Lesson video

Hi, everyone.

Welcome to today's lesson.

My name is Mrs. Jenkins.

Today's lesson is called How AI Systems Shape Online Experiences, taken from the unit, Digital well-being: Stay connected, happy, and healthy online.

Let's get started.

Today's lesson outcome is: I can explain how AI systems influence what I see online.

In our lesson today, we have three keywords.

We have algorithm, recommendation, and personalized.

These words will appear throughout the lesson, so it's really important we know what they mean before we get started.

An algorithm is a precise set of ordered steps that can be followed by a human or a computer to do a task.

A recommendation is a suggestion made by a system.

And personalized is tailored to an individual.

So those three words are algorithm, recommendation, and personalized.

Look out for them as we move through the lesson.

In our lesson today, How AI Systems Shape Online Experiences, we have three learning cycles: understand how AI recommendations work, recognize how AI systems shape content feeds, and evaluate the impact of AI on digital choices.

So our first one is understand how AI recommendations work.

Let's get started.

When you open a video app, a shopping site, or a social media feed, how does it decide what to show you?

Is it random?

Most online platforms use AI systems.

These systems do not think as humans do.

They follow programmed rules.

So we have the user action, the algorithm, and the recommended content.

An algorithm is a set of instructions a computer follows.

For example, if you watch cooking videos, the system may show you more cooking videos.

This is not random.

AI systems collect data about what you watch, how long you watch, what you click, what you like or comment on.

And this behavior creates a data profile.

Okay, let's have a little check-in now.

True or false?

AI systems use information about what users watch or interact with.

What do you think?

Have a go.

How did you get on?

I asked you, "True or false?

AI systems use information about what users watch or interact with.

" The answer is true.

This process is called personalization.

Personalized content is tailored to you.

"That's why my feed looks different to yours.

" That's right, Laura.

Well done.

AI recommendations are based on patterns.

If many people who watch one video also watch another, the algorithm links them.

This linking happens automatically and continuously.

This is pattern recognition.

Let's have a little check-in here.

Which option best describes how AI recommendations work?

A, they are random, B, they follow rules based on data, C, they guess, or D, they copy other phones.

What do you think?

Have a go.

How did you get on?

I asked, "Which option best explains how AI recommendations work?

" A, they are random, B, they follow rules based on data, C, they guess, or D, they copy other phones.

The answer is B, AI recommendations work by following rules based on data.

Great job, everyone.

Laura said, "I thought AI systems just showed random content.

" And Jun has explained, "AI systems use data about users to personalize and recommend content.

" That's right, Jun.

Thank you for explaining.

Okay, we are going to move on to task A.

I would like you to look at the social media feed.

What type of content would likely appear next?

What past behavior caused that recommendation?

And why is this not random?

What do you think?

Have a go.

How did you get on?

I asked you to look at the social media feed.

What type of content would most likely appear next?

What past behavior caused that recommendation?

And why is this not random?

The feed mostly shows sports content, such as football and trainers.

The user may have previously watched sports videos or clicked on sports posts.

It is not random because the algorithm uses past behavior to recommend similar content.

Great job, everyone.

Well done.

We're going to move on to learning cycle two now: recognize how AI systems shape content feeds.

When AI recommends content, it learns from your behavior.

If you click on one type of video, it may show you more of the same.

Over time, your feed becomes shaped by your past choices.

When you repeatedly see similar content, it can start to feel normal.

For example, if you watch many fitness videos, it may seem like everyone prioritizes fitness, but that perception is shaped by the algorithm.

Jun says, "It feels like that's all anyone cares about.

" That's right, Jun, but it doesn't appear like that on everybody's device.

A filter bubble happens when algorithms show similar content again and again.

This can reduce exposure to different viewpoints, reinforce existing interests, and make certain ideas seem more common than they are.

This does not mean the content is wrong.

It means it is filtered.

Okay, let's have a little check-in here.

Which statement best explains how AI systems shape feeds?

A, it shows more of what users interact with, B, it chooses based on color, or C, it randomly selects content.

What do you think?

Have a go.

How did you get on?

I asked, "Which statement best explains how AI systems shape feeds?

" A, it shows more of what users interact with, B, it chooses based on color, or C, it randomly selects content.

AI systems shape feeds by showing more of what users interact with.

Great job, everyone.

When we see repeated content, our brains may assume it is common or important.

Repetition increases influence.

The more something appears, the more normal it feels.

So if you start looking up recipes, you're likely to see more cooking content and more different ingredients and ways of cooking.

