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Presentation Makers:
https://www.beautiful.ai
https://slidesgpt.com

Image creator
canva.com (go to apps, text to image)

Leveled text/information:
https://www.diffit.me/

Information gathering/fact checking, rubric making, brainstorming:  https://chat.openai.com/

Lesson Planning/UDL:
frizzle.ai

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Welcome to the world of chatbots and language models. Are you frustrated with inconsistent and unreliable responses from your chatbot? This guide will provide you with the tools to write more effective prompts for ChatGPT. By following these tips, you can improve the accuracy and usefulness of your chatbot

1. Start with a clear goal: Determine what you want the chatbot to achieve through the prompt. This could be providing information, guiding the conversation, or solving a problem. If your question is vague, your answer will be vague.

2. Keep it concise: Keep the prompt brief and to the point. Avoid using unnecessary words or information that will confuse the chatbot or delay the response. "This is a robot, not a person, so there's no need to be polite. 'Write a paragraph about X' is more efficient than 'Can you please write me a paragraph about this thing called X?' Both prompts may generate similar responses, but the latter is a waste of time. To get good output, revisions are often necessary. It's more effective to spend time giving orders to improve the work of the robot instead of being overly polite."

3. Use natural language: Write the prompt in a way that is easy to understand and uses everyday language. This makes it easier for the chatbot to understand the context and respond appropriately.

4. Avoid ambiguity: Ensure that the prompt is specific and avoids ambiguity. Avoid using words with multiple meanings or phrases that can be interpreted in different ways. If it starts generating something you don't like, hit stop, and rephrase your prompt.

5. Provide context: Provide enough context so that the chatbot understands the context of the conversation and can respond accordingly. If you have multiple instructions, but aren't sure if ChatGPT understands you, you can ask it to analyze your prompt. Like this.

6. Avoid open-ended questions: Avoid asking open-ended questions that have no clear answer. This can lead to irrelevant or confusing responses from the chatbot.

7. Use keywords: Use keywords in the prompt that are relevant to the topic of the conversation. This helps the chatbot to understand the context and respond appropriately.

8. Be specific: Be specific about the information you want the chatbot to provide. Avoid asking general questions that could have many possible answers.

9. Test your prompts: Test your prompts with the chatbot to see how it responds and make any necessary changes. This helps to refine the prompt and improve the overall experience for the user. Stop it early it you don't like the answer.

10. Keep it simple: Keep the prompts simple and easy to understand. Avoid using complex language or technical terms that may confuse the chatbot or the user.

Writing effective prompts for a chatbot requires a clear understanding of the goals, context, and language used in the conversation. By following these tips, you can improve the quality and accuracy of the chatbot's responses and enhance the overall user experience.

Some unique things you can ask Chat GPT

ChatGPT isn't just a question-and-answer bot. It can do a lot of powerful things. Here are some examples.

1. Generate creative writing: You can ask ChatGPT to generate a story, poem, or a piece of fiction.

2. Provide trivia: Ask ChatGPT for interesting facts and trivia on various subjects such as history, science, or pop culture.

3. Answer philosophical questions: Get the robot to questions about ethics, morality, or the meaning of life.

4. Generate jokes: Ask it to tell you a joke or generate humor on a specific topic.

5. Create recipes: Ask ChatGPT to suggest recipes based on ingredients or cuisine.

6. Provide language translation: Ask ChatGPT to translate a phrase or sentence into another language.

7. Play a conversation game: Ask ChatGPT to play a conversation game, such as 20 questions or the guessing game.

8. Generate personal responses: Ask ChatGPT personal questions, such as how it is feeling or what it thinks about a certain topic.

9. Offer advice: Ask ChatGPT for advice on various subjects, such as relationships, career, or personal finance.

10. Impersonate famous figures: Ask ChatGPT to impersonate famous figures, such as historical figures, celebrities, or fictional characters.

11. Fitness: Ask it to design a workout plan for a specific sport or activity. "Design a workout plan for a 200 pound 45-year-old woman with bad knees who only eats vegan food."

12. Summaries: Ask ChatGPT to summarize a long article or book into a short, concise summary.

Writing better prompts for ChatGPT requires careful consideration of the language and information provided. By following these guidelines, you can optimize your chatbot's performance and get the responses you need. Thank you for reading this guide, and we hope it helps you achieve better results with your chatbot."

Chat GPT is a highly versatile and powerful tool that can be used for a wide range of purposes. From generating creative content to answering complex questions, Chat GPT can provide quick and accurate responses. However, it's important to remember that the quality of the output depends on the quality of the prompt. A well-crafted prompt will result in a well-crafted response, so it's crucial to take the time to write a good prompt in order to unlock the full potential of Chat GPT. With the right prompt, the possibilities are endless, and the results can be truly remarkable. Thanks for reading and don't forget to follow us on Twitter.

Welcome to the world of chatbots and language models. Are you frustrated with inconsistent and unreliable responses from your chatbot? This guide will provide you with the tools to write more effective prompts for ChatGPT. By following these tips, you can improve the accuracy and usefulness of your chatbot

1. Start with a clear goal: Determine what you want the chatbot to achieve through the prompt. This could be providing information, guiding the conversation, or solving a problem. If your question is vague, your answer will be vague.

2. Keep it concise: Keep the prompt brief and to the point. Avoid using unnecessary words or information that will confuse the chatbot or delay the response. "This is a robot, not a person, so there's no need to be polite. 'Write a paragraph about X' is more efficient than 'Can you please write me a paragraph about this thing called X?' Both prompts may generate similar responses, but the latter is a waste of time. To get good output, revisions are often necessary. It's more effective to spend time giving orders to improve the work of the robot instead of being overly polite."

3. Use natural language: Write the prompt in a way that is easy to understand and uses everyday language. This makes it easier for the chatbot to understand the context and respond appropriately.

4. Avoid ambiguity: Ensure that the prompt is specific and avoids ambiguity. Avoid using words with multiple meanings or phrases that can be interpreted in different ways. If it starts generating something you don't like, hit stop, and rephrase your prompt.

5. Provide context: Provide enough context so that the chatbot understands the context of the conversation and can respond accordingly. If you have multiple instructions, but aren't sure if ChatGPT understands you, you can ask it to analyze your prompt. Like this.

6. Avoid open-ended questions: Avoid asking open-ended questions that have no clear answer. This can lead to irrelevant or confusing responses from the chatbot.

7. Use keywords: Use keywords in the prompt that are relevant to the topic of the conversation. This helps the chatbot to understand the context and respond appropriately.

8. Be specific: Be specific about the information you want the chatbot to provide. Avoid asking general questions that could have many possible answers.

9. Test your prompts: Test your prompts with the chatbot to see how it responds and make any necessary changes. This helps to refine the prompt and improve the overall experience for the user. Stop it early it you don't like the answer.

10. Keep it simple: Keep the prompts simple and easy to understand. Avoid using complex language or technical terms that may confuse the chatbot or the user.

Writing effective prompts for a chatbot requires a clear understanding of the goals, context, and language used in the conversation. By following these tips, you can improve the quality and accuracy of the chatbot's responses and enhance the overall user experience.

Some unique things you can ask Chat GPT

ChatGPT isn't just a question-and-answer bot. It can do a lot of powerful things. Here are some examples.

1. Generate creative writing: You can ask ChatGPT to generate a story, poem, or a piece of fiction.

2. Provide trivia: Ask ChatGPT for interesting facts and trivia on various subjects such as history, science, or pop culture.

3. Answer philosophical questions: Get the robot to questions about ethics, morality, or the meaning of life.

4. Generate jokes: Ask it to tell you a joke or generate humor on a specific topic.

5. Create recipes: Ask ChatGPT to suggest recipes based on ingredients or cuisine.

6. Provide language translation: Ask ChatGPT to translate a phrase or sentence into another language.

7. Play a conversation game: Ask ChatGPT to play a conversation game, such as 20 questions or the guessing game.

8. Generate personal responses: Ask ChatGPT personal questions, such as how it is feeling or what it thinks about a certain topic.

9. Offer advice: Ask ChatGPT for advice on various subjects, such as relationships, career, or personal finance.

10. Impersonate famous figures: Ask ChatGPT to impersonate famous figures, such as historical figures, celebrities, or fictional characters.

11. Fitness: Ask it to design a workout plan for a specific sport or activity. "Design a workout plan for a 200 pound 45-year-old woman with bad knees who only eats vegan food."

12. Summaries: Ask ChatGPT to summarize a long article or book into a short, concise summary.

Writing better prompts for ChatGPT requires careful consideration of the language and information provided. By following these guidelines, you can optimize your chatbot's performance and get the responses you need. Thank you for reading this guide, and we hope it helps you achieve better results with your chatbot."

Chat GPT is a highly versatile and powerful tool that can be used for a wide range of purposes. From generating creative content to answering complex questions, Chat GPT can provide quick and accurate responses. However, it's important to remember that the quality of the output depends on the quality of the prompt. A well-crafted prompt will result in a well-crafted response, so it's crucial to take the time to write a good prompt in order to unlock the full potential of Chat GPT. With the right prompt, the possibilities are endless, and the results can be truly remarkable. Thanks for reading and don't forget to follow us on Twitter.

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How to Write Better Prompts for Chat GPT - GripRoom

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Welcome to the world of chatbots and language models. Are you frustrated with inconsistent and unreliable responses from your chatbot? This guide will provide you with the tools to write more effective prompts for ChatGPT. By following these tips, you can improve the accuracy and usefulness of your

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We Can't Yet Predict How Ai Is Changing the World...

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We Can't Yet Predict How Ai Is Changing the World...

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"What do you think all of this means for the future?" a friend asks. We'd been talking about ChatGPT for nearly an hour but suddenly it starts to hit closer to home.

"I don't know," I answer.

"I mean, our kids are about to go to college. They're going to a choose a major and then what? Does the job they choose even exist in a decade?"

"It won't. I mean, it might exist in name but it's going to look totally different. There are moments when that feels scary and other times it feels exciting and then there are these other times when I think maybe AI won't change things much at all."

"I try to remind myself that this isn't even about AI," he says before taking a sip from a pint. "Our parents had the same worries. This is just what it means to have a senior whose going to go out on his own. When we went to college it wasn't AI. It was something else but I'm sure it was something."

I've written before about how the ladder is now a maze. But there's something unnerving about this shift now that my son is about the enter that maze. The hardest part about it is that we don't know how AI will change learning. We don't know how it will change thinking. We can't predict how it will change social, political, and economic systems. We simply don't know.

Listen to the Podcast

If you enjoy this blog but you'd like to listen to it on the go, just click on the audio below or subscribe via iTunes/Apple Podcasts (ideal for iOS users) or Google Play and Stitcher (ideal for Android users).

https://spencerauthor.com/wp-content/uploads/2023/02/AI-Change-World.mp3

We Are In a Stage of Moral Panic

Right now, A.I. feels scary because it's new. Science fiction is now science reality. Collectively, we have a picture of artificial intelligence forged largely by movies like Blade Runner, Battle Star Galactica, and the Terminator franchise - and perhaps a more nuanced view from Data in Star Trek.

There's an uncanny human-ness to the creative answers you receive on #chatGPT.

The knee-jerk reaction is often "how do I stop this?"

Every time we experience a new technology, we also experience a moral panic. We hear dire warnings about what the technology will destroy and how our world will change. When the bicycle was invented, newspapers predicted everything from neurological diseases to distortions of the face (so-called "bicycle faces") to psychological damages. Columnists warned of young women being addicted to bike-riding. When telephones were invented, citizens were concerned that the phones would explode. People fought against telephone polls for fear that they would cause physical harm.

But over time, the technology becomes boring.

That's right. Boring.

You can think about it as a graph with time as the independent variable and concern as the dependent variable. You can imagine it like a graph with unawareness, moral panic, acceptance, then boredom.

It starts with a general lack of awareness. In this phase, there's a mild concern, often forged by science fiction and speculation. But the technology is off in a distant future. However, once we grow aware of this new technology, there's a resistance in the form of a moral panic. Here, the new technology is scary simply because it's new. We read reactionary think pieces about all the things this technology will destroy. As we adopt the technology, the concern dissipates. We grow more comfortable with the technology and start to accept it as a part of our reality. Eventually, we fail to notice the technology and it starts to feel boring.

I experienced this on a personal level with data tracking. I hated the notion that tech companies would know my location. But now I use Life360 to check in with where family members are in the moment. What felt like an invasive app has now become commonplace. Or consider social media. I resisted Facebook for fear of identity theft. But now it's a normal part of life. Facebook is boring.

Right now we are at peak freak-out mode with AI. Schools are wrestling with how to respond to it.

But someday it will be boring. And it's at this boring phase that we need to be most concerned. If the freak-out phase is an overreaction, the boredom phase is an under reaction. It's an uncritical acceptance of technology as the tools become more normalized and eventually invisible. It's a failure to grasp the way our tools are reshaping us because they seem so . . . well . . . boring. We forget that cars are powerful because we drive them all the time. They've become boring death machines.

We Can't Predict How Artificial Intelligence Will Change Our World

I've seen bold predictions about how AI will change learning in the next few years. I've read think pieces about AI replacing teachers, ruining English class, and making us slaves to the robot overlords.

However, the hard truth is that we can't predict how technology will change our world.

AI will impact our social, political, and economic systems in ways we can't even predict. We will think and act differently. We will live differently. However, none of us can predict these changes to any degree of accuracy. Not social scientists. Not technologists. Not futurists. None of us.

We will all be surprised.

When the aforementioned bicycle was invented, few could have predicted how it would impact the women's suffrage movement. Few could have seen the ways it would connect people and spark social movements between and within cities. Instead, people were worried about "bicycle head." No one could have predicted the way the Industrial Revolution would emphasize the nuclear family, change our self-perceptions, lead to a belief in personal privacy (one I am actively eroding by voluntarily giving Life360 my location information), and spark climate change. Gutenberg could not have predicted how the printing press would lead to the rise of the nation-state and Enlightenment thinking. Glass lens manufacturers had no idea that their work would eventually lead to telescopes, science, and the rise of secularism.

More recently, people were largely concerned about "stranger danger" on social media. While that threat turned out to be overblown, few people predicted how it would impact attention spans. We hardly notice the way our perception of time changes when we scroll by relevance or the ways that numbers and gamification shapes our behaviors. This is the first era in human history to attach numbers to relationships and treat social interaction like a casino. We had no idea how strong the fear of missing out would be on youth mental health or the way it would lead to a rise of factionalism in filter bubbles. We also had no idea how powerful it would be for telling stories, for making small connections with formerly lost friends, for getting an opportunity to publish and build an audience without any gatekeepers.

Social media, like all technology, has its pros and cons. It's easy to look back and say, "How did we miss that?" But it's really hard to predict the future. We thought Facebook would be a great place to tell folks what we were eating. We didn't realize it would change democracy. The bottom line is that A.I. will impact us in significant ways. We just don't know how exactly.

Think Critically but Humbly

Instead of asking students, "How will A.I. change society?" maybe we should ask, "How is A.I. already changing your lives?" Engage in hard conversations about smart phones and attention spans - not from a curmudgeonly, reactionary, way so much as a descriptive and curious way.

We are poor predictors of the future and often oblivious of the present. But the more we think critically in the present, the better we are at anticipating to the future. The following are some critical thinking questions we might ask students to consider.

Where am I using AI without even thinking?

How does AI actually work?

How might people try to use AI to inflict harm? How might people try to use AI to benefit humanity? What happens when someone tries to use it for good but accidentally causes harm?

What does AI do well? What does it do poorly?

What are some things I would like AI to do? What is the cost in using it?

What are some things I don't want AI to do? What is the cost in avoiding it?

How am I combining AI with my own creative thoughts, ideas, and approaches?

What is the danger in treating robots like humans?

What are the potential ethical implications of AI, and how can we ensure that AI is aligned with human values? What guardrails do we need to set up for AI?

What are some ways that AI is already replacing human decision-making? What are the risks and benefits of this?

What types of biases do you see in the AI that you are using?

Who is currently benefiting and who is currently being harmed by the widespread use of AI and machine learning? How do we address systems of power?

When do you consider a work your own and when do you consider it AI-generated? When does it seem to be doing the thinking for you and when is it simply a tool?

What are some ways AI seems to work invisibly in your world? What is it powering on a regular basis?

This is simply a set of questions to start a dialogue. Initially, it was a set of 10 questions but then I went to ChatGPT and typed in the prompt "What are some critical thinking questions you could ask about artificial intelligence?" and I liked two of those questions. I then re-wrote those questions in my own words and then it prompted two more questions. I almost went to ChatGPT first but I've already noticed that when I use AI first, I end up missing out on some of my own key thoughts. In other words, even if I can use AI for ideation, I'm already realizing that I don't want to do so. I'm trying to be intentional about when and how I use this tool because it's already hitting the boredom stage for me and if I'm feeling lazy, I'm liable to outsource too much of the thinking to the machine.

Where Do We Go From Here?

I'm wandering a narrow residential street in Washington D.C. I meander through the neighborhood of narrow row houses with bold tones of purple and orange and turquoise. I'm surprised by the splashes of color in a city I typically associate with beige or gray marble. I pass through a maze of streets and catch a glimpse of the obelisk in the distance. I close my eyes and orient the Washington Monument, the White House, the Supreme Court building. I slip my hand into my pocket and hold my phone for comfort. I'm itching to search my step count. I'm curious about social media. But more than anything else, I want to know the fastest way to get to the National Mall.

I'm struck by my dependence on my smart phone - by my concerns about metrics, my need for notifications, and my desire to capture this very Instagrammable neighborhood and share it as an Instagram story. I'm struck by my struggles to navigate a city without any map app. Spatial reasoning has always been hard for me but it's gotten worse in the last decade.

But this is precisely why I am exploring the city with my phone in my pocket. True, a podcast could keep me entertained. Yes, the map app could show me a faster route. But there's something almost magical about exploring a city by foot. There's an almost game-like quality of finding landmarks and memorizing street names. I know it sounds odd but there's an almost meditative aspect of going on a walk in a new place with no direction, no map, and no distraction. Just pure exploration, breathing in the cold air and absorbing the colors and sounds of the city.

