Generative AI for scientific work (2027)

Welcome to Generative AI for scientific work

Description:

Generative AI is rapidly reshaping how scientific work gets done, from literature review and coding to data analysis and academic writing, yet PhD students are largely left to figure out for themselves what these tools can and cannot reliably do. This course introduces participants to the practical use of generative AI throughout the research workflow, while engaging critically with the validity, reproducibility, authorship, and ethical questions the technology raises for scientific practice. Aimed at PhD students across the TECH faculty, the course combines hands-on work with current tools and methods alongside structured reflection on how generative AI is changing what it means to do rigorous research.

Preparation for the course:

Ahead of the course, participants complete a short set of pre-readings drawn from recent commentary on generative AI in science (see Key literature) and set up the software environment needed for the hands-on sessions, including API access to a current large language model and a working Python environment. Participants are also asked to come with a concrete research problem from their own PhD work that they can use as a running case throughout the three days.

Prerequisites:

The course is open to PhD students across the TECH faculty. Participants are expected to be comfortable with basic scripting and to have a research project of their own that they can draw on during the practical sessions. No prior experience with generative AI tools is required.

Learning objectives:

By the end of the course, participants will be able to: Apply generative AI tools to concrete tasks in their own research workflow, including literature review, coding, data analysis, and writing. Critically evaluate the outputs of these tools with respect to validity, reproducibility, and bias. Reflect on the implications of generative AI for authorship, research ethics, and scientific practice in their own field. Communicate, in a short presentation, how they have integrated generative AI into a specific research task and what they learned from doing so.

Teaching methods:

The course combines short lectures introducing key concepts and current debates with hands-on lab sessions where participants work directly with generative AI tools on their own research problems. Group work is used throughout to encourage peer discussion and comparison of approaches across disciplines, and the course concludes with short student presentations in which participants share what they have built and what they have learned.

Key literature:

Artificial intelligence and illusions of understanding in scientific research. Nature, 627, 49–58. AI and science: what 1,600 researchers think. Nature, 621, 672–675.

Organizer: Niels van Berkel

Lecturers: Niels van Berkel, Simo Hosio (University of Oulu, Finland)

ECTS: 2.0

Date: 

31 March 2027

2 April 2027

Place: Aalborg University

City: Aalborg

Maximal number of participants: 30

Open for enrolment: 31 November 2026

Deadline for enrolment: 10 March 2027

Important information concerning PhD courses:

There is a no-show fee of DKK 3,000 for each course where the student does not show up. Cancellations are accepted no later than 2 weeks before the start of the course. Registered illness is of course an acceptable reason for not showing up on those days. Furthermore, all courses open for registration approximately four months before start of the course. 

For external PhD students: This course is a general course and is prioritized for PhD Students enrolled at Aalborg University. If there are available seats, PhD students from other universities will be accepted. You will be notified shortly after the deadline if you have been accepted.

To attend courses at the Doctoral School in Medicine, Biomedical Science and Technology you must be enrolled as a PhD student. 

We cannot ensure any seats before the deadline for enrolment, all participants will be informed after the deadline, approximately 3 weeks before the start of the course.

You may find more information in our FAQ: https://phd.moodle.aau.dk/local/page/faq

For inquiries not described in the FAQ, please contact the PhD administration at phdcourses@adm.aau.dk. When contacting us please state the course title and course period. Thank you.