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DM2731 AI for Learning 7.5 credits

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Information per course offering

Termin

Information for Spring 2026 Start 16 Mar 2026 programme students

Course location

KTH Campus

Duration
16 Mar 2026 - 1 Jun 2026
Periods

Spring 2026: P4 (7.5 hp)

Pace of study

50%

Application code

60362

Form of study

Normal Daytime

Language of instruction

English

Course memo
Course memo is not published
Number of places

Min: 20

Target group
Open for all programs from year 3, and for students admitted to a master's program, provided that the course can be included in the program.
Planned modular schedule
[object Object]
Schedule
Schedule is not published
Part of programme
No information inserted

Contact

Examiner
No information inserted
Course coordinator
No information inserted
Teachers
No information inserted

Course syllabus as PDF

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

Course syllabus DM2731 (Autumn 2025–)
Headings with content from the Course syllabus DM2731 (Autumn 2025–) are denoted with an asterisk ( )

Content and learning outcomes

Course contents

  • The historical development of AI-based applications for learning
  • The theoretical foundations of AI-based applications for learning
  • The practical application of AI-based applications for learning
  • Planning, design and evaluation of AI-based applications for learning
  • Ethics and privacy related to AI-based applications for learning

Intended learning outcomes

After passing the course, the student should be able to:

  • account for historical and current perspectives on AI (artificial intelligence) for learning
  • explain the theoretical foundations underlying applications of AI and Learning Analytics (LA) in educational and professional learning environments
  • plan, design and evaluate AI and LA tools to improve learning experiences
  • discuss how AI and LA can contribute to personal and professional development of learning, including strategies for further education and retraining
  • critically evaluate the implications of AI and LA developments in educational and professional learning environments
  • reason about ethical issues in the application and evaluation of AI systems for learning, with a focus on ensuring responsible and fair outcomes

in order to

  • provide students with a broad competence in AI for learning that encompasses theoretical knowledge, practical application, critical thinking and ethical considerations
  • prepare for further studies and the labour market.

Literature and preparations

Specific prerequisites

Knowledge and skills in programming, 6 credits, equivalent to completed course DD1310-D1319/DD1321/DD1331/DD1337/DD100N/ID1018.

At least one of the following:
Knowledge in algorithms and data structures, at least 6hp higher education credits, equivalent to completed course DD1338/DD1320/DD1325/DD1328/DD1338/DD2325/ID1020/ID1021

or

Knowledge in Human-Computer Interaction, 6 higher education credits, equivalent to completed course DH1620/DH1622/DH1624/DH2624.

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

P, F

Examination

  • SEM1 - Seminars, 3.0 credits, grading scale: P, F
  • PRO1 - Project Work, 4.5 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.

Other requirements for final grade

At least 75% attendance at seminars is required to pass the final grade of the course.

Examiner

Profile picture Olga Viberg

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

EECS/Human Centered Technology

Main field of study

Computer Science and Engineering

Education cycle

Second cycle

Supplementary information

In this course, the EECS code of honor applies, see:
http://www.kth.se/en/eecs/utbildning/hederskodex