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LD1016 Learning to learn with AI 2.0 credits

This course introduces the concepts and applications of generative AI, a branch of artificial intelligence that can create novel and realistic content such as text, images, videos, and audio. You will learn how to use generative AI tools such as large language models, text-to-image systems, and video generators to enhance your learning and creativity. You will also learn how to design effective prompts that guide the generative AI models to produce the desired outputs, while at the same time delving deeper into your own learning strategies. Moreover, you will reflect on your own learning process and goals and develop a personal plan for continuous improvement and development after the course.

Choose semester and course offering

Choose semester and course offering to see current information and more about the course, such as course syllabus, study period, and application information.

Application

For course offering

Autumn 2024 Start 28 Oct 2024 single courses students

Application code

10192

Headings with content from the Course syllabus LD1016 (Spring 2024–) are denoted with an asterisk ( )

Content and learning outcomes

Course contents

This course in artificial intelligence (AI) aims to familiarise the students with the basic principles and the concepts around the highly expansive and popular field of AI, including overviews for non-experts of historical developments in the area. The course also provides students with the opportunity to develop their self-reflection and self-analysis skills by analysing generated replies and reflect on how conversational AI can enhance the student's self-awareness. Through seminars and self-study, students are introduced to the possibilities of content creation with AI. Students also apply their critical and analytical skills to determine if AI results are reliable, unbiased and overall useful. Students reach this understanding by the means of:

· Information about the current developments in the field of AI, and research about its pedagogical and non-educational uses,

· Seminars, exercises and discussions of practical aspects of AI use, including ethical challenges,

· Practical exercises in how conversational AI can help them to identify and question existing thought structures.

Intended learning outcomes

On completion of the course, the student should be able to:

· Explain the basic principles and the technologies for generative AI such as transformer architectures, basic models and in-context learning

· Use tools for generative AI as a conversation partner to create different types of contents and process new knowledge

· Apply skills in prompt engineering to ask questions that optimise the quality and relevance of results from generative AI, and to avoid potential pitfalls and bias

· Evaluate the ethical and social consequences of generative AI such as its effect on creativity, authenticity and disinformation

  • Design and carry out a personal learning plan that includes generative AI as a resource and inspiration for the student's future learning and development

Literature and preparations

Specific prerequisites

General entry requirements.

Recommended prerequisites

No information inserted

Equipment

No information inserted

Literature

No information inserted

Examination and completion

If the course is discontinued, students may request to be examined during the following two academic years.

Grading scale

P, F

Examination

  • LEXA - Continuous assessment, 2.0 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.

Opportunity to complete the requirements via supplementary examination

No information inserted

Opportunity to raise an approved grade via renewed examination

No information inserted

Examiner

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

Technology and Learning

Education cycle

First cycle

Add-on studies

No information inserted

Contact

Johan Thorbiörnson (johantor@kth.se)