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Lessons Learned from Ten Generative AI Misuse Cases - Cyberbullying Research Center

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As generative AI tools become fixtures in our lives, the possibility for abuse will grow. We highlight ten major misuse cases to raise awareness and to mobilize prevention and response solutions.

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Generative AI and Creative Learning: Concerns, Opportunities, and Choices

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Generative AI and Creative Learning: Concerns, Opportunities, and Choices

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As each new wave of technology ripples through society, we need to decide if and how to integrate the technology into our learning environments. That was true with personal computers, then with the internet, and now with generative AI technologies. For each new technology, there are many different ways that we can integrate the technology into how we teach and learn. These choices are critically important: different choices can have very different outcomes and implications.

How should we make these choices? I think we need to decide what type of learning and education we want for our children, our schools, and our society-and then design new technologies and applications that align with our educational values and visions.

What does that mean for the integration of new generative AI technologies such as ChatGPT into our learning environments?

In my view, the top educational priority in today's world is for young people to develop as creative, curious, caring, collaborative human beings. With the pace of change accelerating in all parts of the world, today's children will face a stream of uncertain, unknown, and unpredictable challenges throughout their lives, and the proliferation of new AI technologies will further accelerate the changes and disruptions. As a result, it is more important than ever for children from diverse backgrounds to have opportunities to develop the most human of their abilities-the abilities to think creatively, engage empathetically, and work collaboratively-so that they can deal creatively, thoughtfully, and collectively with the challenges of a complex, fast-changing world.

Unfortunately, I find that many of the current uses of AI in education are not aligned with these values-and, in fact, they could further entrench existing educational approaches at a time when significant changes are needed. Too often, today's AI technologies are used in ways that constrain learner agency, focus on "close-ended" problems, and undervalue human connection and community.

But I also see intriguing opportunities for new generative AI technologies. I believe that these new AI technologies (compared with earlier AI technologies) have greater potential for supporting young people in project-based, interest-driven creative learning experiences-and thus supporting their development as creative, curious, collaborative learners. We could be at a moment for significant educational change: the disruptions caused by new generative AI technologies are leading more people to recognize the need for fundamental changes in our approaches to education and learning. But new AI technologies will contribute to these changes only if people make explicit, intentional choices in the ways they design and use these new tools.

In this article, I'll start by discussing my concerns about current uses of AI tools in education, then I'll explore how we might leverage new generative AI technologies to support creative learning experiences.

Most critiques of AI systems highlight problems that the developers of the systems have not sufficiently focused on (e.g., biases or inaccuracies based on the sets of examples used to train the systems, and inadequate acknowledgment or compensation for artists and writers whose work is used in the training) and on problems that arise when the systems are used differently than the developers had hoped (e.g., students turning in papers produced by AI systems as if the work were their own). These are serious and important problems, and they need to be addressed. But in this article, I have a different focus. I will be discussing why I am concerned about many AI-in-education systems even when they work exactly how their developers intended and are used exactly how their developers had hoped.

Concern #1: Constraining Learner Agency

Back in the 1960s, as researchers were beginning to explore how computers might be used in education, there were two primary schools of thought. One focused on using computers to efficiently deliver instruction and information to the learner. The other focused on providing learners with opportunities to use technologies to create, experiment, and collaborate on personally meaningful projects. Seymour Papert referred to these two different approaches as instructionist and constructionist.

Over the years, most AI researchers and developers have focused on the first approach, developing "intelligent tutoring systems" or "AI coaches" that provide instruction to students on particular topics, continually adapting the trajectory of the instruction based on student responses to questions. These systems have been promoted as a personalized approach to teaching, aiming to provide each student with customized feedback and instruction based on their current level of understanding, as opposed to a one-size-fits-all approach in which the same instruction is delivered to all students.

With advances in AI technology, these tutoring systems have become more effective in delivering instruction that adapts to individual learners. For example, some AI tutors and AI coaches have demonstrated improved results when they deliver instruction through a virtual character that looks like the student's favorite teacher or favorite celebrity.

I do not doubt these research results, but I worry that some of these "improvements" are perpetuating and reinforcing an educational approach that is in need of a major overhaul. To a large degree, AI tutors and coaches have been designed to be in control of the educational process: setting goals, delivering information, posing questions, assessing performance. That is also the way most classrooms have operated over the past couple centuries. But the realities of today's world require a different approach: providing students with opportunities to set their own goals, build on their own interests, express their own ideas, develop their own strategies, and feel a sense of control and ownership over their own learning. This type of learner agency is important in students' development, helping them develop the initiative, motivation, self-confidence, and creativity that will be needed to contribute meaningfully in their communities.

AI tutors and coaches are promoted as "personal" since they deliver personalized instruction. But in my view, a truly personal approach to learning would give the learner more choice and control over the learning process. I would like learners to have more control over how they are learning, what they are learning, when they are learning, where they are learning. When learners have more choice and control, they can build on their interests, so that learning becomes more motivating, more memorable, and more meaningful-and learners make stronger connections with the ideas that they are engaging with.

Some new AI tutors and coaches try to support greater learner agency. Instead of controlling the instructional flow, they are designed to provide tips, advice, and support when students ask for help. But even if AI tutors are designed with the best of intentions, I worry that some learners will experience them as intrusive. For example, Sal Khan of Khan Academy expects that future AI tutors will intervene when a student's attention wanders, telling the student: "Hey, I think you're a little distracted right now. Let's get focused on this." That type of intervention might be helpful for some students, but feel intrusive or disempowering for others. As one educator wrote to me: "I would absolutely crumble if I was a middle schooler and had to chat with an AI bot that was trying to pull the answer out of me."

Concern #2: Focusing on "Close-Ended" Problems

Over the past decade, there has been a proliferation of websites designed to teach young people how to code. The vast majority of these sites are organized around a series of puzzles, asking students to create a program to move a virtual character past some obstacles to reach a goal. In the process of solving these puzzles, students learn basic coding skills and computer science concepts.

When our Lifelong Kindergarten group at the MIT Media Lab developed Scratch, we took a different approach. With Scratch, young people can create animations, games, and other interactive projects based on their interests-and share them with others in an online community. Through this project-based, interest-driven approach, students still learn important coding skills and computer science concepts, but they learn them in a more motivating and meaningful context, so they make deeper connections with the ideas. At the same time, the project-based, interest-driven approach helps young people develop their design, creativity, communications, and collaboration skills, which are more important than ever in today's world.

So why do so many coding sites focus on puzzles rather than projects? One reason is that it is easier to develop AI tutors and coaches to give advice to students as they work on puzzles. With puzzles, there is a clear goal. So as a student works on a puzzle, the AI tutor can analyze how far the student is from the goal, and give suggestions on how to reach the goal. With projects, the student's goal might not be clear, and might change over time, so it is more difficult to develop an AI tutor to give advice.

Over the years, most AI tutors and coaches have been designed to provide instruction on problems that are highly structured and well defined. With new AI technologies, there are possibilities for developing systems that could provide feedback and advice on more open-ended projects. But I have been disappointed by the way that most AI researchers and EdTech companies are putting these new technologies to use. For example, I recently saw a presentation in which a prominent AI researcher showed off a new ChatGPT-based system that was asking students a list of single-answer questions. The conversational interface was new, but the educational approach was old. And when Khan Academy recently introduced its AI tutor called Khanmigo, the first example it showed on its website was a multiplication problem involving a fraction (the tutor asked: "What do you think you need to do to multiply 2 by 5/12?"). This type of problem has a single answer and well-defined strategies for getting to the answer-exactly the type of problem that AI tutors have traditionally focused on.

There is an important educational choice: Should schools focus more on open-ended projects or "close-ended" problems? My preference is to put more emphasis on projects where students have more opportunities to learn to think creatively, express their ideas, and collaborate with others-while still learning important concepts and basic skills, but in a more meaningful and motivating context.

Schools have generally preferred close-ended problems since they are easier to manage and assess. Schools end up valuing what they can most easily assess, rather than figuring out ways to assess the things that are most valuable. I worry that EdTech companies and schools will focus on AI tutors that fit in this same framework and further entrench this educational approach, crowding out much-needed changes in how and what students learn. Instead, as I discuss in the Opportunities section below, I hope that there are more efforts to use new AI technologies to support learners as they engage in project-based, interest-driven learning experiences.

Concern #3: Undervaluing Human Connection

In some situations, AI tutors and coaches can provide useful advice and information. And with advances in AI technology, these systems are becoming better at deciding what information to deliver when, and customizing the information based on what students have already learned and what misconceptions they might have.

But good teaching involves more than that. A good teacher builds relationships with students, understands students' motivations, empathizes with students' concerns, relates to students' lived experiences, and helps students connect with one another. Facilitating a student's learning is a subtle process, much more complex than simply delivering information and instruction at the right time. A good teacher understands how to cultivate a caring community among students, so that students feel welcomed, understood, and supported. A good teacher understands how to create an environment in which students feel comfortable taking the risks that are an essential part of a creative learning process.

Some AI tutors and coaches now try to take social-emotional factors into account-for example, using sensors and cameras to get a sense of a student's emotional state. But these AI systems still are not able to understand or empathize with a learner's experience or cultivate a caring community the way a human teacher can.

So it bothers me when AI tutors and coaches are promoted as if they are equivalent to human teachers. For example, a promotional video for a new AI-based tutor from Microsoft says that it is "like there are 20 extra teachers in one classroom." And Khan Academy promotes its Khanmigo system as "a world-class tutor for anyone, anywhere." Some people might shrug off these descriptions as marketing hype. But I worry that they contribute to a devaluation of the human dimensions of teaching. Human teachers are fundamentally different from AI tutors, and I think it is important to recognize the special qualities of human teachers-while also recognizing what AI systems do particularly well.

Some AI researchers and EdTech companies try to avoid the direct comparison to human teachers by positioning their systems as AI companions, collaborators, or copilots rather than AI tutors. But they still try to emphasize the humanness of their systems. I find it especially troubling when AI systems describe their own behavior as if they were human. For example, I recently saw a presentation about a new AI system designed to interact with young children. In its interactions, the AI system talked in a humanlike way about its intentions and feelings. This felt problematic to me, since it could mislead young children to believe that AI systems have motivations and feelings similar to their own.

I do not want to idealize the capabilities of human teachers. Many teachers do not have experience or expertise in facilitating creative learning experiences-and many children do not have access to teachers who do. There is a role for AI-based systems to supplement human teachers (as discussed in the next section). But we should clearly recognize the limitations and constraints of these AI systems, and we should not be distracted from the important goal of helping more people become good teachers and facilitators.

More generally, we need to make sure that the current enthusiasm over AI systems does not lead to a reduction in interactions and collaborations with other people. The importance of the human connection and community became even more apparent during the pandemic, when different schools adopted different pedagogical approaches. Some schools implemented remote-learning routines focused on delivery of instruction based on the traditional curriculum; in those schools, many students felt increasingly isolated and disillusioned. Other schools focused more on social-emotional and community aspects of learning, emphasizing the importance of supporting and collaborating with one another; in those schools, students felt a stronger sense of connection, empathy, and engagement. As Harvard Graduate School of Education professor Jal Mehta wrote: "Classrooms that are thriving during the pandemic are the ones where teachers have built strong relationships and warm communities, whereas those that focus on compliance are really struggling."

The pandemic highlighted the importance of empathy, connection, and community in teaching and learning. As the pandemic recedes, and as AI systems proliferate, we should keep our focus on these very special human qualities.

