MBIB4850 Reviewing literature with AI and advanced information retrieval Emneplan

Global research output generates vast amounts of literature disseminated through fragmented channels and increasingly sophisticated retrieval tools. Navigating, collecting, analyzing, and summarizing literature across diverse research fields places high demands on professionals in information retrieval and literature reviews. This course provides a foundational introduction to reviewing literature and other forms of meta-analysis of research, with a special focus on utilizing new AI-based tools and technologies. This includes tools for searching and browsing, processing data from searches, screening, and bibliometric analyses.

The course language is English.

None.

The student should have the following learning outcomes upon successful completion of the course: Knowledge

The student has

• advanced knowledge of methodological approaches to the development of various types of literature reviews.
• in-depth knowledge of how advancements in AI enable new forms of professional search, processing, and analysis of collected information.

Skills

The student can

• apply relevant tools to automate various processes within the development of literature reviews.
• document and communicate the results of a literature review.
• carry out independent, limited research or development projects under supervision and in accordance with current standards of research ethics.

Generel competence

The student can

• critically evaluate various implications of using AI-based technologies.
• understand and communicate the challenges associated with different types of literature reviews and meta-analysis.

The course is organized as a weekly seminar series on campus. Some weeks will be dedicated to practical workshops where specific tools are applied. Throughout the seminars, students will individually develop a literature review on a chosen topic, using experimental applications of various tools and technologies. The course is structured around three main components where students document:

1. a relevant search strategy
2. work involving processing data from searches
3. an analysis of the processed search results

No coursework requirements or compulsory activities.

The exam in the course is an individual portfolio containing two works:

1) A reflection paper with a scope of 5 pages +/- 10%.

2) Documentation of the literature review that has been developed throughout the course with a scope of 8 to 10 pages.

Font and font size: Arial / Calibri 12 points. Line spacing: 1,5.

In the event of a fail grade, all parts of the portfolio must be resubmitted. Students awarded a fail grade are given one opportunity to submit an improved version of the portfolio for assessment.

All aids are permitted, as long as the student complies with the rules for source referencing.

Grading scale A-F.

All term papers are assessed by one internal and one external examiner.