As large language models (LLMs) become widespread, students increasingly turn to systems like ChatGPT for writing tasks. Educators worry that this reliance may reduce critical engagement with writing and hinder students’ learning processes. Although datasets exist on students’ use of LLMs for writing, how they functionally use ChatGPT in detail—and how this usage shapes their writing and perceptions—remains underexplored. We conducted an online study (n=77) in which students wrote an essay using an in-house ChatGPT we developed to capture their queries. Through qualitative analysis, we identified the types of assistance students sought and presented patterns of use, ranging from asking for opinions on a topic to delegating the entire writing task to ChatGPT. We also found that students’ writing self-efficacy influenced their querying patterns and that levels of ownership and creativity varied depending on how they used ChatGPT. This study contributes empirical data to ongoing discussions about how writing education should incorporate or regulate LLM-powered tools.

Some people suggest that deliberately watching the camera during video calls can simulate eye contact and help build trust. In this study, we investigated the effects of simulated eye contact in video calls and job interviews through an experimental study and a survey. Study 1 involved participants in a mock interview as an interviewer, where a confederate interviewee simulated eye contact half the time. The gaze patterns of the participants were tracked to understand the effects. In Study 2, we conducted an online survey to confirm the findings of Study 1 on a larger scale by asking those with experience interviewing to evaluate interviewees based on interview videos, half of which simulated eye contact. The results of both studies indicate that simulated eye contact had little impact on their evaluation compared to common belief. We discuss how the results motivate future work and how computational approaches to correcting eye gaze can be deceptive.

This poster presents the development and implementation of a 10-day remix-based summer camp curriculum designed to introduce high school students, particularly a multinational cohort of young women, to programming through creative coding. The curriculum integrates music composition using EarSketch and web development with HTML and CSS. The camp aims to inspire participants to gain self-efficacy in programming and motivate them to explore STEM/computing careers. Preliminary results from surveys and interviews indicate increased confidence in programming skills. This ongoing research explores the impact of remixing as a gateway for transitioning into more general-purpose computing domains such as web development.

Despite the existence of search-based recommender systems like Google, Netflix, and Spotify, online users sometimes may turn to crowdsourced recommendations in places like the r/ifyoulikeblank subreddit. In this exploratory study, we probe why users go to r/ifyoulikeblank, how they look for recommendation, and how the subreddit users respond to recommendation requests. To answer, we collected sample posts from r/ifyoulikeblank and analyzed them using a qualitative approach. Our analysis reveals that users come to this subreddit for various reasons, such as exhausting popular search systems, not knowing what or how to search for an item, and thinking crowd have better knowledge than search systems. Examining users query and their description, we found novel information users provide during recommendation seeking using r/ifyoulikeblank. For example, sometimes they ask for artifacts recommendation based on the tools used to create them. Or, sometimes indicating a recommendation seeker's time constraints can help better suit recommendations to their needs. Finally, recommendation responses and interactions revealed patterns of how requesters and responders refine queries and recommendations. Our work informs future intelligent recommender systems design.