Intelligent Environments are rapidly evolving through the integration of Artificial Intelligence, Foundation Models, Generative AI, edge intelligence, digital twins, robotics, pervasive sensing, and autonomous decision-making. These technologies have significantly expanded the capabilities of Intelligent Environments while simultaneously introducing new challenges regarding performance,
reliability, availability, maintainability, resilience, trustworthiness, security, explainability, sustainability, and long-term evolution.

As Intelligent Environments become increasingly AI-driven, software quality can no longer be viewed as a collection of isolated non-functional properties. Performance and dependability now depend on complex interactions between traditional software engineering concerns and emerging AI-specific characteristics such as model uncertainty, data quality, explainability, fairness, robustness,
continuous learning, and governance. Decisions that improve one quality attribute often introduce trade-offs with others, making the engineering of dependable Intelligent Environments considerably more challenging. For example, reliability vs. performance, accuracy vs. explainability, privacy vs. utility.

This Special Issue aims to bring together researchers and practitioners working on the engineering, validation, deployment, and evolution of Intelligent Environments that balance multiple quality objectives while embracing the opportunities and challenges introduced by modern AI technologies. We particularly encourage contributions that move beyond proof-of-concept systems and provide evidence from realistic deployments, empirical studies, industrial case studies, benchmark datasets, and reproducible evaluation methodologies.

The Special Issue welcomes, but is not limited to, the following topics:


AI Engineering and Dependability

Reliable Generative AI for Intelligent Environments
Large Language Models in Intelligent Environments
Foundation Models for Smart Systems
AI Engineering methodologies
AI lifecycle management
Technical debt in AI-enabled systems
Continuous validation and monitoring of AI systems
Sustainable/resource-efficient AI
Agentic AI and multi-agent systems
AI observability and LLMOps
Reliability of GenAI systems (e.g., hallucinations, uncertainty, RAG)
AI security, and technical debt related to data, models, and prompts.
Trustworthy AI

Explainable AI
Responsible AI
Fairness and bias mitigation
Human oversight
Transparency and accountability
AI governance
Safety-critical Intelligent Environments
Software Quality

Reliability, availability, maintainability, safety, and resilience engineering
Software architecture for AI systems
Quality models for Intelligent Environments
Software evolution and maintenance
Self-adaptive systems
Runtime verification
Resilience and fault tolerance
Performance and Dependability Modeling and Assessment

AI- and LLM-assisted generation, evolution, parameterization, and validation of performance and dependability models
Stochastic and probabilistic modeling, including Petri nets, Markov chains, queueing networks, and stochastic workflows
Hierarchical and multimodel approaches
Digital twins for performance and dependability assessment
Performance and dependability assessment of IoT, cloud–fog–edge, and cyber-physical systems, including reliability, availability, maintainability, safety, and resilience
Verification and Validation

Testing AI-enabled systems
Verification of autonomous systems
Model validation
Benchmarking methodologies
Reproducibility and replicability
Digital twins for validation
Formal methods
Data and Learning

Data quality assessment
Synthetic data generation
Continual learning
Federated learning
Edge AI
Privacy-preserving AI
Secure machine learning
Human-Centred Intelligent Environments

Human-AI collaboration
User trust
User experience
Accessibility
Inclusive Intelligent Environments
Adaptive interfaces
Applications

Healthcare
Smart cities
Ambient Assisted Living
Robotics
Industry 5.0
Smart transportation
Education
Agriculture
Energy systems
Empirical Research

Industrial experience reports
Longitudinal studies
Deployment experiences
Lessons learned
Replication studies
Systematic literature reviews
Mapping studies
Benchmark datasets

If you are unsure whether your work would fit the scope of this special issue, please contact the guest editors, by sending an email to a.santokhee@mdx.ac.mu, carlosrodriguez@ugr.es, and/or prmm@cin.ufpe.br