SLMs for Smart Cities Training is a practical training program focused on applying Small Language Models (SLMs) to smart city initiatives. The course explores how lightweight, efficient language models can support urban governance, citizen services, infrastructure management, and decision-making while ensuring data privacy, cost efficiency, and edge deployment readiness. Participants will learn how SLMs can be integrated into smart city ecosystems without heavy cloud dependence.

• Understand Small Language Models (SLMs) and smart city concepts
• Apply SLMs for urban and civic applications
• Analyze data-driven smart city use cases
• Integrate AI solutions for city services and automation
• Evaluate AI performance in smart city environments

• Smart city planners and administrators
• Government and public sector professionals
• Urban technology and innovation teams
• Infrastructure and operations managers
• Consultants working on smart city projects

• Basic understanding of smart city concepts or urban systems
• Familiarity with digital platforms and data-driven services
• No prior AI or machine learning experience required

• Understanding the role of SLMs in smart city ecosystems
• Identifying use cases for SLM-driven urban services
• Designing lightweight AI solutions for public sector needs
• Applying privacy-first and cost-effective AI strategies
• Evaluating SLM deployment options for smart cities

By the end of this training, participants will be able to understand, evaluate, and apply Small Language Models in smart city contexts. Learners will gain practical insights into deploying efficient, privacy-aware AI solutions that support urban services, governance, and infrastructure management.

Introduction to SLMs and Smart City AI
• What are Small Language Models and how they differ from LLMs
• Role of AI in smart city transformation
• Benefits of SLMs for public sector deployments

Smart City Use Cases for SLMs
• Citizen engagement and service chatbots
• Public information systems and knowledge assistants
• Urban planning and policy analysis support

SLMs for Governance and Administration
• Automating reports, notices, and documentation
• Supporting decision-making and data interpretation
• Enhancing transparency and accessibility

Data Privacy, Security, and Local Deployment
• Handling sensitive citizen and city data
• Edge and on-premise deployment considerations
• Compliance and regulatory awareness

SLMs for Infrastructure and Operations
• Supporting transportation and mobility systems
• Monitoring utilities and city services
• Incident reporting and response assistance

Integrating SLMs with Smart City Platforms
• Connecting SLMs with existing city systems
• API-based and workflow-driven integrations
• Interoperability considerations

Performance, Limitations, and Cost Optimization
• Evaluating accuracy and efficiency of SLMs
• Managing trade-offs between model size and capability
• Cost-effective AI strategies for cities

Ethical AI and Responsible Smart City Design
• Avoiding bias in public-facing AI systems
• Ensuring fairness and inclusivity
• Responsible AI governance for smart cities

Hands-on Smart City AI Scenarios
• Real-world urban use case simulations
• Guided SLM workflow design
• Participant exercises and feedback

• Introduction to SLMs and smart cities
• AI applications in urban management
• Smart city data processing and analytics
• AI integration and automation workflows
• Performance, security, and ethical considerations

• Participation in hands-on scenario exercises
• Use case design and analysis assignments
• Scenario-based assessment

Participants will receive course materials, slides, reference materials, exercises and access to resources for further learning.

Participants who successfully complete the training and evaluation will receive an AcadNXT Certificate of Completion in SLMs for Smart Cities Training, validating their understanding of applying Small Language Models in smart city environments.