If someone sees repeated perfect lifestyle posts, it may change expectations, increase comparison, and affect confidence.

Recommendations by AI systems can influence what you buy, what you believe, and how you feel about yourself.

This connects directly to digital well-being.

Okay, for this check-in, I would like you to match each term to its meaning.

So we have the three words: algorithm, recommendation, and personalized.

And the meanings, we have tailored to an individual, a suggestion made by a system, a set of rules a computer follows.

Have a go.

How did you get on?

I asked you to match each term to its meaning.

So we had algorithm, recommendation, and personalized.

And our meanings, we had tailored to an individual, a suggestion made by a system, and a set of rules a computer follows.

So, personalized is tailored to an individual, recommendation is a suggestion made by a system, and an algorithm is a set of rules a computer follows.

Great job, everyone.

We're going to move on to task B now.

I would like you to look at the social media feed.

Identify what types of content dominate the feed, suggest three actions the user could take to widen their feed, and explain how each action might change what the algorithm recommends.

Have a go.

How did you get on?

I asked you to look at the social media feed.

Identify what type of content dominates the feed, suggest three actions the user could take to widen their feed, and explain how each action might change what the algorithm recommends.

So what type of content dominates the feed?

The feed mainly shows food and cookery content.

There are repeated posts about recipes, baking, cooking tips, and food preparation.

So question two would suggest three actions the user could take to widen their feed.

They could search for a different topic, watch videos about a new interest all the way to the end, avoid clicking on cooking videos, like or comment on different types of content, and follow accounts that post different topics.

And explain how each action might change what the algorithm recommends.

If the user searches for a new topic, the algorithm will collect new data and start recommending that topic.

If the user watches different types of videos, the system will detect new viewing patterns.

If the user stops interacting only with cooking content, the feed will become more varied over time.

Great job, everyone.

Well done.

We're going to move on to learning cycle three now: evaluate the impact of AI on digital choices.

Many platforms are designed to keep users engaged.

Longer engagement often means more advertising exposure, more data collected, and more targeted recommendations.

This is part of the system's purpose.

AI recommendations can influence what products you notice, what trends you follow, what opinions feel popular.

If something appears often, it may feel more important.

AI systems do not force choices, but they shape the options you see.

When your choices are shaped, your decisions may change.

This is called influence.

Awareness allows you to pause before clicking, search for alternative viewpoints, and break recommendation patterns.

Critical thinking restores balance.

If I keep seeing gaming videos, I might: watch more gaming, think everyone is gaming, and spend more time playing.

But I can search for something different, take a break, or question why it is appearing.

Okay, let's have a little check-in.

Which example shows AI systems influencing behavior?

A, reading a book, B, turning off your phone, or C, seeing the same product repeatedly and deciding to buy it.

What do you think?

Have a go.

How did you get on?

I asked, "Which example shows AI systems influencing behavior?

" A, reading a book, B, turning off your phone, or C, seeing the same product repeatedly and deciding to buy it.

The answer is C.

Seeing the same product repeatedly and deciding to buy it shows how AI systems influence behavior.

Great job.

We have another check-in here.

True or false?

AI systems force users to make choices.

What do you think?

Have a go.

How did you get on?

I asked, "True or false?

AI systems force users to make choices.

" The answer is false.

The reason why?

AI systems shape options, but they do not force decisions.

Great job, everyone.

Okay, we're going to move on to our third task now, task C, and it is called Break the Bubble.

Create a poster, either digital or by hand, with the title Don't Let the Algorithm Decide Everything.

You are going to include one explanation of how AI systems work, one example of influence, one strategy to widen your feed, and one reminder about balance.

Okay, have a go.

How did you get on?

I asked you to create a poster for Break the Bubble.

So create a poster, either digital or by hand, with the title Don't Let the Algorithm Decide Everything.

Include one explanation of how AI systems work, one example of influence, one strategy to widen your feed, and one reminder about balance.

Share your poster with the rest of your class.

This is a creative task, so your poster is likely to be different to other people's.

Jun said, "My poster reminds people that just because the algorithm shows it, that doesn't mean it's the full story.

" Great job, Jun, and well done, all of you.

You've worked really hard in today's lesson.

Let's summarize what we have learned.

AI systems use algorithms to analyze user data and recommend content.

These recommendations are personalized and based on patterns in behavior.

Repeated recommendations can shape perception and influence choices.

AI systems do not force decisions, but they do shape the options we see.

Awareness and critical thinking helps us protect our digital well-being.

Great job, everyone.

You worked really hard today.

I can't wait to learn with you again soon.