Leveraging Learner Errors in Digital Argumentation Learning: How ALure Helps Students Learn from their Mistakes and Write Better ArgumentsProviding argumentation feedback is considered helpful for students preparing to work in collaborative environments, helping them with writing higher-quality argumentative texts. Domain-independent natural language processing (NLP) methods, such as generative models, can utilize learner errors and fallacies in argumentation learning to help students write better argumentative texts. To test this, we collect design requirements, and then design and implement two different versions of our system called ALure to improve the students’ argumentation skills. We test how ALure helps students learn argumentation in a university lecture with 305 students and compare the learning gains of the two versions of ALure with a control group using video tutoring. We find and discuss the differences of learning gains in argument structure and fallacies in both groups after using ALure, as well as the control group. Our results shed light on the applicability of computer-supported systems using recent advances in NLP to help students in learning argumentation as a necessary skill for collaborative working settings.2025SNSeyed Parsa Neshaei et al.Fighting Misinformation, Building BelievabilityCSCW
Emotionally Aware Moderation: The Potential of Emotion Monitoring in Shaping Healthier Social Media ConversationsSocial 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 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.2025XSXiaotian Su et al.Toxic and Anti-Social BehaviorCSCW
LegalWriter: An Intelligent Writing Support System for Structured and Persuasive Legal Case Writing for Novice Law StudentsNovice students in law courses or students who encounter legal education face the challenge of acquiring specialized and highly concept-oriented knowledge. Structured and persuasive writing combined with the necessary domain knowledge is challenging for many learners. Recent advances in machine learning (ML) have shown the potential to support learners in complex writing tasks. To test the effects of ML-based support on students' legal writing skills, we developed the intelligent writing support system \textit{LegalWriter}. We evaluated the system's effectiveness with 62 students. We showed that students who received intelligent writing support based on their errors wrote more structured and persuasive case solutions with a better quality of legal writing than the current benchmark. At the same time, our results demonstrated the positive effects on the students' writing processes.2024FWFlorian Weber et al.University of KasselHuman-LLM CollaborationAI-Assisted Creative WritingCHI
Intelligent Support Engages Writers Through Relevant Cognitive ProcessesStudent peer review writing is prevalent and important in education for fostering critical thinking and learning motivation. However, it often entails challenges such as high effort and writer's block. Leaving students unsupported may thus diminish the efficacy of the process. Large Language Models (LLMs) offer a potential remedy, but their utility hinges on user-centered design. Guided by design-determining constructs from the Cognitive Process Theory of Writing, we developed an intelligent writing support tool to alleviate these challenges, aiding 1) ideation and 2) evaluation. A randomized experiment (n=120) confirmed users were less inclined to utilize the tool's intelligent features when offered pre-supplied ideas or evaluations, validating our approach. Moreover, students engaged not less but more with their writing if support was available, indicating an enhanced experience. Our research illuminates design choices for enhancing LLM-based tools' usability and user experience, specifically optimizing intelligent writing support tools to facilitate student peer review.2024AGAndreas Göldi et al.University of St.GallenHuman-LLM CollaborationAI-Assisted Decision-Making & AutomationCHI
Fashioning Creative Expertise with Generative AI: Graphical Interfaces for Design Space Exploration Better Support Ideation Than Text PromptsThis paper investigates the potential impact of deep generative models on the work of creative professionals. We argue that current generative modeling tools lack critical features that would make them useful creativity support tools, and introduce our own tool, generative.fashion, which was designed with theoretical principles of design space exploration in mind. Through qualitative studies with fashion design apprentices, we demonstrate how generative.fashion supported both divergent and convergent thinking, and compare it with a state-of-the-art text-based interface using Stable Diffusion. In general, the apprentices preferred generative.fashion, citing the features explicitly designed to support ideation. In two follow-up studies, we provide quantitative results that support and expand on these insights. We conclude that text-only prompts in existing models restrict creative exploration, especially for novices. Our work demonstrates that interfaces which are theoretically aligned with principles of design space exploration are essential for unlocking the full creative potential of generative AI.2024RDRichard Lee Davis et al.EPFLGenerative AI (Text, Image, Music, Video)Graphic Design & Typography ToolsCreative Collaboration & Feedback SystemsCHI
A Design Space for Intelligent and Interactive Writing AssistantsIn our era of rapid technological advancement, the research landscape for writing assistants has become increasingly fragmented across various research communities. We seek to address this challenge by proposing a design space as a structured way to examine and explore the multidimensional space of intelligent and interactive writing assistants. Through community collaboration, we explore five aspects of writing assistants: task, user, technology, interaction, and ecosystem. Within each aspect, we define dimensions and codes by systematically reviewing 115 papers while leveraging the expertise of researchers in various disciplines. Our design space aims to offer researchers and designers a practical tool to navigate, comprehend, and compare the various possibilities of writing assistants, and aid in the design of new writing assistants.2024MLMina Lee et al.Microsoft ResearchHuman-LLM CollaborationAI-Assisted Creative WritingCreative Collaboration & Feedback SystemsCHI
Enhancing Peer Review with AI-Powered Suggestion Generation Assistance: Investigating the Design DynamicsWhile 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.2024SNSeyed Parsa Neshaei et al.Generative AI (Text, Image, Music, Video)Human-LLM CollaborationIUI