All

Papers

Browse and search HCI research papers from All

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Author: 37238

29 results

AI & Writing

AdRecommended

Learn AI Coding at CodeNow

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Session 3e: Trust and Understanding in Explainable AI

Workplace I

XAI 2

Human-AI collaboration; Human-AI collaboration

Paper Title	Authors	Research Topics
	SR
	WM
	AH	AI & Writing
emoji_events	JL
AdRecommended Learn AI Coding at CodeNow Structured lessons, hands-on projects, and continuous updates for people bringing AI into real development work. Explore Now
	KF
emoji_events	SK
	UE	Session 3e: Trust and Understanding in Explainable AI
emoji_events	NS
	UE
	AK	Workplace I
	VL	XAI 2
	VC	XAI 2
	SM
	QL
	MF	Human-AI collaboration; Human-AI collaboration
	JS
	VL
	SN
	SP
emoji_events	UE

All

Papers

Browse and search HCI research papers from All

Active Filters

Author: 37238

29 results

AI & Writing

AdRecommended

Learn AI Coding at CodeNow

Structured lessons, hands-on projects, and continuous updates for people bringing AI into real development work.

Explore Now

Session 3e: Trust and Understanding in Explainable AI

Workplace I

XAI 2

Human-AI collaboration; Human-AI collaboration

Paper Title	Authors	Research Topics
	SR
	WM
	AH	AI & Writing
emoji_events	JL
AdRecommended Learn AI Coding at CodeNow Structured lessons, hands-on projects, and continuous updates for people bringing AI into real development work. Explore Now
	KF
emoji_events	SK
	UE	Session 3e: Trust and Understanding in Explainable AI
emoji_events	NS
	UE
	AK	Workplace I
	VL	XAI 2
	VC	XAI 2
	SM
	QL
	MF	Human-AI collaboration; Human-AI collaboration
	JS
	VL
	SN
	SP
emoji_events	UE

Papers

From Use to Oversight: How Mental Models Influence User Behavior and Output in AI Writing Assistants

Results-Actionability Gap: Understanding How Practitioners Evaluate LLM Products in the Wild

"It was 80% me, 20% AI": Seeking Authenticity in Co-Writing with Large-Language Models

As Confidence Aligns: Understanding the Effect of AI Confidence on Human Self-confidence in Human-AI Decision Making

Learn AI Coding at CodeNow

Canvil: Designerly Adaptation for LLM-Powered User Experiences

Fostering Appropriate Reliance on Large Language Models: The Role of Explanations, Sources, and Inconsistencies

Seamful Explainable AI (XAI): Operationalizing Seamful Design in XAI

Generative Echo Chamber? Effect of LLM-Powered Search Systems on Diverse Information Seeking

The Who in XAI: How AI Background Shapes Perceptions of AI Explanations

Sensing Wellbeing in the Workplace, Why and For Whom? Envisioning Impacts with Organizational Stakeholders

Selective Explanation: Leveraging Human Input to Align Explainable AI

Understanding the Role of Human Intuition on Reliance in Human-AI Decision-Making with Explanations

fAIlureNotes: Supporting Designers in Understanding the Limits of AI Models for Computer Vision Tasks

Designerly Understanding: Information Needs for Model Transparency to Support Design Ideation for AI-Powered User Experience

Human-AI Collaboration for UX Evaluation: Effects of Explanation and Synchronization

Investigating Explainability of Generative Models for Code through Scenario-based Design

Human-AI Collaboration via Conditional Delegation: A Case Study of Content Moderation

Model LineUpper: Supporting Interactive Model Comparison at Multiple Levels for AutoML

Facilitating Knowledge Sharing from Domain Experts to Data Scientists for Building NLP Models

Expanding Explainability: Towards Social Transparency in AI systems

From Use to Oversight: How Mental Models Influence User Behavior and Output in AI Writing Assistants

Results-Actionability Gap: Understanding How Practitioners Evaluate LLM Products in the Wild

"It was 80% me, 20% AI": Seeking Authenticity in Co-Writing with Large-Language Models

As Confidence Aligns: Understanding the Effect of AI Confidence on Human Self-confidence in Human-AI Decision Making

Learn AI Coding at CodeNow

Canvil: Designerly Adaptation for LLM-Powered User Experiences

Fostering Appropriate Reliance on Large Language Models: The Role of Explanations, Sources, and Inconsistencies

