Generative AI (gen AI) tools are rapidly reshaping higher education, influencing research, teaching, learning, and administrative work. This study investigates when, how, and why gen AI is used and not used in a research-intensive university. We conducted in-depth surveys with faculty and staff in mid-2024 (n=102) and mid-2025 (n=101) to capture evolving adoption and perception. Across both years, over 90% of respondents reported intentional use of gen AI, though some described unintentional use or intentional non-use. The most frequently mentioned tools included ChatGPT, Copilot, Gemini, Claude, and Adobe Firefly.

Information credibility is a central concept in information science research and is closely linked to how people evaluate and use information. Credibility perceptions also play an important role in the adoption and continued use of technologies such as generative AI (gen AI). As gen AI becomes increasingly integrated into everyday life, it is essential to understand how people perceive the credibility of information produced by these systems. As part of a larger survey study on the use (and non-use) and perceptions of gen AI in higher education conducted in mid-2024 and mid-2025, we evaluated a set of questionnaire items to assess the perceived credibility of information created by gen AI. In this paper, we present the questionnaire items, descriptive statistics, and correlation matrices from the two surveys, and the results of our exploratory factor analysis examining the underlying structure of the credibility measure. Across both datasets, we identified two distinct factors—output credibility, which refers to the perceived credibility of AI-produced output itself, and relative credibility, which refers to perceptions of AI-produced output relative to human-produced information. We share the instrument and findings to support future refinement and adaptation in studying credibility in the context of gen AI.

Abstract Exploratory searches involve significant cognitively demanding aiming at learning and investigation. However, users gain little support from search engines for their cognitive and metacognitive activities (e.g., discovery, synthesis, planning, transformation, monitoring, and reflection) during exploratory searches. To better support the exploratory search process, we designed a new search assistance tool called OrgBox. OrgBox allows users to drag‐and‐drop information they find during searches into “boxes” and “items” that can be created, labeled, and rearranged on a canvas. We conducted a controlled, within‐subjects user study with 24 participants to evaluate the OrgBox versus a baseline tool called the OrgDoc that supported rich‐text features. Our findings show that participants perceived the OrgBox tool to provide more support for grouping and reorganizing information, tracking thought processes, planning and monitoring search and task processes, and gaining a visual overview of the collected information. The usability test reveals users' preferences for simplicity, familiarity, and flexibility of the design of OrgBox, along with technical problems such as delay of response and restrictions of use. Our results have implications for the design of search‐assisting systems that encourage cognitive and metacognitive activities during exploratory search processes.

In this paper, we describe our use of realism check questions in an exit questionnaire as a method for assessing the realism of scenarios and simulated tasks. We used realism check questionnaire items found in the decision-making literature to assess the realism of the scenario and tasks (i.e., simulated work task scenarios) used as part of a broader study of decision-making. The realism check results indicated that participants could imagine themselves in the scenario (i.e., making recommendations for a friend) and could imagine making recommendations like the ones in the study. The findings suggest that realism check questions are a relatively low effort method to assess the realism of simulated work task scenarios used in an interactive information retrieval study. Future research is needed to understand the relationship between realism and other important factors in IIR studies (e.g., topic interest or prior knowledge, search behaviors).

Prior work in IR has found that searchers under time constraints may adapt their search processes and perceive their task or their performance differently. In many of these prior studies, the task descriptions implicitly or explicitly conveyed an expectation of the amount of information needed to satisfy the task requirements in terms of number of pages (e.g., find N webpages on topic X) or the time to spend on the task (e.g., search until time is up) rather than allowing the participant to determine how much information was needed. In this lab-based study, we investigated the effects of time constraints on information search and decision-making. Participants completed a series of decision-making tasks with half of the participants receiving a 5-minute time constraint and half given no time guidance. They were asked to make good, specific recommendations for a friend, and they had considerable latitude in deciding how much information they needed. Results showed that participants in the time constraint condition made their decisions faster but there were few significant differences in measures of search behaviors between the time constraint conditions (RQ1). Qualitative analysis indicated that participants adapted their decision task by varying their recommendations in their specificity, justification strength, and contents in both time conditions (RQ2). Finally, we found evidence that the impact of the time constraint on time- and task-related perceptions was moderated by the extent to which participants adapted their decision task (RQ3).

Current search systems provide effective support to users engaged in fact-finding and look-up oriented tasks. However, they provide relatively little support for users engaged in exploratory search tasks that involve cognitive and metacognitive activities such as learning, synthesis, planning, and reflection. We conducted a within-subject user study (N=24) that investigated the effects of a novel knowledge organization tool called the OrgBox, designed to assist users with organizing and synthesizing information, and metacognitive activities. The OrgBox included features to allow users to drag-drop information they found through search into "boxes" that could be created, labelled, and re-arranged. Study participants completed two exploratory search tasks, one with the OrgBox, and one with the OrgDoc, a baseline tool that included features of a rich-text editor (e.g., formatting, bullets) for taking notes.

Recently, there has been interest in integrating information retrieval systems more closely with users' knowledge development processes, especially to support exploratory search. In this work, we investigated how people organize and structure information they discover during exploratory searches. In a lab study, we asked 24 participants to take hand-written notes they could use in the future while they were completing an exploratory search. We then asked participants to organize their findings to share with someone else who wants to explore the topic. Finally, we conducted post-session interviews to gain insights into the types of information saved and how participants organized the information they found. In our qualitative analysis of the notes and interviews, we found that the notes included background information about the topic, key concepts, specific details, useful information sources, and information to help with the broader work task. Notes were primarily structured in lists, and reflected a combination of linear note-taking strategies and grouping by information source or topical themes. Participants changed the content and structure of the shared notes to make them easier to understand and to provide a more thorough overview of the topic. Our findings have implications for the design of search tools to help current searchers organize, structure, and synthesize information and to help future searchers engaged in similar information seeking tasks.