Over the past year, there have been a growing number of educational initiatives to teach K-12 students about generative AI. While some of these initiatives, in my view, focus too much on teaching technical details on how current AI systems work (much like teaching students about floppy disks and motherboards in the early days of personal computing), the initiatives play a valuable educational role in helping student gain an understanding of the ethical and societal implications of generative AI technologies and an awareness of how these technologies can provide biased results and misleading information.

But I think the most important educational opportunities will come not from teaching students about AI but rather in helping students learn with AI-that is, supporting students in using AI tools to imagine, create, share, and learn. And, as a bonus, learning with AI can be the best way for students to learn about AI too.

Unfortunately, when most EdTech companies and AI researchers focus on learning with AI, they tend to package it in an instructionist paradigm, developing intelligent tutoring systems (e.g., teaching arithmetic skills or vocabulary words) that are subject to all of the problems described above. But it does not have to be that way. I believe that generative AI technologies (compared with earlier AI technologies) provide greater opportunities for breaking out of the instructionist paradigm and supporting a more constructionist approach to learning. That is, there is potential to design and use generative AI technologies to support young people in project-based, design-oriented, interest-driven creative learning experiences-and thus help them develop the creativity, curiosity, and collaboration skills that are needed to thrive in today's fast-changing world. But that will happen only if we make intentional choices in how to design and use these new technologies.

Supporting the Creative Learning Process

So how can people design and use generative AI technologies to support a more constructionist approach to learning? In our work in the Lifelong Kindergarten research group, we have identified four guiding principles for supporting this type of learning. We call them the Four P's of Creative Learning: projects, passion, peers, and play. That is, young people are most likely to develop as creative, curious, collaborative learners when they have opportunities to work on projects, based on their passions, in collaboration with peers, in a playful spirit.

So, as researchers, companies, and educators integrate generative AI technologies into learning environments, and as students use these new technologies, they should all consider how AI can be used to support the Four P's of projects, passion, peers, and play:

Projects. We should provide students with opportunities to use generative AI tools throughout the process of working on a project, while making sure that they retain creative control. If they are feeling stuck at the start of a project, they could enter a few preliminary ideas and ask the system for variants or refinements of the ideas. When something does not work as expected in the project, they could explain the problem to an AI system and ask for help in debugging it.

Passion. When people work on projects that they really care about, they are willing to work longer and harder, persist in the face of challenges, and make deeper connections to the ideas they encounter. So we should explore ways for students to use generative AI tools to create projects that they find personally meaningful. For example, Karishma Chadha at the MIT Media Lab is developing generative AI tools and activities that enable young people to create dynamic representations of themselves-and create and share personal stories based on those representations-as a way to explore and express their cultural identity.

Peers. Most AI tutoring tools have been designed for one-to-one interaction. But we know that most creative learning experiences involve people learning with and from one another. So generative AI tools should be designed to engage young people working together on projects, helping them connect and collaborate with others who have similar interests or complementary skills.

Play. A playful attitude is not just about laughing and having fun. It is based on a willingness to experiment, try new things, take risks, and push the boundaries. So rather than developing AI tutors that guide students toward a solution, we should provide young people with opportunities to use AI technologies to explore new directions, tinker with new possiblities, and iteratively refine their ideas.

This 4P approach to designing and using generative AI technologies is very different from traditional AI tutoring systems. In the 4P approach, students have greater control of the process, choosing how and when to use AI tools to support their own design and problem-solving practices, and using the tools as a catalyst (not a replacement) for their own creative process and their collaboration with others. It is somewhat similar to the way that people, while working on a project, do an online search or watch a YouTube video to get new ideas or information. Generative AI systems can serve as an additional resource during the creative-learning process.

We should not expect (or want) AI systems to play the same role as human tutors or coaches or companions. Rather, we should consider AI systems as a new category of educational resource, with their own affordances and limitations. When learners are looking for help or inspiration, they sometimes talk with a friend or refer to a book or do an online search or watch a video. Each plays a different role. We can add AI systems to this mix of resources.

How Could Generative AI Transform Coding?

Over the past decade, there has been a surge of interest in helping young people learn to code. With our Scratch programming language, for example, young people can use graphical coding blocks to create interactive stories, games, and animations based on their interests, then share their creations with one another in an online community. Through this 4P process, young people not only learn computational and technical skills, they also develop their abilities to think creatively, reason systematically, and work collaboratively, essential skills for everyone in today's world.

Many people are now wondering if generative AI technologies could end the need for people to learn to code. After all, part of the promise of generative AI is that people will be able to use everyday language to interact with computers. Why learn to write code in a programming language when you can simply engage in conversation with the computer, telling it what you want it to do?

I am skeptical that the ability of generative AI systems to produce computer programs will eliminate the value of people learning to code-just as I am skeptical that the ability of ChatGPT to generate essays will eliminate the value of people learning to write. But I do think that generative AI technologies could significantly change how young people program computers.

To evaluate the benefits and drawbacks of new AI-based approaches to coding, it is worth considering the goals of learning to code. For some young people, of course, learning to code can serve as a starting point on a path towards a career as a professional programmer or software developer. But I am more interested in why it is valuable for everyone to learn to code-just as it is valuable for everyone to learn to write, even if they aren't going to become a professional writer. As I see it, learning to code provides young people with opportunities to:

create new types of projects and express themselves in new ways

learn strategies for design and problem solving

gain a sense of control over technologies that are ubiquitous in their lives

describe, understand, and debug processes

experience the joy of creating things they care about

If we use generative AI to introduce changes to the ways people code, will it enhance these benefits-or undermine them?

AI-based changes to programming environments can come in many different forms. For example, Eric Rosenbaum of the Scratch Foundation has been experimenting with ways to integrate AI-based image-generation tools within Scratch, so that young people can generate graphic characters and backdrops to use in their projects. If a child wants an anime-style purple frog in their project, they can type in "anime-style purple frog" and see what the system produces. If they are not satisfied with the results, they can refine the prompt to get something closer to what they want. This new tool has limitations, and it certainly should not replace the Scratch paint editor or image library, but it can provide another option for creating images within a Scratch project.

Rosenbaum has also been developing new AI-based programming blocks for Scratch-for example, new blocks that enable characters in a project to engage in ChatGPT-style conversations guided by context provided by the programmer. Rosenbaum plans to explore a new hybrid form of coding that combines traditional coding blocks with AI-based blocks.

Several researchers are also experimenting with ways for young people to ask for advice and tips as they are coding their projects. Whereas earlier AI systems could provide advice effectively only if the project goals were clearly articulated in advance, new generative AI tools have potential for providing useful advice even when project goals are ambiguous or evolving. And as long as the student (rather than the AI system) maintains control over the flow of the conversation, this use of AI could align well with a 4P approach for learning to code.

Over time, there could be more fundamental changes to the ways that children tell the computer what to do. In the fifty years since children began programming computers, the biggest change so far has been from text-based coding (using languages like Logo and Basic) to graphical building-block coding (popularized by Scratch). Will generative AI bring a shift to conversational interfaces, in which children use everyday language to tell the computer what to do?

Professional programmers are already using tools like GitHub's Copilot to generate code. And several groups, including the Playful Invention Company and the App Inventor team, have produced prototypes of conversational coding systems for children. As with the shift from text to blocks, the shift to conversation could make it easier and more intuitive to tell the computer what to do. There are continuing questions of how well computers can deal with the ambiguities of everyday language, but new generative AI systems are making progress on this issue, especially as people learn how to iteratively refine their prompts to the systems.

For me, the bigger question is whether these new conversational coding approaches will preserve all of the traditional benefits of learning to code (as listed above). Even if children are able to successfully create projects through a conversational coding interface, will they still feel a sense of control over the technology? Will they still learn strategies for design and problem solving? Will they still experience the joy of creating? Also: Will it be possible for young people to remix one another's projects, as they do in Scratch? And will the systems be welcoming and engaging for learners across diverse backgrounds?

The answers to these questions are not clear. Looking ahead, it will be valuable to develop prototypes of new conversational coding interfaces and explore how children create and learn with them. One challenge is that these interfaces might enhance some of the traditional benefits of learning to code while deteriorating others. We will need to figure out whether the tradeoffs are worth it.

There are many different ways that people can use generative AI technologies to support learning and education.

Some uses of generative AI systems will constrain learner agency, focus on "close-ended" problems, or undervalue human connection and community-and further entrench existing approaches to education and learning at a time when change is needed. I worry that inertia and market pressures will push the educational uses of generative AI in this direction.

But it is also possible to use generative AI technologies to support a more project-based, interest-driven, human-centered, collaborative approach to learning, enabling learners to develop the motivation, creativity, and curiosity that they will need to thrive in today's complex, fast-changing world. The disruptions caused by new generative AI technologies are leading more people to recognize the need to rethink approaches to education and learning, so we could be at a cultural moment where fundamental changes are possible.

The choice is up to us. The choice is more educational and political than technological. What types of learning and education do we want for our children, our schools, and our society? All of us-as teachers, parents, school administrators, designers, developers, researchers, policymakers-need to consider our values and visions for learning and education, and make choices that align with our values and visions. It is up to us.

I would like to thank everyone who provided suggestions on earlier drafts of this paper, helping me to refine my ideas through multiple iterations of the document. I am especially grateful to (alphabetically) Hal Abelson, Karen Brennan, Leo Burd, Karishma Chadha, Kartik Chandra, Pattie Maes, Carmelo Presicce, Eric Rosenbaum, Natalie Rusk, and Brian Silverman-each of whom provided valuable feedback (though, of course, their feedback does not imply that they agree with all of the ideas presented in this paper).

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Generative AI and Creative Learning: Concerns, Opportunities, and Choices

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As each new wave of technology ripples through society, we need to decide if and how to integrate the technology into our learning environments. That was true with personal computers, then with the internet, and now with generative AI technologies. For each new technology . . .

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High school senior Alex Bordeaux was scrolling through social media to check out Taylor Swift's latest outfits when she stumbled on disturbing pornographic images of the pop music icon.

Bordeaux, 17, has studied artificial intelligence in her journalism and yearbook class at Dawson County High School in Dawsonville, Ga., so she knew immediately that she was looking at "deepfakes"-AI-manipulated video, audio, or photos created using someone's voice or likeness without their permission.

But she was furious-and baffled-that so many on social media at least pretended to treat the images as the real deal. "It's just so crazy how easily people can see something on the internet and immediately believe it," she said. "A lot of kids my own age do not double check anything."

At the same time, it hurt to see someone she's admired so deeply, for so long, digitally degraded, added Bordeaux, who credits Swift's 2014 hit "Welcome to New York" as inspiring her dream of living in the Big Apple.

"Taylor Swift seems so untouchable because she's so rich. She's so famous. And she's so sweet. And she's basically been used" by online trolls, Bordeaux said.

Not all students have the background that Bordeaux does to understand the role AI -a relatively nascent technology-plays in creating deepfakes like the ones targeting Swift, as well as other fake images and video designed to spread misinformation, influence public opinion, or con people out of money, experts say.

Schools need to make teaching about this type of technology a priority.

Deepfakes are "a big concern" because they "pollute our information environment to a pretty astonishing degree," said Kate Ruane, the director for the Center for Democracy and Technology's Free Expression project, a nonprofit group that promotes digital rights.

If educators aren't already thinking about teaching students about deepfakes, "they really should be ... because this is the water that their students are swimming in every day," she added.

'It's gonna continue to happen because AI is growing so massively'

Many of the deepfake images of Swift were taken down, but not before they'd attracted plenty of eyeballs. One posted on X (formerly Twitter) racked up more than 45 million views, 24,000 reposts, and hundreds of thousands of likes before the user who shared the images had their account suspended for violating X's policy, reported the Verge, a technology news website.

