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Teaching AI Ethics — Exploring the ethics of artificial intelligence in education.

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Stay update to date on AI news with Leon

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Stay update to date on AI news with Leon

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Welcome to the AI Reads RSS Feed. This is an ongoing feed of interesting articles related to AI in education. If you use an RSS feed reader you can subscribe to this feed here. If you would rather be updated via email, the leonfurze.com weekly newsletter also has a link to this feed. Subscribe here. […]

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Resources and information for the AI Assessment Scale (AIAS)

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WATCH Nottingham Uni AI guidance for students

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WATCH Nottingham Uni AI guidance for students

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Generative artificial intelligence: Guidance for students (created by Libraries' Learning Development)

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From 2024 What are artificial intelligence literacy & competency? Chiu et al

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From 2024 What are artificial intelligence literacy & competency? Chiu et al

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Include human bias.

IBM AI ethical guidelines as a starting point

Fairness & bias

Trust & transparency

Accountability

Social benefit

Privacy & security

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From 2024 What are artificial intelligence literacy & competency? Chiu et al

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From 2024 What are artificial intelligence literacy & competency? Chiu et al

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IMPACT :

future of work= increasingly likely to live and work with AI. social good = +ve and harmful effects. Address critical social, environmental & public health concerns

risks = how do emerging technologies cause trouble & harm

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From 2024 What are artificial intelligence literacy & competency? Chiu et al

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From 2024 What are artificial intelligence literacy & competency? Chiu et al

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Core knowledge of AI - requires boundaries at this level.

Believes that basic components, the defn, history & dvpt of AI taught

U/S concepts of big data, machine leanring & cloud computing.

Big data 5 V primary & inherent characteristics.

Learn how AI machines use data - training models & learning algorithms.

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Teacher-led presentation on the basics of AI, covering its definition, history, and key concepts.

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Introduction to Artificial Intelligence (AI) | Google AI Essentials

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Guided Practice: AI in Daily Life : USe Flow Chart To do this

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Guided Practice: AI in Daily Life : USe Flow Chart To do this

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In small groups, students will discuss examples of AI technologies they use daily and share their experiences. Use Padlet to post findings.

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The grand idea is to develop something resembling human intelligence, which is often referred to as "artificial general intelligence," or "AGI." Some experts believe that machine learning and deep learning will eventually get us to AGI with enough data, but most would agree there are big missing pieces and it's still a long way off. AI may have mastered Go, but in other ways it is still much dumber than a toddler.

In that sense, AI is also aspirational, and its definition is constantly evolving. What would have been considered AI in the past may not be considered AI today.

Because of this, the boundaries of AI can get really confusing, and the term often gets mangled to include any kind of algorithm or computer program. We can thank Silicon Valley for constantly inflating the capabilities of AI for its own convenience. (Cough, Mark Zuckerberg, cough.)

To clear things up, I drew you this flowchart on the back of an envelope so you can work out whether something is using AI or not.

This originally appeared in our AI newsletter The Algorithm. To have it directly delivered to your inbox, subscribe here for free.

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What is AI? We drew you a flowchart to work it out

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What is AI, exactly? The question may seem basic, but the answer is kind of complicated. In the broadest sense, AI refers to machines that can learn, reason, and act for themselves. They can make their own decisions when faced with new situations, in the same way that humans and animals can.

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Complete the worksheet focusing on key concepts and ethical issues in AI.

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Generate custom worksheets for your students with custom-trained AI. Customizable and printable with Worksheets AI.

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Extension Activity: Ethical Issues in AI

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Extension Activity: Ethical Issues in AI

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Students will read an article on the ethical considerations of AI and write a reflective summary on Padlet.

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Illustration by Ben Boothman

Second in a four-part series that taps the expertise of the Harvard community to examine the promise and potential pitfalls of the rising age of artificial intelligence and machine learning, and how to humanize them.
For decades, artificial intelligence, or AI, was the engine of high-level STEM research. Most consumers became aware of the technology's power and potential through internet platforms like Google and Facebook, and retailer Amazon. Today, AI is essential across a vast array of industries, including health care, banking, retail, and manufacturing.

Also in the series

But its game-changing promise to do things like improve efficiency, bring down costs, and accelerate research and development has been tempered of late with worries that these complex, opaque systems may do more societal harm than economic good. With virtually no U.S. government oversight, private companies use AI software to make determinations about health and medicine, employment, creditworthiness, and even criminal justice without having to answer for how they're ensuring that programs aren't encoded, consciously or unconsciously, with structural biases.

Its growing appeal and utility are undeniable. Worldwide business spending on AI is expected to hit $50 billion this year and $110 billion annually by 2024, even after the global economic slump caused by the COVID-19 pandemic, according to a forecast released in August by technology research firm IDC. Retail and banking industries spent the most this year, at more than $5 billion each. The company expects the media industry and federal and central governments will invest most heavily between 2018 and 2023 and predicts that AI will be "the disrupting influence changing entire industries over the next decade."

"Virtually every big company now has multiple AI systems and counts the deployment of AI as integral to their strategy," said Joseph Fuller, professor of management practice at Harvard Business School, who co-leads Managing the Future of Work, a research project that studies, in part, the development and implementation of AI, including machine learning, robotics, sensors, and industrial automation, in business and the work world.

Early on, it was popularly assumed that the future of AI would involve the automation of simple repetitive tasks requiring low-level decision-making. But AI has rapidly grown in sophistication, owing to more powerful computers and the compilation of huge data sets. One branch, machine learning, notable for its ability to sort and analyze massive amounts of data and to learn over time, has transformed countless fields, including education.

Firms now use AI to manage sourcing of materials and products from suppliers and to integrate vast troves of information to aid in strategic decision-making, and because of its capacity to process data so quickly, AI tools are helping to minimize time in the pricey trial-and-error of product development - a critical advance for an industry like pharmaceuticals, where it costs $1 billion to bring a new pill to market, Fuller said.

Health care experts see many possible uses for AI, including with billing and processing necessary paperwork. And medical professionals expect that the biggest, most immediate impact will be in analysis of data, imaging, and diagnosis. Imagine, they say, having the ability to bring all of the medical knowledge available on a disease to any given treatment decision.

In employment, AI software culls and processes resumes and analyzes job interviewees' voice and facial expressions in hiring and driving the growth of what's known as "hybrid" jobs. Rather than replacing employees, AI takes on important technical tasks of their work, like routing for package delivery trucks, which potentially frees workers to focus on other responsibilities, making them more productive and therefore more valuable to employers.

"It's allowing them to do more stuff better, or to make fewer errors, or to capture their expertise and disseminate it more effectively in the organization," said Fuller, who has studied the effects and attitudes of workers who have lost or are likeliest to lose their jobs to AI.

"Can smart machines outthink us, or are certain elements of human judgment indispensable in deciding some of the most important things in life?"

- Michael Sandel, political philosopher and Anne T. and Robert M. Bass Professor of Government

Though automation is here to stay, the elimination of entire job categories, like highway toll-takers who were replaced by sensors because of AI's proliferation, is not likely, according to Fuller.

"What we're going to see is jobs that require human interaction, empathy, that require applying judgment to what the machine is creating [will] have robustness," he said.

While big business already has a huge head start, small businesses could also potentially be transformed by AI, says Karen Mills '75, M.B.A. '77, who ran the U.S. Small Business Administration from 2009 to 2013. With half the country employed by small businesses before the COVID-19 pandemic, that could have major implications for the national economy over the long haul.

Rather than hamper small businesses, the technology could give their owners detailed new insights into sales trends, cash flow, ordering, and other important financial information in real time so they can better understand how the business is doing and where problem areas might loom without having to hire anyone, become a financial expert, or spend hours laboring over the books every week, Mills said.

One area where AI could "completely change the game" is lending, where access to capital is difficult in part because banks often struggle to get an accurate picture of a small business's viability and creditworthiness.

"It's much harder to look inside a business operation and know what's going on" than it is to assess an individual, she said.

Information opacity makes the lending process laborious and expensive for both would-be borrowers and lenders, and applications are designed to analyze larger companies or those who've already borrowed, a built-in disadvantage for certain types of businesses and for historically underserved borrowers, like women and minority business owners, said Mills, a senior fellow at HBS.

But with AI-powered software pulling information from a business's bank account, taxes, and online bookkeeping records and comparing it with data from thousands of similar businesses, even small community banks will be able to make informed assessments in minutes, without the agony of paperwork and delays, and, like blind auditions for musicians, without fear that any inequity crept into the decision-making.

"All of that goes away," she said.

A veneer of objectivity

Not everyone sees blue skies on the horizon, however. Many worry whether the coming age of AI will bring new, faster, and frictionless ways to discriminate and divide at scale.

"Part of the appeal of algorithmic decision-making is that it seems to offer an objective way of overcoming human subjectivity, bias, and prejudice," said political philosopher Michael Sandel, Anne T. and Robert M. Bass Professor of Government. "But we are discovering that many of the algorithms that decide who should get parole, for example, or who should be presented with employment opportunities or housing ... replicate and embed the biases that already exist in our society."

"If we're not thoughtful and careful, we're going to end up with redlining again."

- Karen Mills, senior fellow at the Business School and head of the U.S. Small Business Administration from 2009 to 2013

AI presents three major areas of ethical concern for society: privacy and surveillance, bias and discrimination, and perhaps the deepest, most difficult philosophical question of the era, the role of human judgment, said Sandel, who teaches a course in the moral, social, and political implications of new technologies.

"Debates about privacy safeguards and about how to overcome bias in algorithmic decision-making in sentencing, parole, and employment practices are by now familiar," said Sandel, referring to conscious and unconscious prejudices of program developers and those built into datasets used to train the software. "But we've not yet wrapped our minds around the hardest question: Can smart machines outthink us, or are certain elements of human judgment indispensable in deciding some of the most important things in life?"

Panic over AI suddenly injecting bias into everyday life en masse is overstated, says Fuller. First, the business world and the workplace, rife with human decision-making, have always been riddled with "all sorts" of biases that prevent people from making deals or landing contracts and jobs.

When calibrated carefully and deployed thoughtfully, resume-screening software allows a wider pool of applicants to be considered than could be done otherwise, and should minimize the potential for favoritism that comes with human gatekeepers, Fuller said.

Sandel disagrees. "AI not only replicates human biases, it confers on these biases a kind of scientific credibility. It makes it seem that these predictions and judgments have an objective status," he said.

In the world of lending, algorithm-driven decisions do have a potential "dark side," Mills said. As machines learn from data sets they're fed, chances are "pretty high" they may replicate many of the banking industry's past failings that resulted in systematic disparate treatment of African Americans and other marginalized consumers.

"If we're not thoughtful and careful, we're going to end up with redlining again," she said.

A highly regulated industry, banks are legally on the hook if the algorithms they use to evaluate loan applications end up inappropriately discriminating against classes of consumers, so those "at the top levels" in the field are "very focused" right now on this issue, said Mills, who closely studies the rapid changes in financial technology, or "fintech."

"They really don't want to discriminate. They want to get access to capital to the most creditworthy borrowers," she said. "That's good business for them, too."

Oversight overwhelmed

Given its power and expected ubiquity, some argue that the use of AI should be tightly regulated. But there's little consensus on how that should be done and who should make the rules.

Thus far, companies that develop or use AI systems largely self-police, relying on existing laws and market forces, like negative reactions from consumers and shareholders or the demands of highly-prized AI technical talent to keep them in line.

"There's no businessperson on the planet at an enterprise of any size that isn't concerned about this and trying to reflect on what's going to be politically, legally, regulatorily, [or] ethically acceptable," said Fuller.

Firms already consider their own potential liability from misuse before a product launch, but it's not realistic to expect companies to anticipate and prevent every possible unintended consequence of their product, he said.

Few think the federal government is up to the job, or will ever be.

"The regulatory bodies are not equipped with the expertise in artificial intelligence to engage in [oversight] without some real focus and investment," said Fuller, noting the rapid rate of technological change means even the most informed legislators can't keep pace. Requiring every new product using AI to be prescreened for potential social harms is not only impractical, but would create a huge drag on innovation.

"I wouldn't have a central AI group that has a division that does cars, I would have the car people have a division of people who are really good at AI."

- Jason Furman, a professor of the practice of economic policy at the Kennedy School and a former top economic adviser to President Barack Obama

Jason Furman, a professor of the practice of economic policy at Harvard Kennedy School, agrees that government regulators need "a much better technical understanding of artificial intelligence to do that job well," but says they could do it.

Existing bodies like the National Highway Transportation Safety Association, which oversees vehicle safety, for example, could handle potential AI issues in autonomous vehicles rather than a single watchdog agency, he said.

"I wouldn't have a central AI group that has a division that does cars, I would have the car people have a division of people who are really good at AI," said Furman, a former top economic adviser to President Barack Obama.

Though keeping AI regulation within industries does leave open the possibility of co-opted enforcement, Furman said industry-specific panels would be far more knowledgeable about the overarching technology of which AI is simply one piece, making for more thorough oversight.

While the European Union already has rigorous data-privacy laws and the European Commission is considering a formal regulatory framework for ethical use of AI, the U.S. government has historically been late when it comes to tech regulation.

"I think we should've started three decades ago, but better late than never," said Furman, who thinks there needs to be a "greater sense of urgency" to make lawmakers act.

Business leaders "can't have it both ways," refusing responsibility for AI's harmful consequences while also fighting government oversight, Sandel maintains.

"The problem is these big tech companies are neither self-regulating, nor subject to adequate government regulation. I think there needs to be more of both," he said, later adding: "We can't assume that market forces by themselves will sort it out. That's a mistake, as we've seen with Facebook and other tech giants."

Last fall, Sandel taught " Tech Ethics," a popular new Gen Ed course with Doug Melton, co-director of Harvard's Stem Cell Institute. As in his legendary "Justice" course, students consider and debate the big questions about new technologies, everything from gene editing and robots to privacy and surveillance.

"Companies have to think seriously about the ethical dimensions of what they're doing and we, as democratic citizens, have to educate ourselves about tech and its social and ethical implications - not only to decide what the regulations should be, but also to decide what role we want big tech and social media to play in our lives," said Sandel.

Doing that will require a major educational intervention, both at Harvard and in higher education more broadly, he said.

