Course syllabus - AI in Research Practice, 3 credits
Information about the course
- Course code: FEMT001
- Third-cycle suject: Energy- and Environmental Engineering
- Responsible department: Department of Engineering Science
- Valid from: Spring semester 2026
- Established by: Head of department
- Decision date: 2026-03-30
- Level of education: Third cycle level
Course objective
The purpose of the course is for the doctoral student to deepen their ability to critically evaluate and methodically integrate AI tools into their own research practice. The course highlights responsible and transparent use of AI-based tools, such as language models, code assistants, literature analysis tools, and data analysis tools, in research.
Given the rapid development of AI technologies, the course focuses on long-term principles and criteria for critical and responsible use over time, rather than on individual tools. Furthermore, the course contributes to developing the doctoral student’s ability to make conscious and well-founded decisions regarding the use of AI in the dissemination and publication of research
Course content
The course addresses the use of AI tools in doctoral education across disciplinary and institutional boundaries. The course covers:
- AI as a research tool: opportunities, limitations, risks, and uncertainties in different phases of the research process, including critical evaluation in relation to research design, analysis, and interpretation.
- AI tools in publishing and academic communication, including transparency, authorship, and responsibility.
- Disciplinary and institutional norms governing the responsible use of AI in research.
Intented learning outcomes
After completing the course, the doctoral student shall be able to:
- Critically assess the appropriateness of using AI tools in different phases of their own research project.
- Make independent and well-founded judgments regarding responsibility, transparency, and authorship in relation to the use of AI.
- Apply relevant disciplinary and institutional norms governing the use of AI in their own research practice and justify their choices.
- Present, discuss, and critically defend their own analysis of the use of AI tools in research in academic contexts.
The intended qualitative targets in relation to the Higher Education Ordinance, appendix 2.
Knowledge and understanding
For the Degree of Doctor, the doctoral student shall demonstrate:
- A2: familiarity with research methodology in general and the methods of the specific field of research in particular.
Competence and skills
For the Degree of Doctor, the doctoral student shall demonstrate
- B1: the capacity for scholarly analysis and synthesis as well as to review and assess new and complex phenomena, issues and situations autonomously and critically,
- B2: the ability to identify and formulate issues with scholarly precision critically, autonomously, and creatively, and to plan and use appropriate methods to undertake research and other qualified tasks within predetermined time frames and to review and evaluate such work, and
- B4: the ability in both national and international contexts to present and discuss research and research findings authoritatively in speech and writing and in dialogue with the academic community and society in general.
Judgement and approach
For a Degree of Doctor the doctoral student shall demonstrate
- C1: intellectual autonomy and disciplinary rectitude as well as the ability to make assessments of research ethics, and
- C2: specialised insight into the possibilities and limitations of research, its role in society and the responsibility of the individual for how it is used.
Teaching formats
The course is conducted in the form of lectures, seminars, and workshops. The course concludes with a mini-conference where the doctoral students present and discuss their work.
Examination
INL1, individual written assignment, 2 credits, concerning learning outcomes 1–4, gradd fail (U) or pass (G).
SEM1, oral presentation at the mini-conference, 1 credit, concerning learning outcomes 1–4, grade fail (U) or pass (G).
Active participation in seminars and workshops is mandatory and constitutes a prerequisite for examination.
Grade
Examinations included in the course are assessed according to a two-grade scale, fail or pass.
A person who has not passed the regular examination shall be given the opportunity to retake the test.
Requirements
To participate in the course and the examinations included in the course, the applicant must be admitted to third-cycle (doctoral) studies in an engineering subject or alternatively be a teacher/researcher in an engineering subject.
Specific entry requirements
For participation in the course in the spring semester 2026, participation in the NordTek conference on 1–4 June 2026 in Västerås is mandatory.
Selection criteria
Selection of applicants is carried out in accordance with the following ranking:
- Doctoral students admitted to doctoral studies in an engineering subject at an institution belonging to the NordTek network.
- Other doctoral students in an engineering subject.
- Doctorate-holding teachers/researchers in an engineering subject at an institution belonging to the NordTek network.
Selection criteria
This course corresponds to the 2.5-credit requirement in Research Ethics in the general syllabus for Energy and Environmental Engineering.