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Reporting and Reviewing LLM-Integrated Systems in HCI: Challenges and Considerations

What should HCI scholars consider when reporting and reviewing papers that involve LLM-integrated systems? We interview 18 authors of LLM-integrated system papers on their authoring and reviewing experiences. We find that norms of trust-building between authors and reviewers appear to be eroded by the uncertainty of L…

Karla Felix Navarro et al.Universite de Montreal

How Notations Evolve: A Historical Analysis with Implications for Supporting User-Defined Abstractions

Traditional human-computer interaction takes place through formally-specified systems like structured UIs and programming languages. Recent AI systems promise a new set of informal interactions with computers through natural language and other notational forms. These informal interactions can then lead to formal repre…

Jingyue Zhang et al.

Université de montréal

Semantic Commit: Helping Users Update Intent Specifications for AI Memory at Scale

As AI agents increasingly rely on memory systems to align with user intent, updating these memories presents challenges of semantic conflict and ambiguity. Inspired by impact analysis in software engineering, we introduce SemanticCommit, a mixed-initiative interface to help users integrate new intent into intent speci…

Priyan Vaithilingam et al.Harvard University

Assistance or Disruption? Exploring and Evaluating the Design and Trade-offs of Proactive AI Programming Support

AI programming tools enable powerful code generation, and recent prototypes attempt to reduce user effort with proactive AI agents, but their impact on programming workflows remains unexplored. We introduce and evaluate Codellaborator, a design probe LLM agent that initiates programming assistance based on editor acti…

Kevin Pu et al.University of Toronto

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ChainBuddy: An AI-assisted Agent System for Generating LLM Pipelines

As large language models (LLMs) advance, their potential applications have grown significantly. However, it remains difficult to evaluate LLM behavior on user-defined tasks and craft effective pipelines to do so. Many users struggle with where to start, often referred to as the "blank page problem." ChainBuddy, an AI…

Jingyue Zhang et al.Mila - Quebec AI Institute

Who Validates the Validators? Aligning LLM-Assisted Evaluation of LLM Outputs with Human Preferences

Due to the cumbersome nature of human evaluation and limitations of code-based evaluation, Large Language Models (LLMs) are increasingly being used to assist humans in evaluating LLM outputs. Yet LLM-generated evaluators simply inherit all the problems of the LLMs they evaluate, requiring further human validation. We…

Shreya Shankar et al.University of California

Imagining a Future of Designing with AI: Dynamic Grounding, Constructive Negotiation, and Sustainable Motivation

We ideate a future design workflow that involves AI technology. Drawing from activity and communication theory, we attempt to isolate the new value that large AI models can provide design compared to past technologies. We arrive at three affordances - dynamic grounding, constructive negotiation, and sustainable motiva…

Priyan Vaithilingam et al.Harvard University

An AI-Resilient Text Rendering Technique for Reading and Skimming Documents

Readers find text difficult to consume for many reasons. Summarization can address some of these difficulties, but introduce others, such as omitting, misrepresenting, or hallucinating information, which can be hard for a reader to notice. One approach to addressing this problem is to instead modify how the original t…

Ziwei Gu et al.Harvard University

ChainForge: A Visual Toolkit for Prompt Engineering and LLM Hypothesis Testing

Evaluating outputs of large language models (LLMs) is challenging, requiring making—and making sense of—many responses. Yet tools that go beyond basic prompting tend to require knowledge of programming APIs, focus on narrow domains, or are closed-source. We present ChainForge, an open-source visual toolkit for prompt…

Ian Arawjo et al.Cornell University

Notational Programming for Notebook Environments: A Case Study with Quantum Circuits

We articulate a vision for computer programming that includes pen-based computing, a paradigm we term notational programming. Notational programming blurs contexts: certain typewritten variables can be referenced in handwritten notation and vice-versa. To illustrate this paradigm, we developed an extension, Notate, to…

Ian Arawjo et al.Cornell University

To Write Code: The Cultural Fabrication of Programming Notation and Practice

Writing and its means have become detached. Unlike written and drawn practices developed prior to the 20th century, notation for programming computers developed in concert and conflict with discretizing infrastructure such as the shift-key typewriter and data processing pipelines. In this paper, I recall the emergence…

Ian Arawjo Cornell University

Computing Education for Intercultural Learning: Lessons from the Nairobi Play Project

This paper explores computing education as a potential site for intercultural learning and encounter in post-conflict environments. It reports on ethnographic fieldwork from the Nairobi Play Project, a constructionist educational program serving adolescents aged 14-18 in urban and rural multi-ethnic refugee communitie…

Ian Arawjo et al.Cornell University

Education

Distraction or Life Saver? The Role of Technology in Undergraduate Students' Boundary Management Strategies

Previous research has shown that communication technologies may make it challenging for working professionals to manage the boundaries between their work life and home life. For college students, however, there is a less clear definition of what constitutes work and what constitutes home life. As a result, students ma…

