# Teacher's Guide — Session 7 🟢
# “AI in real life”

**Program:** Applied AI — Beginner Level (from 12 years old, general public)
**Instructor:** Yann Isola
**Recommended duration:** 2 hours (adaptable 1h30 – 2h30)
**Prerequisites:** Sessions 1 to 6 (concepts: AI, machine learning, data, generative AI, critical thinking)

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## 1. Educational objectives

At the end of the session, each participant should be able to:

1. **Name** at least 5 sectors where AI is used today, with a concrete example for each.
2. **Explain** how AI helps doctors (detection on x-rays, drug discovery) without replacing them.
3. **Describe** the levels of autonomy of cars (from 1 to 5) and locate where we really are. ⚠
4. **Give** examples of AI in education, agriculture, entertainment, science, finance and the environment.
5. **Formulate** the golden rule of the session: *AI assists humans, it does not replace them* — and cite 3 situations where AI fails.
6. **Imagine and present** a useful AI application in a sector of your choice (2-minute mini pitch).

> ⚠ **Volatile content**: business examples, autonomous car capabilities, drone delivery programs and scientific records evolve quickly. Check the news **before each session** (15 min wake-up time). The sections marked ⚠ are those to be re-checked as a priority.

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## 2. Overview and common thread

**Code of the session:** *“For 6 sessions, we have been learning what AI is. Today, we're going on a journey: we're going to tour the world of professions and discover where AI is already working — in hospitals, in the fields, in space... and in your pocket. »*

This is THE “wow” session of the program: concrete, full of real examples, without new theory. The highlight is the **final mini pitch**: each participant becomes an inventor and presents THEIR own idea for an AI application. It is also the session that prepares the rest of the program: participants see that AI affects all jobs, including their own (or the one they will do later).

**Pedagogical posture:** you are a travel guide, not a lecturer. Each sector = a “stopover” of 8-10 minutes maximum. Fast pace, lots of interactions, memorable anecdotes.

### Timed course (based on 2 hours)

| Time | Sequence | Format |
|---|---|---|
| 0:00 – 0:08 | Tagline: “AI has already touched you today” | Discussion |
| 0:08 – 0:20 | 🏥 Health: diagnosis, medications, monitoring | Presentation + interactive map |
| 0:20 – 0:32 | 🚗 Transport: levels 1-5, traffic, drones ⚠ | Presentation + express quiz |
| 0:32 – 0:42 | 🎓 Education & 🌾 Agriculture | Presentation + discussion |
| 0:42 – 0:52 | 🎬 Entertainment & 🔬 Science | Presentation + map demo |
| 0:52 – 1:02 | **Pause** | — |
| 1:02 – 1:12 | 💰 Finance & 🌍 Environment | Exposed |
| 1:12 – 1:25 | ⚖ Limits: AI assists, does not replace | Presentation + “impact meter” (web page) |
| 1:25 – 1:50 | **Activity: “Invent your AI” + 2 min pitches** | Workshop |
| 1:50 – 2:00 | Summary + quiz (or quiz at home) | Quiz |

**Adaptation 1 hour 30 minutes:** reduce each stopover to 6-7 minutes, limit pitches to 1 minute each, quiz at home.
**Adaptation 2 hours 30 minutes:** add exercise 1 (sector survey) at the start of the workshop and extend the time for pitch preparation.

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## 3. Detailed content, sequence by sequence### 3.1 Hook (8 min) — “AI has already touched you today”

Start the challenge: *“Raise your hand if an AI has ever done you a favor TODAY, before you even got here. »*

Few hands are raised? Perfect. Scroll down the list and watch the hands go up:
- Did you watch the **weather forecast** this morning? → AI (modern forecasts use AI).
- Did you take a route with **Google Maps or Waze**? → AI (traffic prediction).
- Has your mailbox filtered **spam**? → AI.
- Was a video or music **recommended** to you? → AI.
- Did you pay by card? → an AI verified in a few milliseconds that it was not **fraud**.

**Key message:** AI is not “the future”. It is already everywhere, discreet, often invisible. Today, we are opening the hood, sector by sector.

Announce the program as a **world tour**: 8 stops, then everyone becomes an inventor.

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### 3.2 🏥 Stopover 1 — Health (12 min)

This is the most emotionally rewarding sector: start strong.

#### a) Assisted diagnosis: eyes that never tire

**Tag story:** a radiologist looks at hundreds of images a day. On the 300th radio, the eyes get tired. An AI analyzes the 300th image with the same attention as the first.

**How ​​it works (simple version):** we showed the AI ​​**hundreds of thousands of x-rays** already analyzed by doctors: “this one has a tumor, that one doesn’t”. The AI ​​learned to spot patterns – exactly as it learned to recognize cats in session 2. Except that here, they are breast cancers on mammograms, melanomas on skin photos, lesions on scanners.

