Exploring and Addressing Critical Gaps in AI Education to Advance Biotech, Bioprocessing, and Smart Manufacturing

Abstract

There is a notable disconnect between university programs and the swiftly changing skill requirements of the bio/chemical processing industry, which intensifies the challenge of fulfilling the growing need for skilled candidates. Given the difficulties pharmaceutical employers face in finding qualified professionals, this initiative is particularly important. By bringing together students, academics, government officials, and industry experts from various institutions, we seek to facilitate cross-disciplinary discussions and pinpoint gaps and solutions for swift integration into student curricula. Current K-12 curricula from around the world can be a great starting point for finding out parts of the AI that can be incorporated into student curriculum and reduce fear associated with the AI. Artificial Intelligence (AI), machine learning (ML), and deep learning present opportunities to elevate the field of biotechnology, particularly bioprocessing and smart manufacturing. AI is shifting the way biotech organizations operate, revolutionizing chemical processes and methods for designing and developing new biopharmaceutical products. Meanwhile, academia is seeking to integrate AI into its chemical engineering curricula, while delivering skills and training that meet the demand of the biotech and pharmaceutical industries. Nonetheless, there are some current challenges that academia must address. In building an AI-ready workforce, academia must evaluate critical gaps present in university programs and leverage existing AI curricula from all over the globe, largely improving US Universities competitiveness. In this chapter, we argue that AI-based Quality by Design approaches can help address a critical gap while strengthening the quality of academic programs. Quality by Design tools (QbD) in bioprocessing are already delivering end-to-end results in the product life cycle. However, integrating a range of artificial intelligence-based concepts, such as artificial, convolutional and recurrent neural networks (ANN, CNN and RNN) into bioprocessing academic programs will strengthen the overall framework for integrating AI-based QbD models, yielding comprehensive process improvements. Such models can thus be studied, tested, refined and remodeled as part of an “AI-based QbD for bioprocessing and smart manufacturing” academic program that can bridge the academic-industry gap in the field of CAR-T, CHO and iPSC bioprocessing.