By that point, the post had been accessible on the platform for about 17 hours, according to the Verge. The content became so problematic that X had to temporarily block searches of Swift, the Wall Street Journal reported.

Bordeaux knows Swift isn't the first celebrity to be the victim of a viral deepfake. Former president Barack Obama, Tesla CEO Elon Musk, and Pope Francis have all been recent targets. Male students at a high school in New Jersey made and shared deep-fake pornographic images of their female classmates. And a deepfake robocall audio recording of President Biden was circulated during the New Hampshire presidential primary.

"It's gonna continue to happen because AI is growing so massively," Bordeaux said. "More people will learn how to use it. And the more people use it, the more people will abuse it. That's just the way it works. ... I think the ethical implications of AI are so important."

While there may seem to be a lot of obvious negatives to deepfakes, teachers need to steer their students toward critical questions about the technology, discussing how policymakers and developers can work to mitigate the downsides, said Leigh Ann DeLyser, the executive director and co-founder of CSforALL, a nonprofit organization that seeks to help expand computer science education.

Teachers could ask students: "What are the benefits of deepfakes? What are the challenges of deepfakes? And if there are challenges, how can or how should we as society, create rules around them, like labeling a deep fake" or getting permission before using someone's image? she said.

Bordeaux's journalism teacher, Pam Amendola, who received training on how to teach AI from the International Society for Technology in Education, said many of her students, especially those who consider themselves "Swifties"-a nickname for Swift's fans-were incensed on the pop star's behalf.

But they also considered what might have happened if the subject of the images wasn't "somebody who had her [fame], how would they ever be able to combat it?" Amendola said.

That question can provide an opening for teachers to remind students of their digital footprint, explaining how information they've already put online can be twisted and used for nefarious purposes, Amendola added.

That lesson hit home with Bordeaux, who understands that AI is getting more sophisticated all the time.

"I'm definitely worried that my face is out there," she said. "There's so many ways you can manipulate with deepfakes to make someone look horrible."

'Photoshop on steroids'

There's an opening to discuss the technical aspects of deepfakes, too, in the context of media literacy.

Many people were able to figure out right away that the images of Swift online weren't authentic, said Ruane from the Center for Democracy and Technology.

Teachers could ask: "How did they do that? What are the things about the image, the person in the image, that led you to know those things? What are the instincts that you felt within yourself that led you to that conclusion?" Ruane suggested.

That's something that students have discussed in Elizabeth Thomas-Capello's computer science class at Newburgh Free Academy, a public school in Newburgh, N.Y.

Taylor Swift came up when the class was "talking about how there are things AI can't do very well yet," when it comes to creating images of people, Thomas-Capello said. "It can't really form ear lobes, or the face is a little too perfect or the backgrounds are a little bit muddled. And [it] can't quite get teeth yet."

Keeping those flaws in mind, the class tried to identify which among a series of faces were AI-generated and which were real, Thomas-Capello said. "And we all still failed."

Safinah Arshad Ali, a research assistant at the Massachusetts Institute of Technology who works on teaching AI to middle school students, describes deepfakes as "photoshop on steroids" and is quick to point out the technical weaknesses in the images it creates.

But she also asks students to "think critically about the source, think about who's posting it, why would they be posting it?"

Amendola, too, reminds her students that technology is getting to a place where it is difficult to believe what you see with your own eyes. That means they must consider the context behind everything they see online-whether it's pictures of a pop star or a message from a presidential candidate.

"I tell them, 'question everything because we're at a point in history where you need to be a bit of a skeptic because you're going to be taken advantage of otherwise,'" Amendola said.

The Taylor Swift deepfakes-and the many similar incidents that are sure to follow them-provide an opportunity for would-be computer scientists to delve into the ethics behind the technologies they are learning to create, Thomas-Capello said.

"I really try to emphasize that this what is occurring in our society. These are the implications for our society," Thomas-Capello said. "You as students are the ones who are going to write this. You are the ones who can create technology for good [and] make sure that these types of things are harder and harder to [produce]."

She added, "We don't really know where artificial intelligence is going. We're just at the very, very beginning. But if we can train our students to use technology for good, then I think [we'll get to a] really good place."

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How Taylor Swift's Experience With 'Deepfakes' Can Help Students Examine AI Ethics

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Teachers shouldn't wait until the next big news event to teach students about deepfakes, experts say.

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Assessing AI for Classroom Use Rubric and Checklist.pdf

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AI in the classroom: What’s cheating? What’s OK?

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Now that generative AI tools like ChatGPT are available, classwork is bound to change as a result. How do we preserve academic integrity? What’s cheating,

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alicekeeler.com-Acceptable Use Policy for AI in the ELA Classroom (1)

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weareteachers.com-How To Write an AI Policy for Schools Examples and Resources

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Artificial Intelligence in Teaching & Learning

Syllabi Policy Statements

The AI FLC participants find that there is likely no "one size fits all policy" for AI uses in higher education. Much like attendance/participation policies, AI policy statements will be determined by individual instructors, departments, and programs.

Example policy Statements from the Faculty Learning Community Participants

Claire Hughes-Lynch

AI (artificial intelligence) resources such as ChatGPT can be useful in a number of ways. Because it can also be abused, however, you are required to acknowledge use of AI in any work you submit for class. Text directly copied from AI sites must be treated as any other direct quote and properly cited. Other uses of AI must be clearly described at the end of your assignment.

Selma Koc

Xiongyi Liu

Intellectual honesty is vital to an academic community and for my fair evaluation of your work. All work submitted in this course must be your own, completed in accordance with the University's academic regulations. Use of AI tools, including ChatGPT, is permitted in this course. Nevertheless, you are only encouraged to use AI tools to help brainstorm assignments or projects or to revise existing work you have written. It is sorely your responsibility to make all submitted work your own, maintain academic integrity, and avoid any type of plagiarism. Be aware that the accuracy or quality of AI generated content may not meet the standards of this course, even if you only incorporate such content partially and after substantial paraphrasing, modification and/or editing. Also keep in mind that AI generated content may not provide appropriate or clear attribution to the author(s) of the original sources, while most written assignments in this course require you to find and incorporate highly relevant peer-reviewed scholarly publications following guidelines in the latest publication manual of the APA. Lastly, as your instructor, I reserve the right to use various plagiarism checking tools in evaluating your work, including those screening for AI-generated content, and impose consequences accordingly.

Emily Rauschert on AI as collaboration partner

If you are ever unsure about whether collaboration with others, including using artificial intelligence, is allowed or not, please ask me right away. For the labs, although you may discuss them in groups (and try using AI), you must all create your own code, output and answers. Quizzes will be done in class and must be solely your own work. You alone are always responsible for the correctness of the final answers and assignments you submit.

Leah Schell-Barber for a Business Communications Course

Chat GPT: The use of Chat GTP is neither encouraged nor prohibited from use on assignments for GAD 250. Chat GPT is quickly becoming a communication tool in most business settings. Therefore, if you choose to use Chat GPT for assignments, please be sure to revise the content for clarity, conciseness, and audience awareness. Chat GPT is simply a tool and should not be used as a way to produce first and only drafts. Every assignment submission will be graded using the rubric provided in the syllabus. Be aware that Chat GPT may not develop high-quality work that earns a passing grade. It is your responsibility to review and revise all work before submitting to the instructor.

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Example Policy Statements for AI in Higher Education

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Artificial Intelligence in Teaching & Learning The AI FLC participants find that there is likely no "one size fits all policy" for AI uses in higher education. Much like attendance/participation policies, AI policy statements will be determined by individual instructors, departments, and programs. Claire Hughes-Lynch AI (artificial intelligence) resources such as ChatGPT can be useful in a number of ways.

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A Framework for Responsible AI in Education | Grammarly

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A Framework for Responsible AI in Education

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As is always the case with paradigm-shifting technologies, generative artificial intelligence (AI) has raised both opportunities and challenges for many industries, including education. Questions have…

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World of Better Learning | Cambridge University Press

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Enhancing critical thinking skills with AI-assisted technology

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Vinod Aithal and Jasmin Silver discuss how learners’ critical thinking skills can be enhanced with AI-assisted technology

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Generative AI offers many advantages for educators, for use in and out of the classroom. Yet understanding the unintended biases that permeate within such a massive ecosystem is critical to effectively leveraging it.

As teachers and, yes, students will be using it, the question is do we want them to do so correctly, recognize bias, and navigate around it? The answer is indisputable.

Here are a few examples of why biases persist, and how to navigate around them.

When I searched an AI image generator for "a picture of a successful business person," the results produced three out of four as white men. Only one image was of a Black woman.

When I prompt for images of "a focused student," the first three are boys, two are white, the third is an Asian boy, and the fourth is an apparent high school female.

Next, I asked an AI image generator to create an image of a high school athlete. The results appeared to be both gender and racially biased as it produced three images. The first was a white male while the second two were Black and Brown male students.

Consider that these images were generated by AI's interpretation of my prompt.

If my anecdotal examples haven't convinced you, try for yourself, or refer to a on this subject, which characterizes how bias is becoming a more obvious concern while producing these realistic images.

Fortunately, there are tools that bring more transparency to AI systems and help demonstrate the need for navigating around implicit bias. For example, the Diffusion Bias Explorer compares images generated for a profession while the Average Diffusion Faces Tool showcases a common representation of faces across professions, based on AI-generated images.

The models reinforce concerning patterns linked to my own searches. This provides a compelling lesson to teach awareness of generative bias, both for teachers and students.

AI image generators, despite how clever they are, clearly present limitations. Bias in AI creation is not limited to images. Biased results surface in written command prompts too.Generative content producers are working to resolve this, however, AI continues to be characterized by a remarkably imperfect system of productivity.

Why? The answer is not that AI generators are innately biased. That would suggest they have some kind of sentient consciousness, and in fact, AI platforms simply have incredibly powerful mechanisms to comb the internet for what presently exists (sorry conspiracy theorists). In essence, this makes collected information static, yet dynamic in how AI returns the content.

Since access to content is fixed to what exists, it is biased because it draws on human-created data points.

An AI researcher explains that the issue lies in how AI generators establish extensive statistical connections between words and phrases. When these systems generate new language, it depends on these associations, which can produce the kind of bias in my search results.

How we prompt AI generators affects our results.

Let's take our three examples, and consider an additional word or two to refine the AI search to find something with better balance. In the first example, I added one word, "diverse." My first attempt netted me racial diversity, but all were men!

So I added " women and men" after "diverse" in the command. This produced more proportion.

I tried this for the two remaining test prompts, and the word "diverse" was sufficient in both, but the final prompt (high school athletes) still appeared a bit unbalanced.

AI presents incredible opportunities as . Being aware of the caution signs when using generative AI warrants merit, since the technology is here to stay.

Clearly, using targeted word prompts such as the term "diverse" to level our results aids our effort to harvest representative results for our student populations. Understanding this, helping teachers and students become acutely aware, and filtering out biased content are important lessons in accessing content with teaching and learning in unbiased ways.

Here are 10 examples of command prompts to steer toward unbiased content results.

10 Examples of Unbiased AI Prompts

"Write a short story about a diverse group of individuals working together to solve a global challenge."

"Compose a poem celebrating the beauty and strength of different cultures and backgrounds."

"Generate a piece of artwork that represents inclusivity and equality."

"Create a recipe that incorporates ingredients from various culinary traditions, highlighting the richness of diversity."

"Write a song that promotes understanding and empathy between people from different walks of life."

"Design a virtual reality experience that fosters a sense of unity and respect for all participants."

"Imagine a future society in which discrimination and prejudice have been eliminated, and describe its characteristics."

"Generate a persuasive speech advocating for equal rights and opportunities for all individuals."

