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This work introduces E3Sense, a head-worn platform that co-locates electroencephalography, eye tracking, and electrodermal activity to personalize engagement measurement and provides a proof-of-concept of a head-site, personalized multimodal sensing of engagement for adaptive educational interfaces.
The findings suggest that, contrary to common assumptions, AI assistance may not improve the reliability of code comprehension and review, and highlight the importance of helping developers evaluate machine-generated reliability artifacts, in addition to generating them.
Generative AI is increasingly permeating software engineering, enabling developers to generate functions, files, and even entire applications from natural language specifications. AI systems are also becoming more personalized, adapting outputs based on inferred user characteristics and interaction history. While perso...
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