Kunstliche Intelligenz最新文献

A critical understanding of digital technologies is an empowering competence for citizens of all ages. In this paper we introduce an open educational approach of artificial intelligence (AI) for everyone. Through a hybrid and participative MOOC we aim to develop a critical and creative perspective about the way AI is integrated in the different domains of our lives. We have built and now operate a MOOC in AI for all the citizens from 15 years old. The MOOC aims to help understanding AI foundations and applications, intended for a large public beyond the school domain, with more than 20,000 participants engaged in the MOOC after nine months. This study addresses the pedagogical methods for designing and evaluating the MOOC in AI. Through this study we raise four questions regarding citizen education in AI: Why (i.e., to which aim) sharing such citizen formation? What is the disciplinary knowledge to be shared? What are the competencies to develop? How can it be shared and evaluated? We finally share learning analytics, quantitative and qualitative evaluations and explain to which extent educational science research helps enlighten such large scale initiatives. The analysis of the MOOC in AI helps to identify that the main feedback related to AI is "fear", because AI is unknown and mysterious to the participants. After developing playful AI simulations, the AI mechanisms become familiar for the MOOC participants and they can overcome their misconception on AI to develop a more critical point of view. This contribution describes a K-12 AI educational project or initiatives of a considerable impact, via the formation of teachers and other educators.

Supplementary information: The online version contains supplementary material available at 10.1007/s13218-021-00725-7.

AI has become ubiquitous in our society, accelerated by the speed of the development of machine learning algorithms and voice and facial recognition technologies used in our everyday lives. Furthermore, AI-enhanced technologies and tools are no strangers in the field of education. It is more evident that it is important to prepare K-12 population of students for their future professions as well as citizens capable of understanding and utilizing AI-enhanced technologies in the future. In response to such needs, the authors started a collaborative project aiming to provide a K-12 AI curriculum for Japanese students. However, the authors soon realized that it is important to contextualize the learning experience for the targeted K-12 students. The paper aims at introducing the idea of contextualizing AI education and learning experience of K-12 students with examples and tips using the work-in-progress version of the contextualized curriculum using culturally responsive approaches to promote the awareness and understanding of AI ethics among middle school students.

Over the course of 2 years a global technology education nonprofit engaged ~ 20,000 under-resourced 3rd-8th grade students, parents and educators from 13 countries in a multi-week AI competition. Families worked together with the help of educators to identify meaningful problems in their communities and developed AI-prototypes to address them. Key findings included: (1) Identifying a high level of interest in underserved communities to develop and apply AI-literacy skills; (2) Determining curricular and program implementation elements that enable families to apply AI knowledge and skills to real problems; (3) Identifying effective methods of engaging industry mentors to support participants; (4) Measuring and identifying changes in self-efficacy and ability to apply AI-based tools to real-world problems; (5) Determining effective curricula around value-sensitive design and ethical innovation.

AI Education for K-12 and in particular AI literacy gained huge interest recently due to the significantly influence in daily life, society, and economy. In this paper we discuss this topic of early AI education along four dimensions: (1) formal versus informal education, (2) cooperation of researchers in AI and education, (3) the level of education, and (4) concepts and tools.