About

Robert Capra is a Professor at the School of Information and Library Science at the University of North Carolina at Chapel Hill. His research interests lie at the intersection of human-computer interaction and information retrieval. He focuses on projects related to interactive information retrieval, search interfaces, and personal information management. His work is unified by an interest in how people find, refind, manage, and share information, and how technology can help support these tasks. Capra holds a Ph.D. in Computer Science from Virginia Tech, as well as an M.S. and B.S. in Computer Science from Washington University in St. Louis. He teaches courses including Database Systems I, Usability Evaluation and Testing, and Programming Information Applications.

Research topics

• Computer Science
• Information Retrieval
• Psychology
• Human–computer interaction
• Cognitive psychology
• World Wide Web
• Sociology
• Knowledge management
• Mathematics
• Engineering

Selected publications

• The Effects of Learning Objectives on Searchers' Perceptions and Behaviors

  UNC Libraries · 2026-04-07

  articleOpen access1st authorCorresponding

  In recent years, the "search as learning" community has argued that search systems should be designed to support learning. We report on a lab study in which we manipulated the learning objectives associated with search tasks assigned to participants. We manipulated learning objectives by leveraging Anderson and Krathwohl's taxonomy of learning (A&K's taxonomy)[2], which situates learning objectives at the intersection of two orthogonal dimensions: the cognitive process and the knowledge type dimension. Participants in our study completed tasks with learning objectives that varied across three cognitive processes (apply, evaluate, and create) and three knowledge types (factual, conceptual, and procedural knowledge). We focus on the effects of the tasks cognitive process and knowledge type on participants' pre-/post-task perceptions and search behaviors. Our results found that the three knowledge types considered in our study had a greater effect than the three cognitive processes. Specifically, conceptual knowledge tasks were perceived to be more difficult and required more search activity. We discuss implications for designing search systems that support learning.

  PublisherDOI

• Strengthening the CHIIR Community: Bridging Interactive IR and HCI at CHI 2026

  2026-04-13

  article
  PublisherDOI

• The effects of goal-setting on learning during information seeking with generative AI

  2026-02-28

  articleOpen accessSenior author

  Our research in this paper lies at the intersection of Generative AI (GenAI) and search-as-learning (SAL). GenAI technologies (e.g., ChatGPT) have revolutionized how people search for and interact with information. However, we do not yet fully understand how people use GenAI systems to learn about complex topics. SAL research has studied how different tools can support learning with traditional document retrieval systems. Our research closely relates to SAL work that has investigated the effects of goal-setting on learning during search. We explore the influence of goal-setting on learning during information-seeking sessions with a GenAI system. We report on a between-subjects crowdsourced study (N = 120) in which participants were asked to learn about a complex topic using a GenAI system. The study had four conditions that varied along two factors (a 2 × 2 design). The first factor involved displaying related web results in addition to the GenAI output. The second factor involved giving participants access to the Subgoal Manager (SM), a tool designed to help people develop subgoals and take notes. We investigated the effects of both factors on: (RQ1) perceptions; (RQ2) behaviors; (RQ3) learning and retention; (RQ4) the types of requests issued to the system; and (RQ5) participants’ motivations for engaging (or not engaging) with the related web results. Results found that participants with access to the SM had higher post-task learning outcomes, did less copy/pasting into their notes, perceived the task as more difficult, and requested more examples and support for differentiating concepts from the GenAI system.

  PublisherDOI

• A Questionnaire for Capturing Perceptions of Self-Regulated Learning Processes during Information Seeking

  2026-02-28

  articleOpen accessSenior author

  We present the SRL Perceptions Questionnaire (SPQ), developed to measure perceptions of self-regulated learning (SRL) after information seeking and learning sessions. In a crowd-sourced study (N = 127), participants completed the SPQ after searching to learn about a complex topic. The SPQ asked participants to report their perceptions of particular SRL constructs (e.g., planning, monitoring, strategy use, adapting). A principal component analysis supported a five-factor structure with high reliability (α > =.87). Perceived SRL did not correlate with normalized learning gains, yet pre-task and post-task perceptions showed correlations with several SPQ dimensions. We offer both the SPQ as an instrument for measuring SRL (processes critical to supporting human learning) after information seeking and insights into how perceptions of SRL constructs align with objective learning outcomes, pre-task perceptions, and post-task perceptions while learning during search.

  PublisherDOI

• Designing Search Engine Result Pages for Immersive Virtual Reality: Insights from Eye-Tracking and User Perception Data

  2025-07-13

  articleOpen accessSenior author

  Extensive research exists on user interactions with search engine result pages (SERPs) in desktop and mobile environments. However, relatively little work has focused on understanding how virtual reality (VR) users interact with SERPs in 3D immersive virtual environments (IVEs). Unlike 2D displays with established paradigms (e.g., ranked lists), 3D IVEs lack standardized methods for presenting search results. This work explores how different information arrangements in 3D virtual space impact users' search behaviors and preferences.

  PublisherOA PDFDOI

• Blending Queries and Conversations: Understanding Tactics, Trust, Verification, and System Choice in Web Search and Chat Interactions

  ArXiv.org · 2025-04-07

  preprintOpen access

  This paper presents a user study (N=22) where participants used an interface combining Web Search and a Generative AI-Chat feature to solve health-related information tasks. We study how people behaved with the interface, why they behaved in certain ways, and what the outcomes of these behaviours were. A think-aloud protocol captured their thought processes during searches. Our findings suggest that GenAI is neither a search panacea nor a major regression compared to standard Web Search interfaces. Qualitative and quantitative analyses identified 78 tactics across five categories and provided insight into how and why different interface features were used. We find evidence that pre-task confidence and trust both influenced which interface feature was used. In both systems, but particularly when using the chat feature, trust was often misplaced in favour of ease-of-use and seemingly perfect answers, leading to increased confidence post-search despite having incorrect results. We discuss what our findings mean in the context of our defined research questions and outline several open questions for future research.

