Large-scale analysis of wild LLM chat logs finds that user interaction patterns stabilize quickly after initial use and correlate with long-term outcomes like retention, creating an agency paradox of limited exploration in unconstrained systems.
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Zamfirescu-Pereira, Richmond Y
22 Pith papers cite this work. Polarity classification is still indexing.
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Persona-driven workflow and interface improve automated and human-AI red-teaming of generative AI by incorporating diverse perspectives into adversarial prompt creation.
AttentionBender applies 2D transforms to cross-attention maps in video diffusion transformers, producing distributed distortions and glitch aesthetics that reveal entangled attention mechanisms while serving as both an XAI probe and creative tool.
GUI agents can transform live web interfaces in real-time via DOM manipulations to deliver contextual assistance directly within the application.
Users treat human delegation for long tasks as a flexible compass but AI delegation as rigid railway tracks due to perceived AI limitations in inference and judgment.
IdeaBlocks modularizes divergent intents into Exploration Blocks with multi-level reuse options, enabling 2.13 times more images explored and 12.5% greater visual diversity than baseline in a comparative user study.
CanvasConvo presents a spatial canvas interface for branching LLM conversations, evaluated in a 5-7 day field study with 24 participants that found support for exploratory workflows.
Malleable Prompting reifies subjective preferences from natural language into GUI widgets and modulates LLM token probabilities during decoding to enable controllable generation, with a user study showing improved precision and perceived controllability over standard prompting.
Narrix helps novices identify and reuse narrative strategies from examples through visualization and strategy-steered generation, improving retention, confidence, and adaptation over chat interfaces in a 12-person study.
Higher generative AI error rates reduce user reliance, but task difficulty does not significantly moderate this effect.
A qualitative study with 22 creative writers finds that the reflective value of AI refusals depends on alignment with users' situational thinking phases, cognitive beliefs, and views of AI roles.
Adaptive Prompt Elicitation (APE) uses an information-theoretic framework to generate visual queries that elicit and compile user intent into better prompts for text-to-image models, showing improved alignment in benchmarks and a user study.
Chaplains view AI chatbots as unable to provide attuned pastoral care for non-clinical emotional needs, based on themes of listening, connecting, carrying, and wanting.
A two-stage VLM-LM system that infers actions from screen recordings to detect inefficient workflows and generate tailored recommendations.
PromptDecipher introduces a correction-based authoring workflow that turns live interaction and response editing into the primary way teachers build and validate AI tutoring chatbots.
OOPrompt reifies user intents into structured manipulable artifacts to enable modular and iterative prompting in LLM-based interactive systems.
Hiding generative AI use to signal expertise reduces knowledge sharing and transparency among workplace colleagues.
VizCopilot integrates topic modeling with document visualization to support user oversight of retrieved context in enterprise chatbots, enabling detection of misalignments and adaptation of prompting strategies.
Industry markets AI agents for orchestration, creation, and insight, but a usability study with 31 participants reveals users face challenges from capability misalignment and lack of meta-cognition in tools like Operator and Manus.
VIDEE introduces a human-in-the-loop system using Monte-Carlo Tree Search for task decomposition, executable pipeline generation, and LLM-based evaluation with visualizations to support non-expert text analytics.
Reddit data analysis shows reply-based mobile scams growing nearly twice as fast as click-based ones while evading commercial and open-source detectors.
A survey of user studies on LLM use in programming that identifies interaction behaviors, mixed benefits and weaknesses, and factors influencing human and task performance.
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Priming, Path-dependence, and Plasticity: Understanding the molding of user-LLM interaction and its implications from (many) chat logs in the wild
Large-scale analysis of wild LLM chat logs finds that user interaction patterns stabilize quickly after initial use and correlate with long-term outcomes like retention, creating an agency paradox of limited exploration in unconstrained systems.
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PersonaTeaming: Supporting Persona-Driven Red-Teaming for Generative AI
Persona-driven workflow and interface improve automated and human-AI red-teaming of generative AI by incorporating diverse perspectives into adversarial prompt creation.
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AttentionBender: Manipulating Cross-Attention in Video Diffusion Transformers as a Creative Probe
AttentionBender applies 2D transforms to cross-attention maps in video diffusion transformers, producing distributed distortions and glitch aesthetics that reveal entangled attention mechanisms while serving as both an XAI probe and creative tool.
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Beyond Chat and Clicks: GUI Agents for In-Situ Assistance via Live Interface Transformation
GUI agents can transform live web interfaces in real-time via DOM manipulations to deliver contextual assistance directly within the application.
