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Mittwoch, 07.12.2022 | 17.00 Uhr - 19.30 Uhr

Paderborn Colloquium on Data Science and Artificial Intelligence in School - Session #08 - Part 1: Ute Schmid - Part 2: Jane Waite

Part 1: Learning About and Learning with Artificial Intelligence in School: From Understanding of Basic AI Concepts to Trustworthy and Human-centric AI Tools – Ute Schmid  (Germany)
Artificial intelligence (AI) is currently a much discussed topic evoking high expectations as well as fears. For a realistic assessment of the opportunities and risks of AI, basic understanding of what makes AI applications different from other computer programs is necessary. How and what AI concepts can be introduced in primary and secondary education depends on the age and the computer science background of the students. […]
In the talk, a sample of topics and methods for introducing AI in school is presented – covering machine learning as well as automated reasoning, offering unplugged material for students without background in computer science as well as possibilities for more formal introduction of AI algorithms and AI programming. In the second part of the talk, learning analytics and intelligent tutor systems are introduced as applications of AI methods to support students and teachers. While learning analytics is mainly behavioristic with focus on applying machine learning to prediction of students’ performance, intelligent tutor systems are based on cognitive and constructivist principles with focus on AI methods for individualized diagnosis of misconceptions and feedback generation.

Part 2: A hands-on workshop to develop a set of potential goals for learning about AI – Jane Waite (England)
In this 70-minute workshop, we ask participants to contribute to the process of developing a set of potential goals for learning about AI. We will ask you to join a group that will consider students of a particular age and what they might know about AI. The age groups will be 7, 11, 14, and 18. […]
In this collaborative process, we hope to glean insights, share ideas and reflect on the progression of knowledge and mental models students might develop about AI. To support our group activity, we will introduce the SEAME framework, a simple way to group goals as social and ethical, application, model, or engine focused. We will also provide examples of learning goals and candidate concepts to start the process. It would be great to start to tease out what potential threshold concepts are critical to the progression of AI learning, and we hope this session will get us thinking deeply about this.

Detailed information can be found at

Die Universität der Informationsgesellschaft