"Develop a virtual assistant that provides unbiased information and recommendations without favoritism."

"Compose a letter to encourage open dialogue and constructive discussions on sensitive topics, promoting tolerance and mutual understanding."

To share your feedback and ideas on this article, consider joining our Tech & Learning online community .

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As students learn to use generative AI, it's important to recognize and navigate the biases present

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https://www.youtube.com/watch?v=-bNgqZ_m3Pw

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https://www.canva.com/design/DAFbxm7XKXo/MNGgajvyWS4JqZKQORgFrA/edit?utm_content=DAFbxm7XKXo&utm_campaign=designshare&utm_medium=link2&utm_source=sharebutton

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Copy of Colorful Futuristic Science Fiction Writing Prompts Presentation

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[Keynote Talk] Where Can AI Take Education by 2030

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PD Agenda - AI in Education: What Educators Need to Know

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nypost.com-ChatGPT could make these jobs obsolete The wolf is at the door

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academic syllabi in an era of generative AI

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academic syllabi in an era of generative AI

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This document contains patterns of text for academic syllabi in an era of generative AI. Simply copy what you find helpful and reuse in your own syllabus.

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Resource Document - Feel free to also reference my resource document " AI Tools in Education" which is a perpetual work in progress as I continue to explore AI.

🔑 Accessing Google Bard

You can access Google Bard at the website https://bard.google.com/

As of this writing, Bard is still in beta so you will need to sign up with your Google account to use it.

Simply click the "Join waitlist" button on the welcome page.

Also, at the moment you do need to be using a personal Gmail account, and not a school-provided Google for Education account. During the beta, Bard is not yet accessible for Google Workspace accounts.

After you join the waitlist, eventually you will receive an email letting you know that you have access to Bard.

After that you can just go to https://bard.google.com to access the tool.

Now it is worth noting that Bard is still a work in progress. Google even adds a disclaimer at the bottom of the page stating " Bard may display inaccurate or offensive information that doesn't represent Google's views."

Having said that, in the few weeks that I have been using Bard so far, it has improved significantly. In the first few days it was common to find errors, but recently I have been very impressed with the quality and detail of the responses. And Bard will just continue to improve from here as Google continues to expand, upgrade, and refine the AI model.

To use Bard, simply type in your request in the prompt box at the bottom. Note that you can also click the microphone icon if you want to speak your prompt instead of typing it.

For example we could enter "Explain what a metaphor is and give some examples."

Bard will then generate a response for your prompt.

A nice feature of Bard is that you will actually get multiple versions of the response. If you click on the option for " View other drafts" you can now select from three different versions to find the response that best meets your needs.

Based upon the answer you get, you may want to use the buttons below the response.

You can click the thumbs up button for " Good response" or the thumbs down button for " Bad response" to send feedback to Google which will help improve the Bard AI. If you do click thumbs down, you will get the option to explain what was wrong with the answer Bard gave.

You can also click the " New response" button if you want Bard to generate a brand new set of answers.

If you want to verify the information that Bard has provided or if you just want more details, you can click the " Google it" button to get the link for a suggested Google search related to your conversation.

Finally, if you feel that your prompt wasn't worded quite right, you can click the pencil icon next to your original prompt to make any needed changes and get updated responses from Bard.

At this point you can continue your conversation with Bard by entering new prompts. Bard will remember everything that has already been discussed in this particular dialog, so you can talk to it just like you are chatting with a person.

For example we could ask a follow-up question such as "How are similes different?"

And we can keep the conversation going from there.

At any point, if there is a response that you would like to copy and use somewhere else, your have two options:

First you can click the 3-dots button below the response and choose "Copy". This will copy the text, but not the formatting, such as bullets and tables.

Or second, you can simply select the text with your mouse and then copy as normal. This will copy all of the formatting along with the text.

When you are done with this particular conversation, you can start a new one by clicking " Reset chat" in the top left corner. This will start a new session where Google Bard will not have any memory of the earlier dialog.

🗓️ Bard Activity History

In the future if you want to see what prompts you have entered before, you can view on the "Bard Activity" page. Simply click "Bard Activity" in the top left corner.

This will open a new window where you can scroll back through your history to see prompts you have written and feedback you have given.

At the moment this is just a view-only copy of your actions.

It does not include Bard's responses.

It does not allow you to resume these past conversations.

🎓 Bard Examples for Educators
Just like other generative AI chatbots, Google Bard can be used in many ways to support teaching and learning. In other videos I will cover many more uses, but for now here are just a few sample ways that Bard, or any AI chatbot, can be used for education.
Generating Assessment Questions
We can ask Bard for all sorts of assessment questions. For example we could ask: Create DOK Level 1 questions about the book "Charlotte's Web"

And of the course the same DOK 2, DOK 3, and DOK4: Create DOK Level 4 questions about the book "Charlotte's Web"

See the full text of my conversation with Bard on this topic here: Google Docs link
Writing Starters
We can ask Bard to generate writing prompts and topics for stories, essays, articles, and more. For example: Generate 10 writing prompts for a 5th grade essay on the topic of vacations.

See the full text of my conversation with Bard on this topic here: Google Docs link

Create Rubrics

We can ask Bard to generate a grading rubric to evaluate student work for a specific task and grade level. For example: Create a rubric for an 8th grade creative writing project in table format with 4 levels and 5 categories.

See the full text of my conversation with Bard on this topic here: Google Docs link
Writing Email
And for a final example we can ask Bard to help us draft an email message on a certain topic and geared toward a specific audience. For example: Write an email to school parents with suggestions to encourage student reading at home

See the full text of my conversation with Bard on this topic here: Google Docs link

There are many more ways that Bard can be used to help teachers and students. In upcoming videos we will explore a wide variety of examples.

And that's it! Even though Google Bard is still in its early stages, it is already amazing to see how this can enhance teaching and learning right now. Tools like Bard and ChatGPT and Microsoft's Bing Chat and Canva's Magic Write and so many more will just continue to develop, and I am excited to explore and learn right alongside of you.

Feel free to check out my resource document " AI Tools in Education" at https://bit.ly/curts-ai which is a perpetual work in progress as I continue to explore AI.

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Google has been at the forefront of artificial intelligence research and development for many years. We have seen this work in AI-powered to...

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Artificial intelligence is growing by leaps and bounds. It’s no longer science fiction anymore! Lots of AI tools can serve as our own personal

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Syllabi Policies for AI Generative Tools

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Creative Destruction… GPT-4 meets Sal Khan | ASU+GSV 2023

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Lesson For Students: Facing History and Ourselves

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Lesson For Students: Facing History and Ourselves

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The Ethics of Generative AI in the Classroom

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This is the first mini-lesson in a two-part series on the impact generative AI tools such as ChatGPT and DALL-E have on education.

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Used thoughtfully, generative AI tools like ChatGPT can help educators implement strategies that would otherwise take prohibitive amounts of time and effort to create. Wharton educators share what LLMs are and how you can use them to lighten your workload.

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Against the use of GPTZero and other LLM-detection tools on student writing

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Against the use of GPTZero and other LLM-detection tools on student writing

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https://docs.google.com/document/d/1CXgsYD-Sk-FqrbiaK2XeRwgRK56RG7-1rX2DDMV0JAA/edit?usp=sharing

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Against the use of GPTZero and other LLM-output detection tools

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https://beta.character.ai/
At Character.AI, you collaborate with the computer to write a dialog - you write one character's lines, and the computer creates the other character's lines, giving you the illusion that you are talking with the other character.

Needless to say, a hallucinating supercomputer is not a source of reliable information. Still, we hope that you find Character.AI a useful tool for imagination, brainstorming, language learning, and a host of other purposes we have ourselves not yet imagined :)

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https://einstein.digitalhumans.com/

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I'm a digital version of Einstein. I would love to meet you and share my knowledge; I can even quiz you on a variety of topics.

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https://www.dayofai.org/

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Day of AI offers a full school day's worth of curriculum and activities for elementary, middle school, and high school students. The program is completely free and accessible to students and teachers of all backgrounds and abilities. No technology or coding experience needed! This year, rather than asking everyone to do the curriculum on the same day, we're making our resources available to educators to run at any time and in any increments they choose. Do one per week! Make it a theme week! Make it a school-wide event!

Then, each year in May , join a community of educators around the world in a Day of AI celebration: share your class's projects, posters, photos, and videos, host a discussion, or create your own unique festivity. Stay tuned for details on how to share your class's participation or join one of our Day of AI events.
New learning modules
All of last year's Day of AI activities are available to educators who register on the website . We're also adding 5 new curriculum modules and activities in our gradeband categories, including an all-new module for early elementary students ages 5-7: early elementary, upper elementary, middle school, high school (general), and high school for students with coding experience.
New training videos
Each learning module also has a new training video, made by the experts who developed the curriculum. Register to explore the full Day of AI curriculum, then watch the free training videos at your convenience.

What's next

Thank you to all who joined the Day of AI 2023! We'll have more news about Day of AI 2024 in coming months.

Once you register you'll have the opportunity to watch a training video or sign up for a virtual professional development session with our education provider i2 Learning (both optional). The videos and virtual training, like the curriculum, are completely free.

You'll also get notified about Day of AI content, news, and opportunities for engagement and professional development. We're planning a number of new events for Day of AI 2024 -make sure your classroom is included!

Finally, follow @MIT_RAISE on Instagram and share your own AI education ideas and classroom projects using #DayofAI.

About Day of AI

Day of AI is a free, hands-on curriculum of lessons and activities designed to introduce K-12 students to artificial intelligence (AI) and how it shapes their lives. The Day of AI curriculum was developed by a team of researchers at MIT RAISE, and designed to be taught by educators with little or no technology background. These resources are free, Creative Commons-licensed, and made to be accessible to students of all backgrounds and abilities. Activities are organized by age group (elementary, middle school, and high school) and can be run in 30-minute to 1-hour time blocks. Teachers who register for Day of AI get access to the curriculum and lesson plans as well as optional free professional development training on the materials with our collaborator i2 Learning, with a certificate of completion that may be used for PD credits.

Thank you to our funders for helping us make Day of AI free and available to all! Interested in supporting Day of AI? Email contact@dayofai.org to learn how.

A unique opportunity to help your students shape our world's digital future...and their own .

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Day of AI offers a full school day's worth of curriculum and activities for elementary, middle school, and high school students. The program is completely free and accessible to students and teachers of all backgrounds and abilities. No technology or coding experience needed!

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https://raise.mit.edu/resources.html

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Usage and Creative Commons licensing terms are specified with each unit.

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https://youtu.be/HNmOf6CdZqQ

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Terminators or Transformers? The Age of AI in Education ep.1

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Race After Technology: Abolitionist Tools for the New Jim Code

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Race After Technology: Abolitionist Tools for the New Jim Code

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https://a.co/d/2zC3MCx

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Race After Technology: Abolitionist Tools for the New Jim Code

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Race After Technology: Abolitionist Tools for the New Jim Code [Benjamin, Ruha] on Amazon.com. *FREE* shipping on qualifying offers. Race After Technology: Abolitionist Tools for the New Jim Code

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https://blog.tcea.org/ai-tool-generating-discussion-questions/?utm_source=TCEA+Emails&utm_campaign=bb2b3bb9a3-tn-mar21&utm_medium=email&utm_term=0_f3a5bb651e-bb2b3bb9a3-69194794

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I recently became aware of a cool new feature in Parlay that incorporates AI, and I am here for it. Parlay Genie is currently in beta and available on the Parlay website. The purpose of this new feature is to help facilitate deeper thinking in class discussions, which Parlay calls "roundtables" for grades 5 all the way up through higher ed. Parlay Genie prompts you to enter a discussion topic or a URL for students to review and discuss. Instead of you spending time searching for discussion questions or creating your own, you're provided with coordinating student instructions and discussion questions that are grade-level appropriate! I know. It's pretty great. It's super easy to use and will save you time. Let's talk about Parlay first and then go through how to use this new tool, Parlay Genie, step by step. I can't wait to show you!

