Description:

Generative AI is increasingly used to support knowledge-intensive activities such as literature analysis, requirements engineering, coding, testing, documentation, data analysis, and academic writing. Although these tools can improve efficiency, they may also introduce errors, superficial reasoning, overconfidence, and reduced critical engagement.

This thesis investigates how generative AI changes work practices and how users can benefit from AI assistance without compromising quality, accountability, or professional standards. Depending on the student’s background, the thesis can focus on:

(1) Software engineering practice: effects on development activities, code quality, collaboration, verification, and professional workflows.

(2) Research and education: effects on literature work, scientific reasoning, writing, research skills, and critical evaluation.