Seamful Explainable AI (XAI): Operationalizing Seamful Design in XAI

Generative Echo Chamber? Effect of LLM-Powered Search Systems on Diverse Information Seeking

The Who in XAI: How AI Background Shapes Perceptions of AI Explanations

Sensing Wellbeing in the Workplace, Why and For Whom? Envisioning Impacts with Organizational Stakeholders

Selective Explanation: Leveraging Human Input to Align Explainable AI

Understanding the Role of Human Intuition on Reliance in Human-AI Decision-Making with Explanations

fAIlureNotes: Supporting Designers in Understanding the Limits of AI Models for Computer Vision Tasks

Designerly Understanding: Information Needs for Model Transparency to Support Design Ideation for AI-Powered User Experience

Human-AI Collaboration for UX Evaluation: Effects of Explanation and Synchronization

Investigating Explainability of Generative Models for Code through Scenario-based Design

Human-AI Collaboration via Conditional Delegation: A Case Study of Content Moderation

Model LineUpper: Supporting Interactive Model Comparison at Multiple Levels for AutoML

Facilitating Knowledge Sharing from Domain Experts to Data Scientists for Building NLP Models

Expanding Explainability: Towards Social Transparency in AI systems

Papers

From Use to Oversight: How Mental Models Influence User Behavior and Output in AI Writing Assistants

Results-Actionability Gap: Understanding How Practitioners Evaluate LLM Products in the Wild

"It was 80% me, 20% AI": Seeking Authenticity in Co-Writing with Large-Language Models

As Confidence Aligns: Understanding the Effect of AI Confidence on Human Self-confidence in Human-AI Decision Making

Learn AI Coding at CodeNow

Canvil: Designerly Adaptation for LLM-Powered User Experiences

Fostering Appropriate Reliance on Large Language Models: The Role of Explanations, Sources, and Inconsistencies

Seamful Explainable AI (XAI): Operationalizing Seamful Design in XAI

Generative Echo Chamber? Effect of LLM-Powered Search Systems on Diverse Information Seeking

The Who in XAI: How AI Background Shapes Perceptions of AI Explanations

Sensing Wellbeing in the Workplace, Why and For Whom? Envisioning Impacts with Organizational Stakeholders

Selective Explanation: Leveraging Human Input to Align Explainable AI

Understanding the Role of Human Intuition on Reliance in Human-AI Decision-Making with Explanations

fAIlureNotes: Supporting Designers in Understanding the Limits of AI Models for Computer Vision Tasks

Designerly Understanding: Information Needs for Model Transparency to Support Design Ideation for AI-Powered User Experience

Human-AI Collaboration for UX Evaluation: Effects of Explanation and Synchronization

Investigating Explainability of Generative Models for Code through Scenario-based Design

Human-AI Collaboration via Conditional Delegation: A Case Study of Content Moderation

Model LineUpper: Supporting Interactive Model Comparison at Multiple Levels for AutoML

Facilitating Knowledge Sharing from Domain Experts to Data Scientists for Building NLP Models

Expanding Explainability: Towards Social Transparency in AI systems

From Use to Oversight: How Mental Models Influence User Behavior and Output in AI Writing Assistants

Results-Actionability Gap: Understanding How Practitioners Evaluate LLM Products in the Wild

"It was 80% me, 20% AI": Seeking Authenticity in Co-Writing with Large-Language Models

As Confidence Aligns: Understanding the Effect of AI Confidence on Human Self-confidence in Human-AI Decision Making

Learn AI Coding at CodeNow

Canvil: Designerly Adaptation for LLM-Powered User Experiences

Fostering Appropriate Reliance on Large Language Models: The Role of Explanations, Sources, and Inconsistencies

Seamful Explainable AI (XAI): Operationalizing Seamful Design in XAI

Generative Echo Chamber? Effect of LLM-Powered Search Systems on Diverse Information Seeking

The Who in XAI: How AI Background Shapes Perceptions of AI Explanations

Sensing Wellbeing in the Workplace, Why and For Whom? Envisioning Impacts with Organizational Stakeholders

Selective Explanation: Leveraging Human Input to Align Explainable AI

Understanding the Role of Human Intuition on Reliance in Human-AI Decision-Making with Explanations

fAIlureNotes: Supporting Designers in Understanding the Limits of AI Models for Computer Vision Tasks

Designerly Understanding: Information Needs for Model Transparency to Support Design Ideation for AI-Powered User Experience