Fourth Workshop on Engineering Interactive Systems Embedding AI Technologies

First Workshop on Responsible Software Engineering

Software profoundly influences how we live and work, often reflecting human behavior and societal values. Consequently, software-intensive systems and software engineering technologies must be developed and deployed responsibly to avoid harming users, society, or the environment. This raises questions about what constitutes the responsible development and use of technology, including which human values and which social and environmental norms are embedded in software engineering practices and products. With the increased incorporation of AI in software-intensive systems, software engineers are confronted with even more ethical and moral choices to incorporate into these processes and products. To address these responsibility issues, the workshop hosted a half-day event featuring themed presentation tracks, interactive discussions, and a stimulating concluding panel. The workshop comprised research papers (visionary, exploratory, empirical, and solution-oriented) and industrial papers (lessons learned and challenges) published in the ACM workshop proceedings.

Design-System-Aware Development with AI: Evaluating Productivity and Design Consistency

Design Systems (DS) help standardize front-end development, yet developers still face challenges when translating high-fidelity mockups into consistent, production-ready interfaces. Although AI-assisted tools have emerged as a potential solution, empirical evidence on their effectiveness within DS-centered workflows remains limited. This paper reports a controlled experiment conducted at a large Brazilian enterprise that compares manual development, DS-only development, and DS-aware AI-assisted development across Angular, iOS, and Android stacks. Results from two experimental cycles show that AI assistance significantly reduced time-to-delivery (by 46.7% to 69.4%), increased task completeness, and decreased performance variability. Analysis of break patterns further suggests reduced workflow friction and smoother task execution. These findings provide empirical evidence that DS-aware AI tools can significantly accelerate development, improve design fidelity, and yield practical benefits for industrial front-end workflows.

Spring 2026 Special Workshop on the Futures of Cognitive Architectures and the ACT-R Ecosystem

The Case for Vibe Modeling: A Missing Step in AI-Based Trustworthy Software Development

A student survey study is presented that examines perceptions of LLM output understanding, validation effort, trust and the perceived usefulness of vibe modeling across several AI-assisted development scenarios to inform future studies for trustworthy and explainable AI-based software engineering via vibe modeling.

Comparative Analysis of Approaches to Embedding AI Functionality through In-Application Interfaces and Autonomous Agent Orchestrators in CRM Systems

Customer relationship management platforms now embed artificial intelligence in two structurally different ways, and the choice between them shapes cost, governance, and the pace of organizational change. The first way places assistive models within the interfaces teams already use, so that scoring, summarization, and drafting occur at the point where work is performed. The second way introduces a separate orchestration tier in which semi-autonomous agents pursue goals across several systems while users remain in familiar screens. This review examines the two approaches through a seven-layer reference architecture that places each in a specific tier and clarifies why the boundary between them lies between the model and orchestration tiers. The analysis treats both as design options with measurable trade-offs across change management, expressiveness for multi-step work, architectural coupling, and the surface available for monitoring. Evidence from enterprise practice and the research literature suggests that embedded assistance is more successful during first-generation adoption in mature organizations because it leverages existing processes, whereas a dedicated agent tier yields stronger long-term outcomes for second-wave programs that target genuine cross-system automation. Supporting comparisons across data access, integration style, the trust model, and migration strategy show that the central decision sits inside a wider system of architectural commitments that the framework makes explicit for practicing architects.

Software Engineering for and with GUI Agent

GUI agents have advanced rapidly, producing a growing body of frameworks, benchmarks, and applications. However, this growth has outpaced the maturity of the field. GUI agents remain technically brittle, incompletely engineered, and insufficiently validated for sustained real-world use. They are evolving into closed-loop software systems. Within these systems, model reasoning is coupled with interface perception, execution feedback, recovery, and human oversight. This evolution calls for a software engineering perspective that remains largely absent from existing research. We address this gap by reviewing 336 GUI-agent papers from January 2018 to April 2026. Five research questions examine the research landscape, architectures, evaluation, software lifecycle concerns, and future opportunities. Our findings show that the field has expanded sharply since 2024, while mobile and web settings remain dominant. Architectures increasingly adopt modular perceive-reason-act loops, but recovery, human escalation, safety enforcement, and auditability remain underdeveloped. This architectural imbalance extends to evaluation. Evaluations are becoming more interactive, but they remain centered on task success and are difficult to compare across protocols. More broadly, existing studies provide limited support for testing beyond benchmarks and for maintaining agents after release. Observability, privacy engineering, and systematic human oversight are also underdeveloped. Together, these findings show that capability improvements alone cannot ensure deployment readiness. Future research should connect dependable execution with lifecycle-centered testing and reproducible evaluation. It should also integrate permission and privacy controls with cost-aware, human-centered governance. This integration is necessary to build dependable, maintainable, secure, and deployable GUI-agent systems.