Ellen Bees

@ellenbees.bsky.social

Teacher, learner, parent, bookworm. Living in Winnipeg on Treaty One Territory. She/her. https://teacherbees.ca/

I adore this book that tells myths from many cultures that focus on queer gods, demigods, heroes, and supernatural beings. Such a great read to show that 2SLGBTQIA+ people in society have a long history. teacherbees.ca/queer-mythol...

Queer Mythology

I love Greek mythology. When I was a kid, I spent countless hours reading myths and legends. As a teacher, I have kept up this love with lots of mythology books in my classroom library, and expanding ...

teacherbees.ca

While everyone is talking about banning AI chatbots for young people, perhaps it is also a good time to teach young people to think critically about artificial intelligence too. Giving students information about ethical and environmental considerations is important. teacherbees.ca/ai-part-1/

Teaching to Think Critically About Artificial Intelligence

Artificial intelligence is increasingly being sold by tech companies as a solution to many problems and a way to make life easier. This is especially true in the world of education, where teachers are...

teacherbees.ca

I've been spending a bit of time writing about how to help students understand AI and think critically about it. The first step below is helping them understand what it is and how it is developed, with a focus on some of the ethical issues surrounding its training. teacherbees.ca/ai-part-1/

What is Artificial Intelligence?

Artificial intelligence is increasingly being sold by tech companies as a solution to many problems and a way to make life easier. This is especially true in the world of education, where teachers are...

teacherbees.ca

This paper contains some good arguments about an issue that concerns me a lot when I hear my colleagues talking about LLM use in developing their research: Whose ideas are you presenting as your own? (Though the fatalist argument the authors make at the end of paper is disappointing/bizarre.)

Screenshot (with final two sentences highlighted): “Academic authorship functions not only to communicate knowledge but also to allocate credit for the labour that produced it.
This credit affects hiring, promotion, funding and broader scholarly standing. To plagiarize is to distort the attribution of insight, misleading readers about origin and credit.
The provenance problem introduced by LLMs is troubling for a similar reason.”Screenshot: “Humans, too, can commit what is sometimes called 'cryptomnesia', in which they reproduce ideas that they have encountered previously but mistakenly believe to be origi-nal. However, what was once an idiosyncratic risk of interpersonal exchange is now a systemic feature of our knowledge infrastructure.
As scholars increasingly use generative artificial intelligence (AI) for writing assistance, those whose work is absorbed into training datasets may shape scholarship without ever being cited, while Al-proficient scholars may enjoy reputational gains built partly on others' uncredited ideas.”

(The sentence about “a systemic feature” is highlighted)Screenshot, with second sentence highlighted: “Nevertheless, Al developers bear obligations to improve attribution capabilities: efforts to trace and cite training data sources could help to preserve intellectual credit chains. But because many models rely on copyrighted or unlicensed material, developers face disincentives to improve provenance, even though traceability is crucial for legal and ethical accountability.”
Eryk Salvaggio@eryk.bsky.social · 9mo ago

It’s good to see papers start to address LLMs as structural plagiarism — provenance, more hidden than the original words or training data. www.nature.com/articles/s42...

This term, my students and I focused on thinking more critically about generative AI, how it works and its consequences for the environment, communities, artists, our mental health and our thinking. I’m assessing their thinking in a summative assignment and I’m appreciating how thoughtful they are.

I'm in the process of developing a unit about Generative AI to help students develop a more critical understanding about it. It's going well so far! I found an article that perfectly rounds out my lesson about AI and bias and I'm quite pleased. www.snexplores.org/article/ai-i...

AI image generators tend to exaggerate stereotypes

The racism, sexism, ableism and other biases common in bot-made images may lead to harm and discrimination in the real world.

snexplores.org

One of the most common arguments you hear from fans of generative ‘AI’ is that it’s not plagiarizing people’s work, it’s just learning like a human learns. So I’m going to break down why that’s just not true, and why it can never be true, with the existing systems. 1/