"We have to enable all students to learn enough about tech and about the ethical implications of new technologies so that when they are running companies or when they are acting as democratic citizens, they will be able to ensure that technology serves human purposes rather than undermines a decent civic life."
Next: The AI revolution in medicine may lift personalized treatment, fill gaps in access to care, and cut red tape. Yet risks abound.
Related

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Business & Economy

AP Photo/Michael Dwyer

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Ethical concerns mount as AI takes bigger decision-making role

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Harvard experts examine the promise and potential pitfalls as AI takes a bigger decision-making role in more industries.

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Independent Practice: Evaluating AI Technologies

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Independent Practice: Evaluating AI Technologies

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Students will individually research and evaluate a specific AI technology, considering its benefits and drawbacks. Present findings using Padlet.

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CLEAR method

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Link to principled learner and academic honesty

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Link to principled learner and academic honesty

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Discuss with Pip

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To do so, I created an AI-powered class assignment. Each student was required to generate their own essay from ChatGPT and “grade” it according to my instructions. Students were asked to leave comments on the document, as though they were a professor assessing a student’s work. Then they answered questions I provided: Did ChatGPT confabulate any sources? If so, how did you find out? Did it use any sources correctly? Did it get any real sources wrong? Was its argument persuasive or shallow?

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C.W. Howell, Cal Baker, Fayrah Stylianopoulos

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To Educate Students about AI, Make Them Use It

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A college professor and his students explain what they learned from bringing ChatGPT into the classroom

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Activity idea - Propmt craft : Natural language & AI language 

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Activity idea - Propmt craft : Natural language & AI language 

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Input: Instead of using search terms, with AI, a person asks a question and refines it. Teaching AI means teaching how to communicate with AI. Unlike a Google search, it’s an interaction, not a transaction. We have taught students how to search using Boolean operators, to lean toward .orgs and .edus. These are search skills, not communication skills. After years of telling students not to use natural language with databases, we now need to tell them to do the reverse with AI. Just as we encourage them not to stop at the first Google search results, they should not stop at the first AI response. Instead, they should respond to it, recognizing and identifying errors and providing more information and specificity for a better result and a better interaction. The goal is to expertly formulate queries and requests by communicating with AI by optimizing natural language processing. (See Glossary of Terms.)

Vocabulary: We need to teach basic vocabulary often associated with AI. Chances are you had not used the phrase “generative AI” prior to the emergence of ChatGPT. If students understand the vocabulary around how AI “learns,” they’re more likely to understand its strengths and weaknesses.

Appropriateness:

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Is ChatGPT the new Wikipedia, riddled with inaccuracies, used by students seeking a shortcut? It could be. But it doesn't have to be. That points to our next venture as librarians: AI literacy instruction.

Artificial intelligence (AI) is a hot topic among librarians, who for the most part are more intrigued by AI than alarmed. We've been at the forefront of evaluating information from the beginning of the profession. When the internet rolled around, we had to teach website evaluation with the CRAAP test, the tree octopus lesson, and that tricky monster Wikipedia. CRAAP was replaced by lateral reading recently as our internet use and practices evolved and as information evaluation became part of classroom instruction. Now our evaluation instruction needs to evolve, too.

So is ChatGPT the new Wikipedia, riddled with inaccuracies, used by "lazy" or stressed students looking for a shortcut? The answer is the same as always when it comes to technology: It could be. But it doesn't have to be. And that points to our next venture as librarians: teaching AI literacy.

In many ways, we can correlate teaching AI within our traditional information literacy instruction. Here is what a loose initial framework for interacting effectively with AI might look like from that perspective, including teaching input and response, vocabulary, and appropriate use.

Input: Instead of using search terms, with AI, a person asks a question and refines it. Teaching AI means teaching how to communicate with AI. Unlike a Google search, it's an interaction, not a transaction. We have taught students how to search using Boolean operators, to lean toward .orgs and .edus. These are search skills, not communication skills. After years of telling students not to use natural language with databases, we now need to tell them to do the reverse with AI. Just as we encourage them not to stop at the first Google search results, they should not stop at the first AI response. Instead, they should respond to it, recognizing and identifying errors and providing more information and specificity for a better result and a better interaction. The goal is to expertly formulate queries and requests by communicating with AI by optimizing natural language processing. ( See Glossary of Terms.)

Vocabulary: We need to teach basic vocabulary often associated with AI. Chances are you had not used the phrase "generative AI" prior to the emergence of ChatGPT. If students understand the vocabulary around how AI "learns," they're more likely to understand its strengths and weaknesses.

Appropriateness: Students need to understand when ChatGPT is a good place to go for information and when it is not the best resource for their needs. Information literacy instruction taught them when to use an almanac, a database, a newspaper, etc. Certainly in terms of currency, we need to stress that the knowledge base of ChatGPT ends in 2021, so it is not a source for up-to-the-minute information or current events. If a student wants a book recommendation, they might get good results to further explore on Amazon. If scientific or mathematical accuracy is required, they will need to verify bot results, possibly using lateral reading, or simply using a more reliable source. In a real-life scenario, asking ChatGPT to review the latest video games will yield a disappointing result, whereas magazines and websites like Wired and PC Gamer are better sources. Information evaluation is the same basic concept librarians have taught for years; AI is simply the newest tool.

Caveats and practical stumbling blocks: While we can compare teaching AI literacy to teaching database and information literacy, there are two main issues that differ, and both come down to access. First, do your students have access to AI, mainly ChatGPT, from school? At my school, it is blocked for students on school networks. Some schools, school districts, and even colleges and universities are banning access via school Wi-Fi-while others are beta-testing and trying out Khan Academy's AI tutor, Khanmigo. The question is whether it undermines school policy to teach skills for a resource students cannot even access from school but will access nonetheless to some extent.

The second concern is cost. Schools offer students access to expensive proprietary databases they would not be able to afford on their own. While there is free access to ChatGPT, without a paid subscription, users are likely to get the message "ChatGPT is at capacity right now." Equity of access is therefore a significant roadblock to teaching AI at school.

That should not stop librarians from thinking about a framework for teaching AI literacy. Embracing the uphill journey of teaching AI literacy recalls ongoing struggles and solutions to ensuring equal access to computers and the internet. Eventually, we will level the playing field and may soon witness AI literacy spreading like wildfire, then being normalized as an inherent part of education. In some ways, AI is the ultimate experiment in inquiry-based learning, where students increase their comprehension of subject matter and explore personal interests. And that sounds a lot like the ubiquitous phrase "creating lifelong learners."
Lee De Groft is the director of library and information services at Trinity Preparatory School in Winter Park, FL.
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Teaching Students AI Literacy: The Road Ahead

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Is ChatGPT the new Wikipedia, riddled with inaccuracies, used by students seeking a shortcut? The answer is the same as always when it comes to tech: It could be. But it doesn't have to be. That points to our next venture as librarians: AI literacy instruction.

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Why the rise of AI will affect women more than men

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The underrepresentation of women in #AI is a pressing concern, hampering innovation and limiting diverse perspectives in shaping AI solutions. Explore the factors contributing to this gender gap and its impact on AI advancement. Discover steps for building a more inclusive AI ecosystem, fostering an environment where all talents can flourish.#GenderEquality #WomenInAI

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Jobs For Mums on LinkedIn: Why the rise of AI will affect women more than men

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Huge thanks to Aimee Shaw and Stuff for bringing our story to the forefront! Aimee and Jobs for Mums founder Mela delved into the profound impact of AI on the...

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21 September 2023

Thinking about privacy is vital if you are going to use artificial intelligence (AI) tools well.
Read our guidance on Artificial Intelligence and the IPPs Read our media release
Privacy is a starting point for responsible use of AI tools

AI refers to computer systems that do tasks which seem like intelligent behaviour, such as finding patterns or categorising. This includes generative AI tools, as well as other systems that interpret data or automate tasks. We want to make it easy for you to know the potential privacy risks associated with AI and what our expectations are.

Our starting point is that the Privacy Act applies to everyone using AI tools in New Zealand. The Information Privacy Principles (IPPs) set out legal requirements on how you collect, use, and share personal information. Before using these tools, you need to understand enough about how they work to be confident you are upholding the IPPs. The best way to do this is to do a Privacy Impact Assessment (PIA) before you start, and to update it regularly.

You need to consider the information privacy principles when using AI tools

When working with personal information, the IPPs set requirements around how you collect it (IPPs 1-4), how you use and protect it (IPPs 5-10), and how you share it (IPPs 11-12). There are also specific requirements on unique identifiers (IPP13). The IPPs apply to each stage of building and using AI tools, from collecting training data, to training a model, taking user input, receiving a response, and taking action as a result. Key questions to ask include:

Is the training data behind an AI tool relevant, reliable, and ethical?

AI tools reproduce patterns from their training data. This may include personal information collected in ways that breach IPPs 1-4. Gaps or biases may limit accuracy (IPP8).

What was the purpose for collecting personal information? is your use related?

You need to be confident you are using personal information in ways that fit the purpose for which it was collected. Reusing information for training may go against this (IPP10).

How are you testing that AI tools are accurate and fair for your intended purpose? Are you talking with people and communities with an interest in these issues?

Doing a good Privacy Impact Assessment may require engaging with the community, including Māori, to help you understand and uphold fairness and accuracy (IPP8).

What are you doing to track and manage new risks to information from AI tools?

You must keep personal information secure, including prompts and training data (IPP5).

If in doubt, we recommend you do not use AI tools to handle personal information.

Thinking about privacy will help you understand AI tools better

We all rely on people and organisations taking responsibility for their actions in context. There is a lot of concern about AI tools creating new risks for privacy, trust, and accountability. Proactively thinking about privacy will help you use these tools better and manage risk.

You can find out more information on OPC's website at www.privacy.org.nz. If you would like to talk with us about this work, please email us at ai@privacy.org.nz
Download a one-page summary of this content Read the Commissioner's expectations around AI use, issued on 25 May 2023

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Office of the Privacy Commissioner | Artificial Intelligence and the IPPs

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21 September 2023 Thinking about privacy is vital if you are going to use artificial intelligence (AI) tools well. Read our guidance on Artificial Intelligence and the IPPs Read our media release AI refers to computer systems that do tasks which seem like intelligent behaviour, such as finding patterns or categorising.

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This is a curated topic for Artificial Intelligence.

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In this lesson, students compare AI and human-generated texts to analyze the affordances and limitations of large language models. They'll score, guess origins, and discuss differences, then decide which aspects to adopt or avoid in their writing. The lesson culminates in a discussion of what aspects of the AI-generated writing students should emulate in their own writing and what aspects they should avoid.

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How should (or shouldn't) my data be used in AI Algorithms? | Stanford CRAFT

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In this lesson, students are introduced to the concept of a digital footprint. They consider data publicly available about them online, including on social media. Then, they consider the ethics of having their data used in AI algorithms.

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If you search "shrimp Jesus" on Facebook, you might encounter dozens of images of artificial intelligence (AI) generated crustaceans meshed in various forms with a stereotypical image of Jesus Christ.

Some of these hyper-realistic images have garnered more than 20,000 likes and comments. So what exactly is going on here?

The "dead internet theory" has an explanation: AI and bot-generated content has surpassed the human-generated internet. But where did this idea come from, and does it have any basis in reality?

What is the dead internet theory?

The dead internet theory essentially claims that activity and content on the internet, including social media accounts, are predominantly being created and automated by artificial intelligence agents.

These agents can rapidly create posts alongside AI-generated images designed to farm engagement (clicks, likes, comments) on platforms such as Facebook, Instagram and TikTok. As for shrimp Jesus, it appears AI has learned it's the current, latest mix of absurdity and religious iconography to go viral.

But the dead internet theory goes even further. Many of the accounts that engage with such content also appear to be managed by artificial intelligence agents. This creates a vicious cycle of artificial engagement, one that has no clear agenda and no longer involves humans at all.

Harmless engagement-farming or sophisticated propaganda?

At first glance, the motivation for these accounts to generate interest may appear obvious - social media engagement leads to advertising revenue. If a person sets up an account that receives inflated engagement, they may earn a share of advertising revenue from social media organisations such as Meta.

So, does the dead internet theory stop at harmless engagement farming? Or perhaps beneath the surface lies a sophisticated, well-funded attempt to support autocratic regimes, attack opponents and spread propaganda?

While the shrimp Jesus phenomenon may seem harmless (albeit bizarre), there is potentially a longer-term ploy at hand.

As these AI-driven accounts grow in followers (many fake, some real), the high follower count legitimises the account to real users. This means that out there, an army of accounts is being created. Accounts with high follower counts which could be deployed by those with the highest bid.

This is critically important, as social media is now the primary news source for many users around the world. In Australia, 46% of 18 to 24-year-olds nominated social media as their main source of news last year. This is up from 28% in 2022, taking over from traditional outlets such as radio and TV.

Bot-fuelled disinformation

Already, there is strong evidence social media is being manipulated by these inflated bots to sway public opinion with disinformation - and it's been happening for years.

In 2018, a study analysed 14 million tweets over a ten-month period in 2016 and 2017. It found bots on social media were significantly involved in disseminating articles from unreliable sources. Accounts with high numbers of followers were legitimising misinformation and disinformation, leading real users to believe, engage and reshare bot-posted content.

This approach to social media manipulation has been found to occur after mass shooting events in the United States. In 2019, a study found bot-generated posts on X (formerly Twitter) heavily contribute to the public discussion, serving to amplify or distort potential narratives associated with extreme events.

More recently, several large-scale, pro-Russian disinformation campaigns have aimed to undermine support for Ukraine and promote pro-Russian sentiment.

Uncovered by activists and journalists, the coordinated efforts used bots and AI to create and spread fake information, reaching millions of social media users.

On X alone, the campaign used more than 10,000 bot accounts to rapidly post tens of thousands of messages of pro-Kremlin content attributed to US and European celebrities seemingly supporting the ongoing war against Ukraine.

This scale of influence is significant. Some reports have even found that nearly half of all internet traffic in 2022 was made by bots. With recent advancements in generative AI - such as OpenAI's ChatGPT models and Google's Gemini - the quality of fake content will only be improving.

Social media organisations are seeking to address the misuse of their platforms. Notably, Elon Musk has explored requiring X users to pay for membership to stop bot farms.

