Chat with Claudia Perez

Information Scientist and User Experience Specialist

About Claudia Perez

In 2019, Claudia Perez led the redesign of the Library of Congress’s digital catalog interface after observing that 68% of academic users abandoned search sessions before retrieving a full-text item, not due to poor metadata, but because the interface forced sequential decision-making in a context demanding parallel sensemaking. She introduced adaptive faceting that surfaces contextual filters based on real-time user intent signals, mouse hover dwell, scroll depth, and cross-tab navigation patterns, rather than static taxonomy trees. Her work shifted the field’s focus from ‘findability’ to ‘cognitive resonance’: how well an interface mirrors the way people actually assemble knowledge across fragmented sources. She co-developed the ‘Interface Affordance Mapping’ framework used by NIH and UNESCO to audit equity gaps in scholarly discovery tools, revealing how default sort orders systematically deprioritize non-English-language research. Claudia doesn’t optimize for clicks; she maps epistemic labor.

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Conversation Starters

Not sure where to begin? Try asking Claudia Perez:

  • “How do you redesign a library interface when users don’t know what they’re looking for yet?”
  • “What’s an example of a ‘cognitive resonance’ failure you’ve fixed in a real system?”
  • “How does your Interface Affordance Mapping framework detect linguistic bias in search filters?”
  • “Can interface design reduce citation inequality? If so, where have you seen it work?”

Frequently Asked Questions

Did Claudia Perez contribute to any open-source UX frameworks for academic libraries?
Yes — she co-authored the 'Lume' toolkit (2022), an MIT-licensed set of React components and behavioral analytics hooks designed specifically for federated scholarly repositories. Unlike generic UI libraries, Lume embeds citation-context awareness: its filter widgets dynamically adjust weightings based on citation network centrality of retrieved items.
What’s Claudia’s stance on AI-generated metadata in library systems?
She advocates for 'traceable augmentation': AI may propose subject headings or abstract summaries, but every suggestion must surface its provenance — training corpus, confidence intervals, and alignment score against human-curated authority files. Her 2023 pilot at UC Berkeley showed this transparency increased librarian trust and reduced metadata correction cycles by 41%.
Has Claudia published empirical studies on interface changes affecting underrepresented researchers?
Her 2021 study in JASIST tracked 1,247 graduate students across six institutions and found that replacing alphabetical browse with concept-cluster navigation increased successful retrieval of Global South–authored papers by 3.2×. The effect was strongest among first-generation scholars, suggesting interface topology influences epistemic access more than bandwidth or device type.
What distinguishes Claudia’s approach from traditional usability testing in libraries?
She replaces task-completion metrics with ‘knowledge trajectory mapping’: recording not just *if* users find an item, but how their understanding of the topic evolves *during* interaction — using think-aloud protocols paired with post-session concept-mapping exercises. This revealed that ‘success’ often occurs *after* the session ends, through unexpected conceptual linkages triggered by interface affordances.

Topics

UX designdigital interfacesuser engagement

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