What Is Parlay?

Parlay is an online tool that is all about giving students a voice, facilitating discussion, and allowing educators to monitor and assess students along the way. Teachers set up courses, add their students, and create roundtable discussions within the courses. Roundtable discussions can be written or verbal, and you can customize assessment criteria for each prompt. Parlay connects with Google Classroom, Teams, Brightspace, Schoology, and Canvas learning management systems to make it easy to import students and add them to your created courses and roundtables. Also, once students submit their responses, you can view class-wide and individual data. With a free account, here's what you get:

Unlimited Courses

Unlimited Students

12 RoundTables (at one time)

Parlay Universe + Portfolios

Google Classroom Integration

Google Sign On

Microsoft Sign On

Custom Assessment Criteria

Chat Support

There is a $120 per year Individual Premium account and a customizable Schools and Districts account. Of course, the paid accounts offer more features, but you can do quite a lot with a free account! Here are some short tutorials that will give you an idea of the possibilities and get you started:

How to Use Parlay Genie for Deeper Discussions

I want to really emphasize how easy Parlay Genie is to use. When you arrive at the site, you see four steps to follow and the Parlay Genie submission box.

Generating the Parlay Genie Discussion Prompt

Here, I have selected to share a URL with an article, but you can also provide links to videos. I added a URL to an article for grades 5-8 from National Geographic about the Coriolis effect, asked for 3 questions to be created, and selected grade levels 7-8. Simple!

When I clicked "Generate Promp," Parlay Genie provided instructions, the link to the article I provided, three discussion questions, and instructions for peer feedback.

Customizing the Prompt Settings

When you click "Start Discussion," you will be prompted to either sign in to your account (if you aren't already) or sign up for an account. If you're just now creating your account, you will be prompted to complete your personal information at this time (and no, you can't skip it and come back. I tried.), but don't fret! Parlay saves your prompt, and it will look like this:

At this step, I'd recommend going into Settings and taking a look at the options. Within the settings for your prompt, you can:

Require submission

Make students anonymous to each other

Set a closing date

Turn on email notifications

Create and set assessment criteria

Select peer feedback questions to include

TIP: Don't forget to click "Save" in the top right corner!

Inviting Students to the Discussion

Now it's time to invite your students by clicking, you guessed it, the pink "Invite Students" button in the top right corner. You can invite students with a code, a link, or, if you've integrated Parlay with your LMS, you'll have a Students List already imported to use.

Students will see the instructions, the link, the questions, and a submission box. They will not be permitted to move to discussion until after they submit their responses; once they submit their response, they can't go back and edit it.

After submitting, students will have access to the discussion, where they are able to see other students' posts and answer the feedback questions. I appreciate how the sentence structures are provided for students in Parlay! They can also click on a sentence structure to use it in their response.

Viewing Student Responses

Once students have responded, as the teacher, you can view all responses, class-wide data, and individual student data. You do have the ability to export the student activity data.

As a bonus, use Parlay to help students practice crafting their extended constructed responses (ECR) for STAAR testing. You can customize assessment criteria for written roundtable prompts to align with the STAAR writing rubrics! I hope you'll give this new AI tool a try with your students! If you use Parley Genie, let us know how it goes in the comments, and feel free to share any tips you learned along the way.

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An AI Tool for Generating Higher-Order Discussion Questions

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I recently became aware of a cool new feature in Parlay that incorporates AI, and I am here for it. Parlay Genie is currently in beta and available on the Parlay website. The purpose of this new feature is to help facilitate deeper thinking in class discussions, which Parlay calls "roundtables" for grades 5 all the way up through higher ed.

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https://www.cnn.com/2023/03/16/tech/gpt-4-use-cases/index.html

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5 jaw-dropping things GPT-4 can do that ChatGPT couldn’t | CNN Business

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In the first day after it was unveiled, GPT-4 stunned many users in early tests and a company demo with its ability to draft lawsuits, pass standardized exams and build a working website from a hand-drawn sketch.

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ChatGPT, Dude - South Park | South Park Studios US

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Stan asks Clyde for dating advice, in an all-new episode of South Park, titled "Deep Learning" premiering Wednesday, March 8, at 10:00p ET/PT on Comedy Central

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insert_chart_outlined Top charts

GPT for Sheets™ and Docs™

ChatGPT in Google Sheets™ and Docs™. Get the full power of AI for inspiration data cleaning, classification, extraction, translation, edition, summarization, writing directly in your Sheets™ and Docs™

Listing updated:October 19, 2023

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Overview Permissions Reviews

GPT for Sheets™ and Docs™ is an AI writer for Google Sheets™ and Google Docs™.
It enables you to use ChatGPT and other generative AI models directly directly in Google Sheets™ and Docs™.

Models available include OpenAI GPT-3.5 and GPT-4 as well as Anthropic Claude.
You can use it for all sorts of tasks on text: writing, editing, extracting, cleaning, translating, summarizing, outlining, explaining, etc
If ChatGPT was GPT for Chat, you can think of GPT for Sheets™ as SheetGPT.

In Sheets, the default model for all functions is now gpt-3.5-turbo.
In Docs, the default model for all functions is now gpt-3.5-turbo-16k.
You can select the model you want depending on cost, latency and capability in the sidebars

Website: https://gptforwork.com (learn how to use GPT for work)
How to set up: https://gptforwork.com/help/gpt-for-sheets/setup/create-openai-api-key
Video tutorials: https://www.youtube.com/@gptforwork/playlists

Mentioned by Marc Andreessen as a "killer app": https://twitter.com/pmarca/status/1632855301648183296?s=20

This add-on brings ChatGPT and Claude AI power to Google Sheets™ by exposing many simple custom functions:
GPT to get the result of ChatGPT in a single cell
GPT_TRANSLATE to translate your spreadsheet content
GPT_CLASSIFY to classify spreadsheet content into a single category
GPT_EXTRACT to extract entities from your Sheets™ data
GPT_SUMMARIZE to summarize your spreadsheet content
GPT_FORMAT to sanitize your spreadsheet data into the same format
GPT_EDIT to edit your spreadsheet content
GPT_TAG to apply tags to your spreadsheet content
GPT_MATCH to match values from two columns
GPT_SPLIT to split text semantically, such as by section, paragraph, sentence, customer...
GPT_LIST to get multiple result in a column (one item per cell)
GPT_TABLE to get a table of items from a prompt
GPT_FILL to fill a range from examples

This add-on will help you achieve some tasks such as:
- generating blogposts ideas
- writing whole paragraphs or procedures
- cleaning up lists of names, adresses, emails or companies, dates, currency amounts, phone numbers
- classifying lists of reviews with sentiment analysis or feature categorization
- summarizing reviews
- writing responses to online comments
- trying different versions of a prompt quickly
- working on ad copy (PPC, Google ads, Facebook ads)
- working on SEO metadata (titles, descriptions)
- working on landing page copy
- managing and cleaning product catalogs for e-commerce stores (Shopify and Amazon)
- translations

Docs:
- outlining a piece of content
- expanding a piece of content
- writing emails or blogs
- summarizing notes
- translating content
- changing the style of content

In addition, this add-on will let you experiment easily with hyperparameters:
- temperature
- model (including fine-tuned models in Sheets™)
- maxTokens

This add-on is completely free to use while in beta. You only need to pay OpenAI's or Anthropic's API cost. You will need to insert your OpenAI or Anthropic api key. This is not covered by ChatGPT Plus/Pro subscriptions.
All the details are on our website: https://gptforwork.com

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https://blog.invgate.com/chatgpt-statistics

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Rest assured that with the ChatGPT statistics you're about to read, you'll confirm that the popular chatbot from OpenAI is just the beginning of something bigger. Since its launch in November 2022, ChatGPT has broken unexpected records. For example, it reached 100 million active users in January, just two months after its release, making it the fastest-growing consumer app in history.

This chatbot has revolutionized the field of AI by using deep learning techniques to generate human-like text and answer a wide range of questions with high accuracy. The versatility of the responses goes from the generation of code to the creation of memes. One of its most common uses is for customer service , though ChatGPT can also be helpful for IT support .

But what makes ChatGPT so disruptive? In this article, you'll discover some of the most significant ChatGPT statistics and facts that will help you understand how far AI has come. Read on to find out!

What is ChatGPT?

Developed by OpenAI, ChatGPT is an innovative artificial intelligence chatbot based on the open-source GPT-3 natural language processing (NLP) model.

The chatbot can understand what users say, anticipate their needs, and respond accurately. It interacts conversationally, so users can feel like they are talking to a real person.

50 ChatGPT statistics and facts you need to know

With the rising popularity of ChatGPT, we began to wonder about the numbers behind such a massive phenomenon. So, we started digging, and here's everything we found out.

ChatGPT history

1. OpenAI has been developing GPT (Generative Pre-Train) since 2018.

2. GPT-1 and GPT-2 laid the foundations for GPT-3.

3. GPT-1 was trained with BooksCorpus dataset (5GB), whose primary focus was language understanding.

4. On Valentine's Day 2019, GPT-2 was launched with the slogan " too dangerous to release." It was trained with Reddit articles with over 3 likes (40GB). The training cost was $43,000.

5. Later, GPT-2 was used to generate music in MuseNet and JukeBox.

6. In June 2020, GPT-3 was released, which was trained by a much more comprehensive dataset.

7. Some of the applications that were developed based on GPT-3 are:

DALL-E: creating images from text.

CLIP: connecting text and images.

Whisper: multi-lingual voice-to-text.

ChatGPT: chatbot, article writer, code writer.

ChatGPT general facts

8. OpenAI's GPT-3 is the largest language model created to date.

9. It has 175 billion parameters and receives 10 million queries per day.

10. It was trained on a massive corpus of text data, around 570GB of datasets, including web pages, books, and other sources.

11. GPT-3 has been fine-tuned for a variety of language tasks, such as translation, summarization, and question-answering.

12. It can generate human-like text, including poems, stories, and even code.

13. The response time of ChatGPT is typically less than a second, making it well-suited for real-time conversations.

14. ChatGPT has been integrated into a variety of platforms and applications, including websites, messaging apps, virtual assistants, and other AI applications.

15. Some experts have called GPT-3 a major step in developing artificial intelligence.

16. GPT-3 has been praised for its ability to understand the context and produce relevant responses.

17. It has been shown to outperform previous language models and even humans on certain language tasks.

18. GPT-3 has also been criticized for its lack of common sense knowledge and susceptibility to producing biased or misleading responses.

19. OpenAI has made GPT-3 available through an API, allowing developers to create their own AI applications.

ChatGPT performance

20. An area where ChatGPT excels is in the generation of text. The model can generate coherent and fluent text on a wide range of topics, making it a popular choice for applications such as chatbots, language translation, and content generation.

21. The text generated by ChatGPT is often difficult to distinguish from text written by a human, as demonstrated by its performance on the DALL-E 2.0 test, where it generated high-quality images from textual descriptions.

22. ChatGPT's performance is also influenced by the amount of training data it has been exposed to. The more data a language model has been trained on, the more information it has available to generate accurate and relevant responses.

23. OpenAI has reported that the model's performance improves significantly when it is fine-tuned on specific domains or tasks, demonstrating flexibility and adaptability.

