Innovative learning spaces represent a pioneering educational environment engineered to investigate and exhibit varied pedagogical practices through the application of advanced technologies and versatile classroom configurations. Since its objective is to prepare students for future complexities by incorporating interactive digital tools, promoting cooperation, and facilitating active learning experiences, Science, Technology, Engineering, Arts, and Mathematics workshops are essential in actively involving undergraduate students in research activities. On the other hand, wearable biosensors are making a substantial impact in education, acting as valuable resources for educators and useful tools for students. For instance, bio-metric signals have the potential to optimize academic practices, providing valuable insights into the teaching-learning process. In this context, it is vital for engineering students to acquire the skills and knowledge necessary for the cognitive process of collecting, processing, and modeling data using various wearable sensor technologies. This work presents a case study on project-based learning in engineering education, focusing on a biomechanical analysis project. The project involves analyzing a dancer's performance of improvisations expressing joy or sadness, with the presence of an immersive space. This activity enhances students' understanding of experimental design, signal processing tasks, angular acceleration in joint per participant, and environment-dependent accelerations, while also exploring the user experience of biosensors as a brain-computer interface for electroencephalogram signal monitoring, a medical-grade wearable device that provides real-time physiological data acquisition and streaming, and a camera for tracking joint movements during dance improvisation. In this evaluation, two sets of engineering students were taken into account: a group that attended the course in a traditional classroom setting, and another group that participated in an innovative space. The results suggest that innovative learning environments and medical-grade instruments can positively impact student engagement and understanding of advanced concepts in neuroengineering. Both groups shared positive feedback on the learning experience and expressed their willingness to recommend the course to others.

Traditionally, research and scientific endeavors have predominantly involved graduate students, often sidelining participation of undergraduate students. However, integrating under-graduate students into leading cutting-edge projects significantly enhances their academic experience and professional growth. This manuscript delves into the case study of Conscious Technologies, an innovative research group comprising the Campus City initiative and the IUCRC International BRAIN Affiliate Site at Tecnologico de Monterrey. The group's focus encompasses smart city solutions, computer vision, robotic operating systems, neuroengineering, biometry, and biomechanics, all powered by artificial intelligence. The group efficiently manages a diverse array of projects by employing methodologies such as agile project management and a progressive-phased research approach, which leads to beneficial outcomes for all stakeholders. Undergraduate students gain hands-on experience, increasing their prospects for admission into prestigious graduate programs abroad, while at the same time, research professors expand their international presence through impactful publications. High research output gradually fosters collaboration, including exchanges and stays abroad, further catalyzing the centre's growth. By tackling real-world challenges within a well-structured framework, supplemented with intrinsic and extrinsic rewards and regular meetings, students enjoy autonomy within guided projects, culminating in the publication of 26 scientific works over a span of three years.

Currently, vehicles come equipped with a growing number of electronic devices that enable various driving assis-tance systems. From an academic perspective, it is imperative that engineering students develop the skills and knowledge necessary to handle standardized devices in the automotive industry. To promote interaction with this technology and disseminate theo-retical concepts, innovative ideas, and critical thinking, educative workshops playa crucial role in engaging undergraduate students in the research field. This article presents a case study of an active learning method (project-based learning) in engineering education at Tecnologico de Monterrey, which involves developing an automotive control project using an industrial-level controller, i.e., a Vehicle Control Unit (VCU). This activity helps students to learn signal processing tasks, control design problems, and analyze the capabilities of VCU with simpler environments like Arduino. Typically, students conduct these activities following a research methodology. For this assessment, two groups of engineering students have been defined. One group has previously undergone a control engineering course, while the other group has not. The study evaluated the results obtained from the statisti-cal analysis of the scores of the two groups with respect to their involvement in industrial equipment projects and their perceptions of the project complexity. The first group consisted of undergraduates who had not taken a control course, and the second group included those who have already taken it. Therefore, the first group showed a better understanding of the subject than the second group. However, both groups expressed a preference for conducting engineering projects with real vehicles instead of scaled -down models.

A Challenge based Learning (CBL) activity, with the participation of an industry partner, regarding automotive design for undergraduate engineering students is presented. During an academic semester, senior students in an specialization course became familiar with technical aspects regarding electric vehicles (EV) and learned about battery pack sizing and motor sizing as well. Students worked on a challenging case study to convert a VW Type 1 sedan with an internal combustion engine (ICE) into an EV. The work done by the students involved real-data, simulations, cost analysis, 3D scanning, multiple CAD design iterations, and manufacturing processes. The achieved outcomes of the case were: an improved dynamic behavior and more sym-metrical weight distribution compared with the original vehicle, as well as a comparable power and energy delivery were also achieved at a lower cost compared with commercial conversion solutions. These results are evidence of the skills developed by the students as the CBL activity was developed and shows a promising project option for engineering students. It is observed how challenging students through a motivating situation that demands their intellectual curiosity, seeking to solve problems of global interest, and focusing on an application in their area of expertise, contributes to their professional development. This is an option for future final capstone projects in engineering.

This study aims to develop a biofeedback tool to assess the efficiency of different learning and teaching techniques. Here, neuroengineering tools are used to detect and compare the level of learning obtained through two different modalities: a traditional text and an interactive video. Electroencephalographic signals were recorded in two groups during learning tasks, and performance was evaluated with an exam. Results showed better performance on the video group, as well as power changes in theta and beta bands, mainly in frontal and occipital cortices. A real time monitoring protocol of these features could be implemented to develop a learning evaluation tool based on biometric signals.

This document presents the methodology to implement a project of innovation in the classroom for the course of analysis and synthesis of mechanisms using 3D prototypes that can assist in understanding theoretic concepts that require spatial abilities. This project was conducted with faculty and students that generated different types of models that were improved through iterative exercises of design and experimentation. The application of this project was based on the experiential learning methodology and principles from the Montessori philosophy.

The main challenges currently faced by Higher Education Institutions (HEI) to achieve their integration with social agencies, industry, and government are discussed. Faced with the demand for solutions to increasingly complex problems and the modernization of teaching approaches that address these problems, the TransDisciplinary Learning Community (TDLC) model is proposed as a dynamic path to help educational systems, specifically HEI, to face these challenges.

Higher education institutions have a significant role in the development of the economy of any society. Its primary goal should lie in the generation, transfer, and application of knowledge. The institutions that take their role in this complex task seriously will achieve closer ties of dynamic collaboration and will be able to compete with other economies. This research addresses the mission, function, and challenges of curriculum and studies plan design and development in higher education institutions, to present a model proposal to foster articulated curricula between academic institutions and society.

An approach to find out the training inputs for identification of a Magneto-Rheological (MR) damper is proposed. Reduction of overuse of the damper, number of experiments and configurations of training inputs are main features of this approach. Experimental validation with a commercial MR damper was carried out.Main results show inputs configuration with modulated frequency at fixed amplitude displacement, and random amplitude step with fixed period generate key information. A feed-forward neural network was selected as model emulator. Modelling results showed an error-to-signal ratio lower than milli-thousands.

The precise variation of the magnetorheological (MR) damping force in a semi-active suspension is a key issue in order to assure the desired performances over a suspension control system. The open loop control of this force is a very common strategy. Other schemes propose adaptive controller alternatives while the automotive hardware is a constrained computation resource. This paper proposes the implementation of the damping force control system based on the MR damper using an internal model control approach. The controller and the internal model are proposed as artificial neural networks (ANN) trained and validated with realistic automotive datasets. The results shows good servo control and fast regulation to abrupt disturbances without on-line ANN tuning computations.