Date
Publisher
arXiv
The ubiquitous use of the tools of artificial intelligence (AI) in techno-science, in higher education and in other existing and potential fields of measurement application generates new challenges for teaching measurement science and technology (MST). The aim of this article is to encourage its readers to modernize their approach to teaching MST in a way as to meet these challenges, in particular - to profit from new technological opportunities, and to respond to the needs of our civilization, identified within a humanistic reflection over its development. First, the state of the art in applications of AI in higher education is briefly characterized. Next, a methodology for using AI tools in MST, referring to the author's meta-model of measurement, is outlined. Finally, conclusions concerning an emerging paradigm of teaching MST are summarized. The most important of them are as follows. The challenges implied by the ubiquitous use of AI tools cannot be effectively faced without enhancing, in the corresponding curricula, of the contents related to mathematical modelling of material entities, on the one hand, and of the contents related to ethics of research and engineering, on the other. Knowledge and skills related to the art of mathematical modelling are indispensable for extensively profiting from the convergence of various technologies around IT tools, including AI tools. The knowledge and skills related to ethics of research and engineering are indispensable for developing safe applications of measurement in such domains as autonomous vehicles, social robots, biomedical engineering or automated manufacturing.
Study at a glance
- Question: How should measurement science and technology education be updated to address challenges posed by ubiquitous artificial intelligence?
- Setting and data: Conceptual analysis and literature review covering higher education and engineering, citing a UK undergraduate student survey from December 2024.
- Method: Conceptual analysis and literature review.
Why it matters
The widespread use of artificial intelligence in higher education and technoscience creates new challenges, requiring educators to modernize measurement science and technology instruction to leverage technological opportunities and address societal needs.
Key findings
- A December 2024 survey showed 92% of surveyed British undergraduate students use generative AI tools, primarily for text generation, editing, summarization, and translation.
- Higher education AI applications are categorized into four core areas: profiling and prediction, intelligent tutoring, assessment and evaluation, and adaptive personalization.
- Instruction in measurement science and technology remains largely grounded in a traditional catalog approach that overlooks the fundamental role of mathematical and causal modeling.
Summary drafted with AI from the full paper and may contain errors. See the full paper for details.
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