Student feedback is critical for improving teaching, yet instructors often avoid reading evaluations due to emotional burden and information overload. We present a systematic exploration of how language models can distill and transform student evaluations into adaptive, actionable insights. Through a systematic design space exploration combining 4 feedback strategies (removing harmful content, paraphrasing criticism, sandwiching negatives, adding constructive suggestions) with 4 presentation formats (themes, cards, letters, chatbots), we created six AI-augmented prototypes of teaching evaluations. Interviews with 16 post-secondary instructors revealed that effective use of AI in feedback processing should: (1) support action formation through focused views and divergent thinking, (2) reduce emotional costs while enabling celebration and sharing, (3) facilitate longitudinal engagement and re-contextualization across terms, and (4) maintain transparency and preserve access to original context to build trust. Our work provides design guidelines for AI-augmented feedback systems and demonstrates how language models can adaptively process and present information based on feedback receivers' specific needs and contexts.

With the shift to hybrid meetings in work spaces, there is an increasing need to create a more inclusive hybrid meeting experience where people meeting together in a room interact with those joining remotely. This paper describes a design exploration, implementation, and evaluation of Perspectives, a novel hybrid meeting system that aimed to create an inclusive and equitable space for hybrid meetings. Perspectives digitally composites everyone into a virtual room so that each person has a unique but spatially consistent viewpoint into the meeting. The user study compared Perspectives with three commercially available UX designs for hybrid meetings: Gallery, Together Mode, and Front Row. Results from this study revealed key benefits of Perspectives, including supporting natural interactions, creating a strong sense of co-presence, and reducing cognitive load. Results from the study also helped iterate on the design principles of Perspectives, which offer important insights on supporting hybrid meetings.

Human-AI collaboration for decision-making strives to achieve team performance that exceeds the performance of humans or AI alone. However, many factors can impact success of Human-AI teams, including a user’s domain expertise, mental models of an AI system, trust in recommendations, and more. This article reports on a study that examines users’ interactions with three simulated algorithmic models, all with equivalent accuracy rates but each tuned differently in terms of true positive and true negative rates. Our study examined user performance in a non-trivial blood vessel labeling task where participants indicated whether a given blood vessel was flowing or stalled. Users completed 140 trials across multiple stages, first without an AI and then with recommendations from an AI-Assistant. Although all users had prior experience with the task, their levels of proficiency varied widely. Our results demonstrated that while recommendations from an AI-Assistant can aid in users’ decision making, several underlying factors, including user base expertise and complementary human-AI tuning, significantly impact the overall team performance. First, users’ base performance matters, particularly in comparison to the performance level of the AI. Novice users improved, but not to the accuracy level of the AI. Highly proficient users were generally able to discern when they should follow the AI recommendation and typically maintained or improved their performance. Mid-performers, who had a similar level of accuracy to the AI, were most variable in terms of whether the AI recommendations helped or hurt their performance. Second, tuning an AI algorithm to complement users’ strengths and weaknesses also significantly impacted users’ performance. For example, users in our study were better at detecting flowing blood vessels, so when the AI was tuned to reduce false negatives (at the expense of increasing false positives), users were able to reject those recommendations more easily and improve in accuracy. Finally, users’ perception of the AI’s performance relative to their own performance had an impact on whether users’ accuracy improved when given recommendations from the AI. Overall, this work reveals important insights on the complex interplay of factors influencing Human-AI collaboration and provides recommendations on how to design and tune AI algorithms to complement users in decision-making tasks.

Live streaming is a rapidly growing industry, with millions of content creators using platforms like Twitch to share games, art, and other activities. However, with this rise in popularity, most streamers often fail to attract viewers and grow their platforms. Analytic tools—which have shown success in other business and learning contexts—may be one potential solution, but their use in streaming settings remains unexplored. In this study, we focused on game streaming and interviewed 18 game streamers on Twitch and Mixer about their information needs and current use of tools, supplemented by explorations into their Discord communities. We find that streamers have a range of content, marketing, and community information needs, many of which are not being met by available tools. We conclude with design implications for developing more streamer-centered analytics for video game streamers.

Understanding how racial information impacts human decision making in online systems is critical in today's world. Prior work revealed that race information of criminal defendants, when presented as a text field, had no significant impact on users' judgements of recidivism. We replicated and extended this work to explore how and when race information influences users' judgements, with respect to the saliency of presentation. Our results showed that adding photos to the race labels had a significant impact on recidivism predictions for users who identified as female, but not for those who identified as male. The race of the defendant also impacted these results, with black defendants being less likely to be predicted to recidivate compared to white defendants. These results have strong implications for how system-designers choose to display race information, and cautions researchers to be aware of gender and race effects when using Amazon Mechanical Turk workers.