User study finds that task difficulty affects keystroke dynamics during LLM prompting as a marker of cognitive effort, while device type has weaker effects and keystrokes do not predict perceived output usefulness.
Examining the use and impact of an AI code assistant on developer productivity and experience in the enterprise, in: Yamashita, N., Evers, V., Yatani, K., Ding, S.X
3 Pith papers cite this work. Polarity classification is still indexing.
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A controlled user study and qualitative survey find that AI assistance raises formalization accuracy for math proofs, with users flexibly combining multiple tools while retaining oversight.
Mixed-methods study adapting UTAUT2 shows individual-level perceptions predict continued GenAI use in Italian SME developers (R²=0.647) while social and organisational factors do not.
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Characterizing initial human-AI proof formalization workflows
A controlled user study and qualitative survey find that AI assistance raises formalization accuracy for math proofs, with users flexibly combining multiple tools while retaining oversight.