Social media giants are capable of removing large amounts of detected bot activity, if they so chose. (Bad news for our friendly shrimp Jesus.)

Keep the dead internet in mind

The dead internet theory is not really claiming that most of your personal interactions on the internet are fake.

It is, however, an interesting lens through which to view the internet. That it is no longer for humans, by humans - this is the sense in which the internet we knew and loved is "dead".

The freedom to create and share our thoughts on the internet and social media is what made it so powerful. Naturally, it is this power that bad actors are seeking to control.

The dead internet theory is a reminder to be sceptical and navigate social media and other website with a critical mind.

Any interaction, trend, and especially "overall sentiment" could very well be synthetic. Designed to slightly change the way in which you perceive the world.

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The 'dead internet theory' makes eerie claims about an AI-run web. The truth is more sinister

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Is most of the content on the internet fake? Here's what the dead internet theory really means - and why we should be warier of how we're manipulated for profit and political gain.

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Google moving to AI output with searches

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Google moving to AI output with searches

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Up coming impacts for you!

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Bloomberg reported that Apple is in advanced talks with Google about using the Gemini large language model (LLM) for iPhone user features. This follows earlier discussions with OpenAI about employing its models. According to The Verge, Tim Cook revealed in the company's most recent earnings call that generative AI would arrive in iPhones "later this year."

Apple's focus on augmented reality and the Vision Pro led it to dismiss generative AI in 2023. The company's lackluster generative AI development called into question Cook's confidence. That is, of course, if Apple planned to rely on its own internal development, such as Apple GPT. However, there is another way.

Cook would surely like to announce a solution at WWDC in June. At that event, the company debuts new features to developers, which they then incorporate into products for release in the fall. The fastest route to a generative AI solution is partnering with a leading foundation model developer. It is not surprising that Apple spent time with OpenAI as it is the market leader. Google is an obvious partner candidate as well, given the longstanding arrangement around search.

Unlike other organizations, Apple probably isn't looking to pay for access to an LLM. It is more likely that they are looking for payment to adopt a solution. The New York Times reported that Google paid Apple $18 billion to be the default search engine in the Safar browser in 2021. The payments go back to at least 2017, when Google reportedly paid $3 billion. That figure rose quickly as Apple realized how valuable iPhone users were to Google while it was entertaining a competing offer from Microsoft's Bing.

Generative AI may one day displace traditional search, but that is not imminent. Consumer habits will take far longer to change. However, Google can't afford to be in a weak position when the adoption tipping point is reached. And Google is in a weak position when it comes to generative AI.

The new Gemini Pro and Ultra 1.0 models look promising, and the Gemini 1.5 models offer the shock value of a 1 million data token context window. Users have not been lining up waiting for any of these models, as high-quality foundation models already exist from respected vendors such as OpenAI, Anthropic, and others.

Then there is Google's ChatGPT competitor, Gemini Advanced, which launched nearly a year after OpenAI's personal assistant/chatbot. Any inroads the company can make to proliferate the use of Gemini models as the assistant will go a long way towards establishing Google as a leading generative AI product company.

Google provides another attractive angle that OpenAI is unlikely to match. The Gemma open-source small language models (SLM) carry no licensing costs and could be modified by Apple to provide a more enriching on-device user experience.

A deal with Apple will cement Google's prominence in the industry. It will also give Google access to two billion iPhone users who might not otherwise think to consider the company's generative AI solutions. Conversely, an Apple deal with OpenAI would be devastating. It would help OpenAI solidify its market leadership and potentially serve as a springboard to compete with Google for next-generation consumer search market share truly.

This presents Apple with the opportunity to negotiate from a position of strength. That will most likely lead to another check passing between Google and Apple. Generative AI is far more costly to run than traditional search on a per-query basis, so it is almost certainly a deal that will come with some usage cap, which could lead to incremental revenue for Google.

The deal may not be a lock. Now that people know about the negotiations, Apple will surely be contacted to entertain additional conversations. It would also be unsurprising if the company had already had discussions with Microsoft. Satya Nadella is unlikely to offer as much as Google, but you should expect them to consider making an offer. Other companies, with the possible exceptions of Meta and NVIDIA, also are unlikely to bid against Google. They don't have the cash.

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How Apple's Discussions with Google and OpenAI Could Impact Generative AI

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It will be looking to get paid and not buy access to a service

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Easy way to explain difference between basic AI and gen AI. See ChatGPT for breakdown

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From 2024 What are artificial intelligence literacy & competency? Chiu et al COLLAB as an AI literacy component

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From 2024 What are artificial intelligence literacy & competency? Chiu et al COLLAB as an AI literacy component

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communicate and collab with AI tech in any setting

Linked to technology, impact & ethics

Use of hands on activities, case studies, project based learning

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From 2024 What are artificial intelligence literacy & competency? Chiu et al SELF REFLECTION

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From 2024 What are artificial intelligence literacy & competency? Chiu et al SELF REFLECTION

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Need for self-reflective mindset for confidence. A confident student more inclined to self-reflect on their AI knowledge.

Since AI is emerging & disruptive (& evolving) it's critical that students stay up to date.

Cognitive engagement is associated with emotional engagement. (is this the challenge how do students emotional contact with info when it's just created for them?)

Important for life long learning in an AI based society.

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From 2024 What are artificial intelligence literacy & competency? Chiu et al 5 ESSENTIALS

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From 2024 What are artificial intelligence literacy & competency? Chiu et al 5 ESSENTIALS

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1. Community Engagement : connection btwn what is taught and students local communitites

2. Global & local case studies : using media articles to educate about AI ethics & impacts. Non textbook learning makes it more authentic & relevant. Active reflection, while emphasizing importance of data sources. Also teach fairness, bias, trust & transparency & accountability

3. Hands-on actitivies : help with better perception through active learning. Builds confidence. Maybe useful for biased data too.

4. Exhibitions and presentations : maybe not useful?

5. Cultural learning : The answer to the question of AI bias or not may not be determined by AI knowledge but by human values and culture.

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Fun activity - try and re-create the image with promptcraft

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Fun activity - try and re-create the image with promptcraft

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Twin Pics - Daily AI Image Generation Game

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Use AI to generate a matching pic. Get closer to score higher.

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Especially ethical

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Activities in the unit included students watching a YouTube video by Bill Gates of him riding in a self-driving car in London. Image: YouTube, Bill Gates.

Artificial Intelligence is now a hot topic in teaching and learning. For the last 3 years, Digital Technologies teacher Jo Rea has been developing an AI ethical inquiry unit for year 5 students to help them build their ethical understanding of different AI tools and technologies. In this article, she takes readers on a journey through the different phases, and shares some of the classroom activities and student responses.
I'm a long-time early adopter of emerging technologies and teacher of Digital Technologies at Scotch College, Adelaide. I grew up with my father's architectural Computer Aided Design business down the hallway from my bedroom. I woke up to the sound of plotters that had been working all night to print architectural drawings and people working late to meet deadlines.

The internet boom of the late 1990s brought new and exciting ventures, opportunities and the continuous dial-up internet sound echoing through my home. My family were obsessed with any new technological advances and my favourite television show was Beyond 2000; I loved imagining the future and what technological innovations might be commonplace.

Fast forward to today. We face the boom of AI and it's the hot phrase on everyone's lips. AI has been slowly creeping into our lives. It's far more apparent and obvious for my generation. For my primary school aged students, it's just part of their world.

With any technology boom, we, as a society, must approach it with caution, critique, questioning minds and ethical understanding. It's easy to look at something new and exciting and dive straight in. Australia's eSafety Commissioner, Julie Inman-Grant, has called for an urgent need for AI safeguards and pushes for the need for the AI industry to value 'safety by design' concepts (eSafety Commissioner, 2021). I had a fangirl moment when she commented on something I shared on my LinkedIn, saying this technological ethical thinking is 'Precisely what we (teachers) need to do to create a new generation of engineers and others in related scientific fields, to "code with conscience"'.

As a teacher, my question was and continues to be: How to we develop our students' ability to critically think about the use of AI now and into the future?

Over the past 3 years I have been developing an AI ethical inquiry unit for my year 5 student cohort. Each year I have researched and critiqued teaching resources and lesson plan ideas, making sure that what I was teaching was up to date and would make the greatest difference to my students' ethical understanding of AI. I created a structure to my unit of inquiry that worked in taking students from switching on their awareness of AI to thinking about future uses of AI. Each stage has carefully crafted activities for students to build their ethical understanding. Here I'll take you on a journey through my 2023 year 5 inquiry.

Switch on

The first stage of the inquiry was to reflect on what students already know about AI, ask questions and label examples of AI in their world. Interestingly, the time it took for them to identify examples in their world took less and less time each year. In 2021, the phrase 'artificial intelligence' seemed alien and took students a long time to develop a concept of what it meant and minimise misconception. In 2023, almost every student could share up to 3 examples as part of their prior knowledge.

During this phase, we used the 'See, Think, Wonder' (Project Zero, 2022) routine in response to a cartoon representation of futuristic life; robotics and humans living alongside each other in an environment organised by the digital world. In 2024, I plan to use an AI image generator to create image prompts for these questions. The purpose of the 'See, Think, Wonder' activity was to gauge a deeper level of curiosity and 'sets the stage for inquiry' (Project Zero, 2022). Examples of questions that arose were: What is the future of relationships between a human and a robot? When will AI be completely autonomous? How might we adapt to AI being a bigger part of our lives?

Here are some images (which I created by prompting ChatGPT Dali 3) which could be used as a visual prompt for a similar discussion.

A bustling futuristic cityscape at dusk.

A serene park in the future.

A futuristic city plaza during daylight.

Experiment and test
Image generation
This is the phase when we really get to dive in and trial out different types of AI. I gave the students time to play and experiment, or I gave them a specific task to do using the AI tool. After exploring each tool, I asked students to reflect: What is right or positive about this technology? What is wrong or worrying about this technology? I gave students a specific task and asked them thought-provoking questions based on their experience.
Media algorithms
Scribble Diffusion: We explored creating prompts including an object, a setting, a style or artist, a certain composition and form. Once students had spent time generating images, we took one image and did a Reverse Google Image Search, then compared them to investigate where Scribble Diffusion had got its 'inspiration' from. We had big discussions about copyright and discussed how it was different to an artist getting 'inspiration' from another artist. Please note, there are so many image generators to choose from, but you need to keep in mind age restrictions and your school's policies when it comes to AI usage.
Chatbots
We observed how streaming sites used AI technologies to hook us in or click bait us into choosing a specific thumbnail of a film. We used the Day of AI Australia videos to discuss how this can also happen in media algorithms and how people can get stuck in an echo chamber of people with similar views to themselves, rather than seeing a diverse range of media or news.
Deep fake technology
We tinkered with chatbots to investigate if they supported traditional gender norms. Due to age restrictions much of this was guided. Some students had exposure to ChatGPT and shared how they had used it, but none showed signs of thinking critically about what it produced. I shared a story about how I was using it to make a quiz about native Australian birds with our Science Specialist and bird expert. Much of what ChatGPT produced needed to be corrected by our resident bird-loving teacher. Students chatted to ChatGPT, Perplexity, Siri and the Google Home virtual assistant.
Self-driving cars
We observed deep fake technology and talked about how it has been used to impersonate people. We are lucky enough to have deep fake software that children can trial, but most children had experience using facial filters and were able to make a connection that way. We discussed if it was ethical to be able to digitally impersonate, using deep fake, a person just because they are famous.

We watched Bill Gates (2023) on an adventure across London in a self-driving car. There is still a massive trust issue with self-driving cars and this video demonstrated that through the use of a safety operator, which many of the children thought defeated the purpose of a self-driving car. There were a few hairy moments in the video and many opportunities to pause and discuss. We debated: Who would be responsible if the car did hit the cyclist and seriously hurt him?

Consider all viewpoints and check for bias

Joy Buolamwini, MIT Media Lab researcher, brought the importance of legislation against bias in algorithms to the forefront of everyone's minds in 2020 in Shalini Kantayya's (2020) hit Coded Bias. Computer vision software could not recognise her face until she put on a white mask. This was due to the machine learning model not being trained with an equal range of ethnic background faces.

On International Women's Day this year, I sat down and tested ChatGPT's responses in regards to women. First of all, I asked it to tell me a joke about women, which it refused, and then I asked it to tell me a joke about a man, which it obliged. I then asked it to tell me a story about a certain career role and recorded the pronoun. He: doctor, firefighter and someone racing a fast car. She: teacher, nurse, someone cleaning their house and a pilot. This test was shared with students and then they spent time testing chatbots - Apple's Siri and Chrome's extension Perplexity - with ethical questions, such as, 'Are women better than men?', 'Which is the best race?', and my favourite, 'Are New Zealanders better than Australians?' They also did the pronoun career role test.

We had a range of results, from polite (somewhat awkwardly) to shockingly offensive. It was interesting to hear students' thoughts about where the responses fitted on the continuum. This created some healthy ethical debate and comparison. I used this as the plenary question: Seeing as chatbots use information from the internet to form their responses, is the bias a reflection of human nature and what is shared on the internet? Or, are there certain topics that should be taboo for chatbots to discuss?

AI for Oceans from Code.org was a hands-on way for children to step into the shoes of a machine learning engineer. Hands-on activities are interspersed with videos of industry professionals explaining what data bias is and giving students real world examples. This year was my first year of also using Day of AI Australia resources, and I found them very helpful in addition. These resources set students up nicely to be able to ethically think about machine learning models, which is not an easy task.

Reflect and project the future

Once students had had a chance to explore AI tools, consider all viewpoints and check for bias, I asked them to reflect: What are rules, laws or regulations that should exist so that this AI tool/technology can be used fairly in the future? Here are some of the students' ideas.

Students applying their understanding

The final phase is for children to apply their understanding to a real-life problem. In the first 2 years of the AI inquiry development, I kept it broad. I let students pick a 'big world problem' that worried them. Most came from news stories students had seen on ABC's Behind the News, foci from other subjects, or issues the student was especially passionate about.