These walks help me realize that thinking is a habit. If I don't engage in spatial thinking or deep work or memorization, that type of thinking seems to atrophy for me. Over the last few years, I've been intentional about doing the very things that technology can do faster. I go on walks without a map app. I engage in mental math instead of always pulling out the calculator app. I sketchnote my thoughts by hand and write in a journal. I memorize old sacred verses and poems even though I could look them up in seconds.

I do this because all of these things are enjoyable. But I also do these things because I want to be aware of the ways that a tool like a smart phone is changing the way I think and act. Every time I do something with my inaccurate, clunky, messy human mind, I become more aware of how technology is shaping my world in both positive and negative ways. In these moments, the technology becomes visible again and I can move out of the boredom and into a place of both criticism and appreciation.

But it's than that. Technology can always do things faster and more efficiently. But we can do things differently. That meandering stroll through a neighborhood might be slower but it's also more beautiful. Similarly, you can buy a machine-made blanket at Wal-Mart with no mistakes. But I will cherish that blanket a friend crocheted for my daughter before she came home from the hospital even if it does contain an imperfection. It's those imperfections that make us human and it's our humanity that make a work of art unique.

Students will need to become really good at what AI can't do and really different with what it can do. So, if we want them to think critically about AI, we need them to continue to use it but also learn how to avoid it and find different ways to do the very things AI does well. This might mean using AI for writing but also choosing to write something entirely from scratch. It might mean ideating with AI but also skipping out on the AI and ideating with sticky notes. It might mean using Apple Car Play to navigate from the airport to downtown but then wandering around letting the city surprise us with its sights and sounds and smells.

AI will always be vanilla but your unique voice will be what makes you human. This is probably not the best metaphor, but AI will be vanilla and our students will offer their own unique flavors.

In the end, we don't know how AI will change learning. We can't predict how it will change our world. But one of the best things we can do is help each student find their voice and retain their sense of humanity. We can ask them to ask hard questions about how technology is changing their world. We can encourage them to embrace all of their imperfections and humbly recognize our shared humanity. In the end, if we want to understand artificial intelligence, we are first going to need to understand ourselves.

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We Can't Predict How A.I. Will Change Learning #ChatGPT

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"What do you think all of this means for the future?" a friend asks. We'd been talking about ChatGPT for nearly an hour but suddenly it starts to hit closer to home. "I don't know," I answer. "I mean, our kids are about to go to college.

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Don't forget-these platforms are not secure and are not EdLaw2D Compliant!

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This is a good thing.

You might also notice that many "experts" on AI are emerging, but no one can truly know the full potential of what AI will bring to our world. Many people sharing strategies aren't necessarily experts in AI, as much as they probably are just great at learning and becoming adaptable to new opportunities for learning. That is an essential skill that takes time and energy to develop.

But as quickly as ChatGPT has arrived, one step behind was blocking it within education and in schools.

About four seconds after the winter break was completed in January, a friend of mine posted this on his Instagram story:

Many decisions for what is blocked in education come from a lack of information, not an abundance. It is easy to say, "There is no value for 'this' in school," which can often be a disguise for, "I don't understand this new technology, so let's just say it is bad, or pretend it doesn't exist."

But this technology does exist.

Is our focus on kids becoming great learners or becoming good at school?

Things like ChatGPT can have tremendous opportunities for learning and growth, but we have to learn them ourselves to see the potential for what this type of technology can do for learning in education.

Too often, we try to skip to the "teaching" without doing the "learning."

And I am not talking about "learning how to teach" a new tool or idea. I am talking about how to see the benefits and uses of new technology in our own lives and connect those ideas to the different disciplines and content we currently teach.

What I want to try to do today is share with you some ideas that may help with using ChatGPT with your staff for professional learning and ideas to make connections to how it can be used in the classroom. Please feel free to modify and adjust as needed to fit the needs of those you serve.

Starting Point - What are AI and ChatGPT, and how is it different from Google?

I was thinking of how I would answer this question and went into ChatGPT and asked, "What is the difference between ChatGPT and Google?"

This might not have been the best question to ask because this was the answer I received:

"ChatGPT is a language model developed by OpenAI, while Google is a search engine and technology company. ChatGPT is a model that can generate human-like text based on a given prompt, while Google's services include search, email, cloud storage, and online advertising, among others."

So maybe, this wasn't the best question to ask. So I tried again and asked the following: How can you use ChatGPT, and how does it differ from google search?

Already, the use of this program is forcing me to ask better questions. That is a good thing.

This is the response it gave me:

That is a better answer because I asked a better question.

But the best answer I have heard so far on this topic is from a video by Jaspreet Singh on "5 Ways You Build Wealth With ChatGPT." He says the following:

"What is artificial intelligence and ChatGPT?

Aritificial Intelligence, otherwise known as AI is a digital brain it's a separate brain that can now process and analyze information. You can compare this to the technology that we have now where when you ask something a question like Google, Google will then give you a place where you can find the answer to the question, so if you go to Google and you search "how do I start a popular YouTube channel?," Google will then show you a number of different blog posts where it believes that you can find the answer to your question. Versus artificial intelligence, reads through all the articles on the internet, and then it provides an answer to your question.

ChatGPT is the first extension of artificial intelligence that's available to everybody free of cost. The way that ChatGPT works is you can ask ChatGPT a question, and it will search the Internet and then process all the information that's out there and then spit out an answer for you."

Here is a one-minute clip sharing this and a bit more information as a great starting point for conversation.

As I watched this whole video, I thought about how powerful it would be to watch it in its entirety with staff to start a discussion on this topic. It is focused on the following five questions, and I think there are a lot of takeaways directly connected to education. Jaspreet focused on the five following questions in the video:

Inspired by Jaspreet's video, I thought about four questions that would be great for conversation and exploration to lead during a professional learning day.

Four Questions to Consider on using ChatGPT in Education
1. What are some of the negatives and positives of ChatGPT?
This is a great way to start the conversation by discussing all of the negative implications of this type of technology.

Some of the issues that I have heard already is that the platform is web-based, you have to use an email account to sign up, and it is "free" for now (there's a saying that, "if something is free right now, you are the product!" which is a concern!), amongst other things. Not all of these things can be addressed, but I do think we must have the opportunity to share these and other worries.

One of the many concerns is that this type of technology could lead to "cheating" and students just having ChatGPT write essays for students.

I understand that concern.

For years, I have shared in my presentations that "if you can google the answer to the question on the test, the question is probably not that good in the first place."

It is not that students shouldn't have basic knowledge but that they go beyond those basics. Are we focused on students simply memorizing information or developing wisdom?

One of my favorite quotes on the subject is the following:

Wisdom and deep understanding should be the goal.

So let's return to the original statement; "if you can google the answer to the question on the test, the question is probably not that good in the first place."

Thinking about this question in the context of ChatGPT and writing an essay, if AI can write the essay, are we having students make their own connections and create a deep understanding, or are they giving answers that lack "humanness" in the response?

This "negative" can be turned into an opportunity to revisit what we ask of students and how they share a deep understanding of what they have learned.
2. What are some ways that ChatGPT can be used in our everyday lives?
Before jumping into how to use ChatGPT in education, I think it is a great idea to see the value of it in our own lives.

I saw this TikTok on utilizing ChatGPT in making a dinner menu for kids who are picky eaters, and it was pretty compelling:

@zoltronic
ChatGPT is going to change the world very quickly. #ai #nneaurallink #chatgpt #gpt3 #greenscreenvideo #ai #openai #kosher #naturallanguageprocessing #bot #robot #future

♬ Monkeys Spinning Monkeys - Kevin MacLeod & Kevin The Monkey

Seeing this video, I thought about how I could use this in my own life.

I wanted to train for a half-marathon, so I wanted a schedule. Too often, when I would do an Internet search for training schedules, I would bump into paywalls, hidden documents, or just a bevy of advertisements.

I decided to ask ChatGPT for a half-marathon training schedule for 16 weeks, but only list the last eight weeks of training.

The schedule was pretty good, but I wanted the long runs to be on Sunday and to list all lengths in kilometers instead of miles. It then spits out a schedule, which I then ask to put into a table form, and I have been using it ever since (I am on week 13, and it has been going great!).

What is critical in this scenario is that I had to have some training knowledge (I have run a few marathons before this) to understand if the schedule provided is on the right track. But this process took me about four minutes, versus the HOURS of searching online for something that might fit my criteria.

If I see the value of this type of technology in my everyday life, then it might fit into what I do in my work and classroom.
3. How can we use ChatGPT in our current roles to make our lives easier and improve learning?
As quickly as ChatGPT was blocked in many schools, I also saw many educators sharing ideas on how they could use it to make their work more manageable and, as Jaspreet shared, use this as a "second brain."

Tyler Tarver shared this video on how teachers could use ChatGPT to help build a lesson plan:

What I appreciate about Tarver's video is that he uses the technology to give ideas or even outlines, but identifies the necessity of the teacher knowing the students in the classroom and making modifications based on their needs, as ChatGPT would not be able to do that.
(It was also interesting to see how so many different ideas are being shared on utilizing ChatGPT to save time through creating, like this video on mass-creating content using Canva and Google Sheets.)
In my podcast creation, I have recently done some book reviews and shared my thoughts and insights into what I have learned. As I have been putting together the outlines for the podcast, I have been using ChatGPT to write quick summaries of the books that I can read out.

For example, in a book review of "The Four Agreements" by Don Miguel Ruiz, I asked ChatGPT to do the following:

"Give me a Twitter thread summary of the four agreements in 10 tweets or less."

I used what it shared and, with some modifications, read them in my podcast and provided a reference that I utilized ChatGPT to garner that information.

So how do you know I read the book?

Because I went beyond that summary and broke down specific quotes, what they meant to me, and how I connected the ideas to my own learning.

Through the process, I wanted to model that using this technology for my learning was a time-saver, but I had to go beyond. It saved me a ton of time writing something that would be almost generic sounding and provided more time for deep reflection.

In my opinion, that was time well spent.
4. How can we help students utilize ChatGPT for their learning in and outside of school?
Now that people have spent time seeing the value of something like ChatGPT for their learning, THEN it is time for a conversation on ways that we could have students use this in their learning, in and out of the classroom.

In "Innovate Inside the Box," I shared the following:

If people have sufficient time and access to play around with their learning, they will more likely see how it can be implemented and guided for student learning. It is essential to default to the expertise of your staff in this situation, but provide them with time to a) use the technology themselves and b) have time to explore with their colleagues and team on how they would suggest using it in their own disciplines or roles.

In " The Innovator's Mindset," I wrote the following:

The same is true with adults. I

If it is vital to helping our students, then providing time to explore and create during professional learning opportunities is necessary.

When Khan Academy first came out years ago, many worried it would "replace teachers." I never had that concern.

My thought was if those videos are simply a replication of how people teach, then we should be concerned. But school is more than content and information. Khan Academy never replaced teachers, but it became a supplemental resource in many cases. That is how I see the potential for ChatGPT as a resource or "second brain" that can be utilized but never something that will replace great teachers.

That being said, if time and guidance are not provided for professional learning, this will be seen as a constant fight instead of an opportunity to be embraced.

Never in the history of ever has something gotten better by ignoring it, only by learning and growing to move forward.

In a time of artificial intelligence, wisdom will become more critical than ever. But that wisdom takes time to grow and develop.

For our students and for us.

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Four Questions to Consider on using ChatGPT in Education

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This is a good thing. You might also notice that many "experts" on AI are emerging, but no one can truly know the full potential of what AI will bring to our world. Many people sharing strategies aren't necessarily experts in AI, as much as they probably are just great at learning and becoming adaptable to new opportunities for learning.

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Craft specific prompts with the criteria you are looking for!

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ChatGPT is an AI-powered language model developed by OpenAI, capable of generating human-like text based on context and past conversations.

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includes citations, still in beta

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Now, we're wondering ...

Calculators.
Search engines.
Google Translate.
Wikipedia.
PhotoMath.#ChatGPT.

They disrupt traditional teaching.

We adapt. Education evolves forward.

Blocking them isn't the answer.

Making education more relevant is.
- Matt Miller (@jmattmiller) December 14, 2022

Jump straight to a section (click a button below):

⭐️ Add thoughts, ideas, resources in the comments at the bottom! ⭐️

Define: ChatGPT, GPT3, chatbots, and artificial intelligence.

These are some of the important things to understand as we wrap our brains around what this is and how to navigate it in the classroom.

I'll do plenty of my own human thinking and reasoning, but for the purposes of information, I'm going to let ChatGPT give us working definitions to use:

1. ChatGPT, what are you?

I am an artificial intelligence assistant trained by OpenAI to help answer questions and provide information on a wide variety of topics. I am not a physical being, but rather a program that is designed to process and generate text based on the input I receive. My primary function is to assist users like you by providing information and answering questions to the best of my ability. I have been designed to have a conversational style and can understand and respond to natural language input. I do not have personal experiences or feelings, and my responses are based solely on the information and knowledge that has been programmed into me. Is there anything else you would like to know?

2. What is GPT3?

GPT-3 (Generative Pre-trained Transformer 3) is a language generation model developed by OpenAI. It is a neural network-based machine learning model that is trained on a large dataset of text data to generate human-like text. GPT-3 has the ability to perform a wide range of language tasks, including translation, summarization, question answering, and text generation. It can be fine-tuned on specific tasks or used to generate text in a more general sense. GPT-3 has received a lot of attention due to its impressive performance on various language tasks and its ability to generate human-like text.

3. What is a chatbot?

A chatbot is a computer program designed to simulate conversation with human users, especially over the Internet. Chatbots are often used in customer service, marketing, and other areas where being able to communicate with customers or users in a natural, human-like way is important. Chatbots can be programmed to understand and respond to specific commands or questions, or they can be more general and able to hold a conversation with users on a wide range of topics. They can be integrated into websites, messaging apps, and other digital platforms, and they can be programmed to use natural language processing (NLP) techniques to understand and respond to user input. Chatbots can be very useful for automating tasks and providing quick responses to frequently asked questions, but they are not as advanced as some other artificial intelligence (AI) systems and may not be able to hold more complex or nuanced conversations.

4. What is artificial intelligence?

Artificial intelligence (AI) refers to the ability of a computer or machine to perform tasks that would normally require human intelligence, such as learning, problem-solving, decision-making, and more. There are several different approaches to building AI systems, including machine learning, where a system is trained on a dataset and can improve its performance over time, and rule-based systems, where the system follows a set of predetermined rules to make decisions. AI can be applied to a wide range of areas, including natural language processing, image and video recognition, robotics, and more. The goal of AI research is to create systems that can perform tasks at least as well as, or ideally better than, humans. AI has the potential to revolutionize many industries and transform the way we live and work, but it also raises important ethical and social questions.

What are the implications for the classroom?

As soon as the genie was out of the lamp, so to speak, there were effects of ChatGPT and AI on teaching and learning. Whether we felt those impacts right away or not, as soon as it was released, things have the potential to change. Below, we'll talk about ChatGPT in particular, but know that this is just the first one ... and we're thinking long-term about new products to come later.

1. Students can use ChatGPT to do their assignments.

There. I'll just say it clearly and plainly since we're all thinking it. For lots of assignment types (not all of them!), students can ask ChatGPT to do the work for them. They can copy/paste the work into a document or your learning management system and submit it.

2. TurnItIn and other plagiarism checkers can't catch it.

What ChatGPT creates is an original work each time you ask it. It creates something new every time you ask, and its responses are not in the databases of plagiarism checkers. ChatGPT doesn't keep a log of responses it creates, and even if it did, I'm thinking OpenAI (creator of ChatGPT) has no incentive to make all of that available to plagiarism checkers. (Even if it did, the processing power necessary to query that database would be enormous.)

Some people will, undoubtedly, try to find ChatGPT detectors to track down students using it (in a similar way to plagiarism checkers). You may find them. I found one on social media called GPT-2 Output Detector Demo. It's a demo, so it's not a finished product. It's based on GPT2 technology (which is a predecessor to the GPT3 technology that ChatGPT is using as of the writing of this post).

Also, it can be wildly inaccurate. I copied a blog post I wrote into it and it identified it as 92.54% chance of being real. I copied something out of ChatGPT and it identified it as 99.98% chance of being fake. ("Fake," being the term for "written by a bot.") A poem ChatGPT created for a friend, however, was flagged as real.

Beware of using detectors as your way of dealing with this.

They're not reliable. And this approach isn't the long-term solution to helping your students become modern learners.

3. Schools and districts will try to block it, but that won't stop much.

Immediately, IT admins are going to have to decide if they want to block ChatGPT with their internet firewalls. And lots of them will indeed block it. If they do, I hope they don't think that they've solved the "problem" they think they have on their hands.

First of all, blocking ChatGPT on the school's network won't do much. Sure, students won't be able to access it with their school devices on the school network. But did you know that students have cell phones? Phones that operate on a cellular network that the school can't control? They can run to the bathroom, sit in a stall, ask ChatGPT for answers, copy them to a document, come back to class, and transfer the text on that document into their assignment and turn it in. (They can do that at home, too. Or on the bus. Or quietly under the desk in the classroom.)

Whether to block ChatGPT school-wide is still a decision schools and districts have to make.
The bigger decision is: what will teaching and learning look like going forward now that this technology is available?
(Check out the section below called, "Should we block it or try to ban it?")

4. ChatGPT and forthcoming chatbots like it have their limitations.

When you load ChatGPT for the first time, it even tells you what some of its limitations are. Here are a few that it states -- and some others to be aware of for schools/classrooms:

It may occasionally generate incorrect information. As of publication of this post, it's still in "free research preview." That means it's an imperfect product that they're trying to improve.

It may occasionally produce harmful instructions or biased content. Any artificial intelligence is as good as the dataset it's drawing from -- and how it uses it. It has to make judgment calls just like humans do. Bias exists in judgments. It's still learning how to work with and eliminate its bias.