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Compass vs Railway Tracks: Unpacking User Mental Models for Communicating Long-Horizon Work to Humans vs. AI
Users treat human delegation for long tasks as a flexible compass but AI delegation as rigid railway tracks due to perceived AI limitations in inference and judgment.
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IdeaBlocks: Expressing and Reusing Divergent Intents for Graphic Design Exploration using Generative AI
IdeaBlocks modularizes divergent intents into Exploration Blocks with multi-level reuse options, enabling 2.13 times more images explored and 12.5% greater visual diversity than baseline in a comparative user study.
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Conversations in Space: Structuring Non-Linear LLM Interactions on a Canvas
CanvasConvo presents a spatial canvas interface for branching LLM conversations, evaluated in a 5-7 day field study with 24 participants that found support for exploratory workflows.
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From Words to Widgets for Controllable LLM Generation
Malleable Prompting reifies subjective preferences from natural language into GUI widgets and modulates LLM token probabilities during decoding to enable controllable generation, with a user study showing improved precision and perceived controllability over standard prompting.
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Narrix: Remixing Narrative Strategies from Examples for Story Writing
Narrix helps novices identify and reuse narrative strategies from examples through visualization and strategy-steered generation, improving retention, confidence, and adaptation over chat interfaces in a 12-person study.
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Effects of Generative AI Errors on User Reliance Across Task Difficulty
Higher generative AI error rates reduce user reliance, but task difficulty does not significantly moderate this effect.
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Beyond Compliance: How AI Could Help Creative Writers by Refusing Them
A qualitative study with 22 creative writers finds that the reflective value of AI refusals depends on alignment with users' situational thinking phases, cognitive beliefs, and views of AI roles.
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Adaptive Prompt Elicitation for Text-to-Image Generation
Adaptive Prompt Elicitation (APE) uses an information-theoretic framework to generate visual queries that elicit and compile user intent into better prompts for text-to-image models, showing improved alignment in benchmarks and a user study.
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Chaplains' Reflections on the Design and Usage of AI for Conversational Care
Chaplains view AI chatbots as unable to provide attuned pastoral care for non-clinical emotional needs, based on themes of listening, connecting, carrying, and wanting.
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The Invisible Mentor: Inferring User Actions from Screen Recordings to Recommend Better Workflows
A two-stage VLM-LM system that infers actions from screen recordings to detect inefficient workflows and generate tailored recommendations.
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PromptDecipher: Supporting AI Tutor Authoring Through Editable Simulated Interactions
PromptDecipher introduces a correction-based authoring workflow that turns live interaction and response editing into the primary way teachers build and validate AI tutoring chatbots.
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OOPrompt: Reifying Intents into Structured Artifacts for Modular and Iterative Prompting
OOPrompt reifies user intents into structured manipulable artifacts to enable modular and iterative prompting in LLM-based interactive systems.
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"If You're Very Clever, No One Knows You've Used It": The Social Dynamics of Developing Generative AI Literacy in the Workplace
Hiding generative AI use to signal expertise reduces knowledge sharing and transparency among workplace colleagues.
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VizCopilot: Fostering Appropriate Reliance on Enterprise Chatbots with Context Visualization
VizCopilot integrates topic modeling with document visualization to support user oversight of retrieved context in enterprise chatbots, enabling detection of misalignments and adaptation of prompting strategies.
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Why Johnny Can't Use Agents: Industry Aspirations vs. User Realities with AI Agents
Industry markets AI agents for orchestration, creation, and insight, but a usability study with 31 participants reveals users face challenges from capability misalignment and lack of meta-cognition in tools like Operator and Manus.
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VIDEE: Visual and Interactive Decomposition, Execution, and Evaluation of Text Analytics with Intelligent Agents
VIDEE introduces a human-in-the-loop system using Monte-Carlo Tree Search for task decomposition, executable pipeline generation, and LLM-based evaluation with visualizations to support non-expert text analytics.
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Read This Paper to Get $50 Million:* An Analysis of Mobile Messaging Scams Using Reddit Data
Reddit data analysis shows reply-based mobile scams growing nearly twice as fast as click-based ones while evading commercial and open-source detectors.
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Understanding the Human-LLM Dynamic: A Literature Survey of LLM Use in Programming Tasks
A survey of user studies on LLM use in programming that identifies interaction behaviors, mixed benefits and weaknesses, and factors influencing human and task performance.