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The Impact of Generative AI on Critical Thinking: Self-Reported Reductions in Cognitive Effort and Confidence Effects From a Survey of Knowledge WorkersAdvait Sarkar · 2025The rise of Generative AI (GenAI) in knowledge workflows raises questions about its impact on critical thinking skills and practices. We survey 319 knowledge workers to investigate 1) when and how they perceive the enaction of critical thinking when using GenAI, and 2) when and why GenAI affects their effort to do so. Participants shared 936 first-hand examples of using GenAI in work tasks. Quantitatively, when considering both task- and user-specific factors, a user’s task-specific self-confidence and confidence in GenAI are predictive of whether critical thinking is enacted and the effort of doing so in GenAI-assisted tasks. Specifically, higher confidence in GenAI is associated with less critical thinking, while higher self-confidence is associated with more critical thinking. Qualitatively, GenAI shifts the nature of critical thinking toward information verification, response integration, and task stewardship. Our insights reveal new design challenges and opportunities for developing GenAI tools for knowledge work. CCS Concepts • Human-centered computing →Empirical studies in HCI.

UX of AI & PersonalizationResearch Papers

Real-Time Adaptation of Context-Aware Intelligent User Interfaces, for Enhanced Situational AwarenessZinova Stefanidi, George Margetis · 2022In this work, a novel computational approach for the dynamic adaptation of User Inter- faces (UIs) is proposed, which aims at enhancing the Situational Awareness (SA) of users by leveraging the current context and providing the most useful information, in an optimal and efficient manner. By combining Ontology modeling and reasoning with Combinatorial Optimization, the system decides what information to present, when to present it, where to visualize it in the display - and how, taking into consideration contextual factors as well as placement constraints. The main objective of the proposed approach is to optimize the SA associated with the displayed UI at run-time, while avoiding information overload and induced stress. In the context of this work, we have deployed our computational approach to the use case of an Augmented Reality (AR) system for Law Enforcement Agents (LEAs). To explore the benefits and limitations of the developed system, two evaluations have been conducted. The first one was an expert-based evaluation with LEAs and User Experience (UX) experts, assessing the appropriateness of the system’s decisions. The second one was a user-based evaluation involving LEAs from different agencies, estimating the SA, the mental workload and the overall UX associated with the system, through an AR simulation. The results indicate that the system enhances SA, and while not imposing workload, it provides an overall positive UX. INDEX TERMS Adaptive user interfaces, augmented reality, context-awareness, intelligent user interfaces, ontology modeling, ontology reasoning, situational awareness, user interface optimization.

UX of AI & PersonalizationResearch Papers

Multimodal Differentiation of Obstacles in Repeated Adaptive Human-Computer InteractionsFelix Putze · 2021Human Computer Interaction can be impeded by various interac- tion obstacles, impacting a user’s perception or cognition. In this work, we detect and discriminate such interaction obstacles from different data modalities to compensate for them through User Interface (UI) adaptation. For example, we detect memory-based obstacles from brain activity and compensate through repetition of information in the UI; we detect visual obstacles from user behavior and compensate by complementing visual with auditory informa- tion in the UI. Online cognitive adaptive systems should be able to decide the most suitable UI adaptation given inputs from several obstacles detectors. In this paper, we employ a Bayesian fusion approach upon different underlying obstacles detectors over mul- tiple consecutive interaction sessions. Experimental results show that the model promisingly outperforms the baseline in the first interaction with an average accuracy of 72.5% and further improves drastically in subsequent interactions with additional information, with an average accuracy of 98%. CCS CONCEPTS • Human-centered computing →User models.

UX of AI & PersonalizationResearch Papers

Effect of Adaptive Guidance and Visualization Literacy on Gaze Attentive Behaviors and Sequential Patterns on Magazine-Style Narrative VisualizationsOSWALD BARRAL, SÉBASTIEN LALLÉ et al. · 2021We study the effectiveness of adaptive interventions at helping users process textual documents with embedded visualizations, a form of multimodal documents known as Magazine-Style Narrative Visualizations (MSNVs). The interventions are meant to dynamically highlight in the visualization the datapoints that are described in the textual sentence currently being read by the user, as captured by eye-tracking. These interventions were previously evaluated in two user studies that involved 98 participants reading excerpts of real-world MSNVs during a 1-hour session. Participants' outcomes included their subjective feedback about the guidance, and well as their reading time and score on a set of comprehension questions. Results showed that the interventions can increase comprehension of the MSNV excerpts for users with lower levels of a cognitive skill known as visualization literacy. In this article, we aim to further investigate this result by leveraging eye-tracking to analyze in depth how the participants processed the interventions depending on their levels of visualization literacy. We first analyzed summative gaze metrics that capture how users process and integrate the key components of the narrative visualizations. Second, we mined the salient patterns in the users' scanpaths to contextualize how users sequentially process these components. Results indicate that the interventions succeed in guiding attention to salient components of the narrative visualizations, especially by generating more transitions between key components of the visualization (i.e., datapoints, labels, and legend), as well as between the two modalities (text and visualization). We also show that the interventions help users with lower levels of visualization literacy to better map datapoints to the legend, which likely contributed to their improved comprehension of the documents. These findings shed light on how adaptive interventions help users with different levels of visualization literacy, informing the design of personalized narrative visualizations.

