The increasing use of Large Language Models (LLMs) offers significant opportunities across the engineering lifecycle, including requirements engineering, software development, process optimization, and decision support. Despite this potential, organizations face substantial challenges in assessing the risks associated with LLM use, resulting in inconsistent integration, unknown failure modes, and limited scalability. This paper introduces the LLM Risk Assessment Framework (LRF), a structured approach for evaluating the application of LLMs within Systems Engineering (SE) environments. The framework classifies LLM-based applications along two fundamental dimensions: autonomy, ranging from supportive assistance to fully automated decision making, and impact, reflecting the potential severity of incorrect or misleading model outputs on engineering processes and system elements. By combining these dimensions, the LRF enables consistent determination of corresponding risk levels across the development lifecycle. The resulting classification supports organizations in identifying appropriate validation strategies, levels of human oversight, and required countermeasures to ensure safe and transparent deployment. The framework thereby helps align the rapid evolution of AI technologies with established engineering principles of reliability, traceability, and controlled process integration. Overall, the LRF provides a basis for risk-aware adoption of LLMs in complex engineering environments and represents a first step toward standardized AI assurance practices in systems engineering.
Hybrid Active Teaching Methodology for Learning Development: A Self-assessment Case Study Report in Computer Engineering
University of Luxembourg（卢森堡大学）；Instituto Superior Manuel Teixeira Gomes（曼努埃尔·特谢拉·戈梅斯高等学院）；Lusófona University（卢索福纳大学）；Universidad Nacional de Rosario（罗萨里奥国立大学）
From Code-Centric to Intent-Centric Software Engineering: A Reflexive Thematic Analysis of Generative AI, Agentic Systems, and Engineering Accountability
Teaching Agile Requirements Engineering: A Stakeholder Simulation with Generative AI
University of Applied Sciences Emden/Leer（埃姆登/莱尔应用科学大学）；University of Applied Sciences and Arts Hannover（汉诺威应用科学与艺术大学）；Federal University of São Paulo（圣保罗联邦大学）
Leveraging Generative AI for Enhancing Automated Assessment in Programming Education Contests
University of Bucharest Faculty of Mathematics and Computer Science（布加勒斯大学数学与计算机科学学院）；Softbinator Technologies（Softbinator技术公司）；Delft University of Technology（代尔夫特理工大学）
本文提出利用生成式 AI 自动生成编程竞赛高质量测试用例的 NLP 方法，并在 OJI、Kilonova.ro 和 IIOT 数据集上验证其可发现隐藏错误、减轻教师负担。
Generative AI and Empirical Software Engineering: A Paradigm Shift
School of Computing（计算学院）；Information Systems Singapore Management University Singapore, Singapore（信息系统新加坡管理大学新加坡）；Department of Computer Science University of Victoria Victoria, Canada（计算机科学系维多利亚大学加拿大）
LLM Use, Cheating, and Academic Integrity in Software Engineering Education
University of Calgary（卡尔加里大学）；University of Hawaii at Manoa（夏威夷大学马诺阿分校）；University College London（伦敦大学学院）；Universidade Federal Rural de Pernambuco (UFRPE)（伯南布哥联邦农村大学）；Radboud University（拉德堡德大学）
A Research Roadmap for Augmenting Software Engineering Processes and Software Products with Generative AI
University of Naples "Federico II" Napoli Italy；Ruhr Institute for Software Technology (paluno), University of Duisburg-Essen Essen Germany；Karlsruhe Institute of Technology (KIT) Karlsruhe Germany；Gran Sasso Science Institute (GSSI) L'Aquila Italy；University of Naples "Federico II"；Ruhr Institute for Software Technology (paluno), University of Duisburg-Essen；Karlsruhe Institute of Technology (KIT)；Gran Sasso Science Institute (GSSI)