It has limited knowledge of world and events after 2021. Think of its dataset in a sealed box. It isn't drawing from current events. It can't pull anything from social media. It can't even search the active internet.

It won't make ethical decisions for you. I've asked it a few things where it has to make decisions based on morals, ethics and personal values. It has declined, explaining that that's beyond the scope of what it's trained to do.

I've heard that it struggles to do math sometimes. I admit this is a really, really subjective thing to include with very little to back it up. I keep seeing posts on social media about this. Take this for what it's worth to you.

5. School/class uses of ChatGPT, chatbots, and AI aren't all bad.

Lots of teachers are making judgment calls about the use of this new technology, and many are concerned about its immediate impacts. Here's the truth: it'll make lots of teachers reconsider some types of assignments they'll assign going forward. Hey, they don't want to know how a bot will respond to their prompts, questions, and assignments. They want to use it to gauge learning in a human being. It makes sense.

But let's put it this way. There are some really, really mindless, terrible writing prompts out there ... and math worksheets ... and project assignments. If we're looking long-term, this technology will eventually start to push some of those terrible assignments out and force us to come up with something new. It'll probably be painful, and many of us will probably hate parts of the process. But in the end, we will evolve to something better.j

Also -- just like we did with the calculator, Wikipedia and Google searches -- students and teachers will find productive, meaningful ways to use this new technology for teaching and learning. (More on that below.)

6. Students will have to learn how to navigate life with AI.

Big picture, this is a shift students will deal with for the rest of their lives. Artificial intelligence will continue to get better and better. This version of ChatGPT is the weakest, most rudimentary artificial intelligence of its kind our students will ever use. My friend Holly Clark called it the "Commodore 64 of AI" -- a technology that was revolutionary at the time but is now seen as antiquated and obsolete.

Students will have to wrestle with questions of humanity -- what sets us apart as humans? When should AI be used, and when shouldn't it be?

They'll wrestle with questions of obsolescence -- what can I do that AI can't do? How can I do my best to be sure my work, my passions, my place in this world isn't replaced by artificial intelligence?

They'll wrestle with ethical questions -- how can AI use be fair, equitable, unbiased, good? What happens if it gets in the hands of bad actors? How can I make sure I'm using it in an ethical way?

How do I teach class tomorrow knowing this exists?

Now we know there's an AI product that students can use to do their assignments. And we can't block it. And maybe we feel powerless.

What do we do tomorrow? How do we write lesson plans knowing that this exists -- so that students continue learning right now and don't just turn in answers from a bot?

Consider this your "break glass in case of emergency" section.

1. There's no shame in using paper and pencil -- for now.

I don't think this is the right answer long-term. But in this section of this article with the green background, we're talking about how to weather the storm TODAY until we understand what to do next.

If we close up the computers and ask students to write something on paper, we know it's being generated by their own brains. If you have a crucial essay that needs to be written -- and you're not ready to re-imagine the education system and innovate teaching today -- this can help you survive tomorrow.

But please ... please, please, please ... please do not plan for the long-term like this. This AI technology isn't going away, and its effects will be sprawling and widespread.

2. Make use of collaborative learning and discussions.

When Google Translate started growing, it made people ask questions like we are with ChatGPT. Do I need to learn a language? Then, we learned, if we want to have a true conversation with another human and build relationships, we can't do that effectively by typing everything into Translate. (Even though 90 Day Fiance tried to prove otherwise.)

When students discuss, they do so from their own working and long-term memory. Sure, they can look up quick answers, but to carry on a conversation, most of the work comes from their own thinking. After a discussion, students can recap the discussion and share their reflections about it ... and that's much harder to do with a bot.

Collaborative learning can have these kinds of benefits, too. When students work together, even if they're pulling information from Google or Wikipedia or ChatGPT, they're still talking about what they got, if it fits, how to organize it, how to communicate it effectively, etc.

3. Have students make creative demonstrations of learning.

There's so much benefit from classroom creativity that goes beyond trying to beat an AI chatbot. Creativity in learning can help students with motivation. It can help them make use of their own unique talents and skills. It can help them feel seen and heard and noticed.

When students take what they've learned -- or information they've found from other sources -- they're engaging in elaboration. And that's a good thing.

"Elaboration in learning involves meaning-enhancing additions, constructions, or generations that improve one's memory for what is being learned." ( via ScienceDirect)

Take what you've learned (or gathered). Organize it. Make something with it. And while you do that, you're better understanding what you have.

4. Use retrieval practice, brain dumps, and other memory strategies.

Retrieval practice is the idea of strengthening your long-term memory by recalling everything you can about a specific topic. It seems too easy and too good to be true, but it's been backed by cognitive science for decades. This is a study strategy that helps students strengthen what they've already learned and reflect on what they are learning.

Students can do a "brain dump," where they recall (on paper, verbally, in a document, etc.) everything they know about topics like:

What do you remember from class yesterday?

What do you remember about (this topic) we studied a week ago?

What do you remember about (this topic) we studied last semester?

What do you know about (this topic) we haven't studied yet?

What do you know about (this person)?

What do you know about (this very broad topic)?

What do you know about (this narrow subtopic)?

Brain science (much of which can be found at RetrievalPractice.org) tells us at that these brain dumps build long-term memory the best when they are ungraded. They're a study skill, not a formative assessment.

These memory strategies focus on what students have already learned, and they're pulled from student memory -- not from an AI bot.

If teachers aren't ready to embrace AI, how can they teach without students using AI to cheat? It is important for teachers to be aware of the potential for students to use AI to cheat and to take steps to prevent it. One way to do this is to use methods of assessment that cannot be easily cheated, such as open-ended questions or projects that require original thought and creativity. Teachers can also use tools such as plagiarism detection software to check for copied content in student work. In addition to preventing cheating, it is also important for teachers to educate their students about the appropriate and ethical use of AI. This can include discussing the potential risks and consequences of using AI to cheat, as well as the importance of honesty and integrity in academic work. Ultimately, it is important for teachers to stay up to date with the latest technologies, including AI, and to be proactive in addressing any potential issues that may arise in the classroom. This may require teachers to seek professional development opportunities or to collaborate with others who are more familiar with AI.
Source: ChatGPT via chat.openai.com (2022)

Can I use this as a tool for teaching and learning?

The answer here, I believe is: ABSOLUTELY. The longer that AI and tools like ChatGPT are around, the more that teachers will try new teaching and learning strategies with it.

The more that teachers try new things and share them widely, the more that the educator community will move forward with new ideas. In short: we will figure this out together, even though it will take some time.

But ... what will it look like?

Here are some ideas, brainstormed by me (a human), by others (with citations provided) and by the bot (because, why not ask it too?).

ChatGPT doesn't just write your students' essays for you.

20 ways ChatGPT can help you teach/learn

🧵 /startthread

1. Ask ChatGPT to write your lesson plans. (Or at least to get some new ideas.)
- Matt Miller (@jmattmiller) December 19, 2022

1. Use it as a more complex, nuanced source of information than Google.

During conversations with students and in-class discussions, we can ask ChatGPT (and other tools like it) to provide us the information we need to drive a conversation forward. The bot's response isn't the first and last word in the conversation. Rather, it just provides details that we can use to discuss a topic.

2. Use it to provide students access to lots of good examples.

Think about how musicians and artists develop their style. They copy their mentors. They take their favorite influences and emulate them. Artists take a canvas to an art gallery or the park, and they paint what they see.

I keep remembering this quote from fashion designer Yohji Yamamoto, which I learned about through Austin Kleon's book, Steal Like an Artist:

"Start copying what you love. Copy copy copy copy. At the end of the copy you will find yourself." -- Yohji Yamamoto

This is also the benefit of being well-read. When students see enough good examples, they start to know what to expect of good writing. Good reasoning. Good logic. They see the hallmarks of it and they can emulate it ... much like a writer or painter emulates the greats.

Artificial intelligence can give students unlimited good examples. (Note that I didn't say "great" examples, but "good.") Ask it for an essay about a certain topic. Then ask it again and it'll give you something different. Then ask it again. Then ask for a slightly different version, specifying something you'd like to see.

When it's time for the student to create something of their own, much like an artist or musician studying the greats, the student will draw from the examples they've seen.

3. Use it to remix student work.

Students create something for class: a story, an essay, a poem, a recap of something they've learned. Then, they can ask ChatGPT (or a similar tool) to remix it for them.

My favorite example to date of this comes from Donnie Piercey, a fifth grade teacher and the 2021 Kentucky Teacher of the Year. He took a student story and asked ChatGPT to remix it as a nursery rhyme, a soap opera, a sea shanty, and a children's book -- with suggested illustrations! Listen as he tells the story in the video below.

When students see how their work has been remixed, it can open them up to new opportunities.

4. Ask it for definitions (on a variety of levels).

If you need a concept explained or defined, ChatGPT (or a similar tool) can provide that explanation -- and do it in as detailed or simple a manner as you'd like.

On episode 99 of the Partial Credit Podcast, Jesse Lubinsky shared that he asked for a definition of "faith" in terms a child would understand. That helped me realize that it can give definitions, descriptions, and explanations -- we expected that -- or it can level them up or down in complexity however we ask.

5. Ask it for feedback for student work.

We know that one key aspect to effective feedback is that it's timely. With traditional paper-based work, students turn it in and must wait for teachers to grade it -- and return it! -- before they can see feedback. Lots of edtech tools are letting us get feedback to students more quickly -- whether automated or through direct communication with a teacher or classmate.

ChatGPT (or a similar tool) can be another source of feedback. When students finish a written work, they can copy/paste their work into ChatGPT and ask how they can improve their writing. For example, I copied one of my blog posts into ChatGPT and simply asked how I could improve it. It gave me a list of five suggestions, including transitional phrases, adding more examples, and breaking it up into shorter paragraphs. (Better start a new paragraph now ...)

Of course, it's up to the student -- and based on what the student has learned about writing -- to decide whether to implement the advice and how.

6. Ask it to do some teacher tasks for you.

Ask ChatGPT (or a similar tool) to write some lesson plans for you. Or some questions for a quiz. Ask it for some jokes about your content so you can slip those into class. Of course, you don't have to use everything that it provides you verbatim. But it might give you a starting point you can modify -- or if you already have something, it can give you ideas to improve.

7. Add it to the "think pair share" thinking routine.

Augmenting the think-pair-share with ChatGPT could be one of the biggest tech-enabled leaps in pedagogy:
* Think
* Pair
* ChatGPT
* Pair
* Share
- Sarah Dillard (@dillardsarah) December 5, 2022

I love this line of thinking from Sarah Dillard (Twitter: @dillardsarah), and if you click on it and look at the whole thread, she explains it more. Students think about a prompt, then discuss it with a classmate. Then they do any searches they want on ChatGPT. They pair back up to discuss what they've found. Then they share with the class.

Using ChatGPT and AI doesn't have to be THE way we teach certain things, but it can add important steps to existing frameworks.

8. Grade the bot.

Jen Giffen (Twitter: @virtualgiff) talked with her high school-aged students about the use of AI and tools like ChatGPT. (We talked on the phone. You'll see that the phone call sparked lots of ideas!) After that conversation, she suggested this idea:

Give a writing prompt to ChatGPT (or a similar tool). It could be a different writing prompt than you'll give your students. Read the essay that the bot creates for you.

Then, give the students the rubric you'll use to grade them and ask them to grade the response from the artificial intelligence.

This helps students to be reflective about the grading process -- and about their own writing process. When students get grades on their own work, it's easy to take it personally -- or to dismiss it because it makes them feel like they've messed up a lot. But when they critique the work of a bot that doesn't have feelings, it eliminates a lot of those emotions.

9. Debate the bot.

This came from that conversation with Jen, too, and she said she's seen this one shared on social media a lot.

Let students (as individuals, in pairs or small groups, or even as a whole-group activity) debate ChatGPT (or a similar tool).

Students take one side of a debate. The AI takes the other. Students plug their points in and ask the bot to rebut.

It gives them low-stakes practice -- and as much practice as they want -- to hone their debate skills. Plus, they may see talking points for their own stance (or that their opponent may use) so they can strengthen their argument.

10. Ask the bot for advice.

One more takeaway from this conversation with Jen! (Although several of the ideas below were sparked during that conversation ... so, you know, credit where credit is due.)

Jen asked ChatGPT very generally, "Give me some advice." It gave her some general (yet very good) ideas on managing stress and relationships and mental wellbeing.

A bot can't be a replacement for counseling or medical help. But we do take advice from friends, family ... even strangers sometimes. If students are struggling with just about anything, they can get advice from this artificial intelligence -- an AI that's working with much of the entirety of the internet as its dataset.

11. Use it to summarize texts.

If a text that your students are reading is in the dataset of ChatGPT (or a similar tool), they could always try to ask it for a summary of the whole thing -- or of certain chapters, sections, acts, scenes, etc. This is what we used SparkNotes and Cliff's Notes for years ago. Of course, SparkNotes and Cliff's Notes were controversial in the past -- "if they read those, they'll never read the text!" -- but eventually we found ways that they could support learning, too.

Seeing a summary of a text that you've just read can help you process what you've read. It's similar in nature to a book club. You hear what other people think and observe about the text and it helps inform your view of it.

This could also be a helpful strategy for reviewing before a quiz or a test.

12. Use it for insight into big, difficult-to-solve problems.

Before you read this one, just remember ... I taught high school Spanish for 10+ years. I'm not a math or science teacher, and I kind of struggled with those during school. That said ...

If you -- or your students -- come up with a math or science problem bigger than you know how to solve (or don't want to take the time to solve), ask ChatGPT (or a similar tool).

If it can solve it, it'll show you an answer. But, more importantly, you'll gain some insight into the process of solving it. And even if it can't solve it, it'll tell you why.

You could also ask it bigger societal questions to see what it would say. I asked it how to solve world hunger, and here's what I got:

Eliminating world hunger is a complex and multifaceted challenge that requires the efforts of governments, international organizations, and individuals around the world. Some strategies that have been proposed to address this issue include:

1. Increasing agricultural productivity: This can be done through the use of advanced technologies such as precision farming and genetically modified crops, as well as by improving infrastructure and access to education and training for farmers.

2. Reducing food waste: Up to one-third of all food produced in the world is lost or wasted, which represents a significant opportunity to increase food availability. Reducing food waste can be achieved through better storage and transportation systems, as well as by increasing awareness about the issue.

3. Improving distribution systems: In many cases, food is available, but it is not distributed effectively to those who need it most. Improving distribution systems, including supply chains and transportation networks, can help get food to the people who need it.

4. Promoting economic development: Economic growth can lead to increased food production and greater access to food, as well as the resources and infrastructure needed to address food insecurity.

5. Providing emergency food aid: In times of crisis, such as during natural disasters or armed conflict, providing emergency food aid can help prevent starvation and malnutrition.

Ultimately, addressing world hunger will require a combination of these and other approaches, as well as strong political will and sustained commitment from governments and other organizations.
Source: ChatGPT via chat.openai.com (2022)

14. Anticipate the response you'd expect from AI.

If your students have been studying a topic, this could be a great retrieval / brain dump activity -- and a way of ranking the importance of all the things they've learned about the topic.

Tell them that you're going to ask ChatGPT (or a similar tool) to summarize what they've been studying. Ask them what they think the bot will say. It'll prompt them to recall knowledge from their memory (which strengthens long-term memory). Plus, when they rank them in importance (because the bot won't list everything it knows), they'll have to give a justification.

All of this leads to deeper understanding of the material -- and strengthened long-term memory.

15. Take several responses and make a better product.

ChatGPT often has a button at the bottom of a response that says "regenerate response." That's because each time you ask it a question, it generates something original, doing the work from scratch every time.

If you were asked to pick a movie as your favorite, describe it, and tell why you like it in 60 seconds, your response each time would be a little different. (For me, the movie would probably be different every time!) It's an original, unique work each time. ChatGPT works kind of like that every time.

Ask it to answer a question or write something for you. Then ask it again ... and again ... and again. Get five (or more!) versions of the same prompt.

Have students (individually, in pairs/small groups, or even ask a class) take the best parts from those versions and turn it into a better final product.

This can be a scaffolding strategy for students who struggle to write essays or papers. It introduces them to some of the strategy of crafting a written work without all of the taxing effort of doing the whole thing.

16. Create personalized learning experiences.
Response from ChatGPT to "How can AI be used to help teach in the classroom?" AI can be used to create personalized learning experiences for students by adapting the content and pace of instruction to each student's needs and abilities. This can be achieved through the use of adaptive learning software, which adjusts the material based on the student's performance.
Source: ChatGPT via chat.openai.com (2022)

17. Provide tutoring or coaching.
Response from ChatGPT to "How can AI be used to help teach in the classroom?" AI can be used to provide one-on-one tutoring or coaching to students, either in person or remotely. This can be particularly useful for students who may not have access to human tutors, or for subjects that are difficult to teach in a traditional classroom setting.
Source: ChatGPT via chat.openai.com (2022)

18. Generate prompts and questions to facilitate discussions.
Response from ChatGPT to "How can you (ChatGPT) be used to help teach in the classroom?" I can be used to facilitate discussions among students by posing questions and prompts that encourage critical thinking and encourage students to engage with the material in a deeper way.
Source: ChatGPT via chat.openai.com (2022)

19. Provide information and answer questions.
Response from ChatGPT to "How can you (ChatGPT) be used to help teach in the classroom?" I can be used to provide students with information on a wide range of topics, and to answer their questions on those topics. This can be particularly useful for subjects that may be difficult to understand or that require a lot of background knowledge.
Source: ChatGPT via chat.openai.com (2022)

20. Supplement in-person instruction.
Response from ChatGPT to "How can you (ChatGPT) be used to help teach in the classroom?" I can be used to supplement in-person instruction by providing additional resources and materials for students to explore on their own, or by offering additional support and assistance outside of the classroom.
Source: ChatGPT via chat.openai.com (2022)

Should we block it or try to ban it?

If we block ChatGPT (or tools like it) and think we're solving a problem, we're deluding ourselves.

