workshop facilitation

This study addresses the challenge of process assessment in the absence of supporting documentation by proposing a collaborative evaluation method that integrates Nominal Group Technique, user stories, and Planning Poker. For the first time, it adapts agile practices—specifically user stories and Planning Poker—to the context of CMMI process assessments. The approach transforms CMMI practices into concrete user scenarios and replaces traditional document reviews and audit-style interviews with consensus-driven estimation, enabling fact-finding and validation without reliance on formal documentation. Applied in real-world consulting engagements, the method effectively facilitated structured dialogue among assessors with divergent viewpoints and enabled them to reach agreement on critical process issues, thereby significantly enhancing the collaborativeness and feasibility of the assessment process.

This study addresses the limitations of existing team role modeling approaches, which often lack grounding in educational theory and suffer from poor interpretability, thereby hindering their ability to effectively predict collaborative outcomes. To bridge this gap, the authors develop a theoretically informed framework comprising eight communication roles derived from educational principles. For the first time, this theory-driven role taxonomy is applied to real-world team chat data, integrating expert annotations with large language models for role identification and explicitly modeling the dynamic evolution of roles over time. The research uncovers systematic patterns in how roles shift throughout project progression and demonstrates the cross-contextual validity of these roles in predicting peer recognition and team performance. Experimental results show that the proposed approach significantly outperforms baseline methods—including lexical, conversational, and prompt-engineering strategies—on both prediction tasks.

Hybrid/remote meetings commonly suffer from prolonged duration and declining engagement, while conventional fixed-length summaries fail to satisfy heterogeneous user needs—such as rapid skimming versus deep retrospective review. To address this, we propose Recap, an LLM-driven dual-track meeting summarization system. Grounded in cognitive science and discourse theory, Recap introduces the first complementary summarization paradigm comprising “key highlights (for overview)” and “structured, hierarchical minutes (for retrospective navigation).” It integrates organizational context (e.g., slide links) with personalized adaptation mechanisms, advancing AI-generated summaries from generic outputs toward seamless workflow integration. Through a high-fidelity prototype and qualitative studies in authentic Microsoft meeting contexts (N=7), we empirically validate the synergistic value of both summary types in collaborative discussion and consensus building. Furthermore, analysis of user editing behaviors (additions, deletions, modifications) reveals critical human-AI alignment gaps, providing empirical grounding for explainable and editable AI meeting summaries.

Existing policy design approaches are predominantly top-down and disconnected from community contexts, resulting in low legitimacy and misalignment between policy provisions and actual stakeholder needs. Method: This paper introduces PolicyCraft—a human-computer interaction (HCI)-informed system that embeds real-world cases as negotiation anchors within the policy design workflow. It supports participatory governance through case annotation, structured deliberation, multi-round voting, and versioned collaborative editing, enabling stakeholders to co-author, critique, and iteratively refine policy proposals while dynamically aligning abstract rules with concrete situational constraints. Contribution/Results: Evaluated in two university courses focused on educational policy, groups using PolicyCraft achieved significantly higher consensus and produced policies substantiated by richer, more relevant case evidence—outperforming a baseline system lacking case-based scaffolding. The work advances participatory policy design by grounding abstraction in lived experience and demonstrates scalable HCI methods for democratic, context-sensitive governance.

This study investigates how gender-specific (GS) versus regular-institution (RI) collegiate hackathons differentially influence team dynamics and project selection. Employing a mixed-methods approach—including surveys, behavioral observation, focus groups, and thematic coding—we conduct the first systematic comparative analysis of collaboration patterns, thematic preferences, role distribution, and environmental design across these two hackathon types. Results indicate that GS hackathons foster more equitable collaboration and disproportionately attract health/well-being–oriented projects, whereas RI events emphasize entertainment/environmental themes and outcome-driven competition. Grounded in empirical findings, we propose and validate a hybrid event model that integrates competitive incentives with collaborative inclusivity. This work advances theoretical understanding of STEM intervention design and provides actionable frameworks for enhancing learning experiences and gender equity in computing education.

This work addresses the challenge that presentation authoring is often constrained by factors such as time, audience, and communicative intent, yet existing tools offer little proactive support for leveraging these constraints. Through a qualitative user study, the authors propose the first constraint-driven, multi-session presentation authoring (CMPA) framework, reframing constraints from passive limitations into active design drivers. Based on this framework, they developed ReSlide, a prototype system that enables creators to harness constraints explicitly during narrative construction. User studies demonstrate that ReSlide significantly enhances users’ ability to utilize constraints in shaping their presentations and facilitates flexible content reuse across varying constraint conditions. The findings offer a novel interaction paradigm and design implications for next-generation presentation authoring tools.

This work addresses the common gap in students’ practical experience with user interaction in agile development and their limited understanding of the capabilities and limitations of generative AI in requirements engineering. To bridge this gap, the study introduces an innovative approach that employs a generative AI–powered virtual stakeholder simulation, guided by meta-prompting to facilitate student-led requirement interviews. The method integrates agile practices such as user story mapping and impact mapping for requirements elicitation and documentation, followed by structured reflective discussions to deepen students’ awareness of the technical boundaries and ethical implications of AI tools. Designed to be model-agnostic, the approach demonstrates flexibility and reusability across contexts. Multi-semester teaching evaluations confirm its effectiveness in enhancing students’ integrated competencies in cutting-edge agile requirements engineering and the synergistic application of generative AI.

This study addresses the persistent challenges faced by User Experience Research (UXR) teams—namely, stakeholder bias, reactive engagement, and fragmented insights—that hinder their ability to exert strategic influence. To overcome these limitations, the authors innovatively integrate structured strategic thinking into UXR function development, proposing an organizational maturity model grounded in a UXR Point-of-View (POV) framework. Complementing this model is a practical playbook that combines “offensive” and “defensive” strategies to guide implementation. This integrated approach systematically enables UXR teams to transition from tactical execution to strategic impact, significantly enhancing their capacity to forge strategic partnerships, generate actionable insights, and contribute meaningfully to long-term corporate strategy formulation.

This study addresses key challenges in ESG reporting—namely unstructured data, inconsistent terminology, and complex regulatory standards—compounded by the absence of automation and dynamic feedback in current workflows. To overcome these limitations, this work proposes the first AI-driven, multi-agent framework for end-to-end ESG lifecycle management, systematically integrating five phases: identification, measurement, reporting, stakeholder engagement, and continuous improvement. The framework enables a shift from static disclosure to adaptive, accountable governance by leveraging large language models within three agent configurations: single-model, single-agent, and multi-agent. It supports automated report generation, cross-validation, multi-version comparison, and knowledge base maintenance. A prototype implementation demonstrates significant improvements in report consistency, accuracy, and adaptability, with code and data publicly released.

This study addresses a critical gap in existing qualitative analysis tools, which often prioritize efficiency and consensus at the expense of reflexivity, traceability of analytical processes, and constructive disagreement. To redress this imbalance, the authors design and implement Reflexis, a collaborative analysis workspace that systematically integrates reflexivity, visualizations of code evolution, and stance-aware dialogue into its core architecture. Through embedded reflective prompts, transparent tracking of coding trajectories, and scaffolding for stance-aware discussions, Reflexis facilitates rigorous and nuanced collaborative interpretation. A user study with twelve participants demonstrates that the tool effectively fosters fine-grained reflection, transforms disagreements into productive dialogue, and reveals emergent design requirements such as higher-order memo networks and timely proactive reminders.