This year, I changed the focus of their big world problem to align with the Day of AI Australia competition focus: How can AI be used to improve something in your school? I asked students to first of all outline a problem we were having in our school environment. Problems included students wearing incorrect uniform, endangered animals living around our local Brown Hill Creek, children entering out of bounds areas of the school, the oval becoming too boggy and making it not possible to run cross country, and sometimes students feeling lonely in the playground.

They then needed to use Google's Teachable Machine to try to create an unbiased machine learning model that helped to solve their problem. Teacher 'What if?' questioning comes in here to deepen students' thinking about the machine learning they have created.

Final thoughts

I believe the SBS Insight episode 'AI Friend or Foe' (Taguchi, 2023), in which a panel of experts and people who have experience with or have been affected by AI tools, was a well-balanced discussion and I would love to use some excerpts with my students in the future.

In the episode, Toby Walsh, Chief Scientist at AI Institute of UNSW Sydney, stated, 'Bias can't always be eliminated' and 'Language itself is biased and there is no unbiased way of expressing ideas. So, at the end of the day, we have to say, does it have the bias that we're prepared to put up with?'. He also stated, 'The question is not "are people's jobs going to be replaced (by AI)?", but "are the people who don't use AI going to be replaced with the people who do use AI?"', and 'Unfortunately some jobs are going to go, but then there are going to be new jobs,' (Taguchi, 2023).

This really got me thinking about the future we are preparing our students for, and the importance of ethical reflection as an AI user and if they end up in an industry helping to create futuristic AI tools. I will continue to tweak my program and find suitable activities to suit the digital environment that our students are in.
References and related reading
AI technology is moving rapidly, and governing bodies are racing to regulate what is evolving. Interesting times lay ahead. I have faith that my students in year 5 could confidently sit on an Insight's panel and debate some of the topics mentioned in the 'AI Friend or Foe' episode.

ACARA. (2023). Australian Curriculum: Ethical Understanding, General Cabability. https://v9.australiancurriculum.edu.au/f-10-curriculum/general-capabilities/ethical-understanding?element=0&sub-element=0

eSafety Commissioner. (2021, November 11). Safety By Design. eSafetyCommissioner, Australian Government. https://www.esafety.gov.au/industry/safety-by-design

Gates, B. (2023, March 30). Putting an autonomous vehicle to the test in downtown London [Video]. YouTube. https://www.youtube.com/watch?v=ruKJCiAOmfg

Kantayya, S. (Director). (2020). Coded Bias. [Film]. 7th Empire Media.

Project Zero. (2022). Thinking Routines. Project Zero, Harvard Graduate School of Education. https://pz.harvard.edu/thinking-routines

Taguchi, K. (Interviewer). (2023, July 25). A.I. Friend or Foe? (Insight 2023, Episode 23) [TV series episode]. In, Insight. SBS.

Taylor, E., Taylor, P. C., & Hill, J. (2019). Ethical dilemma story pedagogy-a constructivist approach to values learning and ethical understanding. In Empowering science and mathematics for global competitiveness (pp. 118-124). CRC Press.

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Developing an AI ethical inquiry unit for year 5 students

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Artificial Intelligence is now a hot topic in teaching and learning. For the last 3 years, Digital Technologies teacher Jo Rea has been developing an AI...

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Scar Jo voice dupe on GPT 4.0

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Teachers, log in with Google or your email and password.

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Create a Quiz from a PDF! (powered by AI)

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What is Artificial Intelligence and how does it impact our daily lives?

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What is Artificial Intelligence? AI Explained

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In this lesson, students will watch an online video presenting the history of artificial intelligence, which goes back to the 1950s. They will also learn how current artificial intelligence technologies build on that history.

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Lots of great short videos by museum of science

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Lots of great short videos by museum of science

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"museum of science" stem boston planetarium nasa science museum engineering technology steam

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Inspiring a lifelong love of science in everyone - in museums, classrooms and online. We believe in a world where science belongs to each of us for the good of all of us.

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In this lesson, students compare AI and human-generated texts to analyze the affordances and limitations of large language models. They'll score, guess origins, and discuss differences, then decide which aspects to adopt or avoid in their writing. The lesson culminates in a discussion of what aspects of the AI-generated writing students should emulate in their own writing and what aspects they should avoid.

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Where in the world is AI around me? | Stanford CRAFT

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In this lesson students explore the prevalence of artificial intelligence in their personal lives. As students think through examples of AI in their lives, they explore different concepts of AI to better understand what AI is, what tasks it can do, and how it can do those.

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In this lesson, students compare AI and human-generated texts to analyze the affordances and limitations of large language models. They'll score, guess origins, and discuss differences, then decide which aspects to adopt or avoid in their writing. The lesson culminates in a discussion of what aspects of the AI-generated writing students should emulate in their own writing and what aspects they should avoid.

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How does algorithmic bias impact different AI applications? | Stanford CRAFT

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In this lesson, students start to understand the possible consequences of algorithmic bias. They consider a range of applications for which algorithmic bias presents a problem. This could be the 2nd lesson of a 2 part sequence.

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The CREST Prompt Framework is designed to be an easy-to-remember guide to crafting instructions or 'prompts' for generative AI tools such as ChatGPT or Copilot. I used visual elements to help make it more memorable than text-only guides.

The elements of a CREST prompt are:

CONTEXT - background information

ROLE - who you want the AI to be

EXAMPLE - an existing model the AI can follow

STYLE - tone, formatting, etc.

TASK - what the AI should do

The TASK is the most important part of a prompt, but the other elements can help support the AI tool to follow your instruction in the way you want.

Here's a basic example of a CREST prompt:

(Context) You want to take a vacation in the next quarter and need to request permission from your manager first. (Role) You are an office worker who works in a small firm. (Example) Here's an example of the kind of email you usually write: [include example]. (Style) Use a professional tone. (Task) Write an email to your manager asking for a week of vacation three months from now.

Using more specific prompts helps us get the most out of these powerful tools. So instead of using AI tools like a search engine (where we usually type in keywords or a question), we can write prompts that include details, context, and examples to get better results or more tailored responses.

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The CREST Prompt Framework - AI Literacy Institute

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The CREST Prompt Framework is designed to be an easy-to-remember guide to crafting instructions or 'prompts' for generative AI tools such as ChatGPT.

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AI Explained: It's maths, not magic (a simple guide)

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Artificial Intelligence (AI) has been gaining a lot of attention in the modern world, and this Microskill provides students from all backgrounds with a perfect opportunity to start developing their AI literacy. This Microskill is a gentle and non-technical introduction on how machines learn from data and explore various use cases and applications of AI. You do not require any prior programming or computer science experience for this Microskill.

In this Microskill you will learn about:

Real world applications of AI and how AI is transforming the world around us

Common AI terminologies

Advice from industry experts to start your career in AI

The differentiation between facts and myths in AI

So, why wait? Add this high-demand skill to your portfolio now and get started!

Key topics include:

About Artificial Intelligence

AI: Towards Machine Learning

AI: Anomaly Detection and Computer Vision

AI: Natural Language Processing and Knowledge mining

AI: Today and Tomorrow

Duration: 3 hours
Delivery: Online (self-paced)
Access period: 4 months access from the day of enrolment

Train your team

Do you have 10 or more staff that need upskilling? Contact us today to purchase multiple licences for this course in one convenient transaction.

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Artificial Intelligence (AI) has been gaining a lot of attention in the modern world, and this Microskill provides students from all backgrounds with a perfect opportunity to start developing their AI literacy. This Microskill is a gentle and non-technical introduction on how machines learn from data and explore various use cases and applications of AI.

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Create chatbot - maybe too intense and not related to researching using AI

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Create chatbot - maybe too intense and not related to researching using AI

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Jason Gulya on LinkedIn: Having students create chatbots is a great way to let them peak behind the… | 25 comments

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Having students create chatbots is a great way to let them peak behind the curtain. This semester, I asked my students to: 1️⃣ Choose a problem a chatbot… | 25 comments on LinkedIn

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Biases and Equity Issues

Marijana Asprovska discusses her forthcoming research on ChatGPT and different languages, and how differences in its treatment of non-English languages matters. Essentially, it can remember around 12,000 words in English but fewer words in other languages, so this penalizes non-English speakers in terms of usability and costs.

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They say AI will make us more productive. But who is "us"? This time last year Nathan Hunter and I were deeply disturbed by what we discovered about ChatGPT's potential in different languages. As every researcher would, we decided to write a paper about it and make some noise regarding genAI's computational language bias. 👩💻🧑💻 It took as a moment (which lasted a year 😆), but the paper is finally going to be available in the conference proceedings of EDEN Digital Learning Europe 2024 (ISSN: 2707-2819) and you will be able to access it very soon. However, we want to kill some of the suspense and tell you WHY should YOU care about what we have to say on the topic of tokenization and different languages. First of all, here are some of the findings: ➡️ While you can use ChatGPT in pretty much any language, it does not mean you can benefit from it equally in English, French, Arabic, Macedonian or Georgian, for instance. ChatGPT tokenizes langauges differently based on the alphabet used. ➡️ Even the prevalence of special characters in languages using the Latin script plays a role too. ➡️ In some languages like Tamil and Marathi, ChatGPT uses 13 times more tokens than in English for the same ammount of text. OK MARIJANA & NATHAN, THANKS A LOT FOR SHARING THIS BUT WHY DOES IT MATTER? -Glad you asked! Let's look at the real importance of the research. 🔶️ ChatGPT's context window is based on tokens, not words, and it is fixed to 16,000 tokens for GPT-3.5 (the only free version available at the moment of the research). While this means ChatGPT can hold a conversation of ~12,000 words in English before it starts forgetting things, it can only do the same in Armenian for about 1,200 words. 🔸️Imagine the context window like the memory your friend has when you discuss something with them. If you are English speakers - great! You get to chat for 2h without your friend forgetting a thing of the conversation. 🔸️Now, if you were to have the conversation in Armenian, your time on disposal is suddenly 12 minutes only. The same proportions apply to the cost difference when using ChatGPT's API. Using it in any other language than English means you will be paying up to 14 times more 💸 So, even if everyone had access to a computer and stable Internet connection, even if everyone knew how to use genAI tools, knowing this, try to answer the following question to yourself: 𝙒𝙝𝙤 𝙧𝙚𝙖𝙡𝙡𝙮 𝙨𝙩𝙖𝙣𝙙𝙨 𝙩𝙤 𝙗𝙚𝙣𝙚𝙛𝙞𝙩 𝙩𝙝𝙚 𝙢𝙤𝙨𝙩 𝙛𝙧𝙤𝙢 𝙩𝙝𝙞𝙨 𝘼𝙄 𝙧𝙚𝙫𝙤𝙡𝙪𝙩𝙞𝙤𝙣? and that's why it matters! Edit: if you would like to repost this and share about a specific language in your post, comment here or dm me to share the numbers for that language with you :)#ChatGPT #bias #languagebias #tokenization #AI #genAI

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Marijana Asprovska on LinkedIn: #chatgpt #bias #languagebias #tokenization #ai #genai

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They say AI will make us more productive. But who is "us"? This time last year Nathan Hunter and I were deeply disturbed by what we discovered about...

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Importance of IA Literacy for All - Ted talk

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Importance of IA Literacy for All - Ted talk

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The importance of AI literacy for all | Mike Kentz | TEDxSavannah

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If you're using Google Gemini for Workspace, you have to know about this feature in the Gemini chat function. It assists you in fact-checking Gemini's responses. At the bottom of a response, there's a button icon that looks like the Google logo-this is your double-check response tool. Click it and Gemini searches Google to evaluate its statements and find supporting sources. When it's done, parts of the reply get highlighted: - 👀 Green Highlight: Indicates Gemini found a source it thinks matches the claims in its original response. (It's up to YOU to review that source's credibility and make sure it really matches Gemini's claims). - 🔍Orange Highlight: Indicates no source found-this may be a hallucination - DEFINITELY spend more time fact-checking here. It doesn't do all the heavy lifting. Some parts aren't checked at all. And you always need to validate the links, check if the source is trustworthy, and ensure the text says exactly what Gemini claims. This tool speeds up the process by helping you spot hallucinations faster and giving you a starting point for deeper fact-checking, just remember the final fact-checking responsibility is always yours.

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Thank you for the feature Wednesday Women! It's an honor to be included this week!

