Kursplan för studenter höst 2024

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

- describe and explain basic elements of AI
- identify and describe uses of AI in educational settings

- summarize theoretical literature concerning AI and data-driven practices in education
- develop AI literacy guidelines for a specific educational setting
- basic uses of AI in teaching, learning and research

- critically discuss uses, affordances and limitations with AI solutions and tools in educational settings
- problematize and assess possible practices with AI in teaching and learning

• Theoretical and conceptual perspectives on AI in education
• Elements of AI – modules from an open online course
• Educational and research methods in relation to AI
• Data-driven educational practices

The teaching consists of lectures, seminars and exercises performed individually and in groups.

A learning management system is used.

Students who have been admitted to and registered for a course have the right to receive instruction/supervision for the duration of the time period specified for the particular course instance to which they were accepted. After that, the right to receive instruction/supervision expires.

Undervisningen bedrivs på engelska.

A bachelor's degree (i.e., the equivalent of 180 ECTS credits at an accredited university) with at least 90 credits in education or social science including independent work, i.e., a thesis or the equivalent. English proficiency is required.

Kursen bedöms med betygen A, B, C, D, E, FX eller F.

The examination is based on the intended learning outcomes.

The grades A, B, C, D and E are all passing grades. For courses with more than one element of examination, students are given a final grade based on an overall assessment of all the elements included in the course. The final grade of the course is issued only when all elements of examination have been passed.

The course is examined through two examinations. The student is examined through an online course with questions and tasks graded with pass or fail (75% right answers) (3 credits) as well as an individual written assignment graded with A-F (4.5 credits).

The examination must allow for students to be assessed on an individual basis. Further information concerning assessment of specific intended learning outcomes and grading criteria is provided at the beginning of the course.

Students are guaranteed a minimum of three attempts to pass an examination, including the regular attempt.

If a student has failed the same examination three times, the student can request that the next attempt be graded by a new examiner. The decision to accept or reject such a request is made by the associate dean of education. A student may not make a second attempt at any examination already passed in order to receive a higher grade.

In case a course is terminated or significantly altered, examination according to the earlier syllabus shall be offered on at least two occasions in the course of one year after the termination/alteration.

The examiner has the right to give an adapted examination or let the student carry out the examination in an alternative way provided that the intended learning outcomes can be secured and that there are exceptional reasons for this, including the student's right to targeted study support.

Examinationsmoment	Omfattning	Betyg
Öppen online kurs - Elements of AI	3 hp	U/G
Individuell skriftlig inlämningsuppgift	4,5 hp	A/B/C/D/E/FX/F

The instruction is followed-up throughout the course. A course evaluation is conducted at the end of the course. A summary and comments are published in the learning management system. The evaluation constitutes a basis for future improvements to the course.

Crompton, Helen & Burke, Dane (2023). Artificial intelligence in higher education: the state of the field. International Journal of Educational Technology in Higher Education, 20, 22 p.

Holmes, Wayne & Tuomi, Ikka. (2022). State of the art and practice in AI in education. European Journal of Education, 57(4) p. 542-570. 28 p.

Humble, Niklas, Boustedt, Jonas, Holmgren, Hanna, Milutinovic, Goran, Seipel, Stefan, & Östberg, Ann-Sofie (2023). Cheaters or AI-Enhanced Learners: Consequences of ChatGPT for Programming Education. Electronic Journal of e-Learning. 10 p.

Humble, Niklas, & Mozelius, Peter (2022). The threat, hype, and promise of artificial intelligence in education. Discover Artificial Intelligence, 2(22). 13 p.

Luckin, Rose, Holmes, Wayne., Griffiths, Mark, & Forcier, Laurie B. (2016). Intelligence unleashed. An argument for AI in Education. 50 p.

Velander, Johanna, Taiye, Mohammed Ahmed, Otero, Nuno & Milrad, Marcelo (2024). Artificial Intelligence in K-12 Education: eliciting and reflecting on Swedish teachers' understanding of AI and its implications for teaching & learning. Education and Information Technologies, 29, p. 4085–4105. 20 p.

Zhang, Ke & Begum Aslan, A. (2021). AI technologies for education: Recent research & future directions. Computers and Education: Artificial Intelligence, 2. 11 p.

Elements of AI, open online course, www.elementsofai.com. Interactive material of about 40 working hours

Articles and other written material will be added of about 600 p.


Please note that changes may be made to the reading list up until eight weeks before the start of the course.


Citing Sources – How to Create Literature References
www.ju.se/library/search--write/citing-sources---how-to-create-literature-references

The Interactive Anti-Plagiarism Guide – Jönköping University
Information about plagiarism at higher education institutions
Available in the learning management system