AI Ethics and Professional Responsibility
$20.002 PDH — Technology / AI
Showing all 16 results


A professional-conduct course on using AI responsibly in licensed practice: the paramount duty, verification obligations, bias and hallucination, data privacy and IP, transparency, and where liability rests.

AI in architectural practice: generative and parametric design, AI-assisted BIM and documentation, energy/daylight optimization, scan-to-BIM, and the ethics, IP, and life-safety responsibilities that remain with the licensed architect.

How AI/ML supports civil and structural practice: design optimization, generative/parametric design, structural health monitoring, and infrastructure assessment from imagery — with the verification duties and code-compliance limits.

AI across project delivery: schedule/cost prediction, computer-vision progress monitoring, safety analytics, and document intelligence — with the risk, data, and accountability guardrails for responsible use.

AI/ML in geotechnical practice: site characterization, settlement/slope/pile prediction, seismic and liquefaction screening, and instrumented monitoring — and why subsurface uncertainty keeps the engineer responsible.

AI/ML for surveying and land development: point-cloud classification, feature extraction, GIS analytics, and site/grading optimization — and why boundary determinations and certifications remain the licensed surveyor’s judgment.

AI/ML in mechanical and energy engineering: predictive maintenance, building-energy and HVAC optimization, digital twins, generative/topology design, and fault detection — with the engineering verification and code limits.

AI in transportation: adaptive signal control, traffic prediction and safety analytics, computer-vision detection, connected/automated vehicles, and asset management — with the MUTCD/AASHTO safety guardrails.

AI/ML in water and environmental engineering: hydrologic and flood forecasting, water-quality monitoring, treatment-plant optimization, remote sensing, and leak/asset detection — with permit-compliance limits.


A plain-language primer on AI, machine learning, and generative AI for engineers, surveyors, and architects — how models learn, where they help technical practice, and their limits, risks, and the professional-responsibility duty to verify AI-assisted work.


How generative AI, BIM, and digital twins connect across design, construction, and operations — plus data interoperability, governance, privacy, and the professional-responsibility guardrails.

The data and ML concepts engineers need to use AI critically: data quality, model types, training/validation, overfitting, evaluation metrics, uncertainty, and common pitfalls.

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