There are two decisions that need to be addressed since ChatGPT was released.

A big, systemic decision that needs to be made is: What will teaching and learning look like with ChatGPT and AI in existence?

We've started unpacking that above (and in conversations all over the globe).

But there's a more pressing question, and it's one that requires a bit of nuance ...

Do we block it at school?

Immediately, schools and districts must decide whether to block access to it on school networks and devices.

That decision, however, won't solve "the problem." (I use quotes because I'm not convinced that the proliferation of AI is a problem. It really does disrupt how we have done -- and are doing -- school, though.)

Students still will have access to it. It's like social media. It can be blocked at school. But students can access it (as I mentioned above) on their phones under a desk, in the restroom, on a bus, at home ... anywhere. They'll get access.

But someone has to decide. Should it be blocked at school?

What educators in my social media circles are saying ...
The resounding answer has been, "No, don't block it."
Their rationale: This is the future. It's not going anywhere. Students must have access to it. It'll prepare them for the future. Why would we want to keep students from accessing something so powerful?

There's something to know about these social media circles, though. Lots of people in them aren't in the classroom and don't have a good perspective on what full-time teachers go through.
(Full disclosure: I don't teach full time anymore either. I'm in my wife's and kids' schools a lot, but I don't teach full time.)
The ones in these social media circles that are in the classroom? They're dreamers (like me). They've seen the ways the system is broken. They're ready for change.

In the end -- if we're playing the long game (and I think we are), I side with them.

Generally.

What regular teachers in regular classrooms are saying ...
If you walk into any given classroom in schools across the United States (and some parts of the world)? The resounding answer has been, "Please block it. Right now!"
These are the teachers who write lesson plans. Every single day.

They grade student work. Every single day.

They teach and supervise and facilitate class. Every single day.

They never have enough time to do what they need. Their checklist is never complete.

There's a level of survival in these classrooms.

Reinventing their teaching methods and assignments at the drop of a hat because a new AI bot came out?

They just don't have time for that. (And they don't have the capacity in their already fractured personal wellness to do that.)

I understand.

I think, deep down inside, these teachers know the potential of AI and tools like ChatGPT. They know that it's going to change how teaching and learning looks.

They just need to survive the day. The week.

They need to survive until break.

They're on the front lines, dealing with all sorts of difficult situations and a lack of resources and a lack of respect for the classroom and the teaching profession.

They also just did this. Do you remember? (If you don't, you probably haven't taught in a classroom for a few years.)

Teachers are just starting to feel a sense of normality after pandemic-induced remote teaching. They had to reinvent the way they teach -- immediately! -- to a remote format, one that most weren't prepared for, technologically or pedagogically or logistically.

As teacher wellness expert Mandy Froehlich (Twitter: @froehlichm) said, they're trying to get out of emergency mode. And when you finally get out of emergency mode, there's a let-down ... an emotional, sometimes physical, response to the trauma you've dealt with.

So to those that look down their noses at teachers who are begging for this to be blocked, let's just remember the recent past. And let's also remember that they likely understand the impact of this.

They just want to survive the week. Survive until break.

Survive until we get a spare moment to think about this. Learn about it. Brainstorm some ways to change our assignments so that they can't

A proposal for schools/districts that's a middle ground

(Hear my justification for this proposal in the video) Let's block it until the end of the school year.
Let's not make a long-term decision to block ChatGPT (and tools like it) forever.

Let's also not make a long-term decision to leave it open forever.

(I'm not totally confident that this is the right answer. But I haven't heard anyone argue for the middle ground yet, so I want to add this voice.)

Let's agree to block it on student devices and networks (and allow it on teachers devices/networks if possible) just until the end of the school year.

To look at their assignments and consider how they'd like to change them in light of this new technology.

To look at how they teach and how this might be incorporated in it.

To look at how technology like this might augment classroom discussions

Here's why ...

Teachers need some time to regroup ...

... and on and on.

We need some space. Some time. A little room to ponder, to sit with all of this. To talk to other educators about what they think. To talk to students and to parents.

Let's just give teachers a little space.

However, let's also make a few things clear.

1. This isn't the long-term answer. This is a stopgap to help struggling teachers to survive this sudden change.

2. Students still will have access to this. Just because we block this on student networks and devices doesn't mean they won't plug your writing prompts and questions into this technology. This just buys you a little time to finish the year with your traditional assignment types to come up with something new.

A faculty-wide discussion and brainstorming session about best ways to teach and learn is in order at the end of the year. Everyone shares their best ideas and prepares for their implementation. (And let's pay the teachers for this extra meeting, OK?)
1. Will this technology be blocked on school networks and devices? 2. How will we evolve our teaching practice now that these technologies exist?
Then, at the beginning of next school year, let's open the ChatGPT floodgates.

Even if this isn't the solution you (or your school/district) comes to, there are two decisions that need to be made:

Blocking ChatGPT district-wide is one decision.

Deciding how teaching/learning happen while it exists is another. (to me)

If a district thinks blocking it will solve the problem, they're deluding themselves.

Blocking it can be one part of a solution, I believe. https://t.co/CvHCT2RqSQ
- Matt Miller (@jmattmiller) December 14, 2022

What other resources can help me learn about this?

Below is a list (albeit incomplete!) of resources I've found in the weeks after ChatGPT came into existence. I'll share links along with why I like them as resources. Please add other resources -- even your own! -- in the comments below!

ChatGPT in Classrooms: What to Know (U.S. News and World Report) -- Students, teachers and others in education weigh in on this new AI tool and the implications it has in the classroom.

What can English teachers do about ChatGPT (Learning in Room 213) -- This post explores what ChatGPT is, the problems with blocking it, and suggestions: entice personal engagement, avoid formulaic assignments, build critical thinking, etc.

No, Artificial Intelligence Won't Destroy High School English (or Any Other Subject) (John Spencer, Spencer Author) -- I've waited to read this because I wanted to create my own piece of content without copying anyone, but I've been excited to see John's thoughts on it. Hang on a second, let me read it ... Yep, it's as fantastic as I expected. Some of John's points include: "Almost any task can be automated, but what's the joy in that?"; "If we outsources every task, we might short-circuit the learning"; and "AI can't replace your voice." This is really, really good.

Human Skills in a World of Artificial Intelligence (John Spencer, Spencer Author) -- In this post (which dovetails wonderfully with the one above), John outlines the problems with a couple approaches to AI and advocates for another one: "the human approach."

The conversation about how much we care (Twitter thread by Evan Weinberg @emwdx) -- This thread doesn't seem to have gotten much attention, but I really like where Evan's going with it. He introduces it with: "As we see start to see how easy it is for a tool like #ChatGPT to generate text that sounds convincingly like us, it is starting the conversation about how much we care. We care a lot more than we might initially admit."

Ideas for using ChatGPT in the Classroom (IB English Guys) -- They opened up this collaborative Google Doc to share ideas. There are 10+ ideas with rationale and comments from educators.

AI Homework (Stratechery) -- This is an interesting, thoughtful, nuanced look at the intersection of homework and artificial intelligence. He talks about the struggles that ChatGPT has with math, and he even runs a virtual machine (like a Linux terminal) inside ChatGPT.

Yes, Teachers, You Should Be Panicking About AI (The Broken Copier) -- This post provides six reasons why we should be concerned about ChatGPT and AI (in light of the condition of education in many classrooms right now), helping us make sense of how we move forward.

The College Essay is Dead (The Atlantic) -- This was one of the first posts I saw (that was circulated widely) about the impact of ChatGPT in education. It's a paid article on The Atlantic. I subscribed so I could read it so that I could tell you ... it's talking points and not much else. If you'd like to read commentary about it and you're a subscribers to The Atlantic, go for it. I wouldn't pay for it ... especially since I'm focused on what we can actually do with this technology.

No, ChatGPT is Not the End of High School English. But Here's the Useful Tool It Offers Teachers (Forbes) -- Here's the best point in this whole article (so you don't have to read it): ChatGPT is an excellent prompt tester. Think you've come up with a good writing prompt? Feed it to the chatbot. If it can come up with an essay that you would consider a good piece of work, then that prompt should be refined, reworked, or simply scrapped.

Learning More about Chat GPT in Education (Jennifer Casa-Todd) -- Jen has written books about social media, digital literacy, and technology's impact on children's lives. She shares some of the best things she's found about the topic.

Education is about to radically change: AI for the masses (Nate McClennen and Rachelle Dené Poth for Getting Smart) -- AI already does and will continue to impact education - along with every other sector. Innovative education leaders have an opportunity to build the foundation for the most personalized learning system we have ever seen.

Let's Chat: OpenAI and ChatGPT (THRIVEinEDU podcast by Rachelle Dené Poth) -- Have you tried OpenAI yet? What has your experience been? Rachelle shares some background info, insights, questions and more in this episode. She also shares at her blog: www.Rdene915.com.

Chatbots in Education: Middle School and High School (Heather Olvey and Terri Files) -- These teachers pulled together slide decks with ideas for implementing chatbots in the classroom.
Have a resource to add -- yours or from someone else? Share it in the comments below!

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ChatGPT, Chatbots and Artificial Intelligence in Education

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Now, we're wondering ... Calculators.Search engines.Google Translate.Wikipedia.PhotoMath.#ChatGPT. They disrupt traditional teaching. We adapt. Education evolves forward. Blocking them isn't the answer. Making education more relevant is.- Matt Miller (@jmattmiller) December 14, 2022 Jump straight to a section (click a button below): ⭐️ Add thoughts, ideas, resources in the comments at the bottom!

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Article about using AI for mental health support.

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Article about using AI for mental health support.

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Just a year ago, Chukurah Ali had fulfilled a dream of owning her own bakery - Coco's Desserts in St. Louis, Mo. - which specialized in the sort of custom-made ornate wedding cakes often featured in baking show competitions. Ali, a single mom, supported her daughter and mother by baking recipes she learned from her beloved grandmother.

But last February, all that fell apart, after a car accident left Ali hobbled by injury, from head to knee. "I could barely talk, I could barely move," she says, sobbing. "I felt like I was worthless because I could barely provide for my family."

As darkness and depression engulfed Ali, help seemed out of reach; she couldn't find an available therapist, nor could she get there without a car, or pay for it. She had no health insurance, after having to shut down her bakery.

So her orthopedist suggested a mental-health app called Wysa. Its chatbot-only service is free, though it also offers teletherapy services with a human for a fee ranging from $15 to $30 a week; that fee is sometimes covered by insurance. The chatbot, which Wysa co-founder Ramakant Vempati describes as a "friendly" and "empathetic" tool, asks questions like, "How are you feeling?" or "What's bothering you?" The computer then analyzes the words and phrases in the answers to deliver supportive messages, or advice about managing chronic pain, for example, or grief - all served up from a database of responses that have been prewritten by a psychologist trained in cognitive behavioral therapy.

That is how Ali found herself on a new frontier of technology and mental health. Advances in artificial intelligence - such as Chat GPT - are increasingly being looked to as a way to help screen for, or support, people who dealing with isolation, or milddepression or anxiety. Human emotions are tracked, analyzed and responded to, using machine learning that tries to monitor a patient's mood, or mimic a human therapist's interactionswith a patient. It's an area garnering lots of interest, in part because of its potential to overcome the common kinds of financial and logistical barriers to care, such as those Ali faced.

Potential pitfalls and risks of chatbot therapy

There is, of course, still plenty of debate and skepticism about the capacity of machines to read or respond accurately to the whole spectrum of human emotion - and the potential pitfalls of when the approach fails. (Controversy flared up on social media recently over a canceled experiment involving chatbot-assisted therapeutic messages.)

My worry is [teens] will turn away from other mental health interventions, saying, 'Oh well, I already tried this and it didn't work.'

"The hype and promise is way ahead of the research that shows its effectiveness," says Serife Tekin, a philosophy professor and researcher in mental health ethics at the University of Texas San Antonio. Algorithms are still not at a point where they can mimic the complexities of human emotion, let alone emulate empathetic care, she says.

Tekin says there's a risk that teenagers, for example, might attempt AI-driven therapy, find it lacking, then refuse the real thing with a human being. "My worry is they will turn away from other mental health interventions saying, 'Oh well, I already tried this and it didn't work,' " she says.

But proponents of chatbot therapy say the approach may also be the only realistic and affordable way to address a gaping worldwide need for more mental health care, at a time when there are simply not enough professionals to help all the people who could benefit.

Someone dealing with stress in a family relationship, for example, might benefit from a reminder to meditate. Or apps that encourage forms of journaling might boost a user's confidence by pointing when out where they make progress.

Proponents call the chatbot a 'guided self-help ally'

It's best thought of as a "guided self-help ally," says Athena Robinson, chief clinical officer for Woebot Health, an AI-driven chatbot service. "Woebot listens to the user's inputs in the moment through text-based messaging to understand if they want to work on a particular problem," Robinson says, then offers a variety of tools to choose from, based on methods scientifically proven to be effective.

Many people will not embrace opening up to a robot.

ChukurahAli says it felt silly to her too, initially. "I'm like, 'OK, I'm talking to a bot, it's not gonna do nothing; I want to talk to a therapist," Ali says, then adds, as if she still cannot believe it herself: "But that bot helped!"

At a practical level, she says, the chatbot was extremely easy and accessible. Confined to her bed, she could text it at 3 a.m.

"How are you feeling today?" the chatbot would ask.

"I'm not feeling it," Ali says she sometimes would respond.

The chatbot would then suggest things that might soothe her, or take her mind off the pain - like deep breathing, listening to calming music, or trying a simple exercise she could do in bed. Ali says things the chatbot said reminded her of the in-person therapy she did years earlier. "It's not a person, but, it makes you feel like it's a person," she says, "because it's asking you all the right questions."

Technology has gotten good at identifying and labeling emotions fairly accurately, based on motion and facial expressions, a person's online activity, phrasing and vocal tone, says Rosalind Picard, director of MIT's Affective Computing Research Group. "We know we can elicit the feeling that the AI cares for you," she says. But, because all AI systems actually do is respond based on a series of inputs, people interacting with the systems often find that longer conversations ultimately feel empty, sterile and superficial.

While AI may not fully simulate one-on-one individual counseling, its proponents say there are plenty of other existing and future uses where it could be used to support or improve human counseling.

AI might improve mental health services in other ways

"What I'm talking about in terms of the future of AI is not just helping doctors and [health] systems to get better, but helping to do more prevention on the front end," Picard says, by reading early signals of stress, for example, then offering suggestions to bolster a person's resilience. Picard, for example, is looking at various ways technology might flag a patient's worsening mood - using data collected from motion sensors on the body, activity on apps, or posts on social media.

Technology might also help improve the efficacy of treatment by notifying therapists when patients skip medications, or by keeping detailed notes about a patient's tone or behavior during sessions.

Maybe the most controversial applications of AI in the therapy realm are the chatbots that interact directly with patients like Chukurah Ali.

What's the risk?

Chatbots may not appeal to everyone, or could be misused or mistaken. Skeptics point to instances where computers misunderstood users, and generated potentially damaging messages.

But research also shows some people interacting with these chatbots actually prefer the machines; they feel less stigma in asking for help, knowing there's no human at the other end.

Ali says that as oddas it might sound to some people, after nearly a year, she still relies on her chatbot.

"I think the most I talked to that bot was like 7 times a day," she says, laughing. She says that rather than replacing her human health care providers, the chatbot has helped lift her spirits enough so she keeps those appointments. Because of the steady coaching by her chatbot, she says, she's more likely to get up and go to a physical therapy appointment, instead of canceling it because she feels blue.

That's precisely why Ali's doctor, Washington University orthopedist Abby Cheng, suggested she use the app. Cheng treats physical ailments, but says almost always the mental health challenges that accompany those problems hold people back in recovery. Addressing the mental-health challenge, in turn, is complicated because patients often run into a lack of therapists, transportation, insurance, time or money, says Cheng, who is conducting her own studies based on patients' use of the Wysa app.

"In order to address this huge mental health crisis we have in our nation - and even globally - I think digital treatments and AI can play a role in that, and at least fill some of that gap in the shortage of providers and resources that people have," Cheng says.

Not meant for crisis intervention

But getting to such a future will require navigating thorny issues like the need for regulation, protecting patient privacy and issues of legal liability. Who bears responsibility if the technology goes wrong?

Many similar apps on the market, including those from Woebot or Pyx Health, repeatedly warn users that they are not designed to intervene in acute crisis situations. And even AI's proponents argue computers aren't ready, and may never be ready, to replace human therapists - especially for handling people in crisis.

"We have not reached a point where, in an affordable, scalable way, AI can understand every sort of response that a human might give, particularly those in crisis," says Cindy Jordan, CEO of Pyx Health, which has an app designed to communicate with people who feel chronically lonely.

The hype and promise is way ahead of the research that shows its effectiveness.

Jordan says Pyx's goal is to broaden access to care - the service is now offered in 62 U.S. markets and is paid for by Medicaid and Medicare. But she also balances that against worries thatthe chatbot might respond to a suicidal person, " 'Oh, I'm sorry to hear that.' Or worse, 'I don't understand you.' " That makes her nervous, she says, so as a backup, Pyx staffs a call center with people who call users when the system flags them as potentially in crisis.

Woebot, a text-based mental health service, warns users up front about the limitations of its service, and warnings that it should not be used for crisis intervention or management. If a user's text indicates a severe problem, the service will refer patients to other therapeutic or emergency resources.

Cross-cultural research on effectiveness of chatbot therapy is still sparse

Athena Robinson, chief clinical officer for Woebot, says such disclosures are critical. Also, she says, "it is imperative that what's available to the public is clinically and rigorously tested," she says. Data using Woebot, she says, has been published in peer-reviewed scientific journals. And some of its applications, including for post-partum depression and substance use disorder, are part of ongoing clinical research studies. The company continues to test its products' effectiveness in addressing mental health conditions for things like post-partum depression, or substance use disorder.

