AI in AEC & Cyber Policy Program - Syllabus

This intensive program, developed by the Secure AI Education Foundation (SAIE), explores the transformative impact of Artificial Intelligence (AI) on the Architecture, Engineering, and Construction (AEC) industry. It integrates a critical focus on secure AI practices and relevant policy implications essential for responsible innovation. Participants will gain both theoretical understanding and hands-on experience in leveraging cutting-edge AI technologies, such as advanced analytics and AI models, for real-world AEC use cases.  A particular emphasis will be placed on responsible AI deployment, addressing data privacy, ethical considerations, and emerging policy frameworks. The program blends formal lectures with dynamic, industry-led conversations and intensive, project-based lab work, preparing the next generation of technologists to innovate responsibly in the built environment.

Enrollment Information

The Secure AI Education Foundation (SAIE) delivers this AI in AEC & Cyber Policy Program by partnering directly with organizations, educational institutions, and agencies. We do not offer direct enrollment to individuals.

If you are an individual interested in participating, please be aware that enrollment is handled exclusively through our partner programs. This program will be offered in Fall 2025 at:

  • FAMU Cyber Policy Institute: For enrollment inquiries, please contact Dr. Daryl Scriven at darryl.scriven@famu.edu.
  • Clayton County Schools: For enrollment inquiries, please contact Rod Dunlap at roderick.dunlap@clayton.k12.ga.us.

Organizations or educational institutions interested in offering this program to their members or students should contact us at info@secureaieducation.org to discuss collaboration options.

Learning Objectives

Upon successful completion of this program, participants will be able to:

  • Understand AI Fundamentals: Explain core AI concepts, including machine learning, deep learning, and how AI models apply to the AEC domain.
  • Apply Geospatial AI: Utilize aerial data collection and processing techniques to generate and analyze geospatial models (e.g., orthomosaics, point clouds) for construction site analysis.
  • Implement Predictive AI: Apply predictive AI models for tasks such as construction risk assessment, anomaly detection, and site planning optimization.
  • Deploy AI Responsibly: Understand principles for deploying and managing AI models in controlled environments to enhance data control and privacy.
  • Manage Data Responsibly: Work with large, unstructured, and sensitive datasets within industry-standard data platforms, applying principles of data privacy and responsible data handling.
  • Assess Policy & Ethical Implications: Critically evaluate the ethical, legal, and policy considerations related to AI deployment, data privacy, and responsible use of data in the AEC industry.
  • Utilize Industry Tools: Gain practical proficiency with common development tools and systems used for coding, data management, and geospatial processing.
  • Communicate Effectively: Articulate technical concepts, project outcomes, and policy recommendations clearly and professionally to both technical and non-technical audiences.

Program Structure & Deliverables

This program is structured as a 10-week intensive experience, combining theoretical insights with practical application. It is typically delivered with a 50% Lecture Series and 50% Hands-On Labs.

  • Lecture Series: Features the “Secure AI: Industry & Innovation Talks,” bringing in leading experts to discuss current trends, industry use cases, and challenges in AI, data ethics, and responsible innovation. These sessions provide deep dives into specific topics.
  • Hands-On Labs: Project-based exercises designed to provide practical experience with tools and concepts discussed in lectures. Participants will work on real-world use cases.

Deliverables & Assessment:

  • Lecture Series (50%): Active participation in discussions, insightful contributions to “Industry & Innovation Talks,” and reflection notes/summaries.
  • Lab Assignments & Final Project (50%): Completion of hands-on coding, data processing, AI deployment tasks, and a culminating project, graded on correctness, quality, and adherence to responsible data practices.
  • Note: Grading assessment is optional based on your organization’s specific needs.

Lab Technology Stack & Tools

Participants will engage with industry-standard tools and platforms. We will recommend a lab stack, or we can adapt to standardized tools from your organization.

  • Integrated Development Environment (IDE): A code editor for writing and debugging.
  • Version Control System: For managing code, lab notes, and collaborative project files.
  • Cloud-Based Data Platform: For managing and querying large datasets.
  • Predictive Model Management Platform: For developing, deploying, and managing AI models, including those that process unstructured data.
  • Programming Language: Python with relevant libraries for data analysis, machine learning, and geospatial processing.
  • Photogrammetry/GIS Tools: Software for processing aerial imagery and managing geospatial information.