**Significant figure:** on certain specific tasks (detection of cancers on x-rays), the best AIs do as well – sometimes better – than experienced radiologists. ⚠ (studies evolve, check the most recent)

**THE nuance to hammer home:** the AI **flags** suspicious areas; it is the **doctor who decides**. AI is a second set of eyes, a tireless assistant. No one wants a diagnosis announced by a robot — and legally, the medical decision remains human.

#### b) Drug discovery

Simple explanation: finding a new drug is looking for **a key that fits in a lock** (a molecule that acts on a protein in the body). Problem: there are billions of billions of possible keys. Testing each in the laboratory would take centuries.

AI **simulates** millions of molecules on a computer and suggests the most promising candidates. Researchers only really test the top of the list. Result: years of research saved.

**Star example: AlphaFold** (DeepMind/Google). For 50 years, predicting the 3D shape of a protein was a global puzzle. AlphaFold almost solved it in 2020-2021 and predicted the shape of **more than 200 million proteins** — a gift to all of science. Its creators received the 2024 Nobel Prize in Chemistry. ⚠

#### c) Patient monitoring

- **Connected watches** that detect heart rhythm abnormalities and alert.
- AI which monitors patient constants in the hospital and **warns the team** before a deterioration.
- Medication reminders, fall detection in the elderly.**Question to the group:** *“Would you like an AI to read your radios? And she decides on the treatment on her own? »* → Almost everyone says yes to the first, no to the second. This is exactly the right intuition: **attend ≠ replace**.

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### 3.3 🚗 Stopover 2 — Transportation (12 min)

#### a) Autonomous cars: the 5 levels ⚠

Write the scale on the board — it's a quiz staple:

| Level | Simple name | Who drives? | Example |
|---|---|---|---|
| **1** | Support | Humans, helped with ONE thing | Adaptive cruise control |
| **2** | Combined Support | The human, helped with speed AND direction, **hands ready** | Most current “autopilots” ⚠ |
| **3** | Conditional autonomy | The car, in certain conditions; humans must be able to resume | Traffic jams on motorways, on some models ⚠ |
| **4** | High autonomy | The car, in a defined area, without humans | Robotaxis in certain cities (Waymo…) ⚠ |
| **5** | Total autonomy | The car, everywhere, all the time | **Doesn't exist yet** |

**Myth to bust:** “autonomous cars are for tomorrow”. It has been announced “in 5 years”… for more than 10 years. Level 5 is very difficult: the road is full of unforeseen situations (works, actions of an agent, crossing a ball). AI excels in the predictable, much less in the unexpected – remember this sentence, it comes up in the “limits” section.

**How ​​an autonomous car “sees”:** cameras + radars + lidars (lasers that measure distances) → the AI ​​merges all of this to build a 3D map in real time and predict what other users will do.

#### b) Traffic optimization

- “Smart” red lights that adapt to the actual flow of cars → fewer traffic jams, less pollution.
- Waze/Google Maps: millions of phones send their speed in real time; the AI ​​deduces traffic jams and recalculates the routes. Each driver is a sensor!

#### c) Delivery by drones ⚠

Drones are already delivering medicines to hard-to-reach areas (the emblematic example: Zipline, which has delivered blood and vaccines to Rwanda and Ghana since 2016). Parcel delivery tests exist in several countries. **Honest status:** still limited — air regulations, safety, noise, weather. Check the news before the session.

**Express quiz (1 min):** “A car that maintains its speed AND trajectory on the highway by itself, but where you must remain attentive: what level? » → Level 2.

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### 3.4 🎓 Stopover 3 — Education (5 min)

Three uses to present:

1. **Personalized tutoring:** an AI available 24 hours a day which explains as many times as necessary, without ever getting annoyed, and adapts its explanations to the student's level (examples: Khanmigo from Khan Academy, Duolingo ⚠). The luxury of a private teacher, for everyone.
2. **Automatic correction:** MCQ of course, but also helps with the correction of essays (spelling, structure). The teacher saves time on the repetitive → more time for the human.
3. **Detection of difficulties:** by analyzing a student's answers, the AI ​​can identify EARLY that he or she is stuck on a specific concept (eg: fractions) and alert the teacher before the delay accumulates.**Lightning discussion (2 min):** *“Could an AI replace your favorite teacher? »* → Bring to light: the AI ​​explains, but it does not motivate, does not reassure, does not know your story, does not believe in you. Teaching is about relationships, not just information.

