RESEARCH

This paper adopted an iterative design process to gain insights into programmers’ strategies when using LLMs for programming. We proposed CoLadder, a novel system that supports programmers by facilitating hierarchical task decomposition, direct code segment manipulation, and result evaluation during prompt authoring. A user study with 12 experienced programmers showed that CoLadder is effective in helping programmers externalize their problem-solving intentions flexibly, improving their ability to evaluate and modify code across various abstraction levels, from their task’s goal to final code implementation.

Voice messages, by nature, prevent users from gauging the emotional tone without fully diving into the audio content. This hinders the shared emotional experience at the pre-retrieval stage. Research scarcely explored “Emotional Teasers”—pre-retrieval cues offering a glimpse into an awaiting message’s emotional tone without disclosing its content. We introduce EmoWear, a smartwatch voice messaging system enabling users to apply 30 animation teasers on message bubbles to reflect emotions. EmoWear eases senders’ choice by prioritizing emotions based on semantic and acoustic processing. EmoWear was evaluated in comparison with a mirroring system using color-coded message bubbles as emotional cues (N=24). Results showed EmoWear significantly enhanced emotional communication experience in both receiving and sending messages. The animated teasers were considered intuitive and valued for diverse expressions. Desirable interaction qualities and practical implications are distilled for future design. We thereby contribute both a novel system and empirical knowledge concerning emotional teasers for voice messaging.

Data exploration is an important aspect of the workflow of mixed-methods researchers, who conduct both qualitative and quantitative analysis. However, there currently exists few tools that adequately support both types of analysis simultaneously, forcing researchers to context-switch between different tools and increasing their mental burden when integrating the results. To address this gap, we propose a unified environment that facilitates mixed-methods analysis in a computational notebook-based settings. We conduct a scenario study with three HCI mixed-methods researchers to gather feedback on our design concept and to understand our users’ needs and requirements.