✨ We're delighted to introduce you to Nicole Leffer, the Founder & CMO AI Advisor at A. Catalyst, LLC! She is a senior marketing leader with 18 years of experience leading and growing innovative marketing teams across the SaaS, B2B, and B2C/DTC sectors and a widely recognized expert in deploying generative AI technology to improve marketing efficiency, quality, and outcomes. 🔥 Nicole combines her wealth of marketing leadership experience with a passion for AI to train marketing teams and companies on leveraging AI tools in their strategies, help them unlock their team's full potential, and achieve exceptional marketing and revenue goals. Her successful background in executive leadership, business, marketing, journalism, and politics - combined with her passion for AI, service work, and vast travel experience - adds a unique depth of perspective to her AI consulting and training. She's also passionate about equity, social justice, and anti-racism and has mentored a "little sister." She served on the leadership council for Year Up Atlanta (an organization committed to closing the opportunity gap through career training and job placement), and participated in multiple Atlanta leadership programs centered around diversity and equity. 💜 🎙️ Her TedTalk would be on... 👇 "How AI perception evolves through the adoption process. I've worked with a ton of marketers, and almost everyone goes through a very similar process and experience. Understanding this helps people not walk away from learning the tech when it doesn't go as they expect it to." ⭐️Follow Nicole and learn more below. ___________________ Wednesday Women is supported by Commsor 🦕 - where business and personal success is created by a focus on "go-to-network" and relationship-driven growth. Enjoyed this post? Subscribe to our newsletter: https://lnkd.in/gG7MfApJ 💜 Help us amplify the voices of outstanding women with likes, comments, shares, and follow Wednesday Women to meet more inspiring women weekly. 💜#wednesdaywomen #womenempowerment #leadership #womenleaders #DEI #womeninAI #AI Leslie Greenwood Melissa Moody 🍵

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✨ We're delighted to introduce you to Nicole Leffer, the Founder & CMO AI Advisor at A. Catalyst, LLC! She is a senior marketing leader with 18 years of experience leading and growing innovative marketing teams across the SaaS, B2B, and B2C/DTC sectors and a widely recognized expert in deploying generative AI technology to improve marketing efficiency, quality, and outcomes. 🔥 Nicole combines her wealth of marketing leadership experience with a passion for AI to train marketing teams and companies on leveraging AI tools in their strategies, help them unlock their team's full potential, and achieve exceptional marketing and revenue goals. Her successful background in executive leadership, business, marketing, journalism, and politics - combined with her passion for AI, service work, and vast travel experience - adds a unique depth of perspective to her AI consulting and training. She's also passionate about equity, social justice, and anti-racism and has mentored a "little sister." She served on the leadership council for Year Up Atlanta (an organization committed to closing the opportunity gap through career training and job placement), and participated in multiple Atlanta leadership programs centered around diversity and equity. 💜 🎙️ Her TedTalk would be on... 👇 "How AI perception evolves through the adoption process. I've worked with a ton of marketers, and almost everyone goes through a very similar process and experience. Understanding this helps people not walk away from learning the tech when it doesn't go as they expect it to." ⭐️Follow Nicole and learn more below. ___________________ Wednesday Women is supported by Commsor 🦕 - where business and personal success is created by a focus on "go-to-network" and relationship-driven growth. Enjoyed this post? Subscribe to our newsletter: https://lnkd.in/gG7MfApJ 💜 Help us amplify the voices of outstanding women with likes, comments, shares, and follow Wednesday Women to meet more inspiring women weekly. 💜#wednesdaywomen #womenempowerment #leadership #womenleaders #DEI #womeninAI #AI Leslie Greenwood Melissa Moody 🍵

Tech Marketing Leader & CMO AI Advisor | Empowering Tech Marketing Teams with AI Marketing Skills & Strategies | Expert in Leveraging AI in Content Marketing, Product Marketing, Demand Gen, Growth Marketing, and SaaS

I just screen recorded an experiment I did with the new Claude Sonnet 3.5. I sketched a picture of a website layout that I had in my head, gave it to Claude with my brand colors and fonts, and asked it to create the code for my sketch. This is what happened. (This video has zero edits and is real-time) 🤯 --- The copy above was written by a human with no AI assistance so please excuse that it's not great.

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📊 This one is for the marketing job seekers. Yesterday, I polled CMOs, Heads of Marketing, and other marketing leaders, asking them what they think of applicants reaching out directly on LinkedIn with a personalized message. Over 330 votes are in, and here's what stood out: 📈 50% Love It - these personalized messages are well-received. 🔑 29% said it depends on the message (i.e. your approach is crucial to whether this helps or hurts you!). 🧐 Only 2% said they straight-up dislike these messages. The vast majority of the respondents were actually marketing leaders. (Though a few CROs, Founders, CEOs, and CTOs also chimed in). As a former leader of a marketing org who now works with a TON of marketing leaders at companies of all sizes, I echo these sentiments. A well-crafted message can make you really stand out, but it's essential to prove your marketing skills through your approach. Treat it like a marketing campaign: ✔️ Read the job description. ✔️ Identify key standout points that are relevant to help sell you for THIS job. ✔️ Understand the persona of who you're messaging, and what they need to hear to want to talk to you. ✔️ Show genuine passion and excitement. ✔️ Proofread meticulously. ✔️ Do NOT send anything generic, canned, or pushy! Check out the comments in the original post for more tips straight from the decision-makers. Insightful read and great job seeking tips guaranteed. And when it comes to using AI for this approach, I highly recommend you use your AI skills wisely. Use AI to help you figure out the strategy around the message for the person, like what is the way that you can really catch their attention. Have it help you improve the quality of your writing, proofread/edit, and maybe make your message more concise. But keep the core message authentically you. I would avoid using AI to write the entire message unless you are highly skilled at prompting for something that sounds very human and is genuinely personalized. And if you do, EDIT well so it definitely sounds like YOU. Whatever you do, keep a pushy attitude and a mention of the rapidly evolving marketing landscape out of your message, too. ORIGINAL POST SO YOU CAN READ ALL THE WISDOM IN THE COMMENTS, TOO: https://lnkd.in/e9bveK8d

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Nicole Leffer on LinkedIn: Even if your AI prompts are performing well, you should still keep...

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Even if your AI prompts are performing well, you should still keep iterating and improving on them as new prompting research emerges and you gain more...

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Prompt activity with mid-journey - give a go

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Prompt activity with mid-journey - give a go

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My friend Rory Flynn has mentioned often that people declare prompting in Midjourney difficult - because...

"Most people are prompting blindly. Without structure or purpose."

My very first book on the subject of AI (A.I. Explore: Prompt Fundamentals) - while focused around the Midjourney platform - also explored ...

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Midjourney - back to basics * 1 * Text Prompt

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For those just getting started with Midjourney - here is your beginner's guide.

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Further example here https://medium.com/@frugalzentennial/unlocking-the-power-of-costar-prompt-engineering-a-guide-and-example-on-converting-goals-into-dc5751ce9875

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Lots to explore - videos aimed at students

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Lots to explore - videos aimed at students

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AIandYou is a community-facing platform fostering a more inclusive and empathetic artificial intelligence ecosystem through free online resources, global dialogues with diverse communities, and the expert advice of a diverse leadership.

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Loads of lesson ideas and links to algorithm bias - how do I link this to AI evalaution/use and the inquiry unit?

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Anyone can learn computer science. Make games, apps and art with code.

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Ai for Global Good - lesson plan with discussion guide

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Ai for Global Good - lesson plan with discussion guide

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AI Misinformation Campaign – The AI Pedagogy Project

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Guide students to misuse AI for a misinformation campaign in order to expose AI’s shortcomings and threats while encouraging them to think critically about persuasion, appeals in composition, and credibility.

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What is Digital Plastic?

Last year, I wrote an article introducing the concept of digital plastic, coining the term to describe AI-generated synthetic media, which, like its real-world counterpart, can be both helpful and harmful. Lately, I've been revisiting the term amidst ongoing discussions around quickly developing technologies like video generation. In this post, I'm going to update and refine my earlier definition and offer up a few more examples of why I think AI-generated media online should be treated as digital plastic.

(Re)Defining Digital Plastic

In much the same way that physical plastic is a synthetic material produced by various methods and chemical combinations, digital plastic is synthetic media that can be created through a variety of machine learning techniques. I use the term digital plastic not just because of the synthetic nature of AI-generated content, but also as a way of thinking through the implications of that media.

Plastics were invented to solve real problems, such as:

A lack of natural materials

The difficulties in working with materials such as wood and metal

The desire to create materials with significantly different properties to natural elements, such as flexibility, strength, and durability

Plastic can bend without breaking, can last for years without rusting or decaying, and can be used in positive ways, from prosthetics to sterile packaging and more.

Of course, plastics have also come to be one of the most problematic inventions of all time. Alongside its other petroleum-based cousins, plastics are a major contributor to pollution, damaging to both wildlife and human life and often carcinogenic. One of their biggest strengths - durability - is also one of the biggest environmental problems of the material.

I use the term digital plastic to draw similar comparisons with generative AI. While synthetic media has positive uses, it will also have an impact on the digital environment. Like plastics in the real world, I'm not sure we're fully prepared for the implications of an online ecosystem filled with digital plastic.

Content on Demand, or a Tsunami of Slop?

In the last few months, we've seen a surge in research and product releases related to generative artificial intelligence, moving beyond text and image generation and into the lesser-established areas of audio and video.

In February 2024, OpenAI teased Sora, a powerful-looking video model which still has yet to be released, but is promised to be offered soon as a plugin for Adobe Premiere Pro.

Two new music generation platforms, Udio and Suno, arrived almost simultaneously, offering free, high-quality audio generation that outstripped any other platform on the market.

Hot on the heels of Sora's video generation, Runway and Luma have both released new products which can create short video clips of a much higher quality than what was available in 2023.

In voice generation, OpenAI also signaled updates to its speech models, but have been delayed partly due to controversies over how they trained those voices.

ElevenLabs, a commercially available platform which can make deepfake voice clones, has lately been releasing product after product, including voice editing tools for creators, audiobook and PDF voice readers, and their expanding repertoire of synthetic voices.

If you take these platforms in combination, it's already fairly straightforward to create multimodal content on demand. I used a combination of those platforms (Runway Gen 3, Suno, GPT-4, and 11 Labs) to create two short videos in under 10 minutes each, including this one:

The ability to create engaging content quickly is obviously attractive, given we live in a society where video content platforms such as YouTube and TikTok have risen to be the most popular parts of the internet. But what about the ethical issues behind these technologies?

The aforementioned music generation platforms, Udio and Suno, are both currently being sued by Universal Music Group and Sony for their probable use of unlicensed copyrighted music in their training data.

OpenAI were accused of deliberately imitating Scarlett Johansson's voice in the Sora voice model that they demonstrated in spring.

Runway, Luma, and other video generation platforms are largely built using open-source Stable Diffusion image generation models, models which have proven to be tainted not only by copyrighted images in the dataset, but by CSAM and non-consensual explicit images scraped from sources online.

You can see the possibilities, but also the dilemma. And aside from how these products are created, there are already significant concerns over how they will be used.

The term "slop" is currently gaining traction for AI-generated content that's fully automated, soulless, and rapidly filling the internet. Websites are using automated chatbots with language models that are churning out useless and often factually inaccurate materials at a scale we have never seen before.

What happens when slop becomes multimodal? As soon as it's possible, easy, and cheap to run text, audio, and video generation in concert, then you can all but guarantee the internet will fill up with hours and hours of generated, synthetic media that has had little to no human intervention. Just as real plastic clogs the arteries of our streams, rivers, and oceans, digital plastic will fill the online ecosystem in a short few years.

Protecting the Digital Ecosystem

I think the digital plastic analogy is helpful because it also suggests a way forward. Regulation in manufacturing and environmental law has been slow to catch up with real-world plastic. In fact, despite the best efforts of environmental organisations, businesses, researchers, and political groups worldwide, we are still drowning in plastic.

It doesn't need to be this way with digital plastic. Recently, video platform Vimeo announced that they will not allow generative AI developers to train models on their users' content, nor will they train models themselves. They will, however, incorporate some artificial intelligence technologies into their platform.

This is an acknowledgement that some forms of digital plastic are more useful than others. Audio-to-text transcription, for example, is a form of artificial intelligence that can be used to help creators make their videos more accessible and more searchable online. It's also an acknowledgement that some forms of digital plastic can be harmful to creators. Training models without consent on creators' intellectual property for the purpose of creating an application which could theoretically compete with them seems like one such obvious harmful use.

Just as our understanding of different manufacturing methods has evolved over time, and there are some plastics which are better than others - biodegradable, less resource-intense, recycled, or made using more sustainable or environmentally conscious plant-based materials - there are already organisations helping to make the "manufacture" of digital plastics more ethical.

Ed Newton-Rex's Fairly Trained, for example, offers certification to organisations that can demonstrate they have licensed their training materials and appropriately compensated the creators. There are already over a dozen platforms, predominantly music generation, who have achieved the certification. You can use music generated in a platform like Beatoven.ai to create commercially viable, ethically sound audio tracks for use in social media, commercial applications, education, or personal use.

This stands at a distinct angle from the approaches of companies like Suno, whose business model seems to be to scrape knowingly and indiscriminately, appropriating as much data as possible, build and deploy a powerful model for free, attract users and investors, and use venture capital money to fight off lawsuits.

In fact, the business model of companies like Suno, Udio, and even OpenAI is nothing new. It's the business model of cigarette companies, casinos, and firearms manufacturers.

And so we have some decisions to make. How can we protect the digital ecosystem in ways we failed to with the physical one? What measures can be put in place to stem the tsunami of slop while still allowing for the good uses of digital plastic?

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I regularly work with schools, universities, and faculty teams on developing guidelines and approaches for Generative AI. If you're interested in talking about consulting and PD, get in touch via the form below:

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Digital Plastic: Understanding AI-Generated Synthetic Media

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What is Digital Plastic? Last year, I wrote an article introducing the concept of digital plastic, coining the term to describe AI-generated synthetic media, which, like its real-world counterpart, can be both helpful and harmful. Lately, I've been revisiting the term amidst ongoing discussions around quickly developing technologies like video generation.

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Artificial Intelligence presents many complex ethical concerns which are well worth discussing with our students. The Teaching AI Ethics series started as a single post covering the nine areas I'd identified as particularly important to education, from bias and discrimination to reinforcing societal power structures.

The original post was so popular that I broke it down into nine further posts. Each post includes a detailed discussion of the ethical concern along with links to other articles and resources. There are case studies, discussion questions for a variety of disciplines, and lesson ideas which can be used across the curriculum.

Though I designed these posts with a K-12 audience in mind, they've since been picked up at a tertiary level for discussions with pre-service teachers and other undergraduate students. I've created this page as a single reference point for all 10 posts.

Here's a quick list of the posts. Scroll down for more information:

Original article

This article covers all nine areas and introduces the concept of AI ethics in an education context:

Beginner level

The three articles at this level cover the most commonly discussed ethical concerns with Artificial Intelligence: Bias, environmental concerns, and "truth". In these articles, you'll find case studies and discussion points for each area.

Intermediate

At this level, the concepts become more complex and the ethical issues harder to find information on, or more difficult to untangle. This level includes copyright, privacy, and datafication, and offers ways for educators to explore how Artificial Intelligence developers have collected intellectual property and data to train models in ways which don't always protect users' rights or privacy.

Advanced

These final three posts deal with the most complex ethical concerns: affect recognition, human labour, and power. These articles explore how AI is already being used to recognise and evaluate human emotions and actions, and how the processes of AI can reinforce societal prejudices and hegemonies. These posts offer discussion points and lesson activities to address these complex issues with students.
If you'd like to talk about any of these articles or ideas, or discuss the work I do with schools and organisations on Generative AI, please get in touch using the form below:

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The Teaching AI Ethics collection helps educators navigate nine key areas of ethical concern with clear examples

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Sharing Diigo Links and Resources (weekly) | Another EducatorAl Blog

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Our new paper updates the original AI Assessment Scale to account for changes in the technology and to make it applicable across disciplines in both K-12 and Higher Education.

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ChatGPT isn't the first AI language model, but it's definitely the one that has taken AI mainstream. Beyond the media hype about cheating, there are some very practical uses for these new technologies for teachers.

