Teachers can play a critical role in educating their students about AI, even if they aren’t experts on it. By addressing AI in ways that are designed to build students’ skills and teach them about the pitfalls of relying too much on it, teachers can help build the next generation of citizens who are empowered with the skills necessary to succeed in a world infused with AI technology.
This guide provides an overview of what AI is – and in particular Generative AI – and gives two examples of main AI tools you are likely to encounter. Then it explains some key ethical and social issues related to Generative AI.
The risks that kids encounter in media fall into four categories:
Content risks, where kids are exposed to or engage with harmful content such as violence, hate, or sexualized media;
Conduct risks that come from what kids do or how they interact with other users;
Consumer risks related to money, advertising, and data collection;
Two important ideas relating to teens are the imaginary audience and the personal fable. The imaginary audience makes them overestimate how much attention other people are paying to them. This makes them more self-conscious and leads them to think of privacy primarily in terms of impression management – trying to control how others see them. The personal fable makes teens see themselves as the main character of a story and, as a result, leads many to believe that bad things will simply not happen to them.
The risks that kids encounter in media fall into four categories:
Content risks, where kids are exposed to or engage with harmful content such as violence, hate, or sexualized media;
Conduct risks that come from what kids do or how they interact with other users;
Consumer risks related to money, advertising, and data collection;
In this lesson, students examine a fictional social network profile to learn how online platforms collect data about their users. They then read an article that explains how platforms use this data and explores some of the issues this raises. Finally, they create a mind map of their own online data profile and reflect on how the data they post may be collected and used by others.
This blog was written by Samantha McAleese and three youth participants – Sahil, Erin, and Kate (pseudonyms used to maintain anonymity).
Reflections on Conducting Qualitative Research During a Pandemic
What comes to mind when you hear the word algorithm? Can you explain how machine learning works? Do you have any privacy or data collection concerns regarding the increased reliance on artificial intelligence? These are just a few of the questions that we asked young people in our recent qualitative research project Algorithmic Awareness: Conversations with Young Canadians about Artificial Intelligence and Privacy. From November 2020 to January 2021, we facilitated eight focus groups with 22 youth ages 13 to 17, where we combined game-based learning with discussion and reflection to gain insight into how young Canadians understand the relationship between artificial intelligence, algorithms, and privacy.
Meet Sasha. At age 8, she's a real social butterfly, both online and off, and is very concerned with how the world sees her: she spends a lot of time making sure she looks good in photos online but doesn't always think twice about who might see them. Violet is Sasha's older sister and her polar opposite: she's a hardcore gamer, and just as tough as her Level 65 Barbarian. Though she despairs of her sister sometimes, she's also fiercely protective of her and will unleash her considerable wrath on anyone she thinks is picking on Sasha.
Our older two teens are close to finishing high school, and we’re starting to think about moving them into the adult phase of their lives. That means managing their own online presence and technology, and making sure they have full ownership of their profiles.
And that led to the big password talk.