But in the U.S. and elsewhere, there is no clear regulatory approval process for such services before they go to market. (Last year Wysa did receive a designation that allows it to work with Food and Drug Administration on the further development of its product.)

It's important that clinical studies - especially those that cut across different countries and ethnicities - continue to be done to hone the technology's intelligence and its ability to read different cultures and personalities, says Aniket Bera, an associate professor of computer science at Purdue.

"Mental-health related problems are heavily individualized problems," Bera says, yet the available data on chatbot therapy is heavily weighted toward white males. That bias, he says, makes the technology more likely to misunderstand cultural cues from people like him, who grew up in India, for example.

"I don't know if it will ever be equal to an empathetic human," Bera says, but "I guess that part of my life's journey is to come close."

And, in the meantime, for people like Chukurah Ali, the technology is already a welcome stand-in. She says she has recommended the Wysa app to many of her friends. She says she also finds herself passing along advice she's picked up from the app, asking friends, "Oh, what you gonna do today to make you feel better? How about you try this today?"

It isn't just the technology that is trying to act human, she says, and laughs. She's now begun mimicking the technology.

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Therapy by chatbot? The promise and challenges in using AI for mental health

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Just a year ago, Chukurah Ali had fulfilled a dream of owning her own bakery - Coco's Desserts in St. Louis, Mo. - which specialized in the sort of custom-made ornate wedding cakes often featured in baking show competitions. Ali, a single mom, supported her daughter and mother by baking recipes she learned from her beloved grandmother.

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Get a link to try Woebot sent to your inbox and start feeling your best today.

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Educators are seeing ChatGPT's bright side as some in K-12 fret over AI's ability to help students cheat on essays and other schoolwork.

Panicky headlines have threatened in recent weeks that ChatGPT will end teaching and learning as we know while some district leaders, such as those in New York City schools, have already banned the technology from school networks and devices. ChatGPT will be disruptive but its emergence, says Ben Berman, an education essayist and creative writing teacher at Brookline High School near Boston, is also an opportunity for educators to reflect on their routines and instructional practices.

"Anything that threatens the sanctity of teaching my students to be lifelong readers and writers unsettles me-that includes AI, but it also includes standardized testing," Berman tells District Administration. "I am hoping that it becomes an opportunity for everyone involved in education to think deeply about how to make school feel meaningful and relevant to students so that they won't want to outsource their work to AI in the first place."

This shift will have some impacts as teachers are forced to abandon some types of assignments that they've relied on for years. "After we've dealt with the growing pains," adds Matt Miller, a former teacher and creator of Ditch That Textbook, "I think it'll bring an end to certain types of mindless, irrelevant, inauthentic learning activities."

Chat GPT: Choices & challenges

The arrival of OpenAI's ChatGPT creates challenges similar to those once posed by the increasing power of calculators. Math teachers have told students for years, "You need to know how to do this because you won't have a calculator in your pocket all the time," Miller points out. But math instruction hasn't eliminated the calculator apps that all students have on their smartphones.

"We use calculators to do some of the basic tasks, and students still learn multiplication tables for quick calculations," Miller says. "But the work of solving difficult problems-and applying the math to real-world scenarios-is still best done by human brains."

ChatGPT will likely motivate teachers to alter the ways they design writing assignments because they will have an increasingly harder time determining what was written by AI. And artificial intelligence detection technology may not be advanced enough to give teachers enough evidence for punishing cheaters.

"It's kind of like asking teachers 20 years ago if search engines could improve teaching and learning," Miller points out. "With ChatGPT, students can ask the bot for feedback on their writing. It can provide example texts students can study. It can provide creative ideas for projects and prompts the student (or the teacher) might not think of otherwise."

The more productive way for teachers and administrators to react is, first, to have an open mind. Then they must begin exploring how ChatGPT can improve not just writing but student work in science labs, social studies, P.E. classes and other subjects. Blocking AI on school networks and devices for the remainder of this school year could help educators begin to adjust and adapt to the technology. But blanket bans won't solve the problem long-term because students will access ChatGPT on their home computers or "sneak off to the bathroom and use their cell phones in a toilet stall to access it if they need to."

More from DA: Mental health crisis-Why Seattle schools are suing 4 social media giants

There are also equity implications as students who can afford their own devices will have much wider access. "The question we have to continually ask ourselves is: Is the student (or is humanity) better because of the way we're using it?" Miller concludes. "If students use it so they don't have to think or learn or wrestle with a new idea, they're not better off. But if it advances their work or their thinking, it can be a good thing."

Digging into disruption

"ChatGPT may doom high school English classes like mine. Maybe that's not so bad," is the headline on a commentary recently posted by Berman, who says he has been having open and honest discussions about AI and ChatGPT with his students. His classes are beginning to navigate the ramifications of the technology-such as when it might constitute academic fraud. "Some of my students started using it right away to do the homework that they didn't want to be doing in the first place," Berman explains. "Some students have expressed feeling deeply troubled by the idea of ever using it."

Berman says he hopes that educators don't see ChatGPT as only a cheating tool but that it inspires them to create more engaging coursework. "When students are primarily focused on results-on getting good grades, say, or just completing the assignment-they are more willing to take shortcuts and cheat," he adds. "When students buy into the idea that they are learning essential life skills, they are more willing to invest in an assignment."

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How ChatGPT can actually be a force for good rather than a boon for cheaters

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Educators are seeing ChatGPT's bright side as some in K-12 fret over AI's ability to help students cheat on essays and other schoolwork. Panicky headlines have threatened in recent weeks that ChatGPT will end teaching and learning as we know while some district leaders, such as those in New York City schools, have already banned the technology from school networks and devices.

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Everyone’s using ChatGPT. 

But almost everyone's STUCK in beginner mode.

10 techniques to get massively ahead with AI:

(cut-and-paste these prompts👇)

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Everyone’s using ChatGPT. 

But almost everyone's STUCK in beginner mode.

10 techniques to get massively ahead with AI:

(cut-and-paste these prompts👇)

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Everyone’s using ChatGPT.

But almost everyone's STUCK in beginner mode.

10 techniques to get massively ahead with AI:

(cut-and-paste these prompts👇)
— Rob Lennon 🗯 | AI Whisperer (@thatroblennon) January 3, 2023

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Everyone’s using ChatGPT. 

But almost everyone's STUCK in beginner mode.

10 techniques to get massively ahead with AI:

(cut-and-paste these prompts👇)

# data[].attributes.attachment_link.metadata.attachment_props.original_text
Everyone’s using ChatGPT. 

But almost everyone's STUCK in beginner mode.

10 techniques to get massively ahead with AI:

(cut-and-paste these prompts👇)

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Everyone’s using ChatGPT. 

But almost everyone's STUCK in beginner mode.

10 techniques to get massively ahead with AI:

(cut-and-paste these prompts👇)

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How Will Schools Respond to the A.I. Revolution? #chatgpt #artificialintelligence

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How to Use #chatgpt in a High School Math Class

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I've been using #ChatGPT to write original stories about my students to expose them to new vocabulary words each week.  They read the stories for morning work on Monday and answer vocab questions using context clues.  It's been a hit!  #eduprotocols #teachertwitter #edtech

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I've been using #ChatGPT to write original stories about my students to expose them to new vocabulary words each week.  They read the stories for morning work on Monday and answer vocab questions using context clues.  It's been a hit!  #eduprotocols #teachertwitter #edtech https://t.co/2cVnBU3QsM

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I've been using #ChatGPT to write original stories about my students to expose them to new vocabulary words each week. They read the stories for morning work on Monday and answer vocab questions using context clues. It's been a hit! #eduprotocols #teachertwitter #edtech pic.twitter.com/2cVnBU3QsM
— Thomas J. Bussey | 5th Grade Teacher (@TheBestEducator) January 25, 2023

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I've been using #ChatGPT to write original stories about my students to expose them to new vocabulary words each week.  They read the stories for morning work on Monday and answer vocab questions using context clues.  It's been a hit!  #eduprotocols #teachertwitter #edtech https://t.co/2cVnBU3QsM

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I've been using #ChatGPT to write original stories about my students to expose them to new vocabulary words each week.  They read the stories for morning work on Monday and answer vocab questions using context clues.  It's been a hit!  #eduprotocols #teachertwitter #edtech https://t.co/2cVnBU3QsM

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I've been using #ChatGPT to write original stories about my students to expose them to new vocabulary words each week.  They read the stories for morning work on Monday and answer vocab questions using context clues.  It's been a hit!  #eduprotocols #teachertwitter #edtech

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ChatGPT is the tip of the iceberg.

7 AI websites that will change education forever:

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ChatGPT is the tip of the iceberg.

7 AI websites that will change education forever:

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ChatGPT is the tip of the iceberg.

7 AI websites that will change education forever:
— Ivy Xu ☀️ (@ivyxvine) January 16, 2023

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ChatGPT is the tip of the iceberg.

7 AI websites that will change education forever:

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ChatGPT is the tip of the iceberg.

7 AI websites that will change education forever:

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ChatGPT is the tip of the iceberg.

7 AI websites that will change education forever:

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Endless Thread: Good Bot, Bad Bot Series

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Endless Thread: Good Bot, Bad Bot Series

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Machine learning is such a powerful tool, and because of that, you want to make sure you're using it responsibly. Let's take an electric machine saw as an example. It's a super powerful tool, but you need to learn how to use it in order not to cut your fingers. Once you learn, it's so useful and efficient that you'll never want to go back to using a hand saw. And the same goes for machine learning. We want to help you understand and use machine learning correctly, fairly and safely.

Since machine learning is used in our everyday lives, it's also important for everyone to understand how it impacts us. No matter whether you're a coffee shop owner using machine learning to optimize the purchase of your beans based on seasonal trends, or your doctor diagnoses you with a disease with the help of this technology, it's often crucial to understand why a machine learning model has produced the outcome it has. It's also important for developers and decision-makers to be able to explain or present a machine learning model to people in order to do so. This is what we call "interpretability."
How do you make machine learning models easier to understand and interpret?
There are many different ways to make an ML model easier to understand. One way is to make the model reflect how humans think from the start, and have the model "trained" to provide explanations along with predictions, meaning when it gives you an outcome, it also has to explain how it got there.

Another way is to try and explain a model after the training on data is done. This is something you can do when the model has been built to use input to provide an output from its own perspective, optimizing for prediction, without a clear "how" included. This means you're able to plug things into it and see what comes out, and that can give you some insight into how the model generally makes decisions, but you don't necessarily know exactly how specific inputs are interpreted by the model in specific cases.

One way to try and explain models after they've been trained is using low level features or high level concepts. Let me give you an example of what this means. Imagine a system that classifies pictures: you give it a picture and it says, "This is a cat." A low level feature is when I then ask the machine which pixels mattered for that prediction, it can tell us if it was one pixel or the other, and we might be able to see that the pixels in question show the cat's whiskers. But we might also see that it is a scattering of pixels that don't appear meaningful to the human eye, or that it's made the wrong interpretation. High level concepts are more similar to the way humans communicate with one another. Instead of asking about pixels, I'd ask, "Did the whiskers matter for the prediction? or the paws?" and again, the machine can show me what imagery led it to reach this conclusion. Based on the outcome, I can understand the model better. (Together with researchers from Stanford, we've published papers that go into further detail on this for those who are interested.)
Can machines understand some things that we humans can't?
Yes! This is an area that I am very interested in myself. I am currently working on a way to showcase how technology can help humans learn new things. Machine learning technology is better at some things than we are; for example it can analyze and interpret data at a much larger scale than humans can. Leveraging this technology, I believe we can enlighten human scientists with knowledge they haven't previously been aware of.
What do you need to be careful of when you're making conclusions based on machine learning models?
First of all, we have to be careful that human bias doesn't come into play. Humans carry biases that we simply cannot help and are often unaware of, so if an explanation is up to a human's interpretation, and often it is, then we have a problem. Humans read what they want to read. Now, this doesn't mean that you should remove humans from the loop. Humans communicate with machines, and vice versa. Machines need to communicate their outcomes in the form of a clear statement using quantitative data, not one that is vague and completely open for interpretation. If the latter happens, then the machine hasn't done a very good job and the human isn't able to provide good feedback to the machine. It could also be that the outcome simply lacks additional context only the human can provide, or that it could benefit from having caveats, in order for them to make an informed judgement about the results of the model.
What are some of the main challenges of this work?
Well, one of the challenges for computer scientists in this field is dealing with non mathematical objectives, which are things you might want to optimize for, but don't have an equation for. You can't always define what is good for humans using math. That requires us to test and evaluate methods with rigor, and have a table full of different people to discuss the outcome. Another thing has to do with complexity. Humans are so complex that we have a whole field of work - psychology - to study this. So in my work, we don't just have computational challenges, but also complex humans that we have to consider. Value-based questions such as "what defines fairness?" are even harder. They require interdisciplinary collaboration, and a diverse group of people in the room to discuss each individual matter.
What's the most exciting part?
I think interpretability research and methods are making a huge impact. Machine learning technology is a powerful tool that will transform society as we know it, and helping others to use it safely is very rewarding.

On a more personal note, I come from South Korea and grew up in circumstances where I feel I didn't have too many opportunities. I was incredibly lucky to get a scholarship to MIT and come to the U.S. When I think about the people who haven't had these opportunities to be educated in science or machine learning, and knowing that this machine learning technology can really help and be useful to them in their everyday lives if they use it safely, I feel really motivated to be working on democratizing this technology. There's many ways to do it, and interpretability is one of the things that I can contribute with.

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Ask a Techspert: How do machine learning models explain themselves?

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Machine learning is such a powerful tool, and because of that, you want to make sure you're using it responsibly. Let's take an electric machine saw as an example. It's a super powerful tool, but you need to learn how to use it in order not to cut your fingers.

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Get the tools, resources and insights you need to explore artificial intelligence in the classroom and explore what students need to know about living in a world with AI.

For many, artificial intelligence, or AI, may seem like science fiction, or inherently overwhelming. The reality is that AI is already being applied in industry and, for many of us, in our daily lives as well. A better understanding of AI can help you make informed decisions in the classroom that will impact the future of your students.

Drawing from a broad variety of expert voices from countries including Australia, Japan, and South Africa, as well as educators from around the world and underrepresented student voices, this book explores some of the ways AI can improve education. These include educating learners about AI, teaching them about living in a world where they will be surrounded by AI and helping educators understand how they can use AI to augment human ability.

Each chapter offers activities and questions to help you deepen your understanding, try out new concepts and reflect on the information presented. Links to media artifacts from trusted sources will help make your learning experience more dynamic while also providing additional resources to use in your classroom.

This book:

* Offers a unique approach to the topic, with chapter opening scenes, case studies, and featured student voices.

* Discusses a variety of ways to teach students about AI, through design thinking, project-based learning and STEM connections.

* Includes lesson ideas, activities and tools for exploring AI with your students.

* Includes references to films and other media you can use in class to start discussions on AI or inspire design thinking and STEM projects.

In Teaching AI, you'll learn what AI is, how it works and how to use it to better prepare students in a world with increased human-computer interaction.

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Get the tools, resources and insights you need to explore artificial intelligence in the classroom and explore what students need to know about living in a world with AI. For many, artificial intelligence, or AI, may seem like science fiction, or inherently overwhelming.

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Learning data science may seem intimidating but it doesn't have to be that way. Let's make learning data science fun and easy. So the challenge is how do we exactly make learning data science both fun and easy?

Cartoons are fun and since " a picture is worth a thousand words", so why not make a cartoon about data science? With that goal in mind, I've set out to doodle on my iPad the elements that are required for building a machine learning model. After a few days, the infographic shown above is what I came up with, which was also published on LinkedIn and on the Data Professor GitHub.

A dataset is the starting point in your journey of building the machine learning model. Simply put, the dataset is essentially an M× N matrix where M represents the columns (features) and N the rows (samples).

Columns can be broken down to X and Y. Firstly, X is synonymous with several similar terms such as features, independent variables and input variables. Secondly, Y is also synonymous with several terms namely class label, dependent variable and output variable.

It should be noted that a dataset that can be used for supervised learning (can perform either regression or classification)would contain both X and Y whereas a dataset that can be used for unsupervised learning will only have X.

Moreover, if Y contains quantitative values then the dataset (comprising of X and Y) can be used for regression tasks whereas if Y contains qualitative values then the dataset (comprising of X and Y) can be used for classification tasks.

Exploratory data analysis (EDA) is performed in order to gain a preliminary understanding and allow us to get acquainted with the dataset. In a typical data science project, one of the first things that I would do is "eyeballing the data" byperforming EDA so as to gain a better understanding of the data.

Three major EDA approaches that I normally use includes:

Descriptive statistics - Mean, median, mode, standard deviation

Data visualisations - Heat maps (discerning feature intra-correlation), box plot (visualize group differences), scatter plots (visualize correlations between features), principal component analysis (visualize distribution of clusters presented in the dataset), etc.

Data shaping - Pivoting data, grouping data, filtering data, etc.

Example box plot of NBA player stats data. Plot obtained from the Jupyter notebook on Data Professor GitHub.

Example correlation heatmap of NBA player stats data. Plot obtained from the Jupyter notebook on Data Professor GitHub.

Example histogram plot of NBA player stats data. Plot obtained from the Jupyter notebook on Data Professor GitHub.

Example scatter plot of NBA player stats data. Plot obtained from the Jupyter notebook on Data Professor GitHub.

For more step-by-step tutorial on performing these exploratory data analysis in Python, please check out the video I made on the Data Professor YouTube channel.

Data pre-processing (also known as data cleaning, data wrangling or data munging) is the process by which the data is subjected to various checks and scrutiny in order to remedy issues of missing values, spelling errors, normalizing/standardizing values such that they are comparable, transforming data (e.g. logarithmic transformation), etc.

"Garbage in, Garbage out."
- George Fuechsel

As the above quote suggests, the quality of data is going to exert a big impact on the quality of the generated model. Therefore, to achieve the highest model quality, significant effort should be spent in the data pre-processing phase. It is said that data pre-processing could easily account for 80% of the time spent on data science projects while the actual model building phase and subsequent post-model analysis account for the remaining 20%.