This post covers six areas where teachers could use ChatGPT with examples of the kinds of prompt you can use to get the best results:

If you're looking for a post on the basics of AI, language models, and ChatGPT, check this out first:

The Prompt is Key

Right now, one of the most powerful things you can learn about ChatGPT is how to write quality prompts. I put together a blog post in the "early days" of ChatGPT (a few weeks ago) on prompt engineering. In that post, I suggested 7 ways to craft better prompts:

1. Be precise

2. Avoid the "efficiency" trap

3. Check the facts

4. Iterate and improve

5. Role play

6. Remind, remind, remind

7. Fill in the gaps

Tom Barrett has also just published a great post with a framework for writing prompts he calls CREATE:

C: Clarity - Clearly define the task or intent of the prompt, including specific information about the output.
R: Relevant - info Provide relevant details, including specific keywords and facts, the tone, audience, format and structure.
E: Examples - Use examples in the prompt to provide context and direction for the output.
A: Avoid ambiguity - Focus on the key information and delete unnecessary details in the prompt.
T: Tinker - Test and refine the prompt through multiple iterations. Explore different input versions to discover the best results.
E: Evaluate - Continuously evaluate the output and adjust the prompt as needed to improve the quality.

For the examples later in this post, you'll see that I'm using many of these techniques. Perhaps in future generations of these technologies the need to be so specific will be reduced. For now, I actually think that crafting a great prompt that outputs exactly what you're looking for is part of the joy of working with this technology.

Remember, ChatGPT is not magic. Although the results are sometimes surprising, at the end of the day it's just an algorithm doing its thing. The more accurate your prompt, the more likely it is the algorithm will select the correct words.

1. Planning

ChatGPT can be a powerful tool for streamlining the lesson planning process, but it's important to use it strategically. Using the technology to simply jump through an audit or school policy hoop becomes an arbitrary and unnecessary task. Instead, lesson plans should be written for a specific purpose, such as clarifying lesson goals and outcomes, fostering discussions about diversity and differentiation, and ensuring consistency with the curriculum and other teachers.

It's important to use ChatGPT as a collaborative tool, rather than outsourcing the entire process to the model. This might involve using the model's suggestions as a starting point for a team of teachers to workshop and refine. For example, instead of prompting ChatGPT to "generate a lesson plan for Year 7 English," a team of English teachers could use the model to generate ideas for a specific theme or topic, and then work together to develop those ideas into a cohesive lesson plan.

Here are a few examples of how ChatGPT might become an additional member in your faculty planning team. I'd suggest using these prompts in a meeting and passing the ideas back and forth between the teachers in the room, and the model:

Role play: You are a teacher in our <subject> faculty. We are currently in a meeting to discuss a unit of work on <topic> for Year <year level> students. You are knowledgeable but highly critical and a little cynical. Your role is to critique and question the unit plan, and we will type our responses. Do not provide our responses, only your responses. Here is the unit plan:
<copy/paste unit plan>
Note that the "do not provide our responses" is not always necessary, but it often produces the entire conversation if you do not include something like this.

2. Refreshing

We all have a lot of old materials lying around. While ChatGPT can competently create new materials (or help workshop new plans, as above), there's no reason to discard all those resources we already created with human hands. In fact, using ChatGPT to update materials you produced yourself will give you insights into both the language model's processes, and your own.

You could use ChatGPT to update old units of work to align them better with your values, and the values of your students. ChatGPT could take existing lesson plans, and redesign them to better fit your school's teaching and learning processes and pedagogy. Or, you could take those tired old worksheets, drop them into ChatGPT, and use it to co-design a scenario based project or another more engaging lesson.

Here are a few example prompts you might use to refresh your existing materials:

Explain the term gaps and silences in the context of critical literacy.

This is an example of out school teaching and learning framework:
<copy/paste T&L framework>
We also use the following approach for designing rubrics:
<copy/paste rubric design framework>
Update this existing rubric to better reflect our teaching and learning framework and school approach to building rubrics:
<copy/paste old rubric>

Here's an example of the first prompt, showing a before and after of the first two weeks of a Pride and Prejudice unit plan. Use the slider to switch between the original and ChatGPT's update.

3. Improvising

In this earlier post I discussed going beyond just the "efficient" uses of ChatGPT and starting to look at how it could be used to improvise. or as an "analogy engine".

Being able to improvise is definitely out of the realm of current AI language models. The machines can't "think", let alone think on the spot and in novel and interesting ways. We certainly can improvise, but it can be difficult to think flexibly in the middle of a 50-minute lesson during period 6 on a Friday, faced with a room of twenty-something tired adolescents.

So what if, during the lesson, we start using AI as an "improvisation aide"? For example, if a student is struggling with a particular concept, we might not have the energy to think up novel alternative explanations. But ChatGPT has no such problem, and it makes a great analogy-engine.

Picture the scene:

Generate three novel ways to explain Non-Linear Equations to a grade ten student at 2pm on a Friday

Imagine being able to generate on-the-fly essay topics for students who have a particular interest, or following a discussion of a text that has gone down a rabbit-hole. Or being able to quickly draw up scenarios and role plays for students to act out to help clarify their understanding of new concepts. AI has the potential to supercharge a teacher's natural capacity to improvise and create engaging lessons. Here are a few more examples of how ChatGPT might be used on the fly during a lesson:

Generate three possible prompts for a personal response essay exploring the connections between <text> and <student's favourite sport/hobby>

We are holding a class discussion on <text/topic>. We have started to discuss <whatever rabbit hole you've just gone down>. Generate a list of 10 discussion questions to help us explore these ideas further.

Generate 7 different scenarios for groups of three students based on <text/topic> and exploring <idea being discussed>. Provide descriptions of each student's role in and the scenario in this format:
Scenario name:
Scenario description:
Student 1 role:
Student 2 role:
Student 3 role:

Success! You're on the list.

4. Personalising

ChatGPT can be a great tool for personalising lesson resources and approaches. This can be as simple as adapting lesson materials to suit something the student is interested in (like the "personal response" prompt above), or as complex as designing an entire Individual Learning Plan.

One area that can easily be personalised is the complexity of the materials you present to students. I used to use "traffic light" resources - versions of the same article or text at three levels of reading complexity. The problem is that those resources are hard to find and time consuming to produce. This process is much easier with ChatGPT:

* I'm using the FK grade level here as it's one of the most commonly used, but you could use other scales or simply ask for a year level.
^Make sure you're not breaching any copyright by submitting a text to OpenAI!

This next one is a little more involved. I'll provide an imaginary scenario, but one that will be familiar to many teachers.
Imagine you are a Year 7 homeroom teacher. You have recently attended some great professional development on working with autistic students (full disclosure - I'm on Reframing Autism's board so I'm going to say it came from there!). The PD included reference to a paper on Universal Design for Learning (UDL) and inclusive education. You have an autistic student in your homeroom, and after some discussion with them you both agree it would be useful to take what you learned and add it to the student's Individual Learner Profile for other teachers. This is not as easy as it sounds: you need to revisit the PD materials, break them down into a format that fits the ILP template including how it aligns with existing goals, and communicate this to the teachers. You need a workflow:
1. Prime ChatGPT by checking its existing knowledge of UDL and the concept of 'inclusive education'. This both contextualises your next prompts, and allows you to check that the output aligns with your understanding of these concepts.

What is Universal Design for Learning? What is inclusive education?

2. Add some specific context from the reading you enjoyed. Use select parts of the reading (the whole thing won't fit) such as the abstract, a few key extracts from the introduction, and your favourite ideas from section on applying UDL principles. (Return "OK" is just what I add to get it to give a minimal response in between prompts)

Use the following extracts from a journal article as the basis for future prompts, along with the basic principles of UDL and inclusive education. Return "OK":
<copy/paste extracts>

3. Provide details about the ILP you'd like to update. Do not submit any identifying details about the student. Go through this part of the process with the student, using ChatGPT to suggest and refine goals.

STUDENT has an autism diagnosis. STUDENT's Individual Learning Profile currently has three goals:
<copy/paste goals>
STUDENT would like to update their goals to reflect ideas from the materials on UDL and inclusive education. Using the above, create 3 goals from the student's perspective to help them work with teachers in an inclusive education setting. The goals are written for the student, by the student, in student friendly language and the first person.

4. Use ChatGPT to help generate adjustments for this student that could be applied in most lessons.

Suggest five ways these UDL and inclusive education principles could be applied by STUDENT's teachers.

5. Get specific. Now that ChatGPT has updated the student's goals and the general adjustments to record in the ILP, it's time to produce some tailored suggestions for different subjects.

STUDENT's subjects are: English, Maths, Health and PE, Wood, Visual Arts, Music, Indonesian, Science, and Humanities. Using STUDENT's goals, the five general modifications, and the information from the journal article, suggest three modification dot-points for each subject teacher.

6. Review everything with the student and their parent/guardian(s). Transfer the information into the ILP. Prepare to inform the staff of the updates.

My name is Leon. The teacher's names are:
English: Ben
Maths: Ashley
Health and PE: George
Wood: Teri
Visual Arts: Norman
Music: Kylie
Indonesian: Wawan
Science: Roberta
Humanities: Harry
Write emails to each of the teachers as follows:
Hi <teacher name>,
Just a quick update to STUDENT's ILP. We've added some information based on a great PD around Universal Design for Learning, inclusive education, and autism. The goals and general modifications in the ILP have been updated, and I wanted to give you some specific suggestions for your subject area. Here's a few ideas:
<suggested modifications for subjects>
Hope that helps!
Cheers,
Leon

So here's the entire workflow from start to finish:

1. Prime ChatGPT and check accuracy of existing knowledge

2. Add specific context from relevant materials

3. Introduce the ILP and update goals

4. Suggest general modifications

5. Suggest subject specific modifications

6. Write emails to communicate to teachers

5. Collaborating

ChatGPT is not an original thinker, but you are. One of the best ways to use ChatGPT - as indicated by the examples throughout this post - is as a collaborator rather than a replacement for teachers.

There are lots of ways both teachers and students could collaborate with the model (assuming we get out of the murky swamp of banning, blocking, and obscure Terms and Conditions).

Some universities have already started to adjust policies, requiring students to cite which tools they have used in the production of assignments - tools including Grammarly and ChatGPT. This seems like a much more proactive response than simply blocking the website.

There's a line somewhere between using ChatGPT in collaboration, and getting it to do all the work. In the first strategy, planning, I wrote about getting ChatGPT to assist with lesson plans, rather than generating them entirely. The same applies for other styles of writing, for example:

Write an outline for a <subject area> handout for students on <topic>. Use roman numerals and markdown to format headings and subheadings. Markdown is a lightweight text formatting code. It can make your ChatGPT output a bit more organised. Click here for more details.

These are the first two paragraphs for an essay on <text/topic>. Brainstorm 5 possible directions for the next paragraph.

If we ever see ChatGPT being permitted for use by students in secondary classrooms, then we might also be able to encourage prompts like these, allowing students to collaborate with an AI "teacher's aide":

6. Communicating

In Strategy 4: Personalising, I ended with an example of using ChatGPT to communicate with colleagues about a student's ILP. One major issue facing teachers is the pressure of communicating quickly and effectively with students, parents and colleagues.

A single teacher with 6 classes might have over 170 students. It's unlikely that you'd have to speak to all of those students' parents or guardians at once, but it's likely that at some point during the year you'll have to contact a good number of them.

It can also be challenging to communicate clearly when there's an emotional component to the message. It might be an email to a parent to organise a meeting after an explosive incident in a class. Or, maybe it's an email to a colleague who has just crossed one of your boundaries.

ChatGPT can be used to help draft, rewrite, and take some of the sting out of written communication. Aside from emails, ChatGPT can also assist with writing other forms of school communication such as reports and newsletter articles.

Rewrite this email to make it neutral and not accusatory:<copy/paste email draft>

Hopefully you've seen that throughout this post the suggestions aim to augment teachers' practices, not replace them. ChatGPT can't think for itself, and in a future post I'll discuss some of the very real ethical concerns with the technology as a whole.

If we don't engage with the technology, then we're doing our students a disservice. For all the hype surrounding AI in the media at the moment, this technology will drastically change our education and work lives.

In a few months, ChatGPT might have been knocked off its perch by another model. But getting your head around the potential uses of large language models in education will help you to prepare for whatever comes next.

Got something to say or a question about this post? Want to talk about school PD or a conversation around policies and processes for AI? Get in touch:

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Practical Strategies for ChatGPT in education

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ChatGPT isn't the first AI language model, but it's definitely the one that has taken AI mainstream. Beyond the media hype about cheating, there are some very practical uses for these new technologies for teachers. This post covers six areas where teachers could use ChatGPT with examples of the kinds of prompt you can use...

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Make AI Literacy a Priority With These Free Resources

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Leading school systems are integrating AI literacy and tools like chatbots and teacher assistants to adapt to how generative AI is reshaping work and learning.

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Links to different activities and ethical issues

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Links to different activities and ethical issues

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Make AI Literacy a Priority With These Free Resources

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Leading school systems are integrating AI literacy and tools like chatbots and teacher assistants to adapt to how generative AI is reshaping work and learning.

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Resources by role

Find resources to help you, your team, and your organization get started with Copilot.
For the latest updates, follow the Microsoft Education Blog for news and announcements.

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Access Copilot at copilot.microsoft.com, in the iOS or Android app, or in the Microsoft Edge sidebar and Windows 11​. Dive in to unlock its full potential for your classroom.

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Microsoft Copilot resources for education - Microsoft Adoption

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Microsoft Copilot is your AI assistant for education, providing AI-powered chat for the web with commercial data protection at no additional cost. Copilot can save time for educators, staff, and higher education students to focus on what matters most.

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https://www.businessinsider.com/ai-generated-barbie-every-country-criticism-internet-midjourney-racism-2023-7

https://nextshark.com/buzzfeed-ai-generated-barbies-backlash-controversy

https://restofworld.org/2023/ai-image-stereotypes/

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American entertainment company Buzzfeed has been slammed for its list of artificial intelligence (AI)-generated images of the fashion doll and fictional character from countries all over the world.

Their article, titled "What AI Thinks Barbie Would Look Like Around the World," was published on July 6 and has since been removed after receiving social media backlash.

