Artificial
Intelligence
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16 Key Terms

Scouts prepared these definitions before today. Select any term below to see the official Scoutly definition alongside a counselor's perspective. One term at a time — selecting a new one closes the previous.

These Terms in the Real World

A few terms worth grounding in professional context:

• Machine Learning: In insurance, ML models analyze thousands of claims to predict which cases are likely to escalate to complex litigation, allowing adjusters to intervene weeks earlier than they otherwise could.
• Narrow AI: A document-reading AI model can read a 200-page medical record and extract 40 structured data fields in under a second. Ask it to write a poem, complete blank. That's Narrow AI.
• Digital Workers / RPA: Bots across financial services, insurance, and healthcare process millions of routine transactions, policy renewals, payment confirmations, data entry, that previously required large teams of people. Not AI, but powerful automation.
• General AI vs. Superintelligent AI: We don't have General AI yet. Everything you interact with today, ChatGPT, Siri, Alexa, is Narrow AI. The jump to General AI is one of the biggest unsolved problems in the field.

Scout Presentations (2a, 2b, 2c, 2e)

Scouts prepared these before today. Have each scout share their work and prompt discussion with the questions below.

"AI or Not?", 10 Rounds

Scouts call "AI" or "Not AI" for each scenario. After each answer is revealed, use the discussion prompt to talk through the reasoning. Both the game and the discussion are required.

What "AI in the Workplace" Actually Looks Like

Insurance companies are building complex AI systems to facilitate customer intake, claims handling, and early detection of fraud. When a claim is filed, AI systems can read thousands of pages of medical records, legal filings, and incident reports, extracting structured facts in seconds that would take a human adjuster two to three hours. Healthcare systems use AI to flag anomalies in patient records before a doctor reviews them. Banks use it to detect fraudulent transactions in milliseconds. But here's the pattern that holds across all of them: the AI doesn't make the final decision. The human does.

AI removes the busywork so humans can focus on judgment. That's the model you'll see repeatedly in real enterprise AI: AI handles scale, humans handle accountability. At least for now.

Scout Presentations (3a, 3b, 3c, 3e)

How Automation Works, Three Core Benefits

Have scouts explain this in their own words, then use the comparison below to anchor the discussion.

Automation Saving Lives at 30,000 Feet

Aerospace manufacturers equip jet engines with hundreds of sensors continuously measuring temperature, pressure, vibration, and fuel flow, generating millions of data points per flight. Automated monitoring systems process all of this in real time, 24/7, automatically flagging anomalies and triggering maintenance alerts before anything fails.

Before this kind of automation, engines were serviced on fixed schedules, whether they needed it or not. Now they're serviced when sensor data says they need it. That's automation reducing human error and optimizing resources in a context where failure isn't a customer complaint. It's a crash.

The stakes change the calculus. The same principles you'd apply to automating a homework reminder apply to keeping a commercial aircraft in the air. Worth thinking about.

Scout Share-Out (4a & 4d)

"What Would You Do?", 5 Ethical Scenarios

Read each scenario aloud together. Scouts deliberate and share their thinking. Then reveal the ethical considerations and work through the required discussion questions. Both playing and discussing are required.

Scout-Developed Ethical Guidelines

Each scout developed their own ethical guidelines for AI use. Have them present these now. Use the questions below to facilitate discussion.

What Responsible AI Looks Like Inside a Major Corporation

In regulated industries like insurance, healthcare, and financial services, every AI model goes through a governance process before deployment. This includes bias testing across demographic groups, fairness audits, adversarial probing (trying to break the model on purpose), and documentation of training data sources, known limitations, and decision thresholds. It's not optional, regulators watch closely, and a biased model can mean thousands of customers treated unfairly, with real legal and financial consequences for the company that built it.

The ethical guidelines each of you wrote today are the informal version of what takes teams of engineers, lawyers, ethicists, and compliance officers months to formalize at the enterprise level. You're thinking about the same problems, just without the regulatory filing at the end.

How Deepfakes Actually Work

The technology behind deepfakes is worth understanding, because it is one of the clearest examples of a powerful idea in modern AI: two systems training against each other.

Picture a forger and a detective learning together. The forger creates a fake image and shows it to the detective. The detective says real or fake. The forger adjusts based on what got caught, and tries again. The detective gets better at spotting fakes. The forger gets better at making them. Repeat this millions of times, automatically, at computer speed. After enough rounds, the forger has become precise enough that most people — and even some software — cannot reliably tell the difference.

That is the actual mechanism. It has a technical name — a Generative Adversarial Network, or GAN — but the word "adversarial" just means the two systems are competing. The concept was published in 2014 and changed what AI could create.

Detection is hard for a specific reason: the system was mathematically optimized to fool detectors. It did not stumble into being convincing — that was the training objective. Researchers who build detectors look for patterns the generator leaves behind: a blink rate that is slightly off, lighting that does not match the edge of a swapped face, color values that are statistically inconsistent at seam lines. But when detectors improve, generators adapt. Both sides are machine-learning systems improving on each other.

The broader point: every photo or video you have seen carries an assumption that it records something that actually happened. That assumption is now mathematically breakable. For your generation, verifying the authenticity of digital media is not a solved problem — it is an active area of research.

Scout Presentations (6a, 6b, 6c)

Live Prompt Lab, 3 School-Related Tasks

Each slot shows a weak prompt and a strong prompt for a school-related task. Scouts type their own improved version in the text box, then compare with the strong example. This demonstrates the requirement to write clear instructions for school tasks.

Prompt Engineering in Professional AI Systems

In consumer AI (like ChatGPT), prompt engineering is about getting better answers. In production systems, it's about getting consistent, parseable, reliable outputs at scale. Here's a pattern used in enterprise insurance AI:

The specificity of those constraints is what separates a prototype from a production system. Vague prompts give variable outputs. Variable outputs break downstream code. This is why prompt engineering is an actual job title at major tech companies.

Three Questions That Shape Every Career in AI

Before researching a specific career, scouts should understand that "AI jobs" is not one thing. Every career in this space sits at the intersection of three distinct dimensions. Defining all three changes what you search for and what you find.

One Path Into AI, Many Ways In

There is no single required path into AI. Here's mine:

Reading the Labor Market with Primary Sources

A March 2026 Washington Post interactive analysis — worth bookmarking and having scouts explore directly — examined over 350 occupations for AI exposure and worker adaptability. A few findings worth discussing:

• Jobs in computer programming, marketing, financial analysis, and customer service show high overlap with current AI capabilities. But many workers in those fields also have the skills and flexibility to adapt — higher education, varied experience, access to strong job markets. Exposure does not automatically mean replacement.
• The most at-risk group identified: approximately 6 million clerical and administrative workers who are both highly exposed to automation and have fewer pathways to transition. This skews heavily toward female-dominated occupations — a dimension of AI's labor market impact that rarely makes the headline summaries.
• Previous predictions that ATMs would eliminate bank tellers, that early AI would decimate radiologists, and that a specific 2013 study's estimate that nearly half of jobs could be automated by computers would all have led scouts to make poor career decisions. Economists consistently note: "We do not have a good track record of predicting how technological change will play out in the labor market."
• As of the analysis date, there was no measurable evidence that AI is putting Americans as a whole out of work. That does not mean the transition will be painless — but it does mean that sensational headlines in either direction should be evaluated against the primary data, not accepted at face value.

All 8 Requirements

Confirm each requirement has been met before closing the session.

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