, mainly in the sugar drying process of sugar mills for an updated version of the process. Sugar mills lack process control, leading to unexpected issues. Sugar mills with poor process control cause operational problems. This article presents significant innovation in the field of industrial process optimisation through the integration of digital twins with the k-ε standard model in computational fluid dynamics (CFD). The primary objective of this publication is to predict the ideal conditions of a centrifugal sugar dryer using CFD through the k-ε standard model to analyse the aerodynamic behaviour of the ambient air by applying heat through heat exchangers to obtain a suitable mass flow. The mathematical model was carried out under an energy balance to the thermodynamic system to study the behaviour through a simulation in MATLAB R2017 and an air-fluid simulation of drying with software CFD 2015. The results proved that the model of the thermal system and frontier conditions, when applying CFD, carried our simulation and remained stable. The ideal operating conditions of the centrifugal sugar dryer can be predicted effectively, with an energy saving of 4.25%.