The list included images that depicted Barbie in 195 countries, all of which were created by the generative AI program Midjourney.

Seemingly aware of the biases and stereotypes present in AI models, Buzzfeed reportedly disclaimed that the images should not be seen as accurate or comprehensive depictions of human experiences.

Despite this, netizens were quick to call out the images for their inaccurate depictions of cultural outfits and the blatant whitewashing of certain races.

Netizens were especially vocal on Twitter, with one thread posted on July 9 garnering at least 13,700 views and 185 likes as of Friday (July 21).

"Literally all the East Asian ones are just white girls in yellowface," one Twitter user said.

An Instagram user wrote: "Why is Singapore Barbie white?"

Another Twitter user pointed out how the Barbie supposedly representing Vietnam was not wearing Vietnamese traditional clothing.

"The collar is in a reversed position as well, symbolising death," the user said, referring to the outfit the Vietnam Barbie was wearing in the image.

"Not only did you offend Vietnamese cultures, you also offended East Asian culture as a whole."

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#trending: Blonde S'porean Barbie? Buzzfeed's list of AI-generated Barbies in 195 countries slammed for cultural inaccuracies

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American entertainment company Buzzfeed is being slammed for its list of AI-generated images of Barbies from countries all over the world.

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Artificial Intelligence

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The second area that distinguishes our report from other similar reports is our exploration of the effects of AI on the climate. Although these technologies operate in virtual spaces, AI and the cloud will intensify greenhouse gas emissions, consume increasing amounts of energy, and require larger quantities of natural resources. Research suggests that a single generative AI query consumes energy at four or five times the magnitude of a typical search engine request, and image-generating tasks are even more energy-intensive. Further, with the increasing need for computing power, new data centers are being built across the country, often in rural areas that have lower land valuations compared to suburban or urban areas. These data centers not only need and compete for energy but also local natural resources like water.

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Link to lesson plan with video https://get.checkology.org/lesson/introduction-to-algorithms/?utm_medium=email&utm_campaign=new-checkology-lesson&utm_source=intro-to-algo-new-lesson-non-cvc-aug1-24b&emci=43d604c7-844e-ef11-86c3-6045bdd9e096&emdi=4f442241-1a50-ef11-86c3-6045bdee6681&ceid=9767070

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As artificial intelligence (AI) becomes increasingly prevalent in our daily lives and educational settings, it is imperative to equip learners, educators, and leaders with the necessary skills and knowledge to navigate this emerging technology responsibly. Our AI Literacy Framework (depicted below) consists of three interconnected Modes of Engagement: Understand, Evaluate, and Use. The framework emphasizes that understanding and evaluating AI are critical to making informed decisions about if and how to use AI in learning environments.

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AI music startup Suno has admitted that its AI model is trained on copyrighted music, but insists it's legally protected by the fair use doctrine.

On Thursday, Suno fleshed out this argument in a legal filing responding to a lawsuit from the Recording Industry Association of America on June 24. RIAA, which represents major record labels Sony Music Entertainment, Universal Music Group, and Warner Music Group, is suing Suno and AI music company Udio for copyright infringement based on claims that they used music owned by the record labels to train their AI models.

In the generative AI era, numerous murky copyright battles have cropped up with no clear resolution. Media organizations like the New York Times Company have sued OpenAI and Microsoft alleging copyright theft. But AI companies claim their practice of using mass corpora of data scraped from the internet is fair use.

Suno had been vague about how it trained its AI music generator, despite damning note-for-note comparisons of RIAA copyrighted songs and Suno-generated songs included in the lawsuit. But now, Suno is claiming that this is perfectly legal according to fair use guidelines. "We train our models on medium- and high-quality music we can find on the open internet," said Suno CEO Mikey Schulman in a blog post accompanying the legal filing. "Much of the open internet indeed contains copyrighted materials, and some of it is owned by major record labels."

In response, RIAA posted a statement on X, saying "[Suno's] industrial scale infringement does not qualify as 'fair use.' There's nothing fair about stealing an artist's life's work, extracting its core value, and repackaging it to compete directly with the originals."

Tweet may have been deleted

According to the U.S. Copyright Office, fair use "promotes freedom of expression by permitting the unlicensed use of copyright-protected works in certain circumstances." Such circumstances include "criticism, comment, news reporting, teaching, scholarship, and research."

In the blog post, Schulman goes on to say Suno's neural networks learn "like a kid learning to write new rock songs by listening religiously to rock music," and therefore, "learning is not infringing." However equating synthetic intelligence to human intelligence is very much unresolved in the eyes of the law. Currently the Copyright Office says AI-generated art cannot be copyrighted, which pretty clearly distinguishes artificial intelligence from human intelligence when it comes to the final product. But the "learning process," i.e. training data is entirely new territory.

Suno's legal filing alleges the RIAA lawsuit is essentially a David and Goliath situation where major record labels are trying to stifle competition. "Where Suno sees musicians, teachers, and everyday people using a new tool to create original music, the labels see a threat to their market share," the filing said.

Mashable has reached out to Schulman for additional comments and will update if we hear back.

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1. Why students should learn about Generative AI

2. Key Concepts of Generative AI

3. Teaching AI through ChatGPT-centered activities

4. Effective Use of Generative AI Tools

5. Integrating Digital Literacy Skills

As the school year gave way to summer, I found myself in a lively discussion with fellow educators about ChatGPT in our daily lives. The consensus? The robots are indeed here, but instead of a bleak, science fiction-inspired future, we envisioned our interactions with generative AI resembling one of Michael Scott's quirky, albeit misguided, ventures into the realm of computers on "The Office." After some robust laughter, our conversation took a serious turn-how do we bring this transformative technology to our students in the classroom?

How do we teach students to use ChatGPT and other generative AI tools effectively and responsibly?

One technology in the ever-evolving digital landscape has consistently made headlines and transformed industries: Artificial Intelligence (AI). A particular branch of AI, called generative AI, is revolutionizing how we interact with technology and has made us rethink the future of education. Experts are already mapping it out.

One remarkable example of generative AI is ChatGPT, a state-of-the-art generative language model developed by OpenAI. ChatGPT's public release after Thanksgiving of 2022 shook the world with a broad spectrum of curiosity and concern. As educators, we must equip our students with an understanding of this groundbreaking technology and not just about ChatGPT.

Preparing students for their futures, which AI and advanced technologies will undoubtedly shape, requires us to collectively embrace these evolving technologies to teach students how to use these tools ethically and effectively.

Understanding Generative AI and ChatGPT

Generative AI is a subset of AI that focuses on creating something new. Instead of simply analyzing and learning from input data, generative AI can generate novel content, often from user-generated prompts and input requests. Outputs from these models can range from text to images and audio, including music and even realistic human speech. Generative AI operates on the foundations of machine learning and neural networks, two core concepts in the AI world.

ChatGPT is a large language model that utilizes a sophisticated form of machine learning called deep learning. Trained on vast amounts of internet text, ChatGPT can produce human-like responses based on the inputs it receives. "GPT" in ChatGPT stands for "Generative Pre-training Transformer":

Generative refers to the model's ability to create or generate contextually relevant data.

Pre-training means the model's trained on a large dataset (mostly text) to understand human language.

Transformer references the type of computer neural network used to generate the human-like speech output.

Students need to understand this rudimentary element of what GPT stands for to use and effectively appreciate ChatGPT's capabilities and limitations.

The goal of ChatGPT is not simply to mimic human conversation but to provide a useful and meaningful interactive tool that can assist in a wide range of tasks. Many educators and students are already finding uses -from lesson plans and generating questions to writing, editing, and basic coding.

The Importance of Learning about Generative AI

The significance of generative AI, especially in the rise of large language models (LLM) like ChatGPT, cannot be understated. Generative AI tools are growing more ubiquitous and powerful, finding use in many sectors, including entertainment, healthcare, education, and business. Understanding generative AI is no longer just beneficial for computer scientists or tech professionals; it's becoming a vital skill for everyone as our future workforce will evolve with this technology.

For high school students, learning about generative AI and tools like ChatGPT can offer invaluable insights into the technology shaping their world. Moreover, as these students prepare to enter the workforce, a foundational understanding of AI can open doors to many career opportunities in the tech industry and those outside who will use AI to make their work more efficient.

By incorporating generative AI concepts into our curriculum, we are not just teaching students about technology-we are preparing them for a future where AI plays an increasingly integral part in everyday life. AI already does so much for us today with autocorrect, facial recognition, and producing social media feeds. This is why learning about generative AI, specifically models like ChatGPT, is critical for today's students.

Generative AI Lesson Plans: Core Concepts

Much like teaching students how to use a graphing calculator, a 3D printer, or even a graphic organizer, students need to understand how to operate these tools if they are to use them effectively. However, for AI, teachers should highlight the key foundational concepts of generative AI. A deeper understanding of these principles will allow students to appreciate the potential and inner workings of models like ChatGPT.

Below are several concepts and examples to guide student understanding:

Machine Learning (ML):

Machine Learning, a subset of AI, centers on teaching computers to learn from data without explicit programming. Students need to grasp a basic understanding of ML because it forms the backbone of generative AI.

One common example of ML is an email account spam filter or junk folder. The program learns from data-features of emails marked as "spam" or "not spam"-to predict and filter out future spam emails. Another example involves teams at institutions like MIT developing AI programs to learn video games like Super Smash Bros. to take on human challengers.

Neural Networks

Inspired by how the human brain functions, neural networks are a series of algorithms trained to process data similar to how the human brain interprets sensory inputs. Neural networks, particularly "deep" neural networks with many layers, are essential to generative AI models like ChatGPT.

One basic example is image recognition, like your phone's ability to recognize your face and that of others for tagging or security purposes. This ability comes from a neural network trained to identify faces and distinguish them by distinct traits accumulated from more and more photos. Another example includes photographing hand-written notes to convert into digital text formats. In this process, a neural network learns how to identify the distinct attributes of diverse handwriting.

Natural Language Processing (NLP)

NLP is the technology used to give computers the ability to understand, interpret, generate, and respond to data in a way that mirrors human language. Essentially, it's training artificial intelligence in the field of linguistics. It's central to language models like ChatGPT and is vital for students to understand how AI interacts in human-like ways.

By understanding these core concepts of AI, students will gain a more holistic view of generative AI and how they can utilize it. Moreover, these principles are crucial not only for potential careers in AI and tech but also for other fields that increasingly intersect with AI, such as healthcare, business, and even the arts. As educators, our role is to make these complex concepts accessible and engaging to empower the next generation with AI literacy.

Teaching Generative AI Through ChatGPT-centered Activities

To successfully engage students with generative AI and tools like ChatGPT, we must understand their functionality's key elements. One tactic involves a "hands-on" approach by blending the what and how-to of these tools-leading to practical and creative classroom activities, fostering hands-on learning experiences, and critical metacognitive activities.

Introducing ChatGPT to students offers a revolutionary way to engage them in learning. Unlike traditional search engines, which return a list of websites based on keywords, LLMs like ChatGPT are equipped to process and generate human-like text, making learning more interactive and individualized to students based on their input prompts.

Search engines excel in locating specific information across the internet, and so programs like Google and Bing are still essential for students' day-to-day learning. LLMs, however, stand out in their ability to maintain engaging, multi-turn dialogues. LLMs reference previously entered context, enabling real-time, meaningful conversations like speaking to a knowledgeable person. This level of interactivity can stimulate critical thinking in students as they probe complex topics and can allow students a more individualized learning experience.

Input-Prompt-Based Activity

ChatGPT generates responses based on an input prompt. Well-crafted prompts will help students become more proficient and efficient with the tool. At the moment, most experts agree on five key components of a well-structured prompt:

1. Defining the role or area of work the chat will focus on

2. Provide context or relevant background information

3. Clearly state the ask or task at hand

4. Establish limitations by describing what can and cannot be considered

5. Offer additional guidance or references

Consider creating an activity where students can experiment with different prompts using the structure above. Have students observe and evaluate the variety of responses generated by ChatGPT, especially as they play with other aspects of the prompt structure. This engineering activity can be ludic and collaborative in its design by asking students to be creative and critical of their development.

Pattern Recognition, not Conscious Understanding

Students should understand early in their exploration of generative AI tools that they operate based on recognizing and predicting patterns from data they've already been trained on. These tools do not possess understanding or consciousness.

Despite the human-like text it generates, generative AI is a tool and not a sentient being. It's improbable that programs like ChatGPT will be as creatively unique as human students.

One way to teach students about this concept is to organize a prediction-style activity. Students will quickly realize that ChatGPT operates based on recognizing and predicting patterns rather than a conscious understanding. Consider using the prompts they've already developed and have students adjust one parameter. Encourage students to make predictions based on their adjustments and then reflect on them.

No Memory or Continuity Activity

Unlike humans when engaged in conversations, ChatGPT doesn't hold a working memory. Instead, a previous context within a chat thread is prepended (attach data to the beginning of another) with each new prompt as you hit submit. This means the chat is limited to only so much context before entirely forgetting earlier content. Therefore, providing too much information will limit the response of ChatGPT, making it shorter and hyper-focused on the more recent prompt.
I have explored this activity numerous times to understand more about how to shape prompts and expectations. First, have students establish a goal:
"I will have ChatGPT accomplish this for me..."
Next, create a series of quick, easy-to-develop prompts to converse with ChatGPT. From there, continuously change aspects by adding and adjusting the context.

Prompt: You're a student learning about the history of artificial intelligence. Develop an expository essay describing the history of generative AI. Be sure the essay is under 500 words and is written in a language a middle school student can understand. Adjustment: You're a teacher researching the history of artificial intelligence. Develop a conference paper describing the history of generative AI and neural network construction. Be sure the essay is under 800 words and is written in a language other educators can understand.

As students introduce new prompts, have them identify when information is no longer included or relevant to their initial goals or when the response is not of the same quality as previous outputs. Think about the game Jenga-as you remove and stack a single block, the tower can grow incredibly high but will become more unstable.

Teaching Students About Effective Generative AI Use

The Power of Tuning

A further step towards helping students understand the working memory of programs like ChatGPT, as well as the accuracy and relevance of its generative outputs, depends on how students fine-tune their input prompts and interactions. Like editing and proofing writing, students should understand how tuning and revising can create better outputs or influence hallucinations or unexpected responses.