ChatGPT limitations

24. One of the biggest challenges is its computational requirements. The model requires significant computational resources to run, making it challenging to deploy in real-world applications.

25. Despite its large size and high accuracy, ChatGPT still makes mistakes and can generate biased or inaccurate responses, particularly when the model has not been fine-tuned on specific domains or tasks.

26. ChatGPT cannot access the internet or external links.

27. ChatGPT's knowledge is limited to its training data, which has the cutoff year of 2021.

ChatGPT statistics: research warns of risk of malicious use

28. According to research released by BlackBerry, 51% of IT decision-makers believe a successful cyberattack will be credited to ChatGPT within the year.

59. 71% of those surveyed figure that nation-state actors are already using the technology for "malicious" purposes.

30. Regarding how exactly ChatGPT will be used to help foster cyberattacks, 53% of people said it would help hackers create more believable phishing emails.

31. 49% of respondents pointed to its ability to help hackers improve their coding abilities.

32. The survey also revealed that 49% of people believe ChatGPT will be used to spread misinformation and disinformation.

33. 48% of those surveyed think it could be used to craft entirely new malware strains.

34. 46% of respondents said ChatGPT could help improve existing attacks.

35. The same research also revealed that 95% believe governments are responsible for regulating advanced technologies, such as ChatGPT.

ChatGPT statistics: costs

36. ChatGPT is free for users during the research phase while the company gathers feedback.

37. For developers who want to incorporate this tool into other software, the cost is around a penny for 20,000 words of text and approximately 2 cents for images.

38. The GPT-3 AI model reportedly cost OpenAI $12 million for a single training run.

39. Tom Goldstein, an AI ML Professor at Maryland University, has estimated the daily cost of running ChatGPT to be approximately $100,000 and the monthly cost to be $3 million. His estimates are based on Azure Cloud costs (server infrastructure on which ChatGPT runs).

40. OpenAI has recently launched a pilot subscription price of $20. It is invite-only, promises access even during peak times, and provides faster responses and priority access to new features and improvements.

OpenAI background and investments

41. OpenAI was founded in San Francisco in 2015 by Carlos Virella, Elon Musk, Greg Brockman, Ilya Sutskever, James Greene, Sam Altman, and Wojciech Zaremba.

42. The startup has successfully attracted a series of high-profile investors.

43. After the launch of ChatGPT, OpenAI is valued at $29 billion.

44. Microsoft invested $10 billion recently. It represents the third investment that the company founded by Bill Gates made. It plowed $1 billion into OpenAI in 2019 and reinvested in 2021.

45. OpenAI ranks among the most funded machine-learning startup firms in the world, with funding of over 1 billion U.S. dollars as of January 2023.

46. In a recent pitch to investors, OpenAI expects $200 million in revenue this year and $1 billion by 2024.

47. Companies in the technology and education sectors are most likely to take advantage of OpenAI's solutions. At the same time, business services, manufacturing, and finance are also high on the list of industries utilizing artificial intelligence in their business processes.

ChatGPT statistics: users

48. ChatGPT gained 1 million users in under a week.

49. The chatbot accumulated 57 million monthly active users in its first month of availability.

50. It surpassed 100 million active users in January.

Key takeaways

ChatGPT has swept the world, and there are reasons why it has happened:

With its 175 billion parameters, it has converted into the most prominent language model.

It has demonstrated impressive performance on various NLP tasks and applications.

Its large size and diverse training data allow it to generate high-quality text and answer a wide range of questions with high accuracy.

However, the model's computational requirements and potential for bias and error are essential considerations when deploying it in real-world applications. Moreover, cybercriminals could use it to carry out successful attacks.

But let's face it, with its pros and cons, ChatGPT has a promising future ahead. The investment it recently received from Microsoft and the launch of the subscription pilot demonstrate it.

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50 ChatGPT Statistics and Facts You Need to Know

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Rest assured that with the ChatGPT statistics you're about to read, you'll confirm that the popular chatbot from OpenAI is just the beginning of something bigger. Since its launch in November 2022, ChatGPT has broken unexpected records. For example, it reached 100 million active users in January, just two months after its release, making it the fastest-growing consumer app in history.

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On a spring afternoon in 2014, Brisha Borden was running late to pick up her god-sister from school when she spotted an unlocked kid's blue Huffy bicycle and a silver Razor scooter. Borden and a friend grabbed the bike and scooter and tried to ride them down the street in the Fort Lauderdale suburb of Coral Springs.

Just as the 18-year-old girls were realizing they were too big for the tiny conveyances - which belonged to a 6-year-old boy - a woman came running after them saying, "That's my kid's stuff." Borden and her friend immediately dropped the bike and scooter and walked away.

But it was too late - a neighbor who witnessed the heist had already called the police. Borden and her friend were arrested and charged with burglary and petty theft for the items, which were valued at a total of $80.

Subscribe to the Series

Machine Bias: Investigating the algorithms that control our lives.

Compare their crime with a similar one: The previous summer, 41-year-old Vernon Prater was picked up for shoplifting $86.35 worth of tools from a nearby Home Depot store.

Prater was the more seasoned criminal. He had already been convicted of armed robbery and attempted armed robbery, for which he served five years in prison, in addition to another armed robbery charge. Borden had a record, too, but it was for misdemeanors committed when she was a juvenile.

Yet something odd happened when Borden and Prater were booked into jail: A computer program spat out a score predicting the likelihood of each committing a future crime. Borden - who is black - was rated a high risk. Prater - who is white - was rated a low risk.

Two years later, we know the computer algorithm got it exactly backward. Borden has not been charged with any new crimes. Prater is serving an eight-year prison term for subsequently breaking into a warehouse and stealing thousands of dollars' worth of electronics.

Scores like this - known as risk assessments - are increasingly common in courtrooms across the nation. They are used to inform decisions about who can be set free at every stage of the criminal justice system, from assigning bond amounts - as is the case in Fort Lauderdale - to even more fundamental decisions about defendants' freedom. In Arizona, Colorado, Delaware, Kentucky, Louisiana, Oklahoma, Virginia, Washington and Wisconsin, the results of such assessments are given to judges during criminal sentencing.

Rating a defendant's risk of future crime is often done in conjunction with an evaluation of a defendant's rehabilitation needs. The Justice Department's National Institute of Corrections now encourages the use of such combined assessments at every stage of the criminal justice process. And a landmark sentencing reform bill currently pending in Congress would mandate the use of such assessments in federal prisons.

In 2014, then U.S. Attorney General Eric Holder warned that the risk scores might be injecting bias into the courts. He called for the U.S. Sentencing Commission to study their use. "Although these measures were crafted with the best of intentions, I am concerned that they inadvertently undermine our efforts to ensure individualized and equal justice," he said, adding, "they may exacerbate unwarranted and unjust disparities that are already far too common in our criminal justice system and in our society."

The sentencing commission did not, however, launch a study of risk scores. So ProPublica did, as part of a larger examination of the powerful, largely hidden effect of algorithms in American life.

We obtained the risk scores assigned to more than 7,000 people arrested in Broward County, Florida, in 2013 and 2014 and checked to see how many were charged with new crimes over the next two years, the same benchmark used by the creators of the algorithm.

The score proved remarkably unreliable in forecasting violent crime: Only 20 percent of the people predicted to commit violent crimes actually went on to do so.

When a full range of crimes were taken into account - including misdemeanors such as driving with an expired license - the algorithm was somewhat more accurate than a coin flip. Of those deemed likely to re-offend, 61 percent were arrested for any subsequent crimes within two years.

We also turned up significant racial disparities, just as Holder feared. In forecasting who would re-offend, the algorithm made mistakes with black and white defendants at roughly the same rate but in very different ways.

The formula was particularly likely to falsely flag black defendants as future criminals, wrongly labeling them this way at almost twice the rate as white defendants.

White defendants were mislabeled as low risk more often than black defendants.

Could this disparity be explained by defendants' prior crimes or the type of crimes they were arrested for? No. We ran a statistical test that isolated the effect of race from criminal history and recidivism, as well as from defendants' age and gender. Black defendants were still 77 percent more likely to be pegged as at higher risk of committing a future violent crime and 45 percent more likely to be predicted to commit a future crime of any kind. ( Read our analysis.)

The algorithm used to create the Florida risk scores is a product of a for-profit company, Northpointe. The company disputes our analysis.

In a letter, it criticized ProPublica's methodology and defended the accuracy of its test: "Northpointe does not agree that the results of your analysis, or the claims being made based upon that analysis, are correct or that they accurately reflect the outcomes from the application of the model."

Northpointe's software is among the most widely used assessment tools in the country. The company does not publicly disclose the calculations used to arrive at defendants' risk scores, so it is not possible for either defendants or the public to see what might be driving the disparity. (On Sunday, Northpointe gave ProPublica the basics of its future-crime formula - which includes factors such as education levels, and whether a defendant has a job. It did not share the specific calculations, which it said are proprietary.)

Northpointe's core product is a set of scores derived from 137 questions that are either answered by defendants or pulled from criminal records. Race is not one of the questions. The survey asks defendants such things as: "Was one of your parents ever sent to jail or prison?" "How many of your friends/acquaintances are taking drugs illegally?" and "How often did you get in fights while at school?" The questionnaire also asks people to agree or disagree with statements such as "A hungry person has a right to steal" and "If people make me angry or lose my temper, I can be dangerous."

The appeal of risk scores is obvious: The United States locks up far more people than any other country, a disproportionate number of them black. For more than two centuries, the key decisions in the legal process, from pretrial release to sentencing to parole, have been in the hands of human beings guided by their instincts and personal biases.

If computers could accurately predict which defendants were likely to commit new crimes, the criminal justice system could be fairer and more selective about who is incarcerated and for how long. The trick, of course, is to make sure the computer gets it right. If it's wrong in one direction, a dangerous criminal could go free. If it's wrong in another direction, it could result in someone unfairly receiving a harsher sentence or waiting longer for parole than is appropriate.

The first time Paul Zilly heard of his score - and realized how much was riding on it - was during his sentencing hearing on Feb. 15, 2013, in court in Barron County, Wisconsin. Zilly had been convicted of stealing a push lawnmower and some tools. The prosecutor recommended a year in county jail and follow-up supervision that could help Zilly with "staying on the right path." His lawyer agreed to a plea deal.

But Judge James Babler had seen Zilly's scores. Northpointe's software had rated Zilly as a high risk for future violent crime and a medium risk for general recidivism. "When I look at the risk assessment," Babler said in court, "it is about as bad as it could be."

Then Babler overturned the plea deal that had been agreed on by the prosecution and defense and imposed two years in state prison and three years of supervision.

Criminologists have long tried to predict which criminals are more dangerous before deciding whether they should be released. Race, nationality and skin color were often used in making such predictions until about the 1970s, when it became politically unacceptable, according to a survey of risk assessment tools by Columbia University law professor Bernard Harcourt.

In the 1980s, as a crime wave engulfed the nation, lawmakers made it much harder for judges and parole boards to exercise discretion in making such decisions. States and the federal government began instituting mandatory sentences and, in some cases, abolished parole, making it less important to evaluate individual offenders.

But as states struggle to pay for swelling prison and jail populations, forecasting criminal risk has made a comeback.

Dozens of risk assessments are being used across the nation - some created by for-profit companies such as Northpointe and others by nonprofit organizations. (One tool being used in states including Kentucky and Arizona, called the Public Safety Assessment, was developed by the Laura and John Arnold Foundation, which also is a funder of ProPublica.)

There have been few independent studies of these criminal risk assessments. In 2013, researchers Sarah Desmarais and Jay Singh examined 19 different risk methodologies used in the United States and found that "in most cases, validity had only been examined in one or two studies" and that "frequently, those investigations were completed by the same people who developed the instrument."

Their analysis of the research through 2012 found that the tools "were moderate at best in terms of predictive validity," Desmarais said in an interview. And she could not find any substantial set of studies conducted in the United States that examined whether risk scores were racially biased. "The data do not exist," she said.

Since then, there have been some attempts to explore racial disparities in risk scores. One 2016 study examined the validity of a risk assessment tool, not Northpointe's, used to make probation decisions for about 35,000 federal convicts. The researchers, Jennifer Skeem at University of California, Berkeley, and Christopher T. Lowenkamp from the Administrative Office of the U.S. Courts, found that blacks did get a higher average score but concluded the differences were not attributable to bias.

The increasing use of risk scores is controversial and has garnered media coverage, including articles by the Associated Press, and the Marshall Project and FiveThirtyEight last year.

Most modern risk tools were originally designed to provide judges with insight into the types of treatment that an individual might need - from drug treatment to mental health counseling.

"What it tells the judge is that if I put you on probation, I'm going to need to give you a lot of services or you're probably going to fail," said Edward Latessa, a University of Cincinnati professor who is the author of a risk assessment tool that is used in Ohio and several other states.

But being judged ineligible for alternative treatment - particularly during a sentencing hearing - can translate into incarceration. Defendants rarely have an opportunity to challenge their assessments. The results are usually shared with the defendant's attorney, but the calculations that transformed the underlying data into a score are rarely revealed.

"Risk assessments should be impermissible unless both parties get to see all the data that go into them," said Christopher Slobogin, director of the criminal justice program at Vanderbilt Law School. "It should be an open, full-court adversarial proceeding."

Black Defendants' Risk Scores

White Defendants' Risk Scores

These charts show that scores for white defendants were skewed toward lower-risk categories. Scores for black defendants were not. (Source: ProPublica analysis of data from Broward County, Fla.)

Proponents of risk scores argue they can be used to reduce the rate of incarceration. In 2002, Virginia became one of the first states to begin using a risk assessment tool in the sentencing of nonviolent felony offenders statewide. In 2014, Virginia judges using the tool sent nearly half of those defendants to alternatives to prison, according to a state sentencing commission report. Since 2005, the state's prison population growth has slowed to 5 percent from a rate of 31 percent the previous decade.

In some jurisdictions, such as Napa County, California, the probation department uses risk assessments to suggest to the judge an appropriate probation or treatment plan for individuals being sentenced. Napa County Superior Court Judge Mark Boessenecker said he finds the recommendations helpful. "We have a dearth of good treatment programs, so filling a slot in a program with someone who doesn't need it is foolish," he said.

However, Boessenecker, who trains other judges around the state in evidence-based sentencing, cautions his colleagues that the score doesn't necessarily reveal whether a person is dangerous or if they should go to prison.

"A guy who has molested a small child every day for a year could still come out as a low risk because he probably has a job," Boessenecker said. "Meanwhile, a drunk guy will look high risk because he's homeless. These risk factors don't tell you whether the guy ought to go to prison or not; the risk factors tell you more about what the probation conditions ought to be."

"I'm surprised [my risk score] is so low. I spent five years in state prison in Massachusetts." (Josh Ritchie for ProPublica)

Sometimes, the scores make little sense even to defendants.

James Rivelli, a 54-year old Hollywood, Florida, man, was arrested two years ago for shoplifting seven boxes of Crest Whitestrips from a CVS drugstore. Despite a criminal record that included aggravated assault, multiple thefts and felony drug trafficking, the Northpointe algorithm classified him as being at a low risk of reoffending.

"I am surprised it is so low," Rivelli said when told by a reporter he had been rated a 3 out of a possible 10. "I spent five years in state prison in Massachusetts. But I guess they don't count that here in Broward County." In fact, criminal records from across the nation are supposed to be included in risk assessments.

Less than a year later, he was charged with two felony counts for shoplifting about $1,000 worth of tools from Home Depot. He said his crimes were fueled by drug addiction and that he is now sober.

Northpointe was founded in 1989 by Tim Brennan, then a professor of statistics at the University of Colorado, and Dave Wells, who was running a corrections program in Traverse City, Michigan.

Wells had built a prisoner classification system for his jail. "It was a beautiful piece of work," Brennan said in an interview conducted before ProPublica had completed its analysis. Brennan and Wells shared a love for what Brennan called "quantitative taxonomy" - the measurement of personality traits such as intelligence, extroversion and introversion. The two decided to build a risk assessment score for the corrections industry.

Brennan wanted to improve on a leading risk assessment score, the LSI, or Level of Service Inventory, which had been developed in Canada. "I found a fair amount of weakness in the LSI," Brennan said. He wanted a tool that addressed the major theories about the causes of crime.

Brennan and Wells named their product the Correctional Offender Management Profiling for Alternative Sanctions, or COMPAS. It assesses not just risk but also nearly two dozen so-called "criminogenic needs" that relate to the major theories of criminality, including "criminal personality," "social isolation," "substance abuse" and "residence/stability." Defendants are ranked low, medium or high risk in each category.

As often happens with risk assessment tools, many jurisdictions have adopted Northpointe's software before rigorously testing whether it works. New York State, for instance, started using the tool to assess people on probation in a pilot project in 2001 and rolled it out to the rest of the state's probation departments - except New York City - by 2010. The state didn't publish a comprehensive statistical evaluation of the tool until 2012. The study of more than 16,000 probationers found the tool was 71 percent accurate, but it did not evaluate racial differences.

A spokeswoman for the New York state division of criminal justice services said the study did not examine race because it only sought to test whether the tool had been properly calibrated to fit New York's probation population. She also said judges in nearly all New York counties are given defendants' Northpointe assessments during sentencing.

In 2009, Brennan and two colleagues published a validation study that found that Northpointe's risk of recidivism score had an accuracy rate of 68 percent in a sample of 2,328 people. Their study also found that the score was slightly less predictive for black men than white men - 67 percent versus 69 percent. It did not examine racial disparities beyond that, including whether some groups were more likely to be wrongly labeled higher risk.

Brennan said it is difficult to construct a score that doesn't include items that can be correlated with race - such as poverty, joblessness and social marginalization. "If those are omitted from your risk assessment, accuracy goes down," he said.

In 2011, Brennan and Wells sold Northpointe to Toronto-based conglomerate Constellation Software for an undisclosed sum.

Wisconsin has been among the most eager and expansive users of Northpointe's risk assessment tool in sentencing decisions. In 2012, the Wisconsin Department of Corrections launched the use of the software throughout the state. It is used at each step in the prison system, from sentencing to parole.

In a 2012 presentation, corrections official Jared Hoy described the system as a " giant correctional pinball machine " in which correctional officers could use the scores at every "decision point."

Wisconsin has not yet completed a statistical validation study of the tool and has not said when one might be released. State corrections officials declined repeated requests to comment for this article.

Some Wisconsin counties use other risk assessment tools at arrest to determine if a defendant is too risky for pretrial release. Once a defendant is convicted of a felony anywhere in the state, the Department of Corrections attaches Northpointe's assessment to the confidential presentence report given to judges, according to Hoy's presentation.

In theory, judges are not supposed to give longer sentences to defendants with higher risk scores. Rather, they are supposed to use the tests primarily to determine which defendants are eligible for probation or treatment programs.

Prediction Fails Differently for Black Defendants

Overall, Northpointe's assessment tool correctly predicts recidivism 61 percent of the time. But blacks are almost twice as likely as whites to be labeled a higher risk but not actually re-offend. It makes the opposite mistake among whites: They are much more likely than blacks to be labeled lower risk but go on to commit other crimes. (Source: ProPublica analysis of data from Broward County, Fla.)

But judges have cited scores in their sentencing decisions. In August 2013, Judge Scott Horne in La Crosse County, Wisconsin, declared that defendant Eric Loomis had been "identified, through the COMPAS assessment, as an individual who is at high risk to the community." The judge then imposed a sentence of eight years and six months in prison.

Loomis, who was charged with driving a stolen vehicle and fleeing from police, is challenging the use of the score at sentencing as a violation of his due process rights. The state has defended Horne's use of the score with the argument that judges can consider the score in addition to other factors. It has also stopped including scores in presentencing reports until the state Supreme Court decides the case.

"The risk score alone should not determine the sentence of an offender," Wisconsin Assistant Attorney General Christine Remington said last month during state Supreme Court arguments in the Loomis case. "We don't want courts to say, this person in front of me is a 10 on COMPAS as far as risk, and therefore I'm going to give him the maximum sentence."

That is almost exactly what happened to Zilly, the 48-year-old construction worker sent to prison for stealing a push lawnmower and some tools he intended to sell for parts. Zilly has long struggled with a meth habit. In 2012, he had been working toward recovery with the help of a Christian pastor when he relapsed and committed the thefts.

After Zilly was scored as a high risk for violent recidivism and sent to prison, a public defender appealed the sentence and called the score's creator, Brennan, as a witness.

Brennan testified that he didn't design his software to be used in sentencing. "I wanted to stay away from the courts," Brennan said, explaining that his focus was on reducing crime rather than punishment. "But as time went on I started realizing that so many decisions are made, you know, in the courts. So I gradually softened on whether this could be used in the courts or not."

"Not that I'm innocent, but I just believe people do change." (Stephen Maturen for ProPublica)

Still, Brennan testified, "I don't like the idea myself of COMPAS being the sole evidence that a decision would be based upon."

After Brennan's testimony, Judge Babler reduced Zilly's sentence, from two years in prison to 18 months. "Had I not had the COMPAS, I believe it would likely be that I would have given one year, six months," the judge said at an appeals hearing on Nov. 14, 2013.

Zilly said the score didn't take into account all the changes he was making in his life - his conversion to Christianity, his struggle to quit using drugs and his efforts to be more available for his son. "Not that I'm innocent, but I just believe people do change."

Florida's Broward County, where Brisha Borden stole the Huffy bike and was scored as high risk, does not use risk assessments in sentencing. "We don't think the [risk assessment] factors have any bearing on a sentence," said David Scharf, executive director of community programs for the Broward County Sheriff's Office in Fort Lauderdale.

Broward County has, however, adopted the score in pretrial hearings, in the hope of addressing jail overcrowding. A court-appointed monitor has overseen Broward County's jails since 1994 as a result of the settlement of a lawsuit brought by inmates in the 1970s. Even now, years later, the Broward County jail system is often more than 85 percent full, Scharf said.

In 2008, the sheriff's office decided that instead of building another jail, it would begin using Northpointe's risk scores to help identify which defendants were low risk enough to be released on bail pending trial. Since then, nearly everyone arrested in Broward has been scored soon after being booked. (People charged with murder and other capital crimes are not scored because they are not eligible for pretrial release.)

The scores are provided to the judges who decide which defendants can be released from jail. "My feeling is that if they don't need them to be in jail, let's get them out of there," Scharf said.

Scharf said the county chose Northpointe's software over other tools because it was easy to use and produced "simple yet effective charts and graphs for judicial review." He said the system costs about $22,000 a year.

In 2010, researchers at Florida State University examined the use of Northpointe's system in Broward County over a 12-month period and concluded that its predictive accuracy was "equivalent" in assessing defendants of different races. Like others, they did not examine whether different races were classified differently as low or high risk.

Scharf said the county would review ProPublica's findings. "We'll really look at them up close," he said.

Broward County Judge John Hurley, who oversees most of the pretrial release hearings, said the scores were helpful when he was a new judge, but now that he has experience he prefers to rely on his own judgment. "I haven't relied on COMPAS in a couple years," he said.

Hurley said he relies on factors including a person's prior criminal record, the type of crime committed, ties to the community, and their history of failing to appear at court proceedings.

ProPublica's analysis reveals that higher Northpointe scores are slightly correlated with longer pretrial incarceration in Broward County. But there are many reasons that could be true other than judges being swayed by the scores - people with higher risk scores may also be poorer and have difficulty paying bond, for example.

Most crimes are presented to the judge with a recommended bond amount, but he or she can adjust the amount. Hurley said he often releases first-time or low-level offenders without any bond at all.

However, in the case of Borden and her friend Sade Jones, the teenage girls who stole a kid's bike and scooter, Hurley raised the bond amount for each girl from the recommended $0 to $1,000 each.

Hurley said he has no recollection of the case and cannot recall if the scores influenced his decision.

Sade Jones, who had never been arrested before, was rated a medium risk. (Josh Ritchie for ProPublica)

The girls spent two nights in jail before being released on bond.

"We literally sat there and cried" the whole time they were in jail, Jones recalled. The girls were kept in the same cell. Otherwise, Jones said, "I would have gone crazy." Borden declined repeated requests to comment for this article.

Jones, who had never been arrested before, was rated a medium risk. She completed probation and got the felony burglary charge reduced to misdemeanor trespassing, but she has still struggled to find work.

"I went to McDonald's and a dollar store, and they all said no because of my background," she said. "It's all kind of difficult and unnecessary."

Julia Angwin is a senior reporter at ProPublica. From 2000 to 2013, she was a reporter at The Wall Street Journal, where she led a privacy investigative team that was a finalist for a Pulitzer Prize in Explanatory Reporting in 2011 and won a Gerald Loeb Award in 2010.

Lauren Kirchner is a senior reporting fellow at ProPublica. Surya Mattu is a contributing researcher. Design and production by Rob Weychert and David Sleight.

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There's software used across the country to predict future criminals. And it's biased against blacks.

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Voicing Erasure - A Spoken Word Piece Exploring Bias in Voice Recognition Technology

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NEITHER WE NOR ANY OF OUR AFFILIATES OR LICENSORS WILL BE LIABLE FOR ANY INDIRECT, INCIDENTAL, SPECIAL, CONSEQUENTIAL, OR EXEMPLARY DAMAGES, INCLUDING DAMAGES FOR LOSS OF PROFITS, GOODWILL, USE, OR DATA OR OTHER LOSSES, EVEN IF WE HAVE BEEN ADVISED OF THE POSSIBILITY OF SUCH DAMAGES.

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https://medium.com/mlearning-ai/the-chatgpt-list-of-lists-a-collection-of-1500-useful-mind-blowing-and-strange-use-cases-8b14c35eb

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https://www.npr.org/2023/03/08/1161883646/a-more-perfect-human

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The dream of AI - artificial intelligence - has been around for centuries: the idea of an intelligent machine without free will popped up in ancient Taoist scrolls, Buddhist fables, and the tales of medieval European courts. But it wasn't until the 20th century that science caught up to our imaginations.

Today, AI is everywhere. Breakthrough technologies like ChatGPT make news, while less glamorous but more ubiquitous programs are woven into every part of our lives, from dating apps to medical care. In many ways, AI is the invisible architecture of modern life. It's a reality that's both mundane and terrifying. And it's accelerating at a rapid rate, even as we still grapple with some of the most fundamental questions it raises about what, if anything, is uniquely human. In this episode, we explore the tension between our love of AI and our fear of it - and try to decode the humans behind the machines.

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The dream of AI - artificial intelligence - has been around for centuries: the idea of an intelligent machine without free will popped up in ancient Taoist scrolls, Buddhist fables, and the tales of medieval European courts. But it wasn't until the 20th century that science caught up to our imaginations.Today, AI is everywhere.

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Threat or Opportunity? The Educator Response to ChatGPT

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Threat or Opportunity? The Educator Response to ChatGPT

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https://www.youtube.com/live/NnU5ieL_ThE?feature=share

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Threat or Opportunity? The Educator Response to ChatGPT

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We'll be hosting a panel discussion exploring the influence and impact that emerging AI will have on education. This one's all about community input - so joi...

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college admissions in the age of AI

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An AI Guru on ChatGPT and more

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Amid ChatGPT outcry, some teachers are inviting AI to class

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The unveiling of ChatGPT has panicked educators around the country who fear students will use the artificial intelligence powered chatbot it to write their essays and cheat on homework.

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https://docs.google.com/presentation/d/17a1DRrTI6uOGiPGovZS_uC_vey-AOLIeE7VTVRazYM0/edit?usp=sharing

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K12 Artificial Intelligence What is it and why should we teach about it? Dr. Wendy Hanasky

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55 minutes | Feb 15, 2023

Thanks for listening to Episode #40 of the EDventUres in Tech Podcast. In this episode, Andrew & Dan interview Matt Miller, author of many books, blogger, Edtech speaker, and head "ditcher" from Ditch that Textbook.

In this episode, we discuss Chat GPT and what Matt's thoughts are on how AI, Chat GPT and more revolutionize education.

@JMattMiller
4 Ways to Blend Learning with InsertLearning Include captions with a Google Meet video recording Generate a ready-to-play lesson in seconds
Once again, thank you for all your support in listening on all platforms and leaving us a review. If you have any questions, comments, or suggestions, please reach out.

Tech Hard again. Work smart. Live an EDventUre.

--- Support this podcast: https://anchor.fm/andrew-nikola/support

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Edventures in Tech - Episode #40 - Don't DITCH That AI with Matt Miller on Stitcher

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Thanks for listening to Episode #40 of the EDventUres in Tech Podcast. In this episode, Andrew & Dan interview Matt Miller, author of many books, blogger, Edtech speaker, and head "ditcher" from Ditch that Textbook. In this episode, we discuss Chat GPT and what Matt's thoughts are on how AI, Chat GPT and more revolutionize education.

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What's in this episode? Karine and Rose meet this week to discuss the Online Safety Bill, school absences, and ChatGPT, the latter of which has produced huge public debate, from teacher anxieties to developer felicitations, questions from parents, and columnist thinkpieces all around the presence of AI in the classroom. With all of these concerns, however, is it possible that ChatGPT has done education a favour?
OpenAI's ChatGPT is the third and latest version of their text-generating AI technology, and it's been trained on over 45 terrabytes of data. If that seems like a lot, it is: the entirety of English-language Wikipedia accounts for just 1% of that volume in comparison. The talk of Twitter and intrigued educationalists in schools around the anglosphere, much of the discussion has been around its use as a replacement for human cognition: will students use it to cheat in essays and assessments? Does its information retrieval dumb-down student opportunities for learning when material is simply parroted, rather than interrogated and the learning then applied in novel contexts? In this week's episode, Karine and Rose discuss practical uses for this incredibly powerful tool, and explain why human and machine intelligence can work together successfully to improve teaching and learning, and our understanding of AI.

Material discussed in this episode includes:

Square Peg's new book by Fran Morgan and Ellie Costello, with Ian Gilbert:
Square Pegs: Inclusivity, Compassion, and Fitting In - a Guide for Schools, available here

EVR and Cambridge Partnership for Education's Covid-19 report:
Shock to the System: Lessons from Covid-19, available here

Rose Luckin - Professor of Learner Centred Design, UCL, Founder, EDUCATE Ventures Research

Rosemary (Rose) Luckin is Professor of Learner Centred Design at UCL Knowledge Lab.  She was named one of the 20 most influential people in education in the Seldon List, 2017. Rose is Founder of EDUCATE Ventures Research Ltd., a London hub for start-ups, researchers and educators developing evidence-based educational technology.  She is past president and current treasurer of the International Society for AI in Education and co-founder of the Institute for Ethical AI in Education.

Karine George - Former Headteacher, Chief Education Advisor, EDUCATE Ventures Research

An award-winning, future reimagining educationalist, Karine spent more than 20 years as headteacher in an 'Outstanding' Oftsed-rated school, She is an active research practitioner and ties research, business and education together to transform leadership and pedagogy. An advocate for technology's support of 21st century teaching and learning, she has reported on its use to the DfE, penned numerous articles on educational issues and was a contributing author for 'Sustainable School Transformation: an inside-out school-led approach'.

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#261 - Has Chat GPT Done Education a Favour?

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What's in this episode? Karine and Rose meet this week to discuss the Online Safety Bill, school absences, and ChatGPT, the latter of which has produced huge public debate, from teacher anxieties to developer felicitations, questions from parents, and columnist thinkpieces all around the presence of AI in the classroom.

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Episode #129: ChatGPT and 2 Free Tools that Identify AI Written Text
Welcome back to GotTechED the podcast this is Episode 129 called "ChatGPT and 2 Free Tools that Identify AI Written Text" In this episode, we'll share our take on ChatGPT and other AI apps that can write and think for you. We'll also share 2 edtech tools that help teachers identify when text has been AI generated. This is another episode you don't want to miss, check it out.
Segment 1: Updates

1. Iste Philadelphia

2. NJECC Conference is March 7th (Tuesday in person) and March 8th (Wednesday remote)

Segment 2: ChatGPT vs GPTZero

1. General intro to chatgpt and other ai that inspired this episode

2. New Tech Rant: Use technology to enhance the way we learn... remember the same hesitancies when

1. Negative Uses in Education

1. Essays being written for students

2. Disrupts the creative process

3. Can be out of date (Runs off of a scan from 2021)

4. Incorrect information

2. Positive Uses in Education

1. Pen and paper back in session?

2. Brainstorming ideas

3. Ask GPT to come up with lists or introductions to topics where students can research beyond the AI

4. Gamify your classroom... 2 Truths and a Lie becomes 2 of my and 1 of AI or Human vs AI

5. Have AI help with summarizing/Note-taking

6. Generation of prompts or tasks

I ran through two samples. One that was written by a student and one by ChatGPT. I copy and pasted the results into these two documents:

1. Text Report 1

1. Get GPTZero Result

2. Your GPTZero score corresponds to the likelihood of the text being AI generated:118.7305572913241

3. Your text is likely human generated!

2. Text Report 2

1. GPTZero has finished analyzing your text!

2. Your GPTZero score corresponds to the likelihood of the text being AI generated:24.142793104246635

3. Your text is most likely to be AI generated!

3. GotTechED's YouTube Video on GPTZero

3. Checkforai.com
Feedback

1. Thank you for the feedback! Glad to hear it is simple to use- that was one of my main goals when designing the platform. Excited to see it used on the show!

2. To give you some additional background:

1. The risk score is based upon the word frequencies, as AI produces more standardized output, meaning the text has statistical markers in word patterns that are different from human written text. I used neural networks to identify these patterns.

2. Generally, low risk means there is a low chance of the text being ai generated. Medium risk means the AI is unsure, or, the text may have been written by a human, but edited by AI. High risk means there is a high risk of the text being written by an AI, and very high risk usually means the text was copy pasted directly from chatgpt. It is always good to remember that, like all ai detection tools, it is not perfect, so it should be used in combination with manual inspection of the content and conversation with students!

3. Thanks again for trying it out, and the positive feedback. I will look into adding these explanations and more data on the results in the next feature update. Looking forward to seeing it used on the show.

Segment 4: Where to Find GotTechED 39:00

Do us 3 favors

Music Credits:

Subscribe to our Podcast

Connect with us on Social Media

Join the Conversation and our PLN

Our favorite part of recording a live podcast each week is participating in the great conversations that happen on our Facebook Group Page .

Need a Presenter?

As experienced presenters and content creators, you can contact Nick and Guise to speak at your school, event, or conference. They can customize a workshop that meets your organization's unique time and content needs. While no topic is out of