Designing user interfaces (UIs) is a critical step when launching products, building portfolios, or personalizing projects, yet end users without design expertise often struggle to articulate their intent and to trust design choices. Existing example-based tools either promote broad exploration, which can cause overwhelm and design drift, or require adapting a single example, risking design fixation. We present UI Remix, an interactive system that supports mobile UI design through an example-driven design workflow. Powered by a multimodal retrieval-augmented generation (MMRAG) model, UI Remix enables iterative search, selection, and adaptation of examples at both the global (whole interface) and local (component) level. To foster trust, it presents source transparency cues such as ratings, download counts, and developer information. In an empirical study with 24 end users, UI Remix significantly improved participants’ ability to achieve their design goals, facilitated effective iteration, and encouraged exploration of alternative designs. Participants also reported that source transparency cues enhanced their confidence in adapting examples. Our findings suggest new directions for AI-assisted, example-driven systems that empower end users to design with greater control, trust, and openness to exploration.

Large Language Model (LLM)-based in-application assistants, or copilots, can automate software tasks, but users often prefer learning by doing, raising questions about the optimal level of automation for an effective user experience. We investigated two automation paradigms by designing and implementing a fully automated copilot (AutoCopilot) and a semi-automated copilot (GuidedCopilot) that automates trivial steps while offering step-by-step visual guidance. In a user study (N=20) across data analysis and visual design tasks, GuidedCopilot outperformed AutoCopilot in user control, software utility, and learnability, especially for exploratory and creative tasks, while AutoCopilot saved time for simpler visual tasks. A follow-up design exploration (N=10) enhanced GuidedCopilot with task-and state-aware features, including in-context preview clips and adaptive instructions. Our findings highlight the critical role of user control and tailored guidance in designing the next generation of copilots that enhance productivity, support diverse skill levels, and foster deeper software engagement.

Effective feedback is often timely and consistent but, with large cohorts, this is not always achievable. This study explored the potential of GPT4 to generate feedback on code readability for students enrolled in a CS1 Java course. We developed rubrics based on three readability criteria: naming, commenting, and formatting. We defined feedback criteria and incorporated them into GPT4 prompts to guide feedback generation. Results were mixed: while some feedback messages closely aligned with the rubrics, offering valuable insights, others fell short in providing corrective guidance. This highlights the potential and limitations of using LLMs to generate feedback on code readability. Future research could refine these methods to improve feedback consistency and quality.

Live coding is a powerful teaching technique in programming education that helps students connect theory to practice by observing the coding process in real-time. However, instructors face significant cognitive load challenges when simultaneously coding, explaining, debugging, and managing classroom interactions. This paper introduces Coducate, a code editor extension specifically designed to streamline instructor-led live coding sessions. Coducate aims to reduce the cognitive load of instructors by automating routine tasks while increasing student participation through collaborative coding features.

Live coding is a powerful teaching technique in programming education that helps students connect theory to practice by observing the coding process in real-time. However, instructors face significant cognitive load challenges when simultaneously coding, explaining, debugging, and managing classroom interactions. This paper introduces Coducate, a code editor extension specifically designed to streamline instructor-led live coding sessions. Coducate reduces instructor cognitive load by automating routine tasks while increasing student participation through collaborative coding features. Our showpiece includes an interactive demonstration of Coducate running on tablet devices, accompanied by a poster.

Social media platforms increasingly employ proactive moderation techniques, such as detecting and curbing toxic and uncivil comments, to prevent the spread of harmful content. Despite these efforts, such approaches are often criticized for creating a climate of censorship and failing to address the underlying causes of uncivil behavior. Our work makes both theoretical and practical contributions by proposing and evaluating two types of emotion monitoring dashboards to enhance users' emotional awareness and mitigate hate speech. In a study involving 211 participants, we evaluate the effects of the two mechanisms on user commenting behavior and emotional experiences. The results reveal that these interventions effectively increase users' awareness of their emotional states and reduce hate speech. However, our findings also indicate potential unintended effects, including increased expression of negative emotions (Angry, Fear, and Sad) when discussing sensitive issues. These insights provide a basis for further research on integrating proactive emotion regulation tools into social media platforms to foster healthier digital interactions.

Providing feedback is widely recognized as crucial for refining students' writing skills.Recent advances in language models (LMs) have made it possible to automatically generate feedback that is actionable and well-aligned with humanspecified attributes.However, it remains unclear whether the feedback generated by these models is truly effective in enhancing the quality of student revisions.Moreover, prompting LMs with a precise set of instructions to generate feedback is nontrivial due to the lack of consensus regarding the specific attributes that can lead to improved revising performance.To address these challenges, we propose PROF that PROduces Feedback via learning from LM simulated student revisions.PROF aims to iteratively optimize the feedback generator by directly maximizing the effectiveness of students' overall revising performance as simulated by LMs.Focusing on an economic essay assignment, we empirically test the efficacy of PROF and observe that our approach not only surpasses a variety of baseline methods in effectiveness of improving students' writing but also demonstrates enhanced pedagogical values, even though it was not explicitly trained for this aspect.

While writing peer reviews resembles an important task in science, education, and large organizations, providing fruitful suggestions to peers is not a straightforward task, as different user interaction designs of text suggestion interfaces can have diverse effects on user behaviors when writing the review text. Generative language models might be able to support humans in formulating reviews with textual suggestions. Previous systems use two designs for providing text suggestions, but do not empirically evaluate them: inline and list of suggestions. To investigate the effects of embedding NLP text generation models in the two designs, we collected user requirements to implement Hamta as an example of assistants providing reviewers with text suggestions. Our experiment on comparing the two designs on 31 participants indicates that people using the inline interface provided longer reviews on average, while participants using the list of suggestions experienced more ease of use in using our tool. The results shed light on important design findings for embedding text generation models in user-centered assistants.