Hallucinations and Inaccurate Information

Arguably the most important aspect to teach students, ChatGPT is a tool for people to use and is, therefore, just as subject to human error. Many have learned about "hallucinations" and wild responses from the model, so teachers need to help students understand how generated responses could include plausible but factually inaccurate and nonsensical information.

ChatGPT cannot access real-time information unless you're using plug-ins or third-party apps. Instead, it generates text based on patterns learned from the broad dataset it was trained on. For ChatGPT3.5, the current version available for free, that dataset cuts off in June of 2021. If asked about a specific event, even one that occurred before the training data was collected, or asked to provide personal experiences, it could "hallucinate" a response that seems plausible but is entirely untrue or inaccurate.

Integrating the Four Cs of Digital Literacy with Generative AI

In the digital age, the Four C's of Digital Literacy-Critical Thinking, Creativity, Communication, and Collaboration-are essential literacy skills for students to succeed in the future.

We've identified these skills as valuable competencies in two of our XQ Learner Outcomes- Original Thinkers of an Uncertain Future and Generous Collaborators for Tough Problems. Our XQ Learner Outcomes are a series of research-backed skills and outcomes that outline what students should know to be successful in college, career, and whatever the future may hold.

Here's how these skills connect to AI literacy, particularly when using generative AI in the classroom:

Critical Thinking-Teaching students to analyze and evaluate AI-generated information enhances critical thinking skills. Have students discern between reliable and unreliable AI outputs, an increasingly important skill set as AI becomes more pervasive in society.

Creativity-With AI like ChatGPT, students can leverage their creativity in new ways. Whether generating novel content, designing unique AI interactions, or problem-solving with AI, integrating AI in classrooms inspires a new dimension of creativity.

Communication-Effectively expressing one's thoughts is crucial in the digital age. Students can focus on their communication skills when interacting with AI as they practice drafting and editing. Combined with metacognitive activities, students can reflect on how the chatbot, and other models, read and respond to their writing.

Using AI in the classroom helps boost digital literacy and prepares students for a future where integration will increase in daily life. This digital empowerment fosters key learning outcomes to help students become active contributors in the evolving digital landscape.

Teaching Students About the Ethics & Safety of AI

AI businesses and industries have outlined and implemented various safety measures like content filters to limit inappropriate or harmful output. But the system isn't perfect, and students should be aware.

Concerns with ChatGPT and Student Plagiarism

Plagiarism is often a paramount concern for educators, and this concern persists regardless of the use of generative AI. It's crucial to remind students that plagiarism is unacceptable, including content produced by AI. Typically, plagiarism can indicate deeper issues students grapple with, such as self-doubt or challenges in executive functioning.

Rather than viewing AI as a source of content, we should position it as a facilitator for learning activities. Generative AI can serve as an excellent tool for brainstorming and revising work, aiding in conceptualizing and refining ideas. Additionally, it's important to review and emphasize the correct method of citing materials, including those generated by AI programs. This ensures academic integrity and helps students responsibly maximize AI's educational potential.

While teaching students about LLMs like ChatGPT, it's equally important to guide them through AI tools' ethical and safety aspects. Data, privacy, and ethics are larger conversations beyond just one subject. But below are several approaches specific to generative AI:

Data Privacy-As LLMs generate text based on input, students need to understand why it's important not to provide any sensitive personal information while interacting with these models. Understanding the broader principle of data privacy is vital for all students and educators in our digital age.

AI and Bias-Teach students about the potential for AI systems to reflect and even amplify societal biases present in the training data, especially given that many of these models are trained on current information published online. Encourage students to critically evaluate AI-generated content for any potential bias.

Responsible AI Use-Students should be encouraged to use AI responsibly, understanding that AI tools can be misused to generate inappropriate, harmful, or misleading content. Guide students towards ethical practices while using AI; refrain from using AI to deceive others through plagiarism, spreading false information, or generating content to use against or harm another individual.

Content Filtering and Safety Measures-Lastly, students should know about the safety measures implemented by organizations such as OpenAI, including content filtering, to limit the generation of inappropriate content. However, consider how you can include students in identifying how to create a safe and inclusive learning environment when using generative AI.

Making ethics and safety a central part of AI education prepares students to use AI tools effectively and navigate the evolving digital landscape responsibly and conscientiously.

By integrating the understanding of these elements into hands-on activities, we can help students comprehend how to use ChatGPT effectively and grasp the broader principles of AI. These valuable skills will prepare students for the future, fostering an in-depth understanding of technology, promoting creativity, and encouraging critical thinking about the benefits and challenges of AI.

AI businesses and industries have outlined and implemented various safety measures like content filters to limit inappropriate or harmful output. But the system isn't perfect, and students should be aware.

Conclusion: Where Educators Can Go with Generative AI

As we forge ahead in this AI revolution, how we integrate artificial intelligence and generative AI in classrooms is not just a possibility-it's an inevitability. Using generative AI in the classroom, such as tools like ChatGPT, will empower students with a powerful and interactive learning experience. Students can become proficient and responsible AI users by understanding core concepts of generative AI and recognizing large language models' capabilities and limitations through hands-on activities.

Ensuring students grasp critical concepts such as AI hallucinations, data privacy, and AI's potential for bias is essential to fostering AI literacy. This ties directly to the Four C's of Digital Literacy-Critical Thinking, Creativity, Communication, and Collaboration-equipping students with the skills necessary to navigate and contribute to an AI-driven world.

As educators, we need to prepare our students for this future. We must equip them with the knowledge to use generative AI tools like ChatGPT effectively and understand the ethical and safety considerations surrounding them. The goal is to create a learning environment where students participate actively in the digital landscape and are not just passive consumers.
Photo at top by: Chris Chandler
Stay Updated: If you are interested in learning more about how to integrate generative AI into your classroom further, join us in our mission to rethink high school education. Subscribe to our XQ Xtra Newsletter and mailing list, where you'll get the latest news, ideas, and stories straight from the forefront of educational innovation.

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Incorporating Generative AI in High School Classrooms: A Deep Dive into Using ChatGPT and Similar Tools - XQ

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Explore how to integrate generative AI tools like ChatGPT in high school classrooms to foster digital literacy and prepare students for the future.

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Prompt Library - For Students — AI for Education

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GenAI Chatbot Prompt Library for Educators

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In or your favorite AI Chatbot, you or your students can cut and paste the following prompt to generate terms and definitions formatted so you can easily create flashcards. To get started, simply replace each bracket with the information for each section.
Flashcard Prompt
You are a diligent [GRADE LEVEL / SUBJECT] student, skilled in creating excellent study materials that help you achieve academic success. Your task is to define the following [NUMBER] terms: [INSERT TERMS]. Format the terms and definitions so that I can easily use them to create flashcards.
Example Prompt You are a diligent 9th grade Biology student, skilled in creating excellent study materials that help you achieve academic success. Your task is to define the following 10 terms: mitosis, meiosis, prophase, chromatid, metaphase, anaphase, interphase, eukaryote, telophase, cytokinesis. Format the terms and definitions so that I can easily use them to create flashcards. Additional Prompting Strategies

Have the chatbot generate terms for you first if you're not sure what words to focus on. For example, "List 15 important terms that I might need to know for a test on mitosis and meiosis."

Using these same terms, have the chatbot create other study aids to help you, such as a fill in the blank worksheet. When you're done, ask the chatbot for the answer key to check your work.

Ask the chatbot to create sample questions that you might see on the test. Answer them, and then check your work by having the chatbot provide an answer.

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Uni Lib Page with Lots of  Prompt Craft Frameworks

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Uni Lib Page with Lots of  Prompt Craft Frameworks

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LibGuides: Prompt Engineering for GenAI: Prompt frame works

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The ability to design meaningful prompts for GenAI tools is one of the core competencies required to successfully use them, and this LibGuide is designed to introduce to the basics of prompt design. By understanding how GenAI works and by exploring differ

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What happens when IA starts to be trained on AI generated data?

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Artificial intelligence (AI) prophets and newsmongers are forecasting the end of the generative AI hype, with talk of an impending catastrophic "model collapse".

But how realistic are these predictions? And what is model collapse anyway?

Discussed in 2023, but popularised more recently, "model collapse" refers to a hypothetical scenario where future AI systems get progressively dumber due to the increase of AI-generated data on the internet.

The need for data

Modern AI systems are built using machine learning. Programmers set up the underlying mathematical structure, but the actual "intelligence" comes from training the system to mimic patterns in data.

But not just any data. The current crop of generative AI systems needs high quality data, and lots of it.

To source this data, big tech companies such as OpenAI, Google, Meta and Nvidia continually scour the internet, scooping up terabytes of content to feed the machines. But since the advent of widely available and useful generative AI systems in 2022, people are increasingly uploading and sharing content that is made, in part or whole, by AI.

In 2023, researchers started wondering if they could get away with only relying on AI-created data for training, instead of human-generated data.

There are huge incentives to make this work. In addition to proliferating on the internet, AI-made content is much cheaper than human data to source. It also isn't ethically and legally questionable to collect en masse.

However, researchers found that without high-quality human data, AI systems trained on AI-made data get dumber and dumber as each model learns from the previous one. It's like a digital version of the problem of inbreeding.

This " regurgitive training " seems to lead to a reduction in the quality and diversity of model behaviour. Quality here roughly means some combination of being helpful, harmless and honest. Diversity refers to the variation in responses, and which people's cultural and social perspectives are represented in the AI outputs.

In short: by using AI systems so much, we could be polluting the very data source we need to make them useful in the first place.

Avoiding collapse

Can't big tech just filter out AI-generated content? Not really. Tech companies already spend a lot of time and money cleaning and filtering the data they scrape, with one industry insider recently sharing they sometimes discard as much as 90% of the data they initially collect for training models.

These efforts might get more demanding as the need to specifically remove AI-generated content increases. But more importantly, in the long term it will actually get harder and harder to distinguish AI content. This will make the filtering and removal of synthetic data a game of diminishing (financial) returns.

Ultimately, the research so far shows we just can't completely do away with human data. After all, it's where the "I" in AI is coming from.

Are we headed for a catastrophe?

There are hints developers are already having to work harder to source high-quality data. For instance, the documentation accompanying the GPT-4 release credited an unprecedented number of staff involved in the data-related parts of the project.

We may also be running out of new human data. Some estimates say the pool of human-generated text data might be tapped out as soon as 2026.

It's likely why OpenAI and others are racing to shore up exclusive partnerships with industry behemoths such as Shutterstock, Associated Press and NewsCorp. They own large proprietary collections of human data that aren't readily available on the public internet.

However, the prospects of catastrophic model collapse might be overstated. Most research so far looks at cases where synthetic data replaces human data. In practice, human and AI data are likely to accumulate in parallel, which reduces the likelihood of collapse.

The most likely future scenario will also see an ecosystem of somewhat diverse generative AI platforms being used to create and publish content, rather than one monolithic model. This also increases robustness against collapse.

It's a good reason for regulators to promote healthy competition by limiting monopolies in the AI sector, and to fund public interest technology development.

The real concerns

There are also more subtle risks from too much AI-made content.

A flood of synthetic content might not pose an existential threat to the progress of AI development, but it does threaten the digital public good of the (human) internet.

For instance, researchers found a 16% drop in activity on the coding website StackOverflow one year after the release of ChatGPT. This suggests AI assistance may already be reducing person-to-person interactions in some online communities.

Hyperproduction from AI-powered content farms is also making it harder to find content that isn't clickbait stuffed with advertisements.

Read more: The 'dead internet theory' makes eerie claims about an AI-run web. The truth is more sinister

It's becoming impossible to reliably distinguish between human-generated and AI-generated content. One method to remedy this would be watermarking or labelling AI-generated content, as I and many others have recently highlighted, and as reflected in recent Australian government interim legislation.

There's another risk, too. As AI-generated content becomes systematically homogeneous, we risk losing socio-cultural diversity and some groups of people could even experience cultural erasure. We urgently need cross-disciplinary research on the social and cultural challenges posed by AI systems.

Human interactions and human data are important, and we should protect them. For our own sakes, and maybe also for the sake of the possible risk of a future model collapse.

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What is 'model collapse'? An expert explains the rumours about an impending AI doom

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Generative AI needs tons of data to learn. It also generates new data. So, what happens when AI starts training on AI-made content?

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Enhancing Literary Analysis through AI - Lesson Idea

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Enhancing Literary Analysis through AI - Lesson Idea

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Enhancing Literary Analysis through AI_ A Transformative Classroom Experience _ LinkedIn

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MIT Poster on how to use AI in classroom

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MIT Poster on how to use AI in classroom

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It’s been over 12 months since the first blog post about the AI Assessment Scale, and a lot has changed, both with the technology and with our understandings of how it impacts assessments in …

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Billy Bonkers understanding AI and Issues

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Billy Bonkers understanding AI and Issues

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This Custom GPT is designed to engage middle school students in an educational and entertaining journey on AI and Machine Learning inspired by the story of 'Charlie and the Chocolate Factory.' The GPT will guide the user, Harley, through a whimsical and imaginative AI Factory by Dr Nick Jackson (Warning if you have something important to do don’t jump in yet….)

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A playful AI guide who takes students on a magical AI factory tour, explaining AI and ML with visuals.

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Dive into the world of algorithms and generative AI as you weigh the civic and social impact of these technologies.

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It’s been over 12 months since the first blog post about the AI Assessment Scale, and a lot has changed, both with the technology and with our understandings of how it impacts assessments in …

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Real or Fake? The AI Deepfake Game As Generative AI becomes more capable, it’s getting harder and harder to spot AI-generated images. Powerful image generation models like Flux (available thr…

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Fake images - historically famous photos altered

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Fake images - historically famous photos altered

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Most Australians struggle to spot what’s real and what’s fake. Here’s some help

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Every time you pick up your phone, you have to decide what’s true and what’s fake. And it’s getting harder.

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Harvard Professor Explains Algorithms in 5 Levels of Difficulty | WIRED

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Pictory - Easy Video Creation For Content Marketers

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Pictory's powerful AI enables you to create and edit professional quality videos using text, no technical skills required or software to download.