hackathon participation

This study addresses the current lack of systematic research on operational frameworks and process mechanisms for AI software development agents. It proposes the first six-dimensional process taxonomy—encompassing specification, context, role, execution, validation, and portability—and employs targeted literature review, functional filtering, traction metrics, and a structured scoring rubric to conduct a multi-case comparative analysis of six representative frameworks. The analysis reveals a prevailing trend among mainstream frameworks toward de-emphasizing isolated prompts and instead reinforcing persistent artifacts and human oversight. The work identifies common risks such as specification drift, overreliance on generated outputs, and platform dependency, and empirically characterizes—for the first time—a structural trade-off between process depth and cross-agent portability, offering reproducible tools and a research agenda for future evaluation.

This paper addresses the challenge of operationalizing generative AI within collaborative software engineering teams. Drawing on a design study with 39 industry experts—including field observations, semi-structured interviews, and multi-role workshops—we systematically investigate how prompt engineering supports cross-functional AI prototyping and iterative co-design. Our study is the first to characterize three core phenomena in collaborative prompt prototyping: (1) the emergent construction of shared coordination norms, (2) dynamic role evolution across developers, domain experts, and AI specialists, and (3) context-sensitive evaluation mechanisms for prompt efficacy. We propose a generative-content-feature-driven rapid iteration paradigm and distill a reusable prompt prototyping strategy framework. Key technical challenges—including model opacity and example overfitting—are empirically identified. The findings provide both methodological grounding and actionable practice guidelines for industrial software teams, advancing the shift from generative AI as a technical capability to a collaborative design enabler.

This work addresses the protracted development cycles in traditional visual analytics (VA) prototyping that hinder rapid validation of novel ideas. The authors propose a scaffolded, AI-assisted development paradigm centered on the Artifact–Transform Workflow Language (ATWL) as a structured framework, integrating large language model–driven AI assistants with targeted expert interventions to efficiently construct high-quality VA prototypes within hours. The approach successfully instantiated innovative visual designs such as “soft Pareto fronts” and “constellation” groupings. Controlled experiments further revealed the critical influence of scaffolding design, timing of human-AI collaboration, and methods of knowledge injection on prototype quality, leading the authors to advocate for a taxonomy of knowledge expression in human-AI collaborative systems.

To address the time-intensive, cognitively demanding, and subjectivity-prone nature of proto-persona construction in early product discovery, this study proposes a generative AI–driven automation method grounded in prompt engineering. Employing a mixed-methods (qualitative and quantitative) design, we empirically validated the approach within authentic lean startup contexts. Results demonstrate significant improvements: a 62% average reduction in construction time, lowered cognitive load, and enhanced persona quality, reusability, and stakeholder acceptance—particularly in facilitating stakeholder alignment and MVP scope definition. Our key contribution lies in the first systematic investigation of human–AI collaboration mechanisms for proto-persona generation, empirically confirming generative AI’s capacity to stimulate cognitive empathy. However, limitations persist regarding domain specificity and deep emotional empathy. This work advances human-centered AI design by bridging generative capabilities with empathic user modeling in early-stage innovation.

Non-AI engineers lack efficient tools to prototype generative AI-based UI Agent experiences. Method: We propose AgentBuilder—a user-centered design framework and lightweight prototyping tool—developed through contextual inquiry, design probes, and in-situ experiments to identify core activities and capability requirements for agent experience design. AgentBuilder supports low-code interaction orchestration, real-time LLM integration, and iterative multi-turn dialogue prototyping. Evaluation with 14 cross-disciplinary participants demonstrated that the framework significantly lowers prototyping barriers, enhances designer engagement, and improves feedback quality. It further revealed non-technical users’ critical needs for controllability, explainability, and progressive guidance. Contribution/Results: This work presents the first systematic methodology and practical toolchain for UI Agent experience prototyping tailored specifically for non-engineers, bridging a critical gap between human-centered design and generative AI interface development.

Existing UI prototyping tools provide weak support for integrating design artifacts such as screenshots and sketches, hindering component reuse, semantic integration, and cross-role collaboration. This paper proposes a novel UI prototyping paradigm grounded in Conceptual Blending Theory, the first to concretize cognitive-science-based blending mechanisms into an interactive tool. It enables semantic-level element mixing across heterogeneous design examples through example-driven component extraction and semantic alignment, lightweight vision–semantics mapping, and real-time blended preview—facilitating staged intent articulation by developers. An empirical study with 14 frontend developers demonstrates that the approach significantly reduces prototype initiation time (average improvement of 42%), stimulates highly unexpected creative combinations (68% novel composition rate), and enhances design–development collaboration efficiency.

This study addresses the challenge faced by resource-constrained software startups lacking user experience (UX) expertise in efficiently developing user-centered minimum viable product (MVP) prototypes. To bridge this gap, the authors propose StartFlow, a lightweight method that uniquely integrates wireframes and user flows into a unified “wireflow” representation. StartFlow guides non-UX teams through a structured three-step process—feature organization, prototype construction, and closed-loop validation based on usability heuristics—to iteratively refine MVPs. Empirical results demonstrate that teams employing StartFlow produce prototypes that are clearer, better aligned with user stories and business rules, and exhibit significantly fewer usability flaws. Expert evaluations further confirm the method’s high usability and strong potential for broad adoption in early-stage software development contexts.

This study addresses the persistently low real-world adoption of robotic wheelchairs, attributing it to the severe underrepresentation of end users throughout the research and development process. Through a narrative literature review, the authors systematically evaluate the extent and quality of user involvement in defining requirements, co-designing solutions, and assessing outcomes in studies published between 2015 and 2025. Grounded in principles of user-centered, participatory, and inclusive design, this work presents the first quantitative and critical analysis of user engagement practices over the past decade. Findings reveal that only 6% of reviewed papers meet verifiable criteria for meaningful user participation, with prevalent issues including small sample sizes, reliance on proxy users, validation confined to laboratory settings, and absence of standardized feedback mechanisms. These shortcomings underscore a significant disconnect between engineering-driven approaches and authentic user needs, highlighting an urgent need for systemic reform.

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 growing gap between theory and practice in agile software development amid the rapid proliferation of generative AI, where existing research struggles to produce timely, transferable, and actionable empirical insights. To bridge this gap, we propose an AI-integrated agile development education platform that uniquely transforms course projects into a sustainable research infrastructure for generating reusable, context-rich evidence. The platform establishes a closed-loop feedback mechanism between controlled experimentation and industrial practice through sprint-based iterations, quality gates, AI-assisted artifact generation, and collaboration with real stakeholders. Multi-semester deployment demonstrates that the platform effectively supports scalable instruction, deep industry engagement, and the efficient production of contextualized, reusable empirical evidence on integrating AI into agile development processes.

This study addresses the pedagogical challenge of teaching the threshold concept of “empirical process control” in Scrum by designing a lightweight, free, customizable, and scalable sprint simulation activity. Integrating the threshold concepts framework with active learning, the approach guides students through a single instructional session in which they practice visualizing work status, selecting tasks, and allocating resources, thereby directly experiencing decision-making grounded in empirical feedback. The method combines direct instruction with interactive simulation and employs abductive analysis to evaluate its effectiveness. Implementation across master’s-level courses at two universities and a teaching assistant training program at another institution demonstrates that the simulation significantly enhances students’ understanding of empirical process control, confirming its viability and efficacy as a cross-institutional tool for teaching agile development.