  PublisherOA PDFDOI

• Blending Queries and Conversations: Understanding Trust, Verification, and System Choice in Search and Chat Interactions

  2025-03-24 · 8 citations

  articleOpen access

  This paper presents a user study (N=22) where participants used an interface combining Web Search and a Generative AI-Chat feature to solve health-related information tasks.We study how people behaved with the interface, why they behaved in certain ways, and what the outcomes of these behaviours were.A think-aloud protocol captured their thought processes during searches.Our findings suggest that GenAI is neither a search panacea nor a major regression compared to standard Web Search interfaces.Qualitative and quantitative analyses identified 78 tactics across five categories and provided insight into how and why different interface features were used.We find evidence that pre-task confidence and trust both influenced which interface feature was used.In both systems, but particularly when using the chat feature, trust was often misplaced in favour of ease-of-use and seemingly perfect answers, leading to increased confidence post-search despite having incorrect results.We discuss what our findings mean in the context of our defined research questions and outline several open questions for future research.

  PublisherOA PDFDOI

• Beyond the Surface: Investigating Explicit and Implicit Perceptions of Music Diversity

  2025-03-24

  articleOpen access

  Diversity has been an important concept in both interactive information retrieval (IIR) and recommendation systems (RS), especially in the context of entertainment activities such as music, video, or games.However, diversity itself is a highly abstract concept, and providing diversity to users brings the risk of unsatisfactory results.In this short paper, we investigate diversity in the context of music recommendation.We conducted an online survey with 149 participants to investigate how they perceive music diversity.Our results revealed their explicit ideas about different aspects of diversity, as well as their underlying perceptions shaping their diversity preferences.We propose viewing music diversity using an "above and below-surface" structure, and our findings can help design other entertainment systems such as video or games.

  PublisherOA PDFDOI

• Search+Chat: Integrating Search and GenAI to Support Users with Learning-oriented Search Tasks

  2025-03-24 · 10 citations

  articleOpen accessSenior author

  Generative AI (GenAI) technologies such as ChatGPT are changing the ways people interact with information.To illustrate, popular search engines (e.g., Google) have started integrating responses from GenAI tools with the traditional search results.In this paper, we explore the integration of GenAI technology with traditional search in the context of a learning-oriented task.We report on a between-subjects study ( = 40) in which participants completed a complex, learning-oriented search task.Participants were assigned to one of two conditions.In the SearchOnly condition, participants used a traditional web search system to gather information.In the Search+Chat condition, participants used an experimental system that combined a traditional web search component and an interactive GenAI-based chat component (Chat AI).The study investigated seven research questions.RQ1-RQ3 focused on differences between groups: (RQ1) post-task perceptions, (RQ2) search behaviors, and (RQ3) learning outcomes.To measure learning, participants completed a multiple-choice test before the search task, immediately after, and one week later (to measure retention).RQ4-RQ7 delved deeper into participants' behaviors and experiences in the Search+Chat condition: (RQ4) motivations for (and gains from) engaging with the Chat AI; (RQ5) the phases during which participants engaged with the Chat AI; (RQ6) the types of queries issued to each component; and (RQ7) perceptions about the information returned by each component.

  PublisherOA PDFDOI

• PIM 2024: The Information We Need, When We Need It…: As We Get Ever Closer, Is this Ideal Still Ideal?

  2024-03-08 · 4 citations

  article

  An oft-repeated ideal of personal information management (PIM) is to have “the right information, at the right time, in the right place…” for the current need. But the technologies and innovations that bring us ever closer to this ideal carry costs as well as benefits. In this ninth in a series of PIM workshops, we give closer, critical consideration to the “right time, right place” ideal of PIM. Can we manage the potential downsides involved in achieving this ideal, while preserving its obvious benefits? Or should we revise our ideal of PIM?

  PublisherDOI

Recent grants

• CAREER: Knowledge Representation and Re-Use for Exploratory and Collaborative Search

  NSF · $546k · 2016–2023

• III: Small: Search Assistance Using Search Trails

  NSF · $498k · 2017–2021

Frequent coauthors

• R. J. Wilson

  Colorado State University

  429 shared

• J. Ocariz

  Université Paris Cité

  396 shared

• L. Roos

  Laboratoire de Physique Nucléaire et de Hautes Énergies

  394 shared

• M. Benayoun

  Université Paris Cité

  393 shared

• J. Schwiening

  381 shared

• A. Roodman

  SLAC National Accelerator Laboratory

  381 shared

• M. Rotondo

  Istituto Nazionale di Fisica Nucleare, Laboratori Nazionali di Frascati

  381 shared

• M. Carpinelli

  380 shared

Education

• B.S., Computer Science

  Washington University in St. Louis

• Ph.D., Computer Science

  Virginia Tech

  2006

• M.S., Computer Science

  Washington University in St. Louis

  1994

Awards & honors

• 2022 Best Short Paper Award – ACM SIGIR Conference on Human…
• 2021 Virginia Tech Department of Computer Science Distinguis…
• 2017 Best Paper Award – 2017 European Conference on Informat…