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University of Calgary
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GenAI can streamline systematic literature reviews, but without human oversight, it risks compromising rigor and reliability.
AI-generated responses can subtly distort qualitative survey findings, challenging the reliability of participant insights in software engineering research.
Many software engineering studies rely on fewer than 12 interviews, raising questions about the rigor of qualitative research in the field.
Combining human collaboration with AI support can significantly elevate the clarity and quality of software requirements artifacts.
You can boost inclusivity in software engineering education simply by name-dropping diverse pioneers during lectures, without sacrificing technical depth.
LLMs' apparent competence masks a reliance on stereotype-consistent cues, leading to unreliable and unfair behavior across intersectional settings, especially when stereotype alignment reinforces accuracy.
Forget standalone ethics modules – embedding empathy directly into core software engineering tasks is key to building responsible AI.
Software engineering students are most likely to misuse LLMs on programming assignments and documentation, especially when they feel squeezed for time or lack clear guidance.
