AI-Assisted Engineering Decision-Making The PEARL Framework (Webinar)

In Basics of Earthwork and Compaction: Principles, Methods, and Quality Control, you'll learn ...

• The principles of defensible engineering decision-making and the distinction between decision quality and outcome quality
• The five-stage PEARL lifecycle of Preparation, Evaluation, Action, Reflection, and Learning
• The TRACE process for independently verifying AI-assisted engineering information, analysis, calculations, assumptions, and recommendations
• How to maintain professional accountability, human oversight, and evidence-based judgment when AI contributes to engineering decisions

Overview

---

Artificial intelligence can expand an engineer's ability to develop alternatives, analyze information, and document technical work. It can also introduce confident errors, hidden assumptions, false precision, unreliable sources, and automation bias. This webinar gives professional engineers a practical method for using AI-assisted analysis while retaining control of technical judgment, verification, risk acceptance, and final approval.

The presentation begins by distinguishing decision quality from outcome quality and examining why technically capable teams still make weak or indefensible decisions. Engineering cases involving the Mars Climate Orbiter, Space Shuttle Challenger, Hyatt Regency walkway collapse, Deepwater Horizon, FIU pedestrian bridge collapse, Fukushima Daiichi accident, Oroville Dam spillway incident, and the 2003 Northeast Blackout illustrate failures in problem framing, assumptions, interface verification, communication, authority, monitoring, and stop-work discipline.

Participants then apply the PEARL decision lifecycle: Preparation, Evaluation, Action, Reflection, and Learning. Each stage organizes specific safeguards for defining the decision, separating facts from assumptions, developing alternatives, establishing evaluation criteria, assessing uncertainty, implementing controls, monitoring results, and converting experience into improved procedures and accountability. The TRACE method complements PEARL by requiring engineers to Trace sources, Review reasoning, Assess assumptions, Confirm calculations and requirements, and ensure that a qualified Engineer approves the work before an AI-assisted output influences a professional decision.

A fictional multidisciplinary pump-station case runs throughout the webinar and culminates in an integrated exercise. Participants evaluate an AI-generated recommendation, identify missing evidence and alternatives, define measurable reopening triggers, and determine what a professionally accountable decision record should contain. The webinar is relevant to civil, structural, mechanical, electrical, environmental, systems, aerospace, and other engineers who make decisions involving safety, reliability, cost, schedule, quality, uncertainty, or AI-assisted technical work. General engineering practice is assumed; no prior experience with artificial intelligence is required.

Learning Objectives

Upon completion of this webinar, participants will be able to:

• Identify and describe common failure modes that undermine engineering decisions, including incorrect problem framing, incomplete information, unchecked assumptions, automation bias, inadequate verification, and schedule or cost pressure.
• Distinguish verified facts, inferences, assumptions, and AI-generated suggestions when evaluating information used to support an engineering decision.
• Apply the five stages of the PEARL lifecycle—Preparation, Evaluation, Action, Reflection, and Learning—to structure an engineering decision from initial problem definition through lessons learned.
• Formulate a clear engineering decision statement and establish applicable safety, reliability, quality, cost, schedule, regulatory, and technical criteria before comparing alternatives.
• Evaluate multiple engineering alternatives using predetermined weighted criteria, risk considerations, feasibility findings, and sensitivity analysis to determine how changing assumptions can affect a recommendation.
• Apply the TRACE process by tracing sources, reviewing reasoning, assessing assumptions, confirming calculations and requirements, and obtaining approval from an accountable engineer before relying on AI-assisted work.
• Develop an implementation approach that assigns responsibilities, establishes acceptance criteria, implements identified risk controls, communicates assumptions and limitations, and maintains human oversight.
• Establish measurable monitoring and decision-reopening triggers that identify when changing operating conditions or technical evidence require an engineering decision to be reconsidered.
• Evaluate engineering decisions separately from their eventual outcomes and identify lessons that should modify immediate actions, underlying assumptions, or the broader decision-making system.
• Analyze an integrated engineering case and document a defensible decision that identifies evidence, assumptions, uncertainty, alternatives, risk controls, verification activities, and accountable professional approval.

Certificate of Completion

You will be able to immediately print a certificate of completion after passing a multiple-choice quiz consisting of 5 questions. PDH credits are not awarded until the course is completed and quiz is passed.