Course Outline
Applied Artificial Intelligence Workshop
Format: Intensive, hands-on instructional session
Objective: Establish practical proficiency in artificial intelligence (AI) applications across leading platforms and operational workflows for government personnel.
Module 1 – AI Platform Capabilities and Comparative Analysis (75 minutes)
This module provides an overview of the core functionalities of primary AI platforms utilized in contemporary professional environments.
Platforms evaluated:
- ChatGPT
- Google Gemini
- Microsoft Copilot
Key topics analyzed:
- File processing capacities and document constraints
- Context window mechanics
- Data security and privacy protocols
- Available tools and ecosystem integrations
- Automation potential and workflow integration
- Operational strengths and technical limitations of each platform
Instructor Demonstration
The instructor illustrates key differentiating features and practical applications across platforms.
Independent Practical Exercise
Participants construct an AI tool comparison matrix using a standardized template to assess operational suitability for government use cases.
Evaluation criteria:
- User interface and accessibility
- Accuracy and quality of generated outputs
- Research depth and reliability
- Potential for integration into existing agency workflows
Learning outcomes for this module include the ability to:
- Compare major AI platforms based on functional capabilities
- Identify specific strengths and limitations of each toolset
- Select appropriate AI tools aligned with specific task requirements
Module 2 – Research, Synthesis, and Multimodal Analysis (90 minutes)
This module focuses on leveraging AI for information research, synthesis, and critical evaluation of data integrity.
Exercise 1 – Long-Form Document Reasoning
Independent Practical Exercise
Participants upload a multi-document dataset to test analytical capabilities across two selected AI platforms.
Evaluation criteria:
- Citation accuracy and sourcing
- Logical consistency of analysis
- Depth and coherence of synthesis
- Effectiveness in summarizing complex materials
Exercise 2 – Hallucination Detection Drill
Independent Practical Exercise
Participant activities include:
- Submission of obscure or specific factual queries
- Manual verification of cited sources
- Comparative analysis of responses across different tools
A guided group discussion addresses techniques for identifying and mitigating AI-generated inaccuracies (hallucinations).
Exercise 3 – Voice Interaction Laboratory
Guided Practical Exercise
Participant activities include:
- Ideation and brainstorming via voice interfaces
- Mid-response interruption and correction
- Conversational revision requests
Debrief discussion covers:
- Scenarios where voice interaction enhances efficiency compared to text-based input
- When structured textual prompting yields superior results
Learning outcomes for this module include the ability to:
- Evaluate AI outputs for accuracy, reliability, and adherence to facts
- Utilize AI tools effectively for research and document analysis
- Apply verification protocols to detect potential misinformation
Module 3 – Development of Custom AI Assistants (2 hours)
Participants explore mechanisms for creating specialized AI assistants tailored to specific operational needs.
Platforms introduced:
- ChatGPT Custom GPTs
- Google Gemini Gems
- Microsoft Copilot Agents
Instructor Demonstration
The instructor demonstrates the process for building a custom AI assistant, including:
- Defining operational scope and purpose
- Drafting system instructions and behavioral guidelines
- Integrating proprietary or specialized knowledge documents
- Testing and refining response accuracy
Independent Practical Exercise
Participants select one platform to develop a custom AI assistant.
Participant activities include:
- Defining the assistant’s specific operational purpose
- Configuring instructions and system logic
- Uploading relevant knowledge bases or documentation
- Testing behavior and refining output quality
Instructors provide active facilitation and technical support throughout the development process.
Learning outcomes for this module include the ability to:
- Design custom AI assistants optimized for specific agency workflows
- Configure precise instructions and knowledge sources for government applications
- Deploy assistants to automate routine information management tasks
Module 4 – Workflow Automation Laboratory (90 minutes)
Participants examine methods for streamlining operational processes by integrating AI with productivity suites.
Participants select one of two workflow tracks based on their agency’s technology stack.
Track A – Microsoft Ecosystem Integration
Participants utilize AI with Microsoft tools to:
- Analyze Excel datasets for trends and anomalies
- Generate executive summaries and analytical insights
- Convert findings into structured PowerPoint presentations
Track B – Google Ecosystem Integration
Participants utilize AI with Google tools to:
- Analyze documents stored within Google Drive repositories
- Generate structured reports and memos
- Create summarized communications for email dissemination
Intensive facilitator support is provided during all exercises.
Learning outcomes for this module include the ability to:
- Accelerate analytical workflows using AI assistance
- Seamlessly integrate AI outputs into standard productivity tools
- Design simple automation pipelines for high-volume administrative tasks
Module 5 – Advanced Data Analysis with AI (75 minutes)
Participants explore how AI tools facilitate data exploration, code generation, and visualization for non-technical staff.
Python Code and Data Visualization Exercise
Independent Practical Exercise
Participants upload a dataset and use AI tools to:
- Conduct exploratory data analysis
- Generate code for charts and visualizations
- Refine analytical insights through iterative prompting
Platform comparison focuses on:
- Code transparency and explainability
- Error correction and debugging efficiency
- Clarity and appropriateness of visualizations
Discussion emphasizes strategies for non-developers to leverage AI-assisted data analysis for evidence-based decision-making.
Learning outcomes for this module include the ability to:
- Utilize AI tools for efficient dataset analysis
- Generate visualizations and actionable insights from raw data
- Iterate on data analysis processes using AI-assisted workflows for government reporting requirements
