Abstract:

Historically, industrial revolutions have triggered profound transformations in professions, the economy, education, and the daily activities of society. Each wave of innovation has redefined the nature of work and the skills required to thrive. Today, the fourth industrial revolution, driven by Artificial Intelligence (AI) and task automation, is unfolding at an unprecedented pace and scale, with the potential to reshape the entire labor landscape more radically than ever before.

Many of the professions that have been associated with fields within the higher education system, such as engineering, law, or business administration, now face varying degrees of automation risk, potentially leading to job losses and a decline in the global economy. Monotonous or repetitive tasks such as data entry, scheduling, or basic diagnostics will be carried out entirely by machines, in less time and with greater efficiency than humans can achieve. Therefore, education must be rethought from an evolutionary perspective, with a focus on preparing students for a future shaped by rapid technological change and interdisciplinary collaboration. Learning objectives should aim to foster creativity, adaptability, and critical thinking.

On this premise, this article presents a methodology for the analysis of university degrees as a tool to understand which areas are most susceptible to automation. This methodology is based on the classification of the learning outcomes of university degrees into two categories: "Computational Thinking" (CT) or "Design Thinking" (DT), through the analysis of the verbs used in their formulation (Bloom's taxonomy). This methodology also considers the analysis of the context in which each verb is embedded, for example, whether the verb appears in a technical, creative, or social learning environment, to ensure the accuracy of the classification.

This classification differentiates CT as a type of thinking that can be fully developed by information-processing agents. In contrast, DT is a type of thinking that requires thought processes such as human understanding and interpretation, and the understanding of the specific context in which it is developed.

The proposed methodology analyzes each learning outcome by determining the percentage of CT and DT that is learnt, to anticipate the impact of AI and automation on the profession. As an example of its application, this methodology is proposed for the degree in Industrial Design Engineering and Product Development at CEU Cardenal Herrera University.

This methodology applied in different university degrees should allow the review of the learning objectives and the graduation profile of different professions, including the expected competencies, skills, and roles graduates are prepared to fulfill for the future. Learning processes will be based on the definition of how, when, and why machines could be used as tools to support the evolution of human thinking and societal progress.

Keywords:

Automation, education, AI, STEM, Design Thinking, Computational Thinking, professions, 4th industrial revolution.