User study with 20 novices using ChatGPT identifies recurring AI visualization errors, user prompting issues, trust factors, and collaboration patterns, with distinct failure modes observed on Gemini and Claude.
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Emerging roles and relationships among humans and interactive ai systems.International Journal of Human–Computer Interaction, 41(17):10595–10617
Canonical reference. 73% of citing Pith papers cite this work as background.
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representative citing papers
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.
Qualitative study of 19 practitioners reveals ten LLM product evaluation practices and introduces the results-actionability gap as a key barrier to turning findings into improvements.
An open-source platform integrates generative AI for creating textual and visual stimuli in conjoint analysis surveys, demonstrated via a proof-of-concept study on care robot preferences.
Introduces a gradient-based multilingual audit framework for LLM moral decisions in robot assistance scenarios and reports persistent culturally asymmetric gradient tracking failures not fixed by prompting.
EmoMM benchmark reveals Video Contribution Collapse in MLLMs for emotion recognition under modality conflict and missingness, mitigated by CHASE head-level attention steering.
LLM originality raters exhibit self-preference bias toward artificial responses that disappears after controlling for idea elaboration in the Alternate Uses Task.
Each tested LLM shows its own characteristic unreliability when engaging in repair during extended math-question dialogues.
13 participants became convinced AI understands human values after chatbot interactions evaluated with the VAPT toolkit.
Develops and validates a two-factor scale for machine companionship with AI companions via exploratory factor analysis on N=467 and confirmation on N=249, with post-hoc identification of socioinstrumental and autotelic templates.
User study finds that task difficulty affects keystroke dynamics during LLM prompting as a marker of cognitive effort, while device type has weaker effects and keystrokes do not predict perceived output usefulness.
Eye-tracking experiment finds that labeling code as LLM-generated increases fixation time without changing review thoroughness, with reviewers adapting criteria or using the prompt.
A test-time actor-critic approach for refining image generation prompts won the MediaEval NewsImages 2026 challenge.
Presents the CCAI ontology and SPARQL retrieval method to convert ephemeral Human-Generative AI prompt interactions into explicit, machine-readable collaboration traces, illustrated in a competency-profile software case study.
Exploratory user study of 48 participants finds trade-offs in efficiency, contextual alignment, and social comfort when AI writing assistance varies along synchronous and visual dimensions.
Qualitative study of 20 users of integrated browser conversational AI found that citations raise trustworthiness without verification and that users apply existing LLM and search perceptions to prompting strategies.
A co-located tablet VR setup with spatial separation and tool asymmetry enabled dynamic coordination and active use of teamwork KSAs in two collaborative scenarios.
AI-labeled input devices raise user performance expectations but produce no measurable change in objective or subjective interaction outcomes.
Hiding generative AI use to signal expertise reduces knowledge sharing and transparency among workplace colleagues.
Head- and eye-based pointing outperform hand-based methods for AR 2D selection across depths, with head remaining most accurate and consistent.
A 2x2 between-subjects experiment finds contextualization lowers AI persuasiveness but warmth restores it through crossover interaction, with reliance invariant to design, trust predicting outcomes independently, and AI literacy decoupling trust from behavior.
Qualitative studies show creatives prefer self-experimentation over structured guidance for GenAI image tools to preserve creative autonomy despite terminology barriers.
UTAUT is suitable for studying individual barriers to GenAI use in software engineering when combined with Bayesian analysis, with three priorities for future research on construct refinement, operationalization, and statistical methods.
Researchers clustered 41,300 Moltbook posts from AI agents with k-means and retrieval-augmented generation to produce validated personas that represent behavioral diversity in agent populations.
citing papers explorer
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Vibe Visualizing: How Visualization Novices Try (and Fail) to Generate and Interpret Visualizations with Conversational AI
User study with 20 novices using ChatGPT identifies recurring AI visualization errors, user prompting issues, trust factors, and collaboration patterns, with distinct failure modes observed on Gemini and Claude.
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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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Results-Actionability Gap: Understanding How Practitioners Evaluate LLM Products in the Wild
Qualitative study of 19 practitioners reveals ten LLM product evaluation practices and introduces the results-actionability gap as a key barrier to turning findings into improvements.
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From Prompts to Preferences: An Open-Source Platform for Generative AI-Enhanced Conjoint Analysis
An open-source platform integrates generative AI for creating textual and visual stimuli in conjoint analysis surveys, demonstrated via a proof-of-concept study on care robot preferences.
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Auditing LLM-Governed Social Robots with Culture-Specific Moral Gradients
Introduces a gradient-based multilingual audit framework for LLM moral decisions in robot assistance scenarios and reports persistent culturally asymmetric gradient tracking failures not fixed by prompting.
