AI (Artificial Intelligence) is becoming a topic of increasing social importance. Political reactions like the publication of the AI strategy of the German Government in late 2018 are one indicator for that. But more importantly, we are already interacting with AI systems as if it were the most natural thing in the world, for example, when using language assistants such as Siri or Alexa. Nevertheless, according to surveys, over 50% of Germans do not know what artificial intelligence is.

To address this issue, we have put together a collection of Unplugged Activities related to the topic of AI. Unplugged Activities provide approaches that help learners of all ages to experience the ideas and concepts of computer science actively and do
without the use of a computer.

This brochure contains five activities you can use to teach ideas and concepts of artificial intelligence to learners of all ages.

Nowadays, AI is primarily realized through machine learning, but artificial intelligence is far more than that: AI is not only about technical aspects, but also raises questions of social relevance. This brochure shows possibilities, how these topics can be discussed with children and adults

In this lesson, students will be presented with a project that they will decompose with their partners without having access to its code and without access to a computer. Students will work in teams to recreate the project shown in the following lesson.

Interactive video showing how to add images and use them as billboards in Alice.

Interactive video showing how to add sound files and use them in alice.

Following a brief classroom discussion of relevant principles, each student completes the paper design of several advanced circuits such as multiplexers, sample-and-holds, gain-controlled amplifiers, analog multipliers, digital-to-analog or analog-to-digital converters, and power amplifiers. One of each student's designs is presented to the class, and one may be built and evaluated. Associated laboratory emphasizing the use of modern analog building blocks. Alternate years.

Recent results in cryptography and interactive proofs. Lectures by instructor, invited speakers, and students. Alternate years. The topics covered in this course include interactive proofs, zero-knowledge proofs, zero-knowledge proofs of knowledge, non-interactive zero-knowledge proofs, secure protocols, two-party secure computation, multiparty secure computation, and chosen-ciphertext security.

In this unplugged lesson, students will learn how to develop algorithms and the importance of providing specific instructions while making a simple deli sandwich. This lesson is part of the Virginia K-12 Computer Science Pipeline which is partly funded through a GO Virginia grant in partnership with Chesapeake Public Schools, Loudoun County Public Schools, and the Loudoun Education Foundation.

In this unplugged CS lesson, students will learn the importance of giving detailed directions when sharing ideas. This transfers to programming when students are told that when they provide instructions to the computer, they too need to be detailed and specific. This lesson is part of the Virginia K-12 Computer Science Pipeline which is partly funded through a GO Virginia grant in partnership with Chesapeake Public Schools, Loudoun County Public Schools, and the Loudoun Education Foundation.

This is a textbook for first year Computer Science. Algorithms and Data Structures With Applications to Graphics and Geometry.

This is a textbook for first year Computer Science. Algorithms and Data Structures With Applications to Graphics and Geometry.

In-depth study of an active research topic in computer graphics. Topics change each term. Readings from the literature, student presentations, short assignments, and a programming project. Animation is a compelling and effective form of expression; it engages viewers and makes difficult concepts easier to grasp. Today's animation industry creates films, special effects, and games with stunning visual detail and quality. This graduate class will investigate the algorithms that make these animations possible: keyframing, inverse kinematics, physical simulation, optimization, optimal control, motion capture, and data-driven methods. Our study will also reveal the shortcomings of these sophisticated tools. The students will propose improvements and explore new methods for computer animation in semester-long research projects. The course should appeal to both students with general interest in computer graphics and students interested in new applications of machine learning, robotics, biomechanics, physics, applied mathematics and scientific computing.

In this lesson, students will apply the concept of loops they learned in order to animate their sprites.

This set of cards can be used in a workshop or a "Maker Faire" type of event. They give quick tidbits of code for building mini-apps with App Inventor. Use them in exhibits, parent nights, STEM fairs, after-school clubs, or anywhere that you need to get people jump-started using App Inventor.

This course introduces students to the basic knowledge representation, problem solving, and learning methods of artificial intelligence. Upon completion of 6.034, students should be able to develop intelligent systems by assembling solutions to concrete computational problems, understand the role of knowledge representation, problem solving, and learning in intelligent-system engineering, and appreciate the role of problem solving, vision, and language in understanding human intelligence from a computational perspective.

The excitement for the creative power of programming and the questions about how to assess students’ creative work prompted us to undertake this project, which was funded through the generous support of Google’s Computer Science Education Research program. In this project, we were guided by a central question: How do K-12 computing teachers assess creative programming work? Our approach was simple: during the summer of 2019, we talked to 80 K–12 computing teachers across the U.S. about how they supported and assessed creative work in programming activities. In our conversations, typically between two teachers and a member of our team, teachers brought a pair of assessment examples and used those examples as the foundation for a broader discussion about creativity, programming, and assessment.

Through these conversations, as well as an examination of the assessment research literature, we identified key principles that guide the assessment of creative programming activities:
-Foster a classroom culture that values assessment.
-See student process as well as product.
-Understand what is creative for the student.
-Support students by incorporating feedback from multiple perspectives.
-Scaffold opportunities for students to develop judgment of their own work.

Thanks to these incredible teachers who met with us and generously shared their thinking about their practice, we were able to gather more than 300 assessments, ranging from class project rubrics to examples of student project portfolios. In this document, we are sharing our understandings in two ways: (1) a collection of four case studies, and (2) a selection of 50 assessments. The case studies tell the stories of four teachers who are putting the guiding principles of creative assessment into practice in the complex, real-life contexts of their classrooms.

The 50 assessments represent a curated collection of real assessments that teachers are using in their classrooms, accompanied by quotes from teachers about what the assessment of creative work entails.

Interactive video detailing assignment, display, and input and the way that they are used in programming.

This course provides a challenging introduction to some of the central ideas of theoretical computer science. Beginning in antiquity, the course will progress through finite automata, circuits and decision trees, Turing machines and computability, efficient algorithms and reducibility, the P versus NP problem, NP-completeness, the power of randomness, cryptography and one-way functions, computational learning theory, and quantum computing. It examines the classes of problems that can and cannot be solved by various kinds of machines. It tries to explain the key differences between computational models that affect their power.