PhD Position in Responsible AI in Software Engineering Education

PhD Position in Responsible AI in Software Engineering Education

The Center for Industrial Software (CIS) at the Maersk Mc-Kinney Møller Institute , University of Southern Denmark, invites applications for a three-year PhD position in Responsible AI in Software Engineering Education. The position is based at SDU’s campus in Sønderborg and is available from 1 March 2027or as soon as possible thereafter.

The position is funded by the Novo Nordisk Foundation and is part of PREPARE — Pedagogical Alignment for Responsible AI in Software Engineering Education, a joint project between SDU and the IT University of Copenhagen (ITU).

CIS is a young but fast-growing unit whose research groups span cybersecurity, artificial intelligence, embedded systems, and software engineering. Our students and staff come from Denmark, the rest of the EU, and beyond, and our working language is English.

About the position

Generative AI has arrived in our classrooms faster than our pedagogy has adapted to it. Students increasingly delegate the very things we are trying to teach them — verification, trade‑off reasoning, judgment, accountability — to tools whose reasoning they cannot inspect. Most institutional responses so far have been restrictions or isolated policies, and neither addresses the underlying problem. This project takes the opposite route: if AI is here to stay in software engineering practice, responsible AI use should be deliberately taught, practiced, and assessed.

This is a unique position in that research and its consequences sit in the same place. You will not be studying a distant phenomenon and writing it up; you will be running studies in live courses, designing interventions from what you find, and seeing them change how software engineering is taught. If you see yourself as someone who wants to help our discipline navigate the AI disruption rather than wait for it to settle, this is that opportunity.

Responsibility and opportunities

Concretely, you can expect to:

Design and conduct empirical studies of how software engineering students generally use generative AI — how they justify it, what they delegate to it, and where their agency is preserved or surrendered.

Translate those findings into agency artifacts, practicum activities, and assessment designs that can be used by real instructors in real courses.

Co‑develop and evaluate self‑learning components, and contribute to alignment work on intended learning outcomes, activities, and assessment together with the ITU stream.

Work directly with the head of programs and instructors to implement classroom interventions, whose buy‑in is what makes this kind of change hold. Persuading colleagues is part of the craft here, not an afterthought.

Publish in leading venues for computing education research, and present internationally.

Required scientific qualifications

A master’s degree in software engineering, computer science, or a closely related field, equivalent to a Danish master’s degree and including a substantial thesis component. The degree must be completed before the starting date. Students with master’s in education and related fields are welcome to apply.

A grade average at or above 10 on the Danish 7‑point scale, or a documented equivalent from the awarding institution. Applicants with degrees from outside Denmark should include a grading scale and, where available, a conversion statement.

Documented ability to carry out independent scientific work, evidenced by the master’s thesis.

Qualifications

Required

A software engineering background, with practical familiarity with how software is actually built — you should be able to judge whether an AI‑assisted solution is sound, not only whether it runs.

A genuine motivation for teaching and education, both in delivery and as an object of research.

The ability to design and carry out empirical studies involving students, and to engage stakeholders for support.

Excellent written and spoken English.

Nice to have

Prior research and publication experience.

Experience with empirical research methods — interventions, experiment, interviews, coding, thematic or pattern analysis — and with mixed‑methods designs.

Familiarity with computing education research, or with constructive alignment as a course design framework.

Teaching or teaching‑assistant experience at university level.

Hands‑on experience using generative AI tools in software development, and a considered view of where they help and where they do not.

Personal competences

Critical and ingenious thinking. You question assumptions — including your own, including ours, and including the ones embedded in our education design and the AI tools you will be studying.