Modernizing measurement science education improves skills for ubiquitous AI applications

The increasing prevalence of artificial intelligence tools poses major challenges to traditional approaches in teaching measurement science and technology. Together with his colleagues, Roman Z. Moravski from Warsaw University of Technology is tackling this issue by proposing modern curricula that embrace new opportunities and respond to society's evolving needs. Their research highlights the critical importance of strengthening mathematical modeling skills so that students can take full advantage of the integration of modern IT tools, including AI. Additionally, researchers emphasize the need to incorporate ethical considerations into measurement education to ensure safe and responsible development of applications in areas such as self-driving cars and biomedical engineering.

Researchers classify AI based on its learning capabilities, from task-specific systems to systems that exhibit social intelligence, in line with the definitions proposed by Ategeka and the EU Artificial Intelligence Act of 2024. This classification provides a basis for understanding the potential and limitations of AI tools in measurement contexts. This study traces the historical development of AI applications in measurement, focusing on the early discussions of the 1986 IMEKO TC7 symposium “Intelligent Measurement.”

A comprehensive review of existing literature reveals that there is a growing number of studies exploring the role of AI in measurement, with a significant increase in publications since 2015. The scientists cataloged numerous review articles and applied papers and identified key areas such as biomedical engineering, technical diagnostics, and industrial monitoring. This study demonstrates the rapidly evolving landscape of AI-driven measurement technologies and highlights the convergence of AI tools and established methodologies. Researchers analyzed cases across a variety of disciplines to demonstrate the breadth and depth of AI integration in measurement science and provide a strong rationale for updating educational curricula to reflect these advances. The study ultimately advocates for strengthening curriculum content that focuses on the ethical implications of mathematical modeling and AI-driven measurement systems.

Measurement science curriculum for artificial intelligence

This study details modern approaches to teaching measurement science and technology that address the increasing prevalence of artificial intelligence. Researchers emphasize the need to enhance curricula with advanced mathematical modeling and a solid understanding of research ethics to effectively utilize AI tools. At the heart of this progress is a measurement metamodel that incorporates the common mathematical models essential for defining measurands, calibrating measurement channels, and providing estimates of uncertainty. This study highlights the limitations of current AI tools, particularly their reliance on inductive reasoning, and grapples with the uncertainty inherent in abductive reasoning, a key element of measurement.

The researchers point to a recent systematic review that included 512 peer-reviewed papers published between 2021 and 2023 as evidence of important ongoing research on explainable AI, which is important for assessing the elements of measurement uncertainty that AI generates. This finding highlights the need for curricula that promote the blending of technical and non-technical competencies. The 2024 Nobel Prize in Chemistry, awarded for AI-powered protein structure prediction, serves as a case study in raising ethical questions about intellectual property when AI models are trained in collective research. Researchers are advocating a holistic approach to ethics that combines virtue, deontology, and consequentialist perspectives to address the complex challenges posed by AI in measurement science and technology.

This study demonstrates the need to adapt measurement science and technology education to address the increasing prevalence of artificial intelligence. The researchers highlight that two key areas need to be strengthened to effectively integrate AI tools into the curriculum: mathematical modeling and research ethics. They demonstrate that a solid understanding of mathematical modeling is critical to harnessing the full potential of integrated technologies, including AI, and interpreting the data these systems generate. Furthermore, this study highlights the important role of ethics in the development and application of measurement techniques, especially in sensitive areas such as autonomous vehicles, robotics, and biomedical engineering. By advocating for increased instruction in these areas, scholars aim to prepare future engineers and scientists to create safe, reliable, and responsible measurement applications.