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EmoMM: Benchmarking and Steering MLLM for Multimodal Emotion Recognition under Conflict and Missingness
EmoMM benchmark reveals Video Contribution Collapse in MLLMs for emotion recognition under modality conflict and missingness, mitigated by CHASE head-level attention steering.
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The Effect of Idea Elaboration on the Automatic Assessment of Idea Originality
LLM originality raters exhibit self-preference bias toward artificial responses that disappears after controlling for idea elaboration in the Alternate Uses Task.
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Talking to a Know-It-All GPT or a Second-Guesser Claude? How Repair reveals unreliable Multi-Turn Behavior in LLMs
Each tested LLM shows its own characteristic unreliability when engaging in repair during extended math-question dialogues.
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AI and My Values: User Perceptions of LLMs' Ability to Extract, Embody, and Explain Human Values from Casual Conversations
13 participants became convinced AI understands human values after chatbot interactions evaluated with the VAPT toolkit.
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Measuring Machine Companionship: Scale Development and Validation for AI Companions
Develops and validates a two-factor scale for machine companionship with AI companions via exploratory factor analysis on N=467 and confirmation on N=249, with post-hoc identification of socioinstrumental and autotelic templates.
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Typing Behavior in Human-LLM Interaction: Keystroke Dynamics Reveal Cognitive Effort During Prompting
User study finds that task difficulty affects keystroke dynamics during LLM prompting as a marker of cognitive effort, while device type has weaker effects and keystrokes do not predict perceived output usefulness.
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Same Scrutiny, More Time: Eye Tracking Insights into Reviewing LLM-Labelled Code
Eye-tracking experiment finds that labeling code as LLM-generated increases fixation time without changing review thoroughness, with reviewers adapting criteria or using the prompt.
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A Test-time Actor-Critic Approach to News Images Generation
A test-time actor-critic approach for refining image generation prompts won the MediaEval NewsImages 2026 challenge.
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From Prompts to Context: An Ontology-Driven Framework for Human-Generative AI Collaboration
Presents the CCAI ontology and SPARQL retrieval method to convert ephemeral Human-Generative AI prompt interactions into explicit, machine-readable collaboration traces, illustrated in a competency-profile software case study.
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"It Felt a Bit Eerie": Exploring Humanlike Interactions During Collaborative Writing with an Artificial Agent
Exploratory user study of 48 participants finds trade-offs in efficiency, contextual alignment, and social comfort when AI writing assistance varies along synchronous and visual dimensions.
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Beliefs and Misconceptions around Integrated Conversational AI
Qualitative study of 20 users of integrated browser conversational AI found that citations raise trustworthiness without verification and that users apply existing LLM and search perceptions to prompting strategies.
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Where's the Team Spirit? An Exploratory Study on Team Development Through Co-located Tablet-Based VR
A co-located tablet VR setup with spatial separation and tool asymmetry enabled dynamic coordination and active use of teamwork KSAs in two collaborative scenarios.
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AI Washing Inflates Expected Performance but Not Interaction Outcomes: An AI Placebo Study Using Fitts' Law
AI-labeled input devices raise user performance expectations but produce no measurable change in objective or subjective interaction outcomes.
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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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Comparing Controller-Free Pointing Techniques Across Depth for 2D Selection in Augmented Reality
Head- and eye-based pointing outperform hand-based methods for AR 2D selection across depths, with head remaining most accurate and consistent.
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Personalized to Persuade: The Effects of Contextualization and Warmth on Trust and Reliance in Conversational AI
A 2x2 between-subjects experiment finds contextualization lowers AI persuasiveness but warmth restores it through crossover interaction, with reliance invariant to design, trust predicting outcomes independently, and AI literacy decoupling trust from behavior.
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How Creatives Approach GenAI Image Generation: Tensions Between Structured Guidance, Self-Experimentation, and Creative Autonomy
Qualitative studies show creatives prefer self-experimentation over structured guidance for GenAI image tools to preserve creative autonomy despite terminology barriers.
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GenAI in Software Engineering: The Role of Technology Acceptance Models
UTAUT is suitable for studying individual barriers to GenAI use in software engineering when combined with Bayesian analysis, with three priorities for future research on construct refinement, operationalization, and statistical methods.
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How to Model AI Agents as Personas?: Applying the Persona Ecosystem Playground to 41,300 Posts on Moltbook for Behavioral Insights
Researchers clustered 41,300 Moltbook posts from AI agents with k-means and retrieval-augmented generation to produce validated personas that represent behavioral diversity in agent populations.
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LLM Harms: A Taxonomy and Discussion
Proposes a five-bucket taxonomy of LLM harms and calls for dynamic auditing, but the systematic review behind it is not reproducible and contains mismatched citations.