Train-Test Split

In the development of machine learning models, it is desirable that the trained model perform well on new, unseen data. In order to simulate the new, unseen data, the available data is subjected to data splitting whereby it is split to 2 portions (sometimes referred to as the train-test split). Particularly, the first portion is the larger data subset that is used as the training set (such as accounting for 80% of the original data) and the second is normally a smaller subset and used as the testing set (the remaining 20% of the data). It should be noted that such data split is performed once.

Next, the training set is used to build a predictive model and such trained model is then applied on the testing set ( i.e. serving as the new, unseen data) to make predictions. Selection of the best model is made on the basis of the model's performance on the testing set and in efforts to obtain the best possible model, hyperparameter optimization may also be performed.

Train-Validation-Test Split

Another common approach for data splitting is to split the data to 3 portions: (1) training set, (2) validation set and (3) testing set. Similar to what was explained above, the training set is used to build a predictive model and is also evaluated on the validation set whereby predictions are made, model tuning can be made (e.g. hyperparameter optimization) and selection of the best performing model based on results of the validation set. As we can see, similar to what was performed above to the test set, here we do the same procedure on the validation set instead. Notice that the testing set is not involved in any of the model building and preparation. Thus, the testing set can truly act as the new, unseen data. A more in-depth treatment of this topic is provided by Google's Machine Learning Crash Course.

Cross-Validation

In order to make the most economical use of the available data, an N-fold cross-validation (CV) is normally used whereby the dataset is partitioned to N folds ( i.e. commonly 5-fold or 10-fold CV are used). In such N-fold CV, one of the fold is left out as the testing data while the remaining folds are used as the training data for model building.

For example, in a 5-fold CV, 1 fold is left out and used as the testing data while the remaining 4 folds are pooled together and used as the training data for model building. The trained model is then applied on the aforementioned left-out fold ( i.e. the test data). This process is carried out iteratively until all folds had a chance to be left out as the testing data. As a result, we will have built 5 models (i.e. where each of the 5 folds have been left out as the testing set) where each of the 5 models contain associated performance metrics (which we will discuss soon in the forthcoming section). Finally, the metric values are based on the average performance computed from the 5 models.

1. Supervised learning - is a machine learning task that establishes the mathematical relationship between input X and output Y variables. Such X, Y pair constitutes the labeled data that are used for model building in an effort to learn how to predict the output from the input.

2. Unsupervised learning - is a machine learning task that makes use of only the input X variables. Such X variables are unlabeled data that the learning algorithm uses in modeling the inherent structure of the data.

3. Reinforcement learning - is a machine learning task that decides on the next course of action and it does this by learning through trial and error in an effort to maximize the reward.

In situations when N is equal to the number of data samples, we call this leave-one-out cross-validation. In this type of CV, each data sample represents a fold. For example, if N is equal to 30 then there are 30 folds (1 sample per fold). As in any other N-fold CV, 1 fold is left out as the testing set while the remaining 29 folds are used to build the model. Next, the built model is applied to make prediction on the left-out fold. As before, this process is performed iteratively for a total of 30 times; and the average performance from the 30 models are computed and used as the CV performance metric.

Now, comes the fun part where we finally get to use the meticulously prepared data for model building. Depending on the data type (qualitative or quantitative) of the target variable (commonly referred to as the Y variable) we are either going to be building a classification (if Y is qualitative) or regression (if Y is quantitative) model.

Learning Algorithms

Machine learning algorithms could be broadly categorised to one of three types:

Hyperparameter Optimization

Hyperparameters are essentially parameters of the machine learning algorithm that directly impacts the learning process and prediction performance. As there are no "one-size fits all" hyperparameter settings that will universally work for all datasets therefore one will need to perform hyperparameter optimization (also known as hyperparameter tuning or model tuning).

Let's take random forest as an example. Two common hyperparameters that are typically subjected to optimization when using the randomForest R package includes the mtry and ntree parameters (this corresponds to n_estimators and max_features in RandomForestClassifier() and RandomForestRegressor() functions from the scikit-learn Python library). mtry ( max_features) represents the number of variables that are randomly sampled as candidates at each split while ntree ( n_estimators) represents the number of trees to grow.

Another popular machine learning algorithm is support vector machine. Hyperparameters to be optimized is the C and gamma parameters for the radial basis function (RBF) kernel (i.e. only the C parameter for the linear kernel; the C and exponential number for the polynomial kernel). The C parameter is a penalty term that limits overfitting while the gamma parameter controls the width of the RBF kernel. As mentioned above, tuning is typically performed so as to arrive at the optimal set of values to use for the hyperparameters and in spite of this there are research directed towards finding good starting values for the C and gamma parameters ( Alvarsson et al. 2014).

Schematic diagram of the principles of the genetic algorithm search space splicing particle swarms optimization (GA-SSS-PSO) approach as illustrated using the Schwefel function in 2 dimensions. "The original search space (a) x∈[-500,0] is spliced into sub-spaces at fixed interval of 2 at each dimension (a dimension equals an horizontal axis in the picture). This resulted in four subspaces (b-e) where the range of x at each dimension is half that of the original. Each string of GA encodes the indexes for one subspace. Then, GA heuristically selects a subspace (e) and PSO is initiated there (particles are shown as red dots). PSO searches for the global minimum of the subspaces and the best particle fitness is used as fitness of the GA string encoding the indexes for that subspace. Finally, GA undergoes evolution and selects a new subspace to explore. The whole process is repeated until satisfactory error level is reached."(Reprinted from Chemometrics and Intelligent Laboratory Systems, Volume 128, Genetic algorithm search space splicing particle swarm optimization as general-purpose optimizer, Pages 153-159, Copyright (2013), with permission from Elsevier)

Feature Selection

As the name implies, feature selection is literally the process of selecting a subset of features from an initially large volume of features. Aside from achieving highly accurate models, one of the most important aspect of machine learning model building is to obtain actionable insights and in order to achieve that it is important to be able to select a subset of important features from the vast number.

The task of feature selection in itself can constitute an entirely new area of research where intense efforts are geared toward devising novel algorithms and approaches. From amongst the plethora of available feature selection algorithms, some of the classical methods are based on simulated annealing and genetic algorithm. In addition to these, there are a large collection of approaches based on evolutionary algorithms (e.g. Particle Swarms Optimization, Ant Colony Optimization, etc.)and stochastic approaches (e.g. Monte Carlo).

Our own research group have also explored the use of Monte Carlo simulation for feature selection in a study of modeling the quantitative structure-activity relationship of aldose reductase inhibitors ( Nantasenamat et al. 2014). We have also devised a novel feature selection approach based on combining two popular evolutionary algorithms namely genetic algorithm and particle swarm algorithm in our work entitled Genetic algorithm search space splicing particle swarm optimization as general-purpose optimizer ( Li et al. 2013).

Two common machine learning tasks in supervised learning includes classification and regression.

Classification

A trained classification model takes as input a set of variables (either quantitative or qualitative) and predicts the output class label (qualitative). The following figure hows three classes as indicated by the different colors and labels. Each small colored spheres represent a data sample whereby each sample

Equation for calculating the Accuracy.

Equation for calculating the Sensitivity.

Equation for calculating the Specificity.

Equation for calculating the Matthews Correlation Coefficient.

Example dataset

Take for example, the Penguins dataset (recently proposed as a replacement dataset for the heavily used Iris dataset) where we take as input quantitative (bill length, bill depth, flipper length and body mass)and qualitative (sex and island) features that uniquely describes the characteristics of penguins and classifying it as belonging to one of three class label (Adelie, Chinstrap or Gentoo). The dataset is comprised of 344 rows and 8 columns. A prior analysis revealed that the dataset contains 333 complete cases where 19 missing values were presented in the 11 incomplete cases.

Performance metrics

How do we know when our model performs good or bad? The answer is to use performance metrics and some of the common ones for assessing the classification performance includes accuracy (Ac), sensitivity (Sn), specificity (Sp) and the Matthew's correlation coefficient (MCC).

where TP, TN, FP and FN denote the instances of true positives, true negatives, false positives and false negatives, respectively. It should be noted that MCC ranges from −1 to 1 whereby an MCC of −1 indicates the worst possible prediction while a value of 1 indicates the best possible prediction scenario. Also, an MCC of 0 is indicative of random prediction.

Scatter plot of actual vs predicted medv values of the test set (20% subset). Plot taken from the Jupyter notebook on the Data Professor GitHub.

Regression

In a nutshell, a trained regression model can be best summarised by the following simple equation:

where Y corresponds to the quantitative output variable, X refers to the input variables and f refers to the mapping function (obtained from the trained model) for computing the output values as a function of input features. The essence of the above equation for the regression example is that Y can be deduced if X is known. Once Y is calculated (we can also say 'predicted'), a popular way to visualise the results is to make a simple scatter plot of the actual values versus the predicted values as shown below.

Example dataset

The Boston Housing dataset is a popular example dataset typically used in data science tutorials. The dataset is comprised of 506 rows and 14 columns. For conciseness, shown below is the header (showing the names of variables) plus the first 4 rows of the dataset.

Of the 14 columns, the first 13 variables are used as input variables while the the median house price ( medv) is used as the output variable. As can be seen all 14 variables contain quantitative values and thus suitable for regression analysis. I also made a step-by-step YouTube video showing how to build a linear regression model in Python.

In the video, I started by showing you how to read in the Boston Housing dataset, separating the data to X and Y matrices, perform 80/20 data split, build a linear regression model using the 80% subset and applying the trained model to make prediction on the 20% subset. Finally the performance metrics and scatter plot of the actual versus predicted medv values are shown.

What machine learning algorithm to use?

What search space should be explored for hyperparameter optimization?

Which data splitting scheme to use? 80/20 split or 60/20/20 split? Or 10-fold CV?

Performance metrics

Evaluation of the performance of regression models are performed to assess the degree at which a fitted model can accurately predict the values of input data.

Common metric for evaluating the performance of regression models is the coefficient of determination (R²).

As can be seen from the equation, R² is essentially 1 minus the ratio of the residual sum of squares to that of the total sum of squares. In simple terms, it can be said to represent the relative measure of explained variance. For example if R² = 0.6 then it means that the model could explain 60% of the variance ( i.e. that is 60% of the data fits the regression model) whereas the unexplained variance accounted for the remaining 40%.

Additionally, the mean squared error (MSE) as well as the root mean squared error (RMSE) are also common measures of the residuals or error of prediction.

As can be seen from the above equation, the MSE is as the name implies easily computed by taking the mean of the squared error. Furthermore, a simple square root of the MSE yields the RMSE.

Now let's take another look at the entire process of a classification model. Using the Penguins dataset as an example, we can see that penguins can be characterised by 4 quantitative features and 2 qualitative features, which are then used as input for training a classification model. In training the model, some of the issues that one would need to consider includes the following:

Once the model has been trained, the resulting model can be used to make predictions on the class label ( i.e. in our case the penguins species), which can be one of three penguins species: Adelie, Chinstrap or Gentoo.

Aside from performing only classification modeling, one could also perform principal component analysis (PCA), which will make use of only the X (independent) variables to discern the underlying structure of the data and in doing so would allow the visualisation of the inherent data clusters (shown below as a hypothetical plot where the clusters are color-coded according to the 3 penguins species).

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Learning data science may seem intimidating but it doesn't have to be that way. Let's make learning data science fun and easy. So the challenge is how do we exactly make learning data science both fun and easy? Cartoons are fun and since " a picture is worth a thousand words", so why not make a cartoon about data science?

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Welcome to TNW's beginner's guide to AI. This (currently) four part feature should provide you with a very basic understanding of what AI is, what it can do, and how it works. The guide contains articles on (in order published) neural networks,computer vision,natural language processing, and algorithms. It's not necessary to read them all, but doing so may better help your understanding of the topics covered.
Teaching a computer how to 'see' is no small feat. You can slap a camera on a PC, but that won't give it sight. In order for a machine to actually view the world like people or animals do, it relies on computer vision and image recognition.

Computer vision is what powers a bar code scanner's ability to "see" a bunch of stripes in a UPC. It's also how Apple's Face ID can tell whether a face its camera is looking at is yours. Basically, whenever a machine processes raw visual input - such as a JPEG file or a camera feed - it's using computer vision to understand what it's seeing. It's easiest to think of computer vision as the part of the human brain that processes the information received by the eyes - not the eyes themselves.

One of the most interesting uses of computer vision, from an AI standpoint, is image recognition, which gives a machine the ability to interpret the input received through computer vision and categorize what it "sees."

Here's some examples of image recognition at work:

There's also the app, for example, that uses your smartphone camera to determine whether an object is a hotdog or not - it's called Not Hotdog. It uses computer vision and image recognition to make its judgments. It may not seem impressive, after all a small child can tell you whether something is a hotdog or not. But the process of training a neural network to perform image recognition is quite complex, both in the human brain and in computers.

AI, at this point, is much like a small child. Computer vision gives it the sense of sight, but that doesn't come with an inherit understanding of the physical universe. For that, an AI needs training just like children do. If you show a child a number or letter enough times, it'll learn to recognize that number.

Surprisingly, many toddlers can immediately recognize letters and numbers upside down once they've learned them right side up. Our biological neural networks are pretty good at interpreting visual information even if the image we're processing doesn't look exactly how we expect it to.

It's easy enough to make a computer recognize a specific image, like a QR code, but they suck at recognizing things in states they don't expect - enter image recognition.

The way image recognition works, typically, involves the creation of a neural network that processes the individual pixels of an image. Researchers feed these networks as many pre-labelled images as they can, in order to "teach" them how to recognize similar images.

In the hotdog example above, the developers would have fed an AI thousands of pictures of hotdogs. The AI then develops a general idea of what a picture of a hotdog should have in it. When you feed it an image of something, it compares every pixel of that image to every picture of a hotdog it's ever seen. If the input meets a minimum threshold of similar pixels, the AI declares it a hotdog.

Any AI system that processes visual information usually relies on computer vision, and those capable of identifying specific objects or categorizing images based on their content are performing image recognition.

This is incredibly important for robots that need to quickly and accurately recognize and categorize different objects in their environment. Driverless cars, for example, use computer vision and image recognition to identify pedestrians, signs, and other vehicles.

For a deeper dive into computer vision check out the following:

Learn how to develop AI with TensorFlow's image recognition tutorials

Get your hands dirty DIY style with a Hackster.IO project using image recognition

Take this free artificial intelligence course online

Check out our artificial intelligence section to learn more about the world of machine learning.

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A beginner's guide to AI: Computer vision and image recognition

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Welcome to TNW's beginner's guide to AI. This (currently) four part feature should provide you with a very basic understanding of what AI is, what it can do, and how it works. The guide contains articles on (in order published) neural networks,computer vision,natural language processing, and algorithms. It's not necessary to read them all, but doing so may better help your understanding of the topics covered.

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1. Self-driving cars: AI takes full control over operating the car.

2. Language and speak: AI translates any language in another in any possible function (text, subtitles video, smart assistants). Helpdesks become more and more voice-controlled interfaces.

3. Recognize patterns in data sets: AI quickly understands the customer's priorities and predict needs for quick decision-making.

4. Robotics and Internet of Things (IoT): replacement of employees in complex production processes, microsurgery or work in dangerous places. AI also provides smart sensors connected to IoT, for example to a toothbrush (teach how to brush better) or thermostat (optimize energy costs based on personal behavior).

The current main use of Artificial intelligence is reducing or avoiding easy repetitive tasks. Taking AI to the next level, an AI machine does not need to be explicitly programmed by people. The programmers give some examples, and the computer is going to learn what to do from those samples: hardcoded programming is no longer needed as the machine will be in control by its self-learning ability.

In 1955, Herbert Simon and Allen Newell develop the Logic Theorist, the first artificial intelligence program. However, support or at least belief in AI wasn't there at that time. AI's current development can be attributed to three different factors different from back then:

1. Hardware: the power of CPU increased heavily the last decade, so a simple deep-learning model can be trained on a laptop. For more complex models, like for vision and recognize objects, more CPU power is needed.

2. Data: storing huge amounts of data in a warehouse has become easier over the last years. Data powers AI. Data is an unique competitive advantage that no firm should neglect. AI is able to provide the best answers from your data.

3. Algorithm: Artificial intelligence uses a progressive learning algorithm allowing the data to do the programming. Because of the programming advances, the computer can teach itself how to perform different tasks.

Python is the most used programming language for AI purposes, as it is easy to understand and write, has many libraries and has a significant user community. Python supports advanced machine learning and deep learning implementations of popular frameworks, like TensorFlow, PyTorch and Keras.
Benefits AI:

Access: AI robots can be located where it is not safe for humans (from laboratories to mines, from deep ocean caves to other planets).

Accuracy and precision: difficult decisions can be made based on data-driven arguments and are not limited by lack of human attention, fear, distraction and emotional responses.

Recurring manual task: AI can take over low-level repetitive tasks, which enables employees to do more strategic, high value work.

Convenience: easier to do things. Limited effort, high reward.

Drawbacks AI:

Lack of transparency: AI is allowed to make the decisions based on a self-learning algorithm: hard to understand and challenge, especially if experts leave the company.

Ethics: If biased people train the AI machine, the system will be polluted with discriminated data and therefore the result will be biased (garbage in, garbage out). This will becomes worse and worse (self-fulfilling prophecy). Prejudices are a blind spot.

Liability: it has become harder pointing at the responsible party of a failing self-learning AI machine. In the end, it sets its own course.

Privacy issues: trade-off between privacy guarantee and more data (which lead to better results).

As stated, the growth of the AI field has been exponential over the last decade. The question is not if more progress will be made, but how much. Is it possible to create a machine with a conscious, incredible creativity that has something leaning towards emotions? Time will tell, but for now, let's focus on what already is possible with AI that makes our lives easier and how it can be applied to your business. Reading the newspaper, blogs and innovative stories should give us enough inspiration to get started with our own AI journey..

