Educators Technology

I came across this guide that Marc Watkins shared on his Substack and thought I would share it here.

Better Images of AI: A Guide for Users and Creators by Kanta Dihal and Tania Duarte is an interesting resource to use in critical AI literacy activities with students.

The guide explains how mainstream images of AI, often featuring humanoid robots, glowing brains, blue code, and science-fiction scenes, can be misleading. These images can create fear, exaggerate what AI can do, and hide the human decisions, labour, and accountability behind AI systems.

The authors also invite us to think more carefully about the images we use to represent AI. Instead of relying on futuristic or robotic clichés, they suggest images that are more honest, human-centred, specific, and connected to real-world uses of AI.

Some classroom activity ideas:

Have students use an AI image generator to create an image of “artificial intelligence,” then discuss the patterns that appear. Do the images show robots? Brains? Blue lights? Who is represented? Who is missing?

Ask students to compare common stock images of AI with more realistic images from the Better Images of AI library. They can identify which images are misleading and explain why.

Invite students to redesign an AI image for a specific context, such as AI in healthcare, education, farming, social media, or translation. The goal is to make the image more accurate, inclusive, and less sensational.

Use the guide as a discussion starter on how visuals shape public understanding of technology. Students can reflect on how images influence trust, fear, expectations, and stereotypes around AI.

I just read Eve Fairbanks’s article in The Atlantic, and while I agree with part of her argument, I disagree with the core conclusion.

Fairbanks is right that AI language has become pervasive. We see it everywhere now: in emails, social media posts, student work, and even in the work of established authors. And yes there is a recognizable AI tone that has quietly infiltrated our everyday language.

She is also right to point out some of the weaknesses of bad AI writing such as immaculate and grammatically pruned prose with a bland and soulless voice.

But this is where I disagree.

That is not the inevitable result of using AI. That is the result of using AI lazily.

The bland, generic, and soulless style many people associate with AI usually comes from asking the tool to do the thinking and writing for you. You type a two-sentence prompt, ask it to “write a post,” or “make this sound professional,” and then you copy whatever comes out.

That is the worst use of AI.

And this is not what we should be teaching our students. A delayed introduction of AI in your writing process, I believe, is the 'right way to enlist AI assistance.

I have and still recommend a layered approach to AI use. First write down your own ideas with no AI. Pour into the document your thoughts, go through them to make sure your voice is visibly present then use AI to help with the mechanics of language.

This is where AI can become one of the best editors available to us.

But it only works when you set it up properly. You need to instruct it to preserve your voice and ideation and to avoid clichés and generic motivational prose.

In other words, you need to train the tool to serve your thinking.

This is why I keep saying that AI literacy should never be reduced to prompt engineering. It should focus on cultivating the skills that enable you to think with the tool without surrendering your judgment to it.

And let me tell you this: writing well is hard work with or without AI.

So, yes if your writing style sounds bland, you know you asked AI to write for you before you have done the thinking yourself.

AI is forcing universities to rethink assessment and in a fundamental way.

In this paper, Moorhouse et al. (2023) look at how the world’s top-ranking universities responded to generative AI tools such as ChatGPT.

The paper shows that university guidelines tend to focus on three major areas:

Academic integrity
Assessment design
Communication with students

First, academic integrity.

We can no longer reduce the conversation to “students are cheating with AI.” That is too simplistic. Of course, misuse is real. But the bigger issue is that AI has blurred the boundaries between assistance, authorship, collaboration, and plagiarism.

If a student uses AI to brainstorm, is that misconduct? If they use it to improve grammar, is that allowed? If they use it to generate an outline, where is the line?

If they submit AI-generated text as their own, that is clearly a problem. But many other uses sit in a grey zone, and that is where schools need clarity.

Second, assessment design.

One important recommendation from the paper is that teachers should test their own assignments with AI tools.

Put the prompt into ChatGPT and see what it produces. Then ask yourself: What exactly am I assessing here?

If AI can complete the whole task with little student thinking, then maybe the problem is not only the tool. Maybe the assessment itself needs redesign.

The authors highlight several useful directions:

Focus more on process.
Break large assignments into stages.
Ask for drafts, notes, reflections, and explanations.
Create tasks that connect to class discussions, lived experiences, local contexts, and real-world problems.
Give students opportunities to critique AI outputs rather than simply avoid AI altogether.

This is an important shift. Assessment should not only produce a final answer. It should make learning visible.

Third, communication with students.

This is probably the most practical takeaway for teachers. Students need clear expectations.

Definitely not “AI is forbidden” statements that no one knows how to apply. They need to know what is allowed, what is limited, what must be disclosed, and what crosses the line.

The paper also makes a powerful point: teachers now need a new kind of competence, what the authors call generative AI assessment literacy.

I like this idea.

Teachers do not only need AI literacy in general. They need to understand how AI changes assessment specifically.

That means knowing how AI affects academic integrity, how to redesign tasks, how to guide students toward responsible use, and how to keep assessment meaningful in a world where AI can generate polished work instantly.

Link in the first comment!

Reference:
Moorhouse, B. L., Yeo, M. A., & Wan, Y. (2023). Generative AI tools and assessment: Guidelines of the world’s top-ranking universities. Computers and Education Open, 5, 100151.

The more AI becomes entrenched in education, the more it disrupts the old systems we have relied on for decades.

Today, I want to focus on one of the biggest areas of disruption: plagiarism and academic misconduct.

I recently revisited a paper by Cecilia Chan (2023), where she introduces the concept of AI-giarism.

Chan defines AI-giarism as “an emergent form of academic dishonesty involving AI and plagiarism” (p. 1).

The term itself is interesting, but I think the deeper value of the paper lies in the conversation it opens.

For a long time, plagiarism was mostly understood as using another person’s words or ideas without proper acknowledgment. That definition still matters, of course, but it does not fully capture what is happening with generative AI.

AI does not simply give students someone else’s paragraph to copy. It can help them brainstorm, outline, rewrite, summarize, translate, polish, generate examples, structure arguments, and sometimes produce entire pieces of work.

Are these forms of AI use considered plagiarism in the 'old' academic sense?

This is indeed problematic and it forces us to reconceptualize the entire concept of plagiarism and cheating.

Copy/paste content whether from AI or any other medium is definitely an integrity issue, that's out of the question.

But AI-mediated practices do not always involve these kind of acts. People use it (including me) in more productive ways to help them build their ideas, express them in better ways, and create original contributions.

With this in mind, I think what we need is a different angle and a different set of questions:

What kind of assistance did the student receive?

At what point does assistance become collaboration?

When does collaboration blur authorship?

When does authorship become delegation?

And when does delegation become proxy performance, where the tool performs the intellectual work on behalf of the student?

These, I believe, are the new boundaries we need to define in the AI age.

Chan’s work provides an important starting point for this discussion. It also connects with a growing body of scholarship that invites us to rethink cheating, assessment, and integrity in the age of AI.

I am thinking here of work such as:

Eaton, S. E. (2023). Postplagiarism: Transdisciplinary ethics and integrity in the age of artificial intelligence and neurotechnology.

Corbin et al. (2025). ‘Where’s the line? It’s an absurd line’: Towards a framework for acceptable uses of AI in assessment.

Dawson et al. (2024). Validity matters more than cheating.

Nieminen, J. & Eaton, S. E. (2024). Are assessment accommodations cheating?

All of these point to the same larger shift: The AI age does not make academic integrity less important. It makes it more complex, more urgent, and more central to the future of teaching and learning.