10 Week Course Schedule

(Note: Lab activities reinforce lecture concepts with hands-on application. This schedule is typical and can be customized to your organization’s specific needs and focus areas.)

  • Week 1
    • Lecture Series Topic– Setting the Stage: Technology’s Impact on AEC
    • Lab Topic– Lab Environment Setup: Set up an integrated development environment and version control system. Understand Python environment setup (Python is optional for this course). Initial understanding of the AI in AEC Use Case that will be developed in subsequent labs.
  • Week 2
    • Lecture Series Topic – The Digital Transformation of Architecture & Construction
    • Lab Topic – Aerial Data Collection & Processing (Part 1): Create and execute simulated aerial data collection plans (e.g., drone flight paths). Introduction to data acquisition ethics and best practices.
  • Week 3
    • Lecture Series Topic – Visualizing the Future: Digital Twins and Geospatial Intelligence
    • Lab Topic – Aerial Data Collection & Processing (Part 2): Continue aerial data collection planning. Capture (simulated/provided) site data and generate initial spatial models (2D orthomosaics).
  • Week 4
    • Lecture Series Topic – Data as the New Foundation: Harnessing Information in AEC
    • Lab Topic – Advanced Geospatial & Point Cloud Data Preparation: Refine 2D orthomosaics and generate 3D point clouds. Extract and transform features from point cloud data into structured tabular formats. Introduction to a cloud-based data platform for large dataset management.
  • Week 5
    • Lecture Series Topic – Smarter Decisions: Predictive Analytics for Risk & Opportunity
    • Lab Topic – AI-Driven Risk Analysis (Part 1 – Model Review): Analyze AI-generated excavation risk outputs from provided models (e.g., simulated SiteSenseAI outputs), focusing on understanding how models are presented and what they indicate. Interpret spatial overlays and risk zones.
  • Week 6
    • Lecture Series Topic – Innovation in Action: AI for Optimization and Efficiency
    • Lab Topic – AI-Driven Risk Analysis (Part 2 – Decision Simulation): Deep dive into model outputs. Simulate decision-making using AI model outputs to evaluate risks and optimize site planning. Propose initial mitigation strategies based on data.
  • Week 7
    • Lecture Series Topic – Navigating the AI Landscape: Ethical & Responsible Adoption
    • Lab Topic – Predictive Model Deep Dive (Part 1): In-depth exploration of the AI models used in the previous weeks. Examine the model architecture, input features, and output interpretations. Discuss model limitations and potential biases.
  • Week 8
    • Lecture Series Topic – Policy & the Future of AI in Regulated Industries
    • Lab Topic – Predictive Model Deep Dive (Part 2): Analyze the datasets used to train the AI models. Discuss data quality, data privacy considerations, and ethical implications related to the data.
  • Week 9
    • Lecture Series Topic – Emerging Technologies and Future Trends in AEC
    • Lab Topic – Mock Field Exercises: Conduct structured mock field exercises applying AI model outputs, checklists, and team workflows to real-world use cases. Focus on validating AI predictions in a practical context.
  • Week 10
    • Lecture Series Topic – AI’s Role in Building the Future: Capstone Insights
    • Lab Topic – Culminating Project & Presentation: Teams present their final projects, demonstrating the full workflow (data to insight) and reflecting on outcomes, challenges, and future implications for the AEC industry. Includes discussion of responsible AI deployment.

Program Authors & SMEs

  • Willie Lee, Senior Fellow 
  • Gregory Crooms, Senior Fellow

Important Note

Where AI models and data from specific commercial solutions (e.g., ExcavationIQ’s SiteSenseAI) are utilized, they are integrated for educational purposes only as case studies and examples. These are proprietary properties and may not be used for commercial purposes or integrated into external systems without the explicit permission or commercial agreement of ExcavationIQ. For commercial inquiries regarding ExcavationIQ’s models, please contact wbaker@excavationiq.com.