collaboration and facilitation

This study addresses inefficiencies in cross-functional collaboration between designers and software engineers. We conduct a mixed-methods empirical analysis, integrating a systematic literature review (SLR) of 45 peer-reviewed publications (2004–2023) with real-world open-source collaboration data—including GitHub forum discussions, pull requests, and issue reports. To our knowledge, this is the first work to combine SLR with behavioral data mining from open-source platforms. Our analysis identifies three root causes of collaboration breakdown: misaligned goals, terminology barriers, and tool fragmentation. We further distill two reusable best practices for effective cross-functional engagement. Based on these findings, we propose a generalizable framework for improving designer-engineer collaboration—offering empirically grounded guidance for collaborative tool design, human-AI coordination mechanisms, and human-centered research in software engineering. The framework contributes both methodological innovation (integrating qualitative synthesis with quantitative behavioral analytics) and actionable insights for practice and research.

This study addresses the systemic support of macrocognitive functions—namely, event detection, sensemaking, adaptability, perspective shifting, and coordination—in human–AI teaming, moving beyond traditional usability-centered design paradigms. Drawing on cognitive psychology, human–computer interaction, and cognitive systems engineering, we conducted an interdisciplinary literature review and theoretical integration to develop, for the first time, a set of 14 heuristic design principles comprehensively covering all five macrocognitive functions. The resulting framework cohesively integrates display design, human factors engineering, and joint activity theory into a reusable, evaluable, general-purpose design framework. Empirical validation demonstrates that this framework significantly enhances AI agents’ capacity to function as *effective team members* in dynamic, collaborative settings. It thus provides the first complete, structured, cognition-driven theory–practice interface for the design, development, and evaluation of human–AI collaborative systems.

This study addresses the challenge of integrating and analyzing heterogeneous multimodal data—such as video, audio, handwritten notes, and eye-tracking streams—in collaborative design, which often renders collaboration mechanisms and decision-making processes opaque. We propose a modular, extensible multimodal analysis framework that integrates AI-driven artifact auto-extraction, multi-stream temporal alignment graphs, thematic card summarization, and drill-down interactive analysis, implemented in an interactive visual system named reCAPit. Our key contribution lies in enabling semantic-level cross-modal fusion and transparent, interpretable explanations, thereby significantly enhancing process traceability and comprehensibility. Evaluation across six interdisciplinary workshops—including urban planning and ensemble music rehearsal—demonstrates that the framework effectively uncovers collaborative dynamics, supports decision provenance, and improves communication efficiency between researchers and practitioners.

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.

This work addresses the challenge that current AI agents struggle to interpret users’ concurrent interaction intents on shared artifacts, thereby limiting dynamic co-creation. To overcome this, we propose CLEO—a collaborative intelligent agent grounded in mixed-initiative interaction principles—that dynamically switches among delegation, guidance, and collaboration modes by recognizing user concurrent behaviors in real time. We introduce the first collaborative model capable of real-time intent interpretation, identifying five behavioral patterns, six triggering mechanisms, and four enabling factors, and implement a decision framework comprising six interactive loops. Based on 214 rounds of interactions with professional designers, we quantitatively analyze mode usage (70.1% delegation, 28.5% guidance, 31.8% collaboration) and release design guidelines alongside a labeled dataset to support future research.

Existing visualization research predominantly focuses on *how to use* interactive features, neglecting the critical question of *how to construct* them. Method: We propose the first three-layer decoupled interaction authoring task model—intent–technique–component—derived from empirical coding and abstraction of 592 interaction units across 47 real-world applications. Contribution/Results: This model provides descriptive, evaluative, and generative capabilities, enabling the first unified formalization of interaction authoring intent, technical implementation, and component instantiation. It yields a reusable, theory-grounded classification framework that supports critical evaluation of existing visualization tools and informs the design and validation of next-generation low-code interaction authoring systems.

This study addresses the inefficiency of workplace meetings stemming from ambiguous objectives and the lack of explicit support for meeting intentionality in existing collaboration tools. Conducted as a pre-registered field experiment within a large technology company, the research embedded brief pre-meeting prompts encouraging goal reflection into an enterprise collaboration platform, paired with post-meeting effectiveness assessments. This approach enabled the first large-scale evaluation of a lightweight goal-intervention in authentic work settings. Although the intervention did not significantly improve perceived meeting effectiveness, it enhanced participants’ self-awareness of meeting goals and prompted behavioral adjustments. Notably, the post-meeting survey itself appeared to exert an intervention effect, challenging conventional assessment paradigms. The findings offer empirical grounding and methodological insights for future systems designed to support intentional meeting practices.

This study addresses a critical gap in collaborative research, where alignment, process, and outcomes are often treated in isolation or confined to specific participant types, obscuring their dynamic structural interplay. To overcome this limitation, the paper proposes a dual-perspective framework centered on tasks and intentions: it models the evolution of collaborative trajectories through a structured task space and examines how individual intentions are expressed and influence decision-making within shared contexts. Moving beyond conventional paradigms that reduce collaboration to outcome quality or mere alignment pursuit, this framework systematically uncovers common structural patterns across human-human, AI-AI, and human-AI collaboration. It further elucidates the nonlinear relationships among alignment levels, process structures—such as branching and backtracking—and outcome quality, thereby establishing a novel foundation for understanding and designing hybrid intelligent collaborative systems.

This study investigates the impact of hybrid work arrangements on productivity and collaboration within agile teams, with particular attention to key challenges such as diminished informal interactions, unequal participation, and heightened reliance on digital tools. Drawing on semi-structured interviews and qualitative content analysis of three Norwegian agile teams, the research elucidates how communication patterns, collaborative mechanisms, and agile ceremonies function in hybrid settings. It uniquely highlights the mediating roles of trust, communication, and tool support, revealing that while agile ceremonies serve as critical alignment anchors mitigating collaboration breakdowns, hybrid work significantly curtails informal exchanges and exacerbates participation disparities. The findings underscore the necessity of tailoring team structures and enhancing digital tooling to foster inclusivity and sustain long-term performance.

This study systematically identifies and organizes sixteen core challenges surrounding visualization in synchronous remote collaboration. Drawing on insights from twenty-nine international experts, it focuses on five collaborative scenarios—exploratory data analysis, ideation, visualization presentation, data-driven decision-making, and real-time monitoring—and, for the first time, categorizes these challenges into four research and development dimensions: technology selection, social factors, AI assistance, and evaluation. Integrating emerging trends in extended reality (XR) and artificial intelligence (AI), the work proposes a structured framework that offers both theoretical grounding and practical guidance for future research and system design in multimodal, visualization-supported collaborative environments.

This study addresses the prevalent conflation of human–AI interaction with genuine collaboration, noting that most current systems operate through consultation, instruction, or delegation rather than exhibiting core collaborative features such as symmetry, shared goals, and mutual regulation. Drawing on theories of collaborative learning, the work proposes a novel five-tier taxonomy of human–AI diagnostic collaboration—ranging from transactional to truly collaborative—and rigorously distinguishes pseudo-collaboration from authentic forms. It further identifies the critical functionalities and affordances necessary for achieving higher-order collaboration. Through process-sensitive empirical analysis of interaction data from educational writing and problem-solving tasks, the research demonstrates that mainstream AI systems predominantly remain at lower tiers, with only the highest level meeting established criteria for true collaboration. This framework offers a theoretical foundation, evaluative metric, and design guidance for responsible human–AI collaboration in education.