LLM-based agents for automating the enhancement of user story quality: An early report

Leveraging text generation for enhanced user story quality

SoftBoard: A Multi-Agent Tool for the Creation and Evaluation of Low-Fidelity Prototypes

User Experience (UX) is recognized as a critical factor for the success of digital products, particularly in software startups, environments marked by time constraints, limited resources, and low maturity in design practices. Building Minimum Viable Products (MVPs) through low-fidelity prototyping represents a well-established strategy for rapid validation cycles at reduced cost. A systematic literature mapping, however, revealed gaps in the ecosystem of available tools: a predominance of general-purpose solutions adapted for prototyping, the absence of integrated methodological guidance, and the incipient use of Artificial Intelligence in the design process. This paper presents SoftBoard, a web-based tool for the creation and evaluation of low-fidelity prototypes in the context of MVP development. The tool integrates a prototype editor, team-based project organization, and a multi-agent system based on large language models that supports requirements elicitation and refinement, automates prototype generation, and evaluates interface quality based on usability heuristics. This integration aims to reduce reliance on prior UX expertise, standardize the prototyping process, and support teams in building MVPs aligned with user needs. As future work, a feasibility study with software professionals is currently underway.

Integrating AI into Requirements Quality Learning in Software Engineering Education: A TPACK-Guided Empirical Study

TPACK-guided scaffolding can align AI affordances with pedagogical goals and RE content, offering design guidance for responsible AI integration in RE education.

The Use of Generative AI Tools by Requirements Engineers: An Interview with Industry Professionals

This paper investigates how Generative Artificial Intelligence (GenAI) grounded in Large Language Models (LLMs) are being employed to support activities within Requirements Engineering (RE). Based on interviews conducted with software engineering professionals, the findings indicate that GenAI tools are particularly valuable during requirements elicitation and specification, contributing to improved productivity and reduced operational effort, especially when addressing Functional Requirements (FRs). Despite these benefits, participants also reported important limitations, including sensitivity to prompt formulation, generation of overly generic outputs, and limited capability to adequately support Non-Functional Requirements (NFRs).

Unpacking Human–GenAI Collaboration in Software Engineering Education

Generative AI tools such as ChatGPT, Claude, and Gemini are increasingly shaping how students engage with software engineering (SE) problem-solving. However, there is limited understanding of how learners collaborate with GenAI across different stages of the software development lifecycle (SDLC), and how learner agency shifts during this process. This research unpacks the problem-solving strategies, collaboration patterns, and agency shifts underlying human–GenAI collaboration in software engineering education. Using a mixed-methods approach, it draws on multimodal data including screen recordings, GenAI transcripts, task artefacts, and retrospective think-aloud interviews. Preliminary findings suggest that learners engage with GenAI differently across SDLC phases, with variation in reliance, evaluation, and decision-making. Future work will extend this analysis to larger and more diverse learner groups and inform pedagogical supports for effective, critical, and agentic GenAI-supported problem-solving.

GAMIFICATION FOR THE IMPLEMENTATION OF A SYSTEMS INTEGRATION PROJECT

This article analyzes the application of gamification strategies as a tool to optimize Information Technology (IT) project management within the agribusiness sector, specifically in contexts that combine predictive and agile approaches. The study is based on a literature review that establishes the theoretical framework for a future operational systems integration project. The proposal explores how the introduction of game-like elements—such as badge systems, avatars, visual goal tracking, and rewards for completed milestones—can boost team engagement and motivation. Additionally, the article addresses the use of generative Artificial Intelligence tools to develop the project's visual identity, enabling the creation of unique, customized graphic assets. In practical terms, the work outlines guidelines for a future implementation focused on automating data flow via standardized communication protocols, aiming to eliminate manual processes, mitigate human error, and establish continuous feedback mechanisms.