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### 3.5 🌾 Stopover 4 — Agriculture (5 min)

The sector that surprises the most — AI in rubber boots:

1. **Surveillance drones:** they fly over the fields and photograph everything. The AI ​​analyzes the images: here there is water missing, there an area is abnormally yellowing. The farmer knows **exactly where to act** instead of treating the entire field.
2. **Harvest prediction:** weather + soil condition + satellite images → AI estimates the harvest in advance. Useful for planning, selling, anticipating shortages.
3. **Detection of plant diseases:** mobile apps (e.g. PlantVillage ⚠) where the farmer photographs a diseased leaf and the AI ​​identifies the disease — even in villages without an agronomist miles away.

**Key message:** it's “precision agriculture” — less water, less pesticides, more yield. AI serving the planet AND the plate.

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### 3.6 🎬 Stopover 5 — Entertainment (5 min)

The sector they know best — reverse: make them tell it.

1. **Recommendations:** Netflix, YouTube, Spotify, TikTok. AI observes what you watch, like, spend — and predicts what you'll like. Reminder of session 6: this is also how **filter bubbles** are born.
2. **Video games:** AI animates non-player characters, adapts the difficulty, and even helps **generate worlds** (landscapes, textures, dialogues). ⚠
3. **Special effects in cinema:** rejuvenation of actors, generated crowds, dubbing where the lips follow the new language. Opportunity to recall the question of deepfakes (session 5) and the debate on digital actors.

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### 3.7 🔬 Stopover 6 — Science (5 min)

AI as a super-assistant for researchers:

1. **New molecules and materials:** beyond drugs (see health), AI offers materials for better batteries and more efficient solar panels.
2. **Climate:** AI digests mountains of data (satellites, oceans, atmosphere) to refine climate models and predict extreme events.
3. **Space:** automatic sorting of millions of images from telescopes (AI has already spotted exoplanets in data that humans had already looked at!), Martian rovers which choose their route — essential when a message takes many minutes to travel between Earth and Mars.

**Anecdote that works well:** citizen science + AI projects team up — volunteers classify galaxies, the AI ​​learns from them, then classifies the remaining millions. Human + AI = winning team, again and again.

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### 3.8 💰 Stopover 7 — Finance (5 min)

1. **Fraud detection:** your bank knows your habits. An unusual payment (amount, country, time) → the AI ​​spots it in **a few milliseconds** and can block or ask for confirmation. It is one of the oldest and most effective AI applications.
2. **Algorithmic trading:** a large part of stock market orders are placed by programs, in fractions of a second. Honestly mention the downside: “flash crashes” have occurred when algorithms run amok on-chain. Speed ​​≠ wisdom.
3.**Banking chatbots:** common questions 24 hours a day (“what is my balance?”, “how do I object?”). Useful for the simple, frustrating for the complicated – hence the famous “speak to an advisor” button. Once again: AI manages the repetitive, humans manage the delicate.

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### 3.9 🌍 Stopover 8 — The environment (5 min)

Ending the world tour on a note of hope:

1. **Monitoring deforestation:** satellites continuously photograph forests; the AI ​​compares images and **alerts in near real time** when trees disappear (e.g. Global Forest Watch ⚠). Before, we discovered deforestation months later.
2. **Improved weather forecasts:** recent AI models (DeepMind's GraphCast, among others ⚠) predict the 10-day weather faster — and often better — than traditional supercomputers. Better anticipation of cyclones = lives saved.
3. **Energy optimization:** AI adjusts the heating/cooling of buildings and data centers (Google reduced the cooling energy of its data centers by around 40% thanks to AI ⚠), balances electrical networks, better integrates solar and wind.

**Honest nuance to give:** AI itself **consumes** a lot of energy (training large models, data centers). It is both part of the solution AND part of the problem. Good critical thinking reflex inherited from session 6.

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### 3.10 ⚖ Limits — AI assists, does not replace (13 min)

The most important sequence. After the exciting world tour, we rebalance.

**The golden rule (to be repeated to the group):** *“AI does not replace humans — it assists them. »*

In ALL sectors visited, the pattern is the same:
- Health → AI signals, **doctor decides**
- Transport → AI assists, **the driver remains responsible** (levels 1-3)
- Education → AI explains, **the teacher motivates and supports**
- Finance → AI alerts, **the advisor manages delicate cases**

**Where the AI fails (the 3 red zones):**

1. **Cultural context and common sense.** AI learns from data; what is rare or local escapes him. A joke, an innuendo, a regional custom, irony — it misses the point. A telling example: a recruitment AI trained on past CVs reproduced past discrimination (Amazon case, abandoned in 2018).
2. **Real emotions.** An AI can *simulate* empathy (“I understand that this is difficult”) but **feel** nothing. Breaking bad news to a patient, consoling a student, negotiating a conflict: human, human, human.
3. **Pure creativity and the unexpected.** AI excels at recombining what exists. Inventing an entirely new artistic genre, reacting to a never-before-seen situation (the famous road under construction with an agent making gestures): that's where it stalls. She interpolates, humans invent.

