Objective: This worksheet provides an in-depth understanding of the technical fundamentals and ethical implications of artificial intelligence at the upper secondary level. The aim is to go beyond mere application and understand the underlying mechanisms (such as neural networks and machine learning) and critically reflect on their social implications.
Content and methods: The content covers fundamental technical concepts of artificial intelligence such as machine learning, deep learning, and neural networks, the distinction between weak and strong AI, and the analysis of everyday digital applications according to criteria such as data volume, algorithms, and learning ability. This is supplemented by practical case studies on personalized learning systems and ethical and social issues such as bias, responsibility, and the impact on learning and creativity. The methods include technical term explanations, comparative classification tasks, scenario-based case analyses, multi-perspective role-plays from different points of interest, and stimulating discussion and position exercises in the classroom, in which learners justify and reflect on their attitudes.
Competencies:
- Technical expertise: Confident use of technical terms such as algorithms, training data, and artificial neurons.
- Analytical skills: Ability to identify the role of training data and bias in AI models.
- Ethical judgment: Reflection on data protection and discrimination risks.
- Discussion and argumentation skills: Ability to represent complex positions within a role-play and in a plenary discussion
Target group and level: From grade 10 onwards