In this paper, we analyze the impact of task time limits as described by 24 participants in an experimental study investigating the use of a search assistance tool, the Search Guide (SG), during four search tasks with varying levels of cognitive complexity. Participants were given a 12 minute task time limit and a warning notification when 3 minutes remained. In post-experiment interviews, participants reported two different impacts of the time limit on use of the SG: ten described using the SG as a way to save time given the time limit while five reported less SG use due to uncertainty whether it would contain useful information. Participants also described skimming and reading pages more shallowly, selecting easier to read search results, and bookmarking pages more freely because of the limited time they had to complete the task.

This full-day tutorial provides general instruction about the design of controlled laboratory experiments that are conducted in order to better understand human information interaction and retrieval. Different data collection methods and procedures are described, with an emphasis on self-report measures and scales. This tutorial also introduces the use of statistical power analysis for sample size estimation and introduces and demonstrate two data analysis procedures, Multilevel Modeling and Structural Equation Modeling, that allow for examination of the whole set of variables present in interactive information retrieval (IIR) experiments, along with their various effect sizes. The goals of the tutorial are to increase participants' (1) understanding of the uses of controlled laboratory experiments with human participants; (2) understanding of the technical vocabulary and procedures associated with such experiments and (3) confidence in conducting and evaluating IIR experiments. Ultimately, we hope our tutorial will increase research capacity and research quality in IR by providing instruction about best practices to those contemplating interactive IR experiments.

During information search, people often experience time pressure. This might be a result of a deadline, the system's performance or some other event. In this paper, we report results of a study with forty-five participants which investigated how time constraints and system delays impacted the user experience during information search. We randomly assigned half of our study participants to a treatment condition where they were only allowed five minutes per search task (the other half were given no time limits). For half of participants' search tasks, five second delays were introduced after queries were submitted and SERP results were clicked. We used multilevel modeling to evaluate a number of hypotheses about the effects of time constraint, system delays and user experience. We found those in the time constraint condition reported significantly greater time pressure, experienced higher task difficulty, less satisfaction with their performance, increased importance of working fast and engaged in more metacognitive monitoring. We found when experiencing system delays participants reported slower system speeds when encountering delays on the second task. This work opens a new line of inquiry into how time pressure impacts the search experience and how tools and interfaces might be designed to support people who are searching under time pressure. It also presents an example of how multilevel modeling can be used to better understand and model the complex interactions that occur during interactive information retrieval.

This full-day tutorial provides general instruction about the design of controlled laboratory experiments that are conducted in order to better understand human information interaction and retrieval. Different data collection methods and procedures are described, with an emphasis on self-report measures and scales. This tutorial also introduces the use of statistical power analysis for sample size estimation and introduces and demonstrate two data analysis procedures, Multilevel Modeling and Structural Equation Modeling, that allow for examination of the whole set of variables present in interactive information retrieval (IIR) experiments, along with their various effect sizes. The goals of the tutorial are (1) to increase participants? understanding of the uses of controlled laboratory experiments with human participants; (2) to increase participants? understanding of the technical vocabulary and procedures associated with such experiments and (2) to increase participants? confidence in conducting and evaluating IIR experiments. Ultimately, we hope our tutorial will increase research capacity and research quality in IR by providing instruction about best practices to those contemplating interactive IR experiments.

In this paper, we study how users interact with a search assistance tool while completing tasks of varying complexity. We designed a novel tool referred to as the search guide (SG) that displays the search trails (queries issued, results clicked, pages bookmarked) from three previous users who completed the task. We report on a laboratory study with 48 participants that investigates different factors that may influence user interaction with the SG and the effects of the SG on different outcome measures. Participants were asked to find and bookmark pages for four tasks of varying complexity and the SG was made available to half the participants. We collected log data and conducted retrospective stimulated recall interviews to learn about participants' use of the SG. Our results suggest the following trends. First, interaction with the SG was greater for more complex tasks. Second, the a priori determinability of the task (i.e., whether the task was perceived to be well-defined) helped predict whether participants gained a bookmark from the SG. Third, participants who interacted with the SG, but did not gain a bookmark, felt less system support than those who gained a bookmark and those who did not interact. Finally, a qualitative analysis of our interviews suggests differences in motivation and benefits from SG use for different levels of task complexity. Our findings extend prior research on search assistance tools and provide insights for the design of systems to help users with complex search tasks.

The primary purpose of this research is to explore the impact of perceived time pressure on search behaviors, searcher perceptions of the search system and the search experience. Are there observable behavioral changes when a searcher is time-pressured? To what extent are search behavior differences attributable to objective experimental manipulation versus to the subjective experience of time pressure? An important secondary purpose of this work is to identify appropriate outcome measures that allow for the comparison of session-level search behaviors when time is manipulated.

We report preliminary results of the impact of time pressure and system delays on search behavior from a laboratory study with forty-three participants. To induce time pressure, we randomly assigned half of our study participants to a treatment condition where they were only allowed five minutes to search for each of four ad-hoc search topics. The other half of the participants were given no task time limits. For half of participants' search tasks (n=2), five second delays were introduced after queries were submitted and SERP results were clicked. Results showed that participants in the time pressure condition queried at a significantly higher rate, viewed significantly fewer documents per query, had significantly shallower hover and view depths, and spent significantly less time examining documents and SERPs. We found few significant differences in search behavior for system delay or interaction effects between time pressure and system delay. These initial results show time pressure has a significant impact on search behavior and suggest the design of search interfaces and features that support people who are searching under time pressure.