AI-labeled input devices raise user performance expectations but produce no measurable change in objective or subjective interaction outcomes.
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2 Pith papers cite this work. Polarity classification is still indexing.
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The paper describes ongoing efforts to characterize developer diversity in cognition and context and to use personalization to make LLM-based conversational programming assistants more inclusive.
citing papers explorer
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AI Washing Inflates Expected Performance but Not Interaction Outcomes: An AI Placebo Study Using Fitts' Law
AI-labeled input devices raise user performance expectations but produce no measurable change in objective or subjective interaction outcomes.
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Personalizing LLM-Based Conversational Programming Assistants
The paper describes ongoing efforts to characterize developer diversity in cognition and context and to use personalization to make LLM-based conversational programming assistants more inclusive.