AI Applications
Senior 2 · ICT
AI Applications
Lesson 1 of 4
25 min
Since October 2016, drones run by the company Zipline have flown blood to hospitals across Rwanda, first from a centre in Muhanga and, since 2018, also from Kayonza. The drones navigate and avoid obstacles on their own, using software instead of a pilot. A 2022 study found that the service cut blood delivery times by 61% and reduced the number of blood units that expired unused by 67%.
Closer to home, your phone keyboard guesses your next word, a translation app turns English into Kinyarwanda, and a video app decides what to show you next. All of these use artificial intelligence (AI). This unit teaches you what AI is, how to use AI tools well, and how to use them responsibly.

A program that follows rules. A programmer writes: "if the temperature is above 30 °C, show 'Hot'". The computer follows that rule exactly and never changes it.
A program that learns. A spam filter is shown thousands of emails that people have already marked "spam" or "not spam". It finds patterns on its own, such as certain words, links and senders, and uses them to sort new emails it has never seen. Nobody wrote a rule for each pattern; the program learned them from examples.
Artificial intelligence is the ability of computer systems to do tasks that usually need human intelligence, such as learning, reasoning, solving problems, recognising speech or images, and making decisions. Most modern AI uses machine learning: the system learns patterns from large amounts of examples, called training data, instead of following only rules written by a programmer. What the system produces from its training is called a model.
A fruit cooperative wants a phone app that sorts mangoes into "ripe" and "not ripe" from a photo.
Now suppose every training photo showed a variety of mango that turns yellow when ripe. Shown a variety that stays green when ripe, the model will say "not ripe" every time. A model is only as good as its training data.
| Area | What the AI does | Example |
|---|---|---|
| Health | Autonomous navigation; reading medical scans | Zipline drones delivering blood and vaccines in Rwanda |
| Language | Speech recognition, translation, chatbots | Voice tools and chatbots that work in Kinyarwanda |
| Agriculture | Spotting crop disease from a leaf photo; weather forecasts | A farmer photographs a bean leaf to check it for disease |
| Finance | Spotting unusual transactions that may be fraud | A bank blocks a suspicious card payment |
| Transport | Finding the fastest route from live traffic data | A map app sends a moto around a traffic jam |
| Entertainment | Recommending what to watch or hear next | Video and music apps |
| Content creation | Generating text, images, audio and video from a prompt | Chatbots and image generators (Lessons 2 and 3) |
An AI system can only understand a language it has been trained on, and for a long time very little recorded Kinyarwanda speech was available. Volunteers have changed that. Mozilla's Common Voice is an open project in which volunteers record and check sentences in their own language, and the recordings are given away free for anyone to use. Kinyarwanda is now one of the five languages with the most recorded hours on it. In Kigali, the company Digital Umuganda collects voice, text and translation data for African languages and builds tools on that data, such as its Mbaza chatbot. Open data like this lets developers build speech tools for Kinyarwanda speakers.
In 2023 Rwanda adopted a National Artificial Intelligence Policy to guide how AI is developed and used in the country.
A speech-recognition system is trained mostly on recordings of adult men from Kigali. It is tested on 100 sentences from each of three groups. It gets 94 right for adult men from Kigali, 81 for women, and 72 for children from rural districts. The gap between the best and worst groups is 94 − 72 = 22 percentage points. The cause is the training data: the system rarely heard voices like those of rural children. The fix is to collect recordings from many kinds of speakers, which is exactly why Common Voice asks for many different voices and accents.
Tip
Four questions before you trust or use an AI tool: Is it TRUE (have I checked it)? Is it FAIR (could it disadvantage some people)? Is it PRIVATE (am I sharing anyone's personal data)? Is it HONEST (am I open about using AI)?
Rules or machine learning? (a) a calculator adding up marks; (b) an app that learned to recognise bean diseases from 10,000 labelled leaf photos; (c) a traffic light that changes every 60 seconds.
Solution: (a) rules; (b) machine learning, because it learned from labelled examples; (c) rules.
A model is tested on 250 photos and labels 225 correctly. What is its accuracy?
Solution: 225 ÷ 250 = 0.9, so the accuracy is 90%.
Uwase is using an AI tool to write a speech for her aunt's wedding. To "make it personal", she is about to paste in her aunt's phone number and ID number. Advise her.
Solution: She should not. These are personal data that the tool may store, and the speech does not need them. She should use only first names and stories that her aunt is happy to share.
Give one benefit and one risk of AI in health care.
Solution: Benefit: for example, drones that navigate themselves deliver blood faster to remote hospitals. Risk: for example, a model trained mainly on patients from other countries may make more mistakes with patients here; or medical records could be exposed.
Summary
Sources and credits
Facts about Digital Umuganda are from its own website, digitalumuganda.com (facts only; no text reused). The worked examples, the TRUE–FAIR–PRIVATE–HONEST check and the practice items were written by Ganzaa.
This lesson adapts CC BY-SA material and is shared under CC BY-SA 4.0.