UX of AI & PersonalizationResearch Papers

Context-Aware Online Adaptation of Mixed Reality InterfacesDavid Lindlbauer, Anna Maria Feit et al. · 2019We present an optimization-based approach for Mixed Reality (MR) systems to automatically control when and where appli­ cations are shown, and how much information they display. Currently, content creators design applications, and users then manually adjust which applications are visible and how much information they show. This choice has to be adjusted every time users switch context, i.e., whenever they switch their task or environment. Since context switches happen many times a day, we believe that MR interfaces require automation to alleviate this problem. We propose a real-time approach to automate this process based on users’ current cognitive load and knowledge about their task and environment. Our system adapts which applications are displayed, how much informa­ tion they show, and where they are placed. We formulate this problem as a mix of rule-based decision making and combina­ torial optimization which can be solved effciently in real-time. We present a set of proof-of-concept applications showing that our approach is applicable in a wide range of scenarios. Finally, we show in a dual-task evaluation that our approach decreased secondary tasks interactions by 36%. Author Keywords Mixed Reality, Context-Awareness, UI Optimization CCS Concepts •Human-centered computing → Mixed / augmented real­ ity; Virtual reality; User interface management systems; Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for proft or commercial advantage and that copies bear this notice and the full citation on the frst page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specifc permission and/or a fee. Request permissions from permissions@acm.org. UIST’19, The 32nd Annual ACM Symposium on User Interface Software and Tech­ nology, October 20–23, 2019. New Orleans, LA, USA © 2019 Copyright held by the owner/author(s). Publication rights licensed to ACM. ISBN 978-1-4503-6816-2/19/10...$15.00 DOI: 10.1145/3332165.3347945

UX of AI & PersonalizationResearch Papers

The Influence of Personality Traits and Cognitive Load on the Use of Adaptive User InterfacesHarvard SEAS · 2017One of the problems adaptive interfaces must solve is the is- sue of stability—users must be able to complete a familiar task reliably. Split Adaptive Interfaces, where a limited part of the screen contains copies of the interface elements pre- dicted to be of immediate use, are one technique for resolv- ing this difficulty. While prior work demonstrated that Split Adaptive Interfaces improve performance on average, the re- sults of our study demonstrate systematic individual differ- ences in the utilization of the adaptive features, which cor- relate with the stable user traits of Need for Cognition and Extraversion. Specifically, higher Need for Cognition (a will- ingness to undertake difficult mental activities) is correlated with increased utilization rates, while higher Extraversion (a general orientation towards seeking gratification from the ex- ternal world) is negatively correlated with utilization rates. Our results also demonstrate a significant negative correlation between cognitive load induced by a secondary task and the utilization of the adaptive features. This effect, however, is very small (less than two percentage points). Together, these results provide additional evidence of the usefulness of the split adaptive interface approach and a negligible effect of ad- ditional cognitive load, but also demonstrate that the approach does not benefit all users equally. Author Keywords Adaptive user interfaces, cognitive load, extraversion, need for cognition ACM Classification Keywords H.5.m. Information Interfaces and Presentation: Miscella- neous

UX of AI & PersonalizationResearch Papers

Towards a Human-centered E-Commerce Personalization FrameworkMarios Belk · 2015This paper presents a personalization framework, namely PersonaWeb that adapts the visual and interaction design of E-Commerce Web environments based on human cognitive differences. In particular, it describes a user model formalization that incorporates a set of human cognitive factors (i.e., cognitive styles and working memory capacity) and an adaptation engine that personalizes the visual and interaction design attributes of E-Commerce product views. The proposed framework has been applied in a real-life E-Commerce Web-site and two subsequent user studies were conducted in which 135 users interacted with the personalized and the original (non-personalized) version of the same Web environment. Results indicate the added value of personalizing content and functionality of E-Commerce product views in terms of users’ task completion performance.

UX of AI & PersonalizationResearch Papers