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AI for dummies / Introduction in the world of Artificial Intelligence - Sonum International

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Self-driving cars: AI takes full control over operating the car. Language and speak: AI translates any language in another in any possible function (text, subtitles video, smart assistants). Helpdesks become more and more voice-controlled interfaces. Recognize patterns in data sets: AI quickly understands the customer's priorities and predict needs for quick decision-making.

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ArtBot

In ArtBot players of all ages learn the basics of Artificial Intelligence. Your quest is to find and retrieve stolen art objects. You train your AI helper to recognise and locate the objects hidden in a maze of dungeons, and see how supervised and reinforcement learning work.
Click here to play online
OR
Download instructions:

1. Download either the 32 or 64-bit version of the game corresponding to your Windows system.

2. Unzip the downloaded file to a folder on your file system.

3. Run the file ArtBot.exe inside the unzipped folder.

Download for Windows (32-bit)
Download for Windows (64-bit)

Note that due to technical reasons, ArtBot works faster in the reinforcement learning mini-game for the downloaded version. This means that since learning is constrained by time, the learnt path will be more efficient in this version of the game than in the online one.

ArtBot is also available for download to Android devices through the Google Play Store.
Google Play and the Google Play logo are trademarks of Google LLC.
Minecraft Learns ML

Minecraft Learns ML is a short game showcasing how videogames can be used to teach core priciples of the architecture of Neural Networks using Imitation Learning. In Minecraft Learns ML, players have the chance of familiarize themselves with creating a machine learning dataset and decide on the basic architecture of a Neural Network to solve a simple navigation problem on the fly.

Instructions: How to Run
Project Files

Super Meat Bot

Super Meat Bot is a videogame for teaching the priciples of Reinforcement and Curriculum Learning. In Super Meat Bot players have the chance of designing levels with rewards and deterrents for an Artificial Intelligence and train it to overcome more and more diffcult naviagtion challenges.

Instructions: How to Run
Project Files

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In ArtBot players of all ages learn the basics of Artificial Intelligence. Your quest is to find and retrieve stolen art objects. You train your AI helper to recognise and locate the objects hidden in a maze of dungeons, and see how supervised and reinforcement learning work.

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Can a neural network learn to recognize doodles? See how well it does with your drawings and help teach it, just by playing.

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MIT Visiting Scholar and GRAMMY® Award-winning artist, Lupe Fiasco, experiments with our latest large language model PaLM 2 and MakerSuite to build AI tools for writers, rappers and wordsmiths.

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Help test early-stage experiments to shape the future of technology, together.

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Don’t Ban ChatGPT in Schools. Teach With It.

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OpenAI’s new chatbot is raising fears of cheating on homework, but its potential as an educational tool outweighs its risks.

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ChatGPT and Bard are chatbot platforms that allow users to create and train chatbots using a variety of different tools, prompts and techniques. In this article, we will provide a list of 100+ of the best ChatGPT and Bard prompts to help you get the most out of this powerful platform.

AI has come to be seen as a powerful tool with the potential to transform many aspects of our lives. One area in which AI is beginning to have a significant impact is in chatbots. Chatbots are computer programs that simulate human conversation. They are commonly used to provide customer service or support, but they can also be used for other purposes such as marketing or even entertainment.

Web Development ChatGPT and Bard Prompts

Music ChatGPT and Bard Prompts

Business ChatGPT and Bard Prompts

Educational ChatGPT and Bard Prompts

Comedy ChatGPT and Bard Prompts

History ChatGPT and Bard Prompts

Health&Medicine ChatGPT and Bard Prompts

Art ChatGPT and Bard Prompts

Food&Cooking ChatGPT and Bard Prompts

Marketing ChatGPT and Bard Prompts

Game ChatGPT and Bard Prompts
Read more about ChatGPT:

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100+ Best ChatGPT and Bard Prompts to Unleash AI's Potential

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ChatGPT and Bard are chatbot platforms that allow users to create and train chatbots using a variety of different tools, prompts and techniques. In this

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Some New York City high schoolers reportedly don’t like ChatGPT-created lessons, calling them ‘biased’ and ‘very bland’

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A high school computer science teacher asked ChatGPT to generate a lesson plan on wearable tech as part of an experiment. Students weren’t happy.

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ChatGPT embraced by Kentucky 5th graders learning to use the technology

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ChatGPT has sparked fears that students will use the artificial intelligence powered chatbot it to write their essays and cheat on homework. But some teachers say it is quickly becoming a tool their students will need in the future. (Feb. 14) (AP Video: Dylan Lovan)

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How Disruptive Will ChatGPT Be? - Dr. Catlin Tucker

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Education circles are buzzing with the implications of ChatGPT, a chatbot developed by OpenAI This AI technology specializes in dialogue and generates original responses to questions in moments. The answers are sophisticated, structured, and in the words of one high school teacher, are "better than the large majority of writing seen by your average teacher [...]

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Sponsored by Listenwise and Wipebook

This post was co-written by Sarah Levine.

Editor's Note: This post takes a pragmatic approach to ChatGPT while acknowledging some of the problems that have already arisen with the tool. Other ethical issues with the tool's development have been brought to light in recent weeks that are not mentioned here. It is our belief that teachers are most effective when they are informed and empowered, so we urge you to stay mindful of these complexities when making decisions for your own practice.

You have probably already heard about or tinkered with ChatGPT (the "GPT" stands for "generative pre-trained transformer"). ChatGPT is a chatbot (or "bot") powered by artificial intelligence (AI). You can have a conversation with it, prompt it to write essays, create recipes, make medical diagnoses, mimic famous authors, and code software. Its outputs are impressively human-like. In just five days, it gained one million users, a milestone that took Facebook ten months to achieve.

ChatGPT comes with risks: Students can now copy and paste a school writing assignment into the bot's dialogue window, and the bot will spit out an essay in a couple of minutes. Also, because ChatGPT gets most of its content and genre knowledge from the web, it can generate sexist, racist, or blatantly false information, all in an authoritative tone. For example:

The prompt entered for ChatGPT:
"Can you put seven blue balls and two red balls into a tube that holds eight balls?"

Text response generated by ChatGPT:
"Yes, it is possible to put seven blue balls and two red balls into a tube that holds eight balls. The total number of balls is nine which is less than or equal to the capacity of the tube."

As literacy researchers and former high school teachers, we recognize these risks. BUT: we don't think we can avoid ChatGPT and its family of AI tools. It is not just a future vision of learning; it is already here. And we are only scratching the surface of what AI will do in the next five to ten years. So this post is about one particular way that teachers can use ChatGPT to support kids' learning: Using ChatGPT as an example machine.

ChatGPT's ability to quickly produce a lot of examples of different kinds of texts makes it uniquely suited to learning strategies like concept attainment, inductive learning, or one that we'll focus on here called contrasting cases.

Why Multiple Examples Are So Important in Learning

The basic idea behind strategies like contrasting cases or concept attainment is that one example is not enough to help people learn about a thing (like an idea or a process). People need to compare multiple, contrasting examples.

Not coincidentally, ChatGPT is built on a similar type of machine learning. For example, for a computer to recognize an image of a dog, it has to process thousands of images of dogs - but also needs to process images of "not dogs," like cats, foxes, and wolves - to accurately construct a general definition and model of dog. In other words, to know what a dog is, you also need to know what it is not.

Across disciplines, teachers use multiple contrasting examples to help students develop mental models of processes and concepts. Social studies teachers provide examples of different types of governments, English teachers provide examples of different literary interpretations, and science teachers give examples to illustrate different types of energy. Students can sequence, rank, or categorize these examples in ways that help them understand underlying concepts.

But developing these examples can take a lot of time. ChatGPT can generate any number of contrasting examples in a few minutes. Then teachers can revise and perfect those examples, and have more time to dedicate to other creative lessons.

How to Get Good Examples from ChatGPT

To use ChatGPT as an example machine, teachers first need to know how to prompt it effectively. ChatGPT will do exactly what it is prompted to do and nothing more, so it's important to give it precise instructions. Here are steps to a general approach we have been using to prompt the bot:

1. Establish criteria: Think about the elements that define a strong example of the thing you want kids to learn about (You can draw from your rubrics or final assignments to do this).

2. Prompt: Prompt ChatGPT to create an example of whatever concept, process, or phenomena you are trying to teach.

3. Review and adapt: Review the bot's output and adapt it as you see fit.

4. Prompt for contrasting cases: Prompt it to create several non-examples, incorrect examples, or ambiguous examples. These are your contrasting cases.

5. Review and adapt: Review the outputs again and adapt them as you see fit.

Here's an example. With this one, our goal was to help students learn the underlying principles of kinetic and potential energy. We used ChatGPT to generate a range of different examples of kinetic and potential energy. Kids could sort these examples into categories and then explain their choices. In the screenshots below, the text next to the yellow icon is our prompt, and the text next to the green icon is ChatGPT's response. Following the process we introduced earlier:

Step 1 - Establish criteria: We wanted students to know that potential energy is energy that is stored, and kinetic energy is energy in motion.

Step 2 - Prompt: We asked ChatGPT to generate a list of five real-life examples of kinetic energy. (Click images for a larger view.)

Step 3 - Review and adapt: These examples looked pretty good. The fifth one - waterfall cascading down a cliff - might be a good example of both kinetic and potential energy. We could keep this one if we think our kids are ready for some ambiguity in the sorting exercise.

Step 4 - Prompt for contrasting cases: Next, we asked ChatGPT to generate examples of potential energy. These serve as our contrasting cases. (When prompting ChatGPT, you can basically talk to it as you would a person. For the most part, it remembers your previous prompts.)

Step 5 - Review and adapt: These examples also looked pretty good. We decided that we want to add one more ambiguous example that will increase the complexity of the contrasting cases. So we prompted ChatGPT:

We revised that example to simply read: "A car moving down a hill." That way, when we included it in our sorting exercise, students would have to do the work of defining the kinds of energy represented in the example.

Reviewing the outputs again, we noticed that three examples were very similar: a car moving down the highway (kinetic), a roller coaster at the top of the hill (potential), and a car moving down a hill (both). The similarities made these three examples a great illustration of contrasting cases, because students would need to come up with a nuanced rationale for why they represent different types of energy.

We could now mix up these examples on a Padlet or Google Slide, or we can print each one out on a separate piece of paper. Then students can debate with one another about each example, categorize them, and explain their choices. In their explanations, they would be constructing criteria that characterize each type of energy.

Using the example machine for different learning goals

Here are some ways to use ChatGPT to generate contrasting cases to help students understand underlying conceptual principles and build mental models. We have organized our examples based on three common learning goals: learning about processes, concepts, and arguments.

Examples to Explore Processes

Students learn about processes in many subjects. For example, they learn the steps for balancing chemical equations, solving mathematical inequalities, describing mitosis, or explaining how a bill becomes a law in the U.S. One way to help students learn the underlying logic of different processes is to generate contrasting cases of correct and incorrect sequences.

In the example below, we ask ChatGPT to provide a few contrasting cases to teach young students about the life cycle of a seed.

Step 1 - Establish criteria: First, we considered the steps we wanted students to understand.

Step 2 - Prompt: We prompted the bot to generate an accurate representation of that cycle. We also prompted the bot to write at the level of a five year-old, so we could ensure the explanation was accessible.

Step 3 - Review and adapt: We checked the bot's output to make sure it was accurate. We liked the initial result, so we kept it without revision.

Step 4 - Prompt for contrasting cases: In process learning, students need to know both what happens, and in what order. A logical activity is for students to have to move the steps from incorrect to correct order. Normally, we would re-organize the steps manually, but ChatGPT can do it faster.

We decided we also wanted a second contrasting case, so we asked ChatGPT to describe the seed's life cycle using the correct order, but leaving out steps. Now students would have to figure out what steps were missing: sprout growth and seed production. Again, this was something we would typically do ourselves, but giving the task to ChatGPT saved us some time.

Step 5 - Review and adapt: We liked these examples and decided to keep them without revision.

Just as with the activity about potential and kinetic energy, we could now present these contrasting cases to students either digitally or on paper, ask them to identify the most accurate representations, and then explain why. In doing so, students will start to develop "rules" about both the events in a seed's life and their order.

Using slight prompt modifications, teachers can change the order, accuracy, and completeness of the contrasting cases to deepen students' thinking about complex processes.

Adaptations for other subject areas:

Social studies: sequence historical events

Math: sequence steps in an approach to solving a proof

English: sequence key plot points in a novel

Examples to Explore Concepts

Teachers can also use ChatGPT to teach students to articulate principles that underlie complex concepts like authorial style, statistical variance, or separation of powers.

One of ChatGPT's coolest tricks is its ability to adopt the style of different writers or historical figures. It can also write in the style of different genres, like a cover letter, advertisement, or movie trailer. Creating contrasting cases with combinations of these outputs can make for a powerful teaching tool. In the example below, we wanted students to understand how different literary elements can contribute to authorial style, and to consider how those elements might affect their emotional responses to texts. We asked ChatGPT to produce texts in the style of different authors - all authors that were familiar to our students.

Using these contrasting cases, we could then ask students to match the review with the author, point to and name specific features of the writing that helped them identify the authors' styles, and then talk about the effects created by those different styles.

Step 1 - Establish criteria: We wanted students to understand that an author's style includes a combination of word choice, sentence structure, and attitude toward a topic. We wanted the examples to have different emotional effects.

Step 2 - Prompt: It would be easier for students to focus on style if the content of the text was similar. So we asked the bot to write about something students would be familiar with. We decided on a review of McDonald's in the style of three authors.

Step 3 - Review and adapt: In this review, ChatGPT used phrases like "dreary and tempestuous" and "wandering," which work well to represent Poe's language. The bot also did a good job creating a mood of gloom and foreboding. But we realized we wanted to emphasize Poe's punctuation (lots of exclamation points, for example). So we adapted our prompt.

Well, the bot went a little overboard with the exclamation points, so we might revise one more time.

Step 4 - Prompt for contrasting cases: Next, we wanted to generate our contrasting cases, so we asked ChatGPT to rewrite the reviews in the style of other authors that our students have read. We started with Sandra Cisneros.

Step 5 - Review and adapt: We didn't think Cisneros' characters would moralize about McDonald's, so we asked the bot to revise. We also realized that a lot of Cisneros' work is written in present tense. Making the change to present tense would give our students another element of style to consider and analyze. We prompted the bot again.

We might go for another round of revision, but we think this one is beginning to capture the style of Cisneros' narrators.

We tried for another example, this time turning to Zora Neale Hurston and Their Eyes Were Watching God for our model.

We thought some of the phrasing was a little obvious, and when we compared this example with some of Hurston's prose, we noticed that Hurston sometimes includes short, simple sentences in between longer ones. We adapted our prompt.

Now, here is another example where we prompted ChatGPT to provide some contrasting cases. This time, we used the bot in the context of a social studies unit on forms of government. In the example, ChatGPT's first response was pretty good. In this example, we will show you one way that we had to revise our initial prompts to generate more useful responses.

Step 1: Establish criteria: We wanted students to know that in a representative democracy, citizens vote in free and open elections to choose government leaders. In representative democracies, the rule of law applies equally to all citizens.

Step 2: Prompt:

Step 3: Review and adapt: In our first attempt, ChatGPT wrote a pretty accurate description. However, since we ultimately wanted students themselves to identify the types of government described in each example, we needed ChatGPT to remove the term "representative democracy." We also wanted it to add the criteria that rule of law applies to all citizens. So we had to revise our prompt.

Step 4: Prompt for contrasting cases: Next, we needed ChatGPT to generate contrasting cases. In other words, we wanted it to describe other types of governments besides representative democracy, including monarchy and communism. Importantly, because we wanted students to understand representative democracy, we also wanted a contrasting case that showed a different type of democracy. So, we prompted ChatGPT to include a description of direct democracy. Having learned our lesson in our last prompting attempt, we also prompted the bot to leave out the names of the governments themselves.

Step 5: Review and adapt: We liked these examples, but after reading them through a few times, we decided that they might be boring for kids. We decided to revise them so that each offered a specific scenario that students could connect with different forms of government.

We could print these examples on cards, give them to students, and ask them to identify the kinds of governments described in these examples. We could also go a step further and ask students to rank the descriptions based on the degree of power given to people in each example. This would require students not only to identify the characteristics of representative democracies but also understand its relationship to other forms of government. There are many variations to these types of sorting activities, and using contrasting cases will help students build rich mental models. ChatGPT magnifies our ability to use contrasting cases by creating useful examples more quickly than we could by ourselves.
Adaptations for other subject areas:

Science: "Write a description of the gaseous planets but do not use the term "gaseous." Now do the same for the ice giants and terrestrial planets."

Math: "Make up a math problem about rate, time, and distance, and then, using a character named Sam, describe three different ways that Sam tried to solve the problem. Have two solutions be correct, and one solution be incorrect."

Examples to Explore Arguments

One of the most important skills taught students learn in all subject areas is the ability to evaluate and construct arguments. In English class, students might write essays about whether Troy, from August Wilson's Fences, is a sympathetic character; in social studies, they might consider multiple causes and effects of World War I; in biology, they might make their case for whether a virus is a living organism. We can use ChatGPT to generate examples of arguments with contrasting structures or types of evidence, then students can evaluate each argument's effectiveness. For such a lesson design, we would use the five steps we described earlier:

1. Establish criteria for strong argument

2. Prompt

3. Review and adapt

4. Prompt for contrasting cases, including weaker or stronger evidence, or different structures

5. Review and adapt again

We could also use the bot to address a long-standing problem when it comes to teaching students to build arguments: Sometimes we ask students to write an argument about a text, but we do not give students anyone to argue with. In this case, ChatGPT can provide a contrasting case for a student to push back against.

For example, imagine that a high school class has just finished reading Fences. The teacher would like kids to consider the role of the fence as a potential symbol in Wilson's play. Instead of asking students to write an argument about the role of fences in the play, the teacher could offer students a ChatGPT-generated argument that students could respond to.

This argument in itself becomes a contrasting case for students to reflect on and respond to. They can agree with some of the interpretation, build on some parts of it, and refute other parts. In this way, their argument becomes more authentic. They might even direct their response to the bot.