Hajin Lim et al.Cornell University

Children, Teens, and Young Adults

Paper Title	Authors	Research Topics
Reporting and Reviewing LLM-Integrated Systems in HCI: Challenges and Considerations What should HCI scholars consider when reporting and reviewing papers that involve LLM-integrated systems? We interview 18 authors of LLM-integrated system papers on their authoring and reviewing experiences. We find that norms of trust-building between authors and reviewers appear to be eroded by the uncertainty of L…	KN Karla Felix Navarro et al.Universite de Montreal
How Notations Evolve: A Historical Analysis with Implications for Supporting User-Defined Abstractions Traditional human-computer interaction takes place through formally-specified systems like structured UIs and programming languages. Recent AI systems promise a new set of informal interactions with computers through natural language and other notational forms. These informal interactions can then lead to formal repre…	JZ Jingyue Zhang et al.Université de montréal
Semantic Commit: Helping Users Update Intent Specifications for AI Memory at Scale As AI agents increasingly rely on memory systems to align with user intent, updating these memories presents challenges of semantic conflict and ambiguity. Inspired by impact analysis in software engineering, we introduce SemanticCommit, a mixed-initiative interface to help users integrate new intent into intent speci…	PV Priyan Vaithilingam et al.Harvard University
Assistance or Disruption? Exploring and Evaluating the Design and Trade-offs of Proactive AI Programming Support AI programming tools enable powerful code generation, and recent prototypes attempt to reduce user effort with proactive AI agents, but their impact on programming workflows remains unexplored. We introduce and evaluate Codellaborator, a design probe LLM agent that initiates programming assistance based on editor acti…	KP Kevin Pu et al.University of Toronto
AdRecommended Learn AI Coding at CodeNow Structured lessons, hands-on projects, and continuous updates for people bringing AI into real development work. Explore Now
ChainBuddy: An AI-assisted Agent System for Generating LLM Pipelines As large language models (LLMs) advance, their potential applications have grown significantly. However, it remains difficult to evaluate LLM behavior on user-defined tasks and craft effective pipelines to do so. Many users struggle with where to start, often referred to as the "blank page problem." ChainBuddy, an AI…	JZ Jingyue Zhang et al.Mila - Quebec AI Institute
Who Validates the Validators? Aligning LLM-Assisted Evaluation of LLM Outputs with Human Preferences Due to the cumbersome nature of human evaluation and limitations of code-based evaluation, Large Language Models (LLMs) are increasingly being used to assist humans in evaluating LLM outputs. Yet LLM-generated evaluators simply inherit all the problems of the LLMs they evaluate, requiring further human validation. We…	SS Shreya Shankar et al.University of California
Imagining a Future of Designing with AI: Dynamic Grounding, Constructive Negotiation, and Sustainable Motivation We ideate a future design workflow that involves AI technology. Drawing from activity and communication theory, we attempt to isolate the new value that large AI models can provide design compared to past technologies. We arrive at three affordances - dynamic grounding, constructive negotiation, and sustainable motiva…	PV Priyan Vaithilingam et al.Harvard University
An AI-Resilient Text Rendering Technique for Reading and Skimming Documents Readers find text difficult to consume for many reasons. Summarization can address some of these difficulties, but introduce others, such as omitting, misrepresenting, or hallucinating information, which can be hard for a reader to notice. One approach to addressing this problem is to instead modify how the original t…	ZG Ziwei Gu et al.Harvard University
emoji_events ChainForge: A Visual Toolkit for Prompt Engineering and LLM Hypothesis Testing Evaluating outputs of large language models (LLMs) is challenging, requiring making—and making sense of—many responses. Yet tools that go beyond basic prompting tend to require knowledge of programming APIs, focus on narrow domains, or are closed-source. We present ChainForge, an open-source visual toolkit for prompt…	IA Ian Arawjo et al.Cornell University
emoji_events Notational Programming for Notebook Environments: A Case Study with Quantum Circuits We articulate a vision for computer programming that includes pen-based computing, a paradigm we term notational programming. Notational programming blurs contexts: certain typewritten variables can be referenced in handwritten notation and vice-versa. To illustrate this paradigm, we developed an extension, Notate, to…	IA Ian Arawjo et al.Cornell University
emoji_events To Write Code: The Cultural Fabrication of Programming Notation and Practice Writing and its means have become detached. Unlike written and drawn practices developed prior to the 20th century, notation for programming computers developed in concert and conflict with discretizing infrastructure such as the shift-key typewriter and data processing pipelines. In this paper, I recall the emergence…	IA Ian Arawjo Cornell University
emoji_events Computing Education for Intercultural Learning: Lessons from the Nairobi Play Project This paper explores computing education as a potential site for intercultural learning and encounter in post-conflict environments. It reports on ethnographic fieldwork from the Nairobi Play Project, a constructionist educational program serving adolescents aged 14-18 in urban and rural multi-ethnic refugee communitie…	IA Ian Arawjo et al.Cornell University	Education
Distraction or Life Saver? The Role of Technology in Undergraduate Students' Boundary Management Strategies Previous research has shown that communication technologies may make it challenging for working professionals to manage the boundaries between their work life and home life. For college students, however, there is a less clear definition of what constitutes work and what constitutes home life. As a result, students ma…	HL Hajin Lim et al.Cornell University	Children, Teens, and Young Adults