The impact of large language models (LLMs) on critical thinking has provoked growing attention, yet this impact on actual performance may not be uniformly negative or positive. Particularly, the role of time—the temporal context under which an LLM is provided—remains overlooked. In a between-subjects experiment (n=393), we examined two types of time constraints for a critical thinking task requiring participants to make a reasoned decision for a real-world scenario based on diverse documents: (1) LLM access timing—an LLM available only at the beginning (early), throughout (continuous), near the end (late), or not at all (no LLM), and (2) time availability—insufficient or sufficient time for the task. We found a temporal reversal: LLM access from the start (early, continuous) improved performance under time pressure but impaired it with sufficient time, whereas beginning the task independently (late, no LLM) showed the opposite pattern. These findings demonstrate that time constraints fundamentally shape whether an LLM augments or undermines critical thinking, making time a central consideration when designing LLM support and evaluating human-AI collaboration in cognitive tasks.

AI demonstrates unprecedented reasoning capabilities, but its increasing integration into human reasoning via automated reading and summarization has provoked debate about its use for cultural interpretation. Close reading—the practice of understanding, analyzing, and critiquing cultural texts for pleasure—is a skill at the core of such interpretation, traditionally being seen as exclusive to humans. To test AI’s impact on close reading, both in terms of interpretative performance and pleasure, we conducted a preregistered randomized experiment (n = 400) investigating the impact of AI assistance by presenting single or multiple AI interpretations, on close reading poems, compared to no AI assistance. We found that single AI interpretation boosted both performance and pleasure, while multiple AI interpretations only improved performance. Further exploration revealed a trade-off: participants who heavily relied on AI showed better performance on the task but lower pleasure. Our results contribute to discussion on whether and how to calibrate AI assistance for cultural interpretation: “less is more.”

The rise of Generative AI (GenAI) has demonstrated significant potential to improve productivity and foster creativity among content creators, social media influencers with large audiences on platforms such as Instagram, TikTok, and YouTube. However, as GenAI tools became increasingly integrated into creative workflows, significant concerns have emerged about potential risks and harms, including misinformation, social biases, and threats to authenticity. While prior research in HCI and CSCW has documented the pressures content creators face within algorithmic ecosystems, relatively little is known about how creators practically manage responsibility work when using GenAI tools. To address this gap, we conducted semi-structured interviews (N = 16) with content creators active on popular social media platforms such as YouTube, Instagram, and TikTok, examining their motivations, practices, and specific challenges related to responsible GenAI use. Our findings reveal that creators’ motivations for practicing responsible AI use span personal reputation management, audience trust-building, and broader social responsibility. However, they face persistent tensions, as integrating GenAI significantly intensifies conflicts between responsible AI practices and the pressures of visibility, engagement, and monetization imposed by platform algorithms. Content creators are required to perform extensive and often invisible responsibility work, which directly conflicts with the rapid production cycles and engagement demands of algorithm-driven platforms. Based on these insights, we propose concrete socio-technical design implications at the individual, community, and institutional levels, advocating solutions that shift responsibility beyond individual creators alone.

There have been increasing calls within HCI to build sustained partnerships with communities that go beyond surface-level engagement. However, little is known about how communities view such partnerships and their outcomes. In collaboration with a community-based organization, we co-analyzed a series of interviews to understand the impacts of university-led research initiatives and publicly deployed technologies on local communities, and to explore strategies for more equitable community-university partnerships. Our findings reveal that local communities often perceive technology companies and academic institutions as potential threats due to their shared role in a series of projects, including predictive policing, surveillance, and broader concerns on technological bias and exclusion against minoritized groups. While interviewees named material benefits, sustained relationships, and meaningful accountability as desirable from universities, they pointed to academia's institutional priorities that pose barriers to forming effective partnerships. Drawing from la paperson's concept of a Third University, we argue that researchers and academic institutions must contend with these complexities, while taking a decolonizing approach to community-university partnerships through the lens of revestment.

Ruiyi Wang, Stephanie Milani, Jamie C. Chiu, Jiayin Zhi, Shaun M. Eack, Travis Labrum, Samuel M Murphy, Nev Jones, Kate V Hardy, Hong Shen, Fei Fang, Zhiyu Chen. Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing. 2024.

Food waste and food insecurity are two problems that co-exist worldwide. A major force to combat food waste and insecurity, food rescue platforms (FRP) match food donations to low-resource communities. Since they rely on external volunteers to deliver the food, communicating rescue task difficulty to volunteers is very important for volunteer engagement and retention. We develop a hybrid model with tabular and natural language data to predict the difficulty of a given rescue trip, which significantly outperforms baselines in identifying easy and hard rescues. Furthermore, using storyboards, we conducted interviews with different stakeholders to understand their perspectives on how to integrate such predictions into volunteers' workflow. Motivated by our findings, we developed three explanation methods to generate interpretable insights for volunteers to better understand the predictions. The results from this study are in the process of being adopted at Food Rescue Hero, a large FRP serving over 25 cities across the United States.