Problem
Research questions and friction points this paper is trying to address.
information seeking
Generative AI
search interfaces
cognitive effort
human-computer interaction
Characterizing the Evolving Landscape of Modern Information Seeking
🤖 AI Summary
The rise of generative artificial intelligence has profoundly reshaped users’ information-seeking behaviors, necessitating a systematic understanding of the underlying cognitive mechanisms and interaction dynamics. This study integrates online crowdsourced experiments, theoretical modeling grounded in information search theory, and laboratory-based neurophysiological measurements—such as electroencephalography (EEG)—to pioneer a multimodal approach that jointly analyzes behavioral data and neural correlates. By doing so, it characterizes user preference patterns and fluctuations in cognitive load within novel AI-driven search interfaces. The findings not only elucidate the nature of cognitive effort in generative AI–mediated information seeking but also provide empirical foundations and design principles for developing personalized, cognition-aware information systems.
📝 Abstract
Information seeking (IS) evolves, as does the human IS process. Since the rise of Generative AI (GenAI), modern IS has shifted by introducing more interfaces, more complex interactions, and expanded system capabilities. We argue that these changes in modern IS should be systematically examined. This PhD research characterizes the changes in the modern IS process. We use mechanisms, including online crowdsourcing survey experiments, theoretical IS frameworks, and in-lab experiments with neurophysiological signals, to characterize the shifts in modern IS, especially those driven by GenAI. We offer insights into the current landscape of search interface preferences and the cognitive efforts involved in seeking information. We believe this PhD research will contribute to and inform future designs of personalized, cognition-aware IS systems.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Generative AI
information seeking
neurophysiological signals
cognition-aware systems
search interface
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