Comparative Analysis of Two Implementations of Global Shared Learning in Biotechnology Engineering: Teacher Preparation and AI Integration for Future Classrooms Academic Article in Scopus uri icon

abstract

  • In an increasingly interconnected world, the ability to collaborate across borders and disciplines is essential for future engineers. This paper presents a comparative analysis of two implementations of the Global Shared Learning Classroom (GSLC) in biotechnology engineering education, conducted in collaboration with international partner institutions. The study highlights the impact of crossinstitutional collaboration on developing critical skills such as interdisciplinary problem-solving, sustainability awareness, and global communication, while also proposing the integration of artificial intelligence (AI) tools for future GSLC implementations. To ensure the success of these future initiatives, this paper presents a comprehensive teacher preparation strategy for integrating AI technologies into the classroom, alongside the development of rubrics using Magic School to measure the educational impact of these innovations. Previous implementations integrated the Tec21 educational model from Tecnológico de Monterrey, México, which emphasizes challenge-based learning, personalized instruction, and global perspectives. In both cases, students from the Biotechnology Engineering program addressed the challenge of producing biodegradable bioplastics through microbial fermentation, aiming to reduce reliance on petroleum-based plastics and minimize environmental impact. The first implementation, in collaboration with the University of Bio Bio, Chile, focused on the selection of microorganisms and optimizing the bioreactor design for bioplastic production. The second implementation, in partnership with the Universidad Tecnológica Metropolitana, Chile, similarly tackled the bioplastics challenge but incorporated the use of SuperPro Designer to simulate bioprocess engineering and evaluate production scalability. Key findings from this comparative analysis reveal that both implementations enhanced critical thinking, global collaboration, and technical skills, with a focus on sustainability and bioprocess optimization. The first implementation fostered deeper exploration of interdisciplinary teamwork, while the second leveraged simulation technologies for more practical applications of theoretical knowledge. The MUSIC® Model of Academic Motivation Inventory was used to assess student motivation in both cases, offering insights into how different pedagogical approaches affect engagement and skill development. Additionally, this paper presents a structured set of steps for future GSLC implementations, beginning with the design of rubrics using Magic School to measure the impact of AI technologies such as genmo.ai for image generation, fliki.ai for educational video creation, and gamma.app for interactive presentation development. These rubrics serve as a key tool for evaluating the effectiveness of integrating AI into the learning process. Alongside this, a methodological strategy for teacher preparation is outlined, providing educators with a clear guide on how to successfully implement these AI tools. This guide ensures that teachers are fully prepared to incorporate AI into their classrooms, remaining at the forefront of educational innovation while enhancing student engagement and learning outcomes. These steps act as a roadmap for educators to follow, helping them integrate AI seamlessly into future GSLC classrooms and fostering a more dynamic and interactive learning environment for students. This integration of AI tools and teacher preparation strategies aligns with the broader educational objectives of the Tec21 model, which seeks to promote personalized learning, foster creativity, and prepare students for the demands of a globalized, technology-driven workforce. © 2025 IEEE.

publication date

  • January 1, 2025