domain expert collaboration

This study addresses collaboration friction between domain experts (DEs) and software developers (SDEs) in cross-disciplinary software development (CDSD), stemming from misaligned expectations. Using activity theory as an analytical framework, we employed a mixed-methods approach—combining semi-structured interviews with large-scale questionnaire surveys—to empirically identify six core expectations of DEs and eight of SDEs, and to systematically categorize 21 typical friction points and their conflict patterns. Our key contribution is the first activity-theoretic, actionable diagnostic model for cross-disciplinary collaboration, which explicates how expectation discrepancies translate into practical conflicts via mediating elements—including tools, rules, and division of labor. The findings provide both theoretical grounding and practical guidance for role alignment, process design, and collaborative mechanism optimization in CDSD teams.

This work addresses the challenge that non-experts often produce low-quality visualizations in specialized domains due to insufficient domain knowledge, thereby consuming substantial expert time and creating organizational bottlenecks. To overcome this, the paper introduces the first systematic framework that structures experts’ tacit knowledge into explicit rules and design principles, integrating a request classifier, retrieval-augmented generation (RAG), and a multi-agent architecture to enhance large language models’ (LLMs) autonomous, reactive, proactive, and social capabilities. Evaluated across five engineering scenarios, the approach achieves a 206% improvement in output quality, with all generated visualizations rated at expert level; furthermore, the synthesized code exhibits higher quality and lower variance compared to baseline methods.

This study addresses the limited understanding of how domain experts can effectively engage in the design and evaluation of large language models (LLMs) within complex professional domains. Through a 12-week ethnographic investigation involving field observations, semi-structured interviews, and qualitative analysis, the research examines collaboration dynamics between experts and developers in a pedagogical chatbot development team. It identifies four key practices: adaptive data collection strategies, knowledge augmentation techniques under constrained expert input, co-constructed evaluation criteria, and a hybrid assessment framework integrating expert, developer, and LLM perspectives. The study also highlights core challenges in expert involvement—such as insufficient motivation, lack of trust, and ambiguous collaboration structures—and proposes future workflow designs to support evolving expert roles, including enhanced AI literacy and transparent consent mechanisms, thereby underscoring the indispensable value of expert knowledge in LLM development.

This study addresses persistent challenges in scientific communication and aerospace engineering—namely data silos, insufficient collaboration incentives, and legal barriers—that hinder the implementation of FAIR (Findable, Accessible, Interoperable, Reusable) principles. To overcome these limitations, this work proposes a novel, scalable knowledge infrastructure framework that integrates human–AI collaboration, knowledge graphs, and user-centered design across technological, social, and legal dimensions. The framework encompasses automated information processing workflows, a wiki-style digital library, and demand-driven interactive interfaces. Pilot implementations demonstrate its effectiveness in consolidating fragmented knowledge resources and establishing a viable collaborative paradigm for sparsely networked domains. Nevertheless, institutional and sociocultural barriers remain significant and require further intervention to fully realize the framework’s potential.

How can domain experts’ tacit knowledge—regarding data provenance, quality, and usage—be efficiently elicited to improve domain adaptability in visualization design? This paper introduces the “Data Therapist” paradigm: an LLM-driven web tool integrating hybrid active questioning and interactive annotation. It supports multi-granularity structured annotation and iterative follow-up queries to systematically externalize and model tacit knowledge. The method synergizes large language models, interactive knowledge elicitation interfaces, and qualitative user studies. Empirical validation across molecular biology, accounting, political science, and usable security reveals cross-domain patterns in data reasoning. Results demonstrate significant improvements in visualization systems’ understanding and support of domain semantics, enabling more robust, domain-informed visualization design. By formalizing and structuring expert knowledge, this work establishes a scalable, reusable knowledge infrastructure for data-driven, automated visualization generation.

This work addresses the challenge that domain experts face in translating natural language descriptions of data quality requirements into executable analyses, a process often hindered by reliance on data engineers, resulting in inefficiency and high technical barriers. To overcome this, the paper proposes a no-code, model-driven pipeline that leverages a QPM metamodel to define domain-specific quality analysis templates. Coupled with the Constrainify toolchain, it automatically transforms natural language requirements into executable and reusable analytical logic. By integrating model-driven engineering, metamodeling, and no-code web technologies, the approach significantly reduces dependency on technical expertise, enabling efficient, reproducible, and semantically aligned data quality assessments. This advancement enhances both the accessibility and automation of data quality analysis for non-technical domain practitioners.

Existing benchmarks for knowledge work evaluation largely adhere to traditional NLP task paradigms, failing to capture systems’ capabilities in real-world knowledge-intensive settings. This work proposes a three-step framework—explicitly defining work activities, establishing realistic test environments, and focusing evaluation on deliverable outputs—and derives 18 core knowledge work activities from the O*NET database. Innovatively integrating role responsibilities, local tool usage, and downstream usability into benchmark design, the approach establishes a coherent “work activity–test setup–scoring artifact” alignment. Validation through three case studies (GDPval, OfficeQA Pro, and APEX-SWE) exposes critical misalignments in current benchmarks between tasks, environments, and actual work objectives, offering a new paradigm for evaluating knowledge work systems in practical, application-oriented contexts.

This study addresses the gap between existing Bodies of Knowledge (BoKs) in computing and their effective translation into assessable, competency-oriented curricula. The authors propose an innovative competency-mapping methodology that systematically aligns BoKs with a structured competency framework, resulting in a five-year engineering curriculum encompassing 23 core competencies, organized into five thematic modules and three specialization tracks, and integrating mandatory work-integrated learning projects. To support explicit linkage, collaborative maintenance, and continuous evolution of knowledge-to-competency mappings, the authors developed ISANUMpedia—a semantic web–based collaborative platform. Implemented in the ISANUM engineering degree program, this approach successfully mapped 494 knowledge topics to the 23 competencies, significantly enhancing the curriculum’s professional relevance and assessability.

This study addresses the limited diversity and perceived relevance of problem domains in software modeling instruction, which often undermine student motivation and inclusivity. Through parallel surveys of 90 students and 22 instructors, combined with quantitative and qualitative analyses, it reveals a significant mismatch between instructor assumptions and student preferences: learners prioritize socially relevant problem contexts and value autonomy in topic selection. Furthermore, their sense of engagement markedly increases when they perceive their feedback has been explicitly incorporated. The work proposes a learner-centered strategy for selecting problem domains that foregrounds social relevance and autonomy as critical enablers of inclusive learning. It also highlights how seemingly minor instructional design choices can inadvertently foster exclusion, offering empirical insights and actionable guidance for improving pedagogical practice.

This study addresses the challenge that existing organizations deploy specialized AI tools in silos across multiple domains, relying heavily on domain experts and struggling to achieve anticipated workforce transformation. To overcome this, we propose “Augment Engineering,” a methodology that leverages transferable prompt engineering and context engineering to orchestrate collaborative workflows among diverse AI tools across disciplines. We introduce a novel paradigm for cross-tool, cross-domain AI collaboration, formalize a six-stage orchestration pipeline, and define four metrics for assessing transferability. Our framework integrates a multi-tool orchestration stack with quantitative evaluation methods, including Cochran–Armitage trend tests and Wright’s Law fitting. Empirical results demonstrate that a single practitioner can accomplish tasks traditionally requiring multiple experts across seven domains and ten system components, confirming a positive correlation between prompt complexity and first-pass success rate, thereby significantly enhancing overall productivity.