Improving domain-specific AI education

A structured approach to design domain-specific AI courses
and help teachers to bring AI education into their respective domain.

  • Open


    Open

  • Living


    Living

  • Impactful


    Impactful

Freely available to everyone

The AI Course Design Planning Framework can be downloaded by anyone free of charge and can be used for their own projects, whether in research, education or beyond (CC BY-SA 4.0 license)

A living project

The development of the AI Course Design Planning Framework is a continuous process. It is regularly re-evaluated and improved by the latest scientific findings.

Interdisciplinary collaboration

The AI Course Design Planning Framework builds an interdisciplinary bridge between computer science education and other academic fields (“domains”). It is applicable in every domain of higher education.

Structure of the AI Course Design Planning Framework

The framework is divided into three pillars: AI in the Domain (left), Learning Environment (middle), and Course Implementation (right).
Taken together, these pillars reflect the key aspects for developing and implementing an AI course.

  • AI Usecases in the Domain

    Impact of AI on the domain, today as well as in the future. Well-established or cutting-edge AI use cases in the domain.

  • Data in the Domain

    Most common data types in the domain.

  • Implications of using AI in the Domain

    Important legal, ethical and social implications of using AI in the domain.

  • Additional Learning Resources

    Additional resources that can be used to teach AI in the domain, such as open educational resources.

  • Learners and their interaction with AI

    Learners’ pre-existing knowledge and skills and the role that learners will take after completing the course.

  • Instructors

    AI knowledge, skills, and competencies of all course instructors and organizers.

  • Internal Support

    Internal resources such as available data, time, and money. Support from the university.

  • Learning Outcomes

    Knowledge and skills that participants should have upon completion of the course.

  • Assessment

    Methods with which the achievement of the learning outcomes will be evaluated.

  • Learning Activities

    Actual activities that will be used to teach AI to the participants.

Try it for yourself

The framework is available for download here.