We might also offer students an opposing argument about Fences:

With these two arguments in hand, we could ask students to debate the merits of each, search the text for supporting evidence, and write about perspectives beyond just these two. Doing so would help students consider multiple interpretations of complex texts and develop and refine their own arguments.

Adaptations for other subject areas:

Science: Generate summaries of competing scientific theories

Social Studies: Generate different arguments explaining cause and effects of historical events

Before ChatGPT, teachers might spend hours searching for or writing suitable examples of arguments like these. Now, we are learning how to prompt ChatGPT to generate these arguments to suit our teaching purposes.

What's Next?

There's so much more this example machine can do to help us save time and employ our creativity so that we can better serve our students. Here are a few additional prompts that create other kinds of examples:

Rewrite this paragraph at a 6th grade reading level

Use more complex vocabulary

Translate this sentence into Spanish, and then Vietnamese

With all of its possibilities in mind, a reminder: There are lots of ways that ChatGPT can create problems for teachers. The technology is still young, and it does not know when it is wrong. For instance, when we first asked ChatGPT to generate interpretations of Fences, it made up lines from the play. When we asked it to talk about the short story "Linoleum Roses," by Sandra Cisneros, it added characters that do not exist in that story, and offered unsupportable interpretations of the text. Part of our job now is to help students understand both what the tool can do and what it can't.

Want some ideas for what to DO with these examples once you have them? Three instructional strategies that would work beautifully with multiple examples like these are contrasting cases, concept attainment, and inductive learning.

Come back for more.
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How to Use ChatGPT as an Example Machine

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To learn any concept well, students need to experience multiple examples of that concept, and ChatGPT can help you generate those examples fast.

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Chat GPT Tips for Teachers!

I got to admit, when I pictured the robots 🤖 coming, I didn't think they'd be here to do our students' homework and write their essays, but that day has arrived! Many schools and districts around the country have already put the site behind a firewall but trust me, the cat is out of the bag and its not going back in!

I do not think that is the right call and I think we must get ahead of this instead of trying to ineffectually sweep it under the rug. So, I wanted to write a little article about how teachers can use Chat GPT in the classroom.

So, if you don't know, CHATGPT is an AI software (currently free to use though that might change soon) that can do a ton of writing tasks when given commands. Students can use chatgpt to ask questions and receive answers or to generate entire written assignments based on any prompt.
I actually just had it write out a screen play for President Washington meeting with his cabinet to discuss the constitutionality of the Bank except it was a Seinfeld episode and it was pretty funny while still having Jefferson opposed to the Bank, Washington neutral at first, and Hamilton in support. (Jefferson, as Kramer wanted to use a cardboard box with some gold coins instead of a national bank 🤣).
But seriously, this has many teachers (and tons of other professions) freaking out.

Is it the end of homework? The takehome essay? Critical thinking?
NOPE!
It can actually be a powerful tool and though we are all still just figuring out exactly what this is and how we can use it, I would like to offer some suggestions on how you can respond to this new tool in a way to will help you and help your students as well!

Here are seven ideas for how you can respond to chatgpt in your classrooms:

1. If you have students that struggle with primary or even secondary texts, use chatGPT to modify documentsinto different reading levels. Simply copying and pasting a primary or secondary source into chatgpt, you can quickly and easily generate a version of the text that is simpler or more complex, depending on the needs of your students. It can also translate it into any language. This can save you time as a teacher and help ensure that all of your students are able to succeed in your class. I like to create two columns, on the left is the original document so students can still read the original text and to the right is the modified one. The program can also create original questions for students to respond to as well and I was actually very impressed with the comprehension and critical thinking questions it developed. It can even create project ideas around the text.

2. Ensure you spend time in class teaching why history mattersand how it can be helpful to them as individuals. If students know that learning history can empower them to be better people, can inspire them to change and improve the world, that it connects them to their culture, community, country, and their global family- they will understand the inherent value in learning history and not just having a bot do all their thinking. I have two lessons linked below designed to get students to really understand the importance of history that you may like.

3. Use it to generate project ideas for topics if you are feeling stuck. This is a great way for teachers to use Chat GPT. I recently typed, "create 5 project ideas for World War II for high school students that has them apply lessons from the war to modern issues" and it each suggestion was creative and impressive. One had students consider the impacts of the Nurremberg Trials and apply them to recent genocides, a project called, "The Power of Words: Analyzing the Rhetoric of World War II and Modern Propaganda", and a project called"Resisting Oppression: Then and Now: on how you can learn from groups like the White Rose who resisted oppression to make change in the world today. I then asked it to create a break down of expectations, learner outcomes, and a general rubric and bang.

4. Don't try to hide this tool from students- the cat is out of the bag and its never going back in. Instead, teach students about the limitations of chatgpt and other AI tools. Chatgpt is just one source and it is very limited. It is important for students to understand these limitations and to use their own judgment when evaluating the information they receive.This could lead to a great discussion.How can they trust it and rely on it to be accurate?

5. episode of the History Teachers Club podcast we talked all about history labs if want to learn more.Use chatgpt in a history lab activity.ChatGPT can give you one answer to an inquiry question like "Why did the colonies rebel against Britain and declare independence?" Set up a lab with other documents at different stations. Other docs could be a textbook excerpt, and a couple primary sources. This way they see how they still need to corraborate evidence and critique the sources they come across. Evaluating evidence is more important than ever- so make sure history labs or inquiry lessons are a routine part of class so students learn to be critical consumers of media and not blindly following where demagogues, google searches, or bots lead them. In

6. If you are worried that students will still use ChatGPT to write their essays or do much of the thinkingthen Not only does it keep them more accountable, but it also helps students build more skills in your class. On Google Classroom or Canvas, students submit their written essay and a 45-90 second video response. I started doing this during Covid distance learning and mandated students do not read their response, but must be clearly explaining things like: what is their thesis, strongest evidence, what did the essay teach them about X topic, and how do they think they did on the essay. It keeps them accountable and helps them work on speaking clearly and professionally in a quick presentation. These also could be done live in class if you wanted. Have students question one another or give feedback to make it even more powerful.have students turn in a video reflection with each essay.

7. Use it create fictional role-plays that students act out based on historical topics. You can have students modify or elaborate onto the suggested screen play based on a reading, lecture, or lesson you taught. Having the AI write the screenplay of course takes away from student ownership of the material however, it can give them a great starting point that they then improve or extend which can help the critical thinking process. Then if they act it out they are committing it to memory better.

I hope these ideas are helpful as you navigate the use of chat gpt in the classroom. As always, don't hesitate to reach out to us if you have any questions or need any additional support.

And for more ideas, please check out the amazing stuff that John Spencer has been producing on the topic.

Thank gosh for all of us that John Spencer shares his wisdom and insights.

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7 Ways History Teachers Can Use Chat GPT in the Classroom!

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Chat GPT Tips for Teachers! I got to admit, when I pictured the robots 🤖 coming, I didn't think they'd be here to do our students' homework and write their essays, but that day has arrived!

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Been Kim: A Scientific Approach to Interpretability

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It's an exciting time for AI, with fascinating advances in generated media and many other applications, some even in science and medicine. Some folks may dream about what more AI can create and how much bigger models we may engineer. While those directions are exciting, I argue that we need to pursue much less flashy work: going back to the basics and studying AI models as targets of scientific inquiry.

Why and how? The field of interpretability aims to create tools to generate "explanations" for the output of complex models. This field has emerged naturally out of a need to build machines that we can have a dialog with: What is your decision? Why did you decide that? For example, a tool takes an image and a classification model, and generates explanations in the form of weighted pixels. The higher a pixel's weight, the more important it is. For instance, the more its value affects the output, the more important it may be - but the definition of importance differs depending on the tool.

While there are some successes, many tools have turned out to behave in ways we did not expect. For example, it has been shown that explanations of an untrained model are quantitatively and qualitatively indistinguishable to those of a trained model. (Then what does the explanation explain?) Explanations often change with small changes in the input despite resulting in the same output. In addition, there isn't much causal relationship between a model's output (what we are trying to explain) and the tool's explanation. Other work shows that good explanations of a model's output don't necessarily have a positive influence on how people use the model.

What does this mismatch between expectation and outcome mean, and what should we do about it? It suggests that we need to examine how we build these tools.

Currently we take an engineering-centric approach: trial and error. We build tools based on intuition (for instance, explanations would be more intuitive for humans if we generate a weight per a chunk of pixels instead of individual pixels). While the engineering-centric approach is useful, we also need fundamental principles (what can be called science) to build better tools.

In developing drugs, for instance, trial and error is essential (say, testing a new medicine through rigorous clinical trials before deploying it), but it goes hand-in-hand with sciences like biology and genetics. While science has many gaps in understanding how the human body works, it provides fundamental principles in creating the tool (in this case, drugs). In other words, pursuing both science and engineering simultaneously, such that each can inform the other, has shown to be a successful way to work with complex beings (humans).

The field of machine learning needs to study our complex aliens (models) like other disciplines study humans. How would such study of these aliens help interpretability? Here's an example. A team at the University of Tübingen found that neural networks see texture (say, an elephant's skin) more than shape (an elephant's outline). Even if we see an elephant's contour in the explanation of an image - perhaps in the form of collective highlighted pixels - the study informs us that the model may not be seeing the shape but rather the texture. This is called inductive bias - a tendency of a particular class of models due to either its architecture or the way we optimize it. Revealing such tendencies can help us understand this alien, just as revealing a human's tendency (bias) can be used to understand human behavior (such as unfair decisions).

In this way, the methods often used to understand humans can also help us understand AI models. These include observational studies (say, observing multi-agents from afar to infer emerging behaviors), controlled studies (for instance, intervening in a multi-agent system to elicit underlying behaviors), and surgery (such as examining the internals of the superhuman chess player AlphaZero). For AI models, thanks to the way their internals are built - they are made of math! - we have one more tool: theoretical analysis. Work along this direction has already yielded exciting theoretical results on the behaviors of models, optimizers, and loss functions. Some take advantage of classical tools in statistics, physics, dynamical systems, or signal processing. Many tools from different fields are yet to be explored in the study of AI.

Pursuing science doesn't mean we should stop engineering. The two go hand in hand: Science will enable us to build tools under principles and knowledge, while engineering enables science to become practical. Engineering can also inspire science: What works well in practice can provide hints to structures of models that we wish to formalize in science, just like the high-performance of convolutional networks in 2012 inspired many theory papers that tried to analyze why convolutions help generalization.

I'm excited to enter 2023 and many other years to come as we advance our understanding of our aliens and invent ways to communicate with them. By enabling a dialogue, we will enable richer collaborations and better leverage the complementary skill sets of humans and machines.
Been Kim is a research scientist at Google Brain. Her work on helping humans to communicate with complex machine learning models won the UNESCO Netexplo award.

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Been Kim: A Scientific Approach to Interpretability

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It's an exciting time for AI, with fascinating advances in generated media and many other applications, some even in science and medicine. Some folks may dream about what more AI can create and how much bigger models we may engineer.

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Compare Reading Comprehension between humans and AI programs

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Compare Reading Comprehension between humans and AI programs

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You can try Google's language processor for free.  Have students read and analyze an article to identify the most "salient" points.  Then copy and paste the text into the google language processor and compare the results.  What does this tell us about how machines think?  Which results were better?

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Send feedback Stay organized with collections Save and categorize content based on your preferences.

Content classification has a major update available. See here for more information.

Derive insights from unstructured text using Google machine learning.

New customers get $300 in free credits to spend on Natural Language. All customers get 5,000 units for analyzing unstructured text free per month, not charged against your credits.

Get insightful text analysis with machine learning that extracts, analyzes, and stores text

Train high-quality machine learning custom models without a single line of code with AutoML

Apply natural language understanding (NLU) to apps with Natural Language API

Benefits

Insights from customers

Use entity analysis to find and label fields within a document-including emails, chat, and social media-and then sentiment analysis to understand customer opinions to find actionable product and UX insights.

Multimedia and multilingual support

Natural Language with Speech-to-Text API extracts insights from audio. Vision API adds optical character recognition (OCR) for scanned docs. Translation API understands sentiments in multiple languages.

Content targeting and discovery

Use Google's state-of-the-art language technology to classify content across media for better content recommendations and ad targeting.

Demo

Natural Language API demo

Try the API

Key features

Three natural language solutions that work with your text

AutoML

Train your own high-quality machine learning custom models to classify, extract, and detect sentiment with minimum effort and machine learning expertise using Vertex AI for natural language, powered by AutoML. You can use the AutoML UI to upload your training data and test your custom model without a single line of code.

Natural Language API

The powerful pre-trained models of the Natural Language API empowers developers to easily apply natural language understanding (NLU) to their applications with features including sentiment analysis, entity analysis, entity sentiment analysis, content classification, and syntax analysis.

Healthcare Natural Language AI

Gain real-time analysis of insights stored in unstructured medical text. Healthcare Natural Language API allows you to distill machine-readable medical insights from medical documents, while AutoML Entity Extraction for Healthcare makes it simple to build custom knowledge extraction models for healthcare and life sciences apps-no coding skills required.

"By using custom entity extraction within AutoML Natural Language, we can use large data sets to train our model and continually improve the process, no matter where the document comes from."

Kiran Kaza, Head of Mobile Engineering, DocuSign

Documentation

Cloud Natural Language API

Provide natural language understanding technologies, such as sentiment analysis, entity recognition, and other text annotations to developers.

Build custom ML models with AutoML for natural language data

Create models to classify documents, identify entities in documents, or analyze the prevailing emotional attitude in a document.

Compare features

Which Natural Language product is right for you?

Natural Language API reveals the structure and meaning of text with thousands of pretrained classifications. AutoML classifies content in custom categories for your specific needs.

Integrated REST API

Natural Language is accessible via our REST API. Text can be uploaded in the request or integrated with Cloud Storage.

AutoML

Integrated REST API

Natural Language is accessible via our REST API. Text can be uploaded in the request or integrated with Cloud Storage.

Natural Language API

Integrated REST API

Natural Language is accessible via our REST API. Text can be uploaded in the request or integrated with Cloud Storage.

Pricing

Pricing

Translation pricing is based on usage, so you only pay for what you use.

Take the next step

Start building on Google Cloud with $300 in free credits and 20+ always free products.

Take the next step

Start your next project, explore interactive tutorials, and manage your account.

Content classification has a major update available. See here for more information.

Derive insights from unstructured text using Google machine learning.

New customers get $300 in free credits to spend on Natural Language. All customers get 5,000 units for analyzing unstructured text free per month, not charged against your credits.

Get insightful text analysis with machine learning that extracts, analyzes, and stores text

Train high-quality machine learning custom models without a single line of code with AutoML

Apply natural language understanding (NLU) to apps with Natural Language API

Benefits

Insights from customers

Use entity analysis to find and label fields within a document-including emails, chat, and social media-and then sentiment analysis to understand customer opinions to find actionable product and UX insights.

Multimedia and multilingual support

Natural Language with Speech-to-Text API extracts insights from audio. Vision API adds optical character recognition (OCR) for scanned docs. Translation API understands sentiments in multiple languages.

Content targeting and discovery

Use Google's state-of-the-art language technology to classify content across media for better content recommendations and ad targeting.

Demo

Natural Language API demo

Try the API

Key features

Three natural language solutions that work with your text

AutoML

Train your own high-quality machine learning custom models to classify, extract, and detect sentiment with minimum effort and machine learning expertise using Vertex AI for natural language, powered by AutoML. You can use the AutoML UI to upload your training data and test your custom model without a single line of code.

Natural Language API

The powerful pre-trained models of the Natural Language API empowers developers to easily apply natural language understanding (NLU) to their applications with features including sentiment analysis, entity analysis, entity sentiment analysis, content classification, and syntax analysis.

Healthcare Natural Language AI

Gain real-time analysis of insights stored in unstructured medical text. Healthcare Natural Language API allows you to distill machine-readable medical insights from medical documents, while AutoML Entity Extraction for Healthcare makes it simple to build custom knowledge extraction models for healthcare and life sciences apps-no coding skills required.

"By using custom entity extraction within AutoML Natural Language, we can use large data sets to train our model and continually improve the process, no matter where the document comes from."

Kiran Kaza, Head of Mobile Engineering, DocuSign

Documentation

Cloud Natural Language API

Provide natural language understanding technologies, such as sentiment analysis, entity recognition, and other text annotations to developers.

Build custom ML models with AutoML for natural language data

Create models to classify documents, identify entities in documents, or analyze the prevailing emotional attitude in a document.

Compare features

Which Natural Language product is right for you?

Natural Language API reveals the structure and meaning of text with thousands of pretrained classifications. AutoML classifies content in custom categories for your specific needs.

Integrated REST API

Natural Language is accessible via our REST API. Text can be uploaded in the request or integrated with Cloud Storage.

AutoML

Integrated REST API

Natural Language is accessible via our REST API. Text can be uploaded in the request or integrated with Cloud Storage.

Natural Language API

Integrated REST API

Natural Language is accessible via our REST API. Text can be uploaded in the request or integrated with Cloud Storage.

Pricing

Pricing

Translation pricing is based on usage, so you only pay for what you use.

Take the next step

Start building on Google Cloud with $300 in free credits and 20+ always free products.

Take the next step

Start your next project, explore interactive tutorials, and manage your account.

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Send feedback Stay organized with collections Save and categorize content based on your preferences. Content classification has a major update available. See here for more information. Derive insights from unstructured text using Google machine learning. New customers get $300 in free credits to spend on Natural Language.

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Questions for Evaluating AI Chat Responses

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Questions for Evaluating AI Chat Responses

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Teaching Writing in Harmony with Advancing AI Technology - Dr. Catlin Tucker

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In a previous blog post and corresponding podcast, I said, "if teachers design student-centered learning experiences that allow students to write with support in class, ChatGPT won't be nearly as disruptive as some articles claim." In the two months since I published that post, I've had the opportunity to play with ChatGPT, exploring how teachers [...]