Abstract
The learning environment is an essential part of teaching and learning. Its personalization has several advantages (e.g., guaranteeing learning quality or effective learning). In vocational education, a personalized learning environment might provide training most suitable to each professional according to individual characteristics, skills, or career path. Artificial intelligence's ability to process big data can be harnessed to personalize a learning environment. This work intends to investigate the personalization of a learning environment using artificial intelligence (AI) in vocational training that can provide relevant training based on the trainees' skills required. A framework will be proposed to personalize a learning environment in this scope. Its development will follow the design science research (DSR) methodology. During the process, the survey methodology (expert interviews and focus groups) will be conducted to validate the artifact requirements and evaluate our future framework.

Abstract
A Learning management system (LMS) is considered appropriate for company training. It is increasingly used in companies or organizations as a tool to manage their online training. The company or organization should consider the implementation of an LMS that provides ease in training content selection to achieve the best use and satisfaction of its employees in the learning process. From this perspective, the present study aims to investigate the implementation of a personalized LMS to facilitate the formative content selection tailored to employees’ roles. A Survey research methodology was used to achieve this objective. Based on the literature and survey results, we propose an approach to reach the personalization of content selection.

Abstract
Training employees in organizations is essential for enhancing productivity and profitability, updating their knowledge, and better preparing them for market demands. Through digital platforms (LMS) and the e-Learning method, training occurs in web-based environments, enabling content management and accessibility across multiple devices. e-Learning typically follows a modular structure, ensuring adaptability, flexibility, and asynchronous learning. This study applies to the Design Science Research method to implement a data protection training course via an LMS, facilitating knowledge dissemination and employee self-assessment. The organization faces challenges in rapidly spreading knowledge due to its widespread locations, diverse working hours, and geographical constraints. The study evaluates training dissemination through microlearning, leveraging Moodle (LMS) and Digital Storytelling techniques. Additionally, it assesses the pedagogical and engagement aspects to ensure training is efficient, standardized, flexible, and more appealing to employees, increasing their receptivity and interest.

Abstract
Software businesses are continuously increasing their presence in the cloud. While cloud computing is not a new research topic, designing software for the cloud is still challenging, requiring engineers to invest in research to become proficient at working with it. Design patterns can be used to facilitate cloud adoption, as they provide valuable design knowledge and implementation guidelines for recurrent engineering problems. This work introduces a pattern language for designing software for the cloud. We believe developers can significantly reduce their R&D time by adopting these patterns to bootstrap their cloud architecture. The language comprises 10 patterns, organized into four categories: Automated Infrastructure Management, Orchestration and Supervision, Monitoring, and Discovery and Communication.

Abstract
General-purpose AI-assisted tools, such as ChatGPT, have recently gained much attention from the media and the general public. That raised questions about in which tasks we can apply such a tool. A good code design is essential for agile software development to keep it ready for change. In this context, identifying which design pattern can be appropriate for a given scenario can be considered an advanced skill that requires a high degree of abstraction and a good knowledge of object orientation. This paper aims to perform an exploratory study investigating the effectiveness of an AI-assisted tool in assisting developers in choosing a design pattern to solve design scenarios. To reach this goal, we gathered 56 existing questions used by teachers and public tenders that provide a concrete context and ask which design pattern would be suitable. We submitted these questions to ChatGPT and analyzed the answers. We found that 93% of the questions were answered correctly with a good level of detail, demonstrating the potential of such a tool as a valuable resource to help developers to apply design patterns and make design decisions.

Abstract
Observability helps ensure the reliability and maintainability of cloud-native applications. As software architectures become increasingly distributed and subject to change, it becomes a greater challenge to diagnose system issues effectively, often having to deal with fragmented observability and more difficult root cause analysis. This paper builds upon our previous work and introduces three design patterns that address key challenges in monitoring cloud-native applications. Distributed Tracing improves visibility into request flows across services, aiding in latency analysis and root cause detection, Application Metrics provides a structured approach to instrumenting applications with meaningful performance indicators, enabling real-time monitoring and anomaly detection, and Infrastructure Metrics focuses on monitoring the environment in which the system is operated, helping teams assess resource utilization, scalability, and operational health. These patterns are derived from industry practices and observability frameworks and aim to offer guidance for software practitioners. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2026.