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Challenges of a digital society and response of Dutch universities. One programme, seven themes: Citizenship & Democracy, Responsible Data Science, Health & Well-Being Learning & Education, Work & Organisations, Digital Cities & Communities, Safety & Security

By Jeannette M. Wing, corporate vice president, Microsoft Research "Not in my lifetime." That's what I said when I was asked whether we would ever see computer science taught in K-12. It was 2009, and I was addressing a gathering of attendees to a workshop on computational thinking convened by the National Academies. I'm happy...

All primary school children in England are required to write computer programs and learn about computational thinking. There are moves in other countries to this effect such as the U.S. K-12 Computer Science Framework (CSF) for development.

Computational Thinking (CT) is a problem solving process that includes a number of characteristics and dispositions. CT is essential to the development of computer applications, but it can also be used to support problem solving across all disciplines, including the humanities, math, and science. Students who learn CT across the curriculum can begin to see a relationship between academic subjects, as well as between life inside and outside of the classroom."

"The DigCompEdu Framework has been used as the basis for developing a self-reflection tool for educators, provisionally called "DigCompEdu CheckIn". This tool is currently being pre-tested with small groups of educators in different European Member States. During the test phase, the tool is open for testing to all educators worldwide. All educators are invited and encouraged to provide feedback on their experience with the tool via the corresponding feedback survey."

In recent years, many European countries have revised their statutory compulsory education curriculum, introducing basic Computer Science concepts. This has paved the way for the development of students' Computational Thinking (CT) skills. Further impetus in this direction is coming from the European Commission´s Digital Education Action Plan 2021-2027, where quality Computing Education is a key element under the priority "Enhancing digital skills and competences for the digital transformation."

aTrain is a tool for automatically transcribing speech recordings utilizing state-of-the-art machine learning models without uploading any data to the internet. It was developed by researchers at the Business Analytics and Data Science-Center at the University of Graz and tested by researchers from the Know-Center Graz.

"The teaching professions face rapidly changing demands, which require a new, broader and more sophisticated set of competences than before. The ubiquity of digital devices and applications, in particular, requires educators to develop their digital competence. The European Framework for the Digital Competence of Educators (DigCompEdu) is a scientifically sound framework describing what it means for educators to be digitally competent. It provides a general reference frame to support the development of educator-specific digital competences in Europe. DigCompEdu is directed towards educators at all levels of education, from early childhood to higher and adult education, including general and vocational education and training, special needs education, and non-formal learning contexts."

"This study investigates how cognitive, affective, and behavioral variables related to artificial intelligence (AI) build AI self-efficacy among university students. Based on these variables, we identify three meaningful student groups, which can guide educational initiatives. We recruited 1465 undergraduate and graduate students from the United States, the United Kingdom, and Germany and measured their AI self-efficacy, AI literacy, interest in AI, attitudes towards AI, and AI use. Using a path model, we examine the correlations and paths among these variables. Results reveal that AI usage and positive AI attitudes significantly predict interest in AI, which in turn and together with AI literacy, enhance AI self-efficacy."

As AI pushes the boundaries of chip design, it raises a dilemma: can we trust systems we don't fully understand?