How digital transformation can influence workflows, teaching practices and curricula in (bio)process science and engineering—An interview series with stakeholders

Abstract

A massive digital transformation is underway in biotechnology and process engineering fueled by recent advances in machine learning and so-called artificial intelligence, especially in the large language model field (e.g., ChatGPT). Training courses and curricula will need to adapt to keep pace, but the speed of progress is such that guidelines for the implementation of a digital transformation are probably already outdated. We therefore interviewed stakeholders from the fields of didactics, biotechnology and process engineering to collect the latest perspectives on the impact of digital transformation and to solicit recommendations for the adaptation of curricula and training courses to reflect new work profiles in academia and industry. We conducted semi-structured interviews with 17 stakeholders and used a framework analysis approach to structure and evaluate the collected information. For example, data handling was the dominant general activity affected by digital transformation, whereas multitasking was relevant to work, and the design and implementation of new didactic methods and content was linked to teaching. The interviews revealed that an increasingly diverse set of skills and competences (in addition to those in current curricula) will be expected from the next generation of biotechnologists and process engineers. This includes profound programming skills, model building abilities, as well critical data interpretation and data literacy in the widest sense. The corresponding key challenges will be a reasonable and structured disinvestment in other areas to provide slots for the new content and to secure resources for the implementation of necessary modifications.