**Summary formula to remember:** *“AI is unbeatable in the repetitive, the massive and the rapid. The human remains unbeatable on context, emotion and the unprecedented. The best teams combine the two. »*

**Blitz activity — the impact meter (web page):** for a series of tasks (“spot a tumor on 10,000 x-rays”, “consolate a patient”, “predict the weather”, “write a sincere poem about your grandmother”…), participants place the cursor between “AI excels” and “humans are irreplaceable”.Discuss borderline cases — that’s where the learning happens.

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### 3.11 🛠 Final activity — “Invent your AI” + mini pitches (25 min)

See exercise 2 of the exercise file for the complete procedure. In summary:

1. **Choose a sector** (among the 8, or another: sport, justice, cooking, fashion, etc.)
2. **Identify a real problem** in this sector
3. **Imagine an AI application** that helps (without replacing humans!)
4. **Prepare a 2-minute pitch** with the canvas: Problem → AI solution → Necessary data → What the human keeps → Name of the app
5. **Pitch** in front of the group

The “AI design” of the web page can serve as a warm-up: it allows you to manipulate the logic *problem → data → method → result* in a fun way before the paper exercise.

**Role of the instructor during the pitches:** time strictly (2 min!), applaud each passage, and ask ONE question per pitch, always the same: * “And what do humans keep in your system? »* If the student has an answer, the lesson of the session is acquired.

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## 4. Frequently asked questions from participants (and ready answers)

**“Will AI eliminate jobs? »**
Honest answer: it will transform many, eliminate some (very repetitive tasks) and create others (like every great technology: the computer has eliminated typist jobs and created millions of IT jobs). Jobs combining expertise + human relations are the strongest. The best part: knowing how to work WITH AI — that's exactly what we learn here.

**“Why aren’t self-driving cars everywhere yet?” »**
Because the 1% of unforeseen situations are extremely difficult, liability in the event of an accident is a thorny legal issue, and public trust is earned slowly. Driving is 99% easy, but it's the last 1% that counts.

**“Can AI make mistakes in medicine? »**
Yes — that's exactly why the doctor keeps the decision. The AI ​​can make mistakes on rare cases or populations that are poorly represented in its training data. Two looks (RN + doctor) are better than one.

**“What is the most protected profession in AI? »**
Nice trap: there is no “protected” job, but **tasks** that can be more or less automated. Nurse, plumber, educator, craftsman: lots of context, unforeseen events and human relationships → very difficult to automate.

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## 5. Educational pitfalls to avoid

- **The catalog effect.** 8 sectors in 50 minutes = risk of indigestion. The remedy: ONE notable story per sector, not an exhaustive list. Better they remember AlphaFold and Zipline than 40 skimmed names.
- **The techno-smug.** Only success stories = dishonest. The nuances (non-existent level 5 car, biased recruitment AI, energy consumption) make the point credible.
- **Reverse catastrophism.** “AI will destroy everything” is not more accurate. Hold the line: great assistant, bad replacement.
- **Skip the pitches.** If time runs out, cut into the presentations, NEVER in the final activity. She is the one who anchors everything.
- **Give outdated figures.** Everything marked ⚠ is checked the day before. A false number detected by a participant ruins your credibility (and it's a great session 6 lesson that they would apply against you!).

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## 6. Materials and preparation- [ ] Video projector + session slides
- [ ] Interactive web page (`webpage/index.html`) tested — works **offline**
- [ ] A4 sheets + markers for pitch canvases
- [ ] Stopwatch visible for pitches (phone or online timer)
- [ ] Printouts of the pitch canvas (exercise 2) — 1 per participant or pair
- [ ] 15 min of news monitoring on the subjects ⚠ (autonomous cars, drones, tools mentioned)
- [ ] Optional: small symbolic rewards for the “public’s favorite pitch”

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## 7. The final word (to be said at the end)

*“We traveled around the world: hospitals, roads, fields, cinemas, laboratories, banks, forests. Everywhere, the same story: the AI ​​does the repetitive, massive and rapid work — and the human keeps the context, the emotion and the decision. The question is no longer “will AI change your future job?” — she will. The real question is: will you be the one who knows how to work with her? After tonight, you have a head start. »*

**Bridge to session 8:** *“Next time, we'll talk about what all this involves: ethics, privacy, and how AI should be framed. Because such a powerful tool deserves rules. »*