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DM2588 Generative AI for Media Technology and Interaction Design 7.5 credits

Information per course offering

Course offerings are missing for current or upcoming semesters.

Course syllabus as PDF

Please note: all information from the Course syllabus is available on this page in an accessible format.

Course syllabus DM2588 (Autumn 2026–)
Headings with content from the Course syllabus DM2588 (Autumn 2026–) are denoted with an asterisk ( )

Content and learning outcomes

Course contents

  • The bases of generative artificial intelligence, AI (historical, theoretical, conceptual and practical).
  • Common applications of generative AI (including trends).
  • Application programming interfaces for generative AI.
  • Creativity and generative AI (views on creativity, art and artistic work and related applications of generative AI).
  • Generative production in media, generative design and art.
  • Education and generative AI (challenges, possibilities and risks).
  • Ethical perspectives on use of generative AI (for example values, independence and ownership).
  • Gender equality, diversity and equally conditions perspectives and problems for generative AI (for example gender partiality).
  • The future for generative AI (the development we can expect).

Intended learning outcomes

After passing the course, the student should be able to

  • describe the fundamental theoretical and conceptual bases of generative AI
  • describe how generative AI has been developed historically
  • describe the application space for generative AI
  • analyse and describe possibilities and limitations to create texts and other content with generative AI
  • use generative AI to create content as texts, images, number, music and video
  • use application programming interfaces for generative AI
  • analyse the possibilities about how generative AI should be able to support creativity
  • analyse ethical challenges and risks with generative AI as for instance ownership and independence related to digital content that partly or completely has been generated with AI
  • analyse and discuss possibilities with generative AI in relation to sustainability
  • assess and discuss questions around equal opportunities, diversity and equal conditions related to generative AI

in order to be able to use generative AI for media technology and human computer interaction from human, ethical and sustainable perspectives.

Literature and preparations

Specific prerequisites

Knowledge and skills in programming, 6 credits, equivalent to completed course DD1337/DD1310-DD1319/DD1321/DD1331/DD1333/DD100N/ID1018/ID1022.

Skills in scientific thinking and writing through any of the following:

  • a university degree of at least 180 credits
  • a completed or begun degree project aiming at a university degree of at least 180 credits
  • a completed course in research methodology/theory of science, 6 credits. 

Knowledge in English corresponding to the upper secondary course English B/English 6.

Literature

You can find information about course literature either in the course memo for the course offering or in the course room in Canvas.

Examination and completion

Grading scale

A, B, C, D, E, FX, F

Examination

  • LIT1 - Literature Assignment, 2.5 credits, grading scale: P, F
  • PRO1 - Project Work, 5.0 credits, grading scale: P, F

Based on recommendation from KTH’s coordinator for disabilities, the examiner will decide how to adapt an examination for students with documented disability. The examiner may apply another examination format when re-examining individual students. If the course is discontinued, students may request to be examined during the following two academic years.

Examiner

No information inserted

Ethical approach

  • All members of a group are responsible for the group's work.
  • In any assessment, every student shall honestly disclose any help received and sources used.
  • In an oral assessment, every student shall be able to present and answer questions about the entire assignment and solution.

Further information

Course room in Canvas

Registered students find further information about the implementation of the course in the course room in Canvas. A link to the course room can be found under the tab Studies in the Personal menu at the start of the course.

Offered by

Main field of study

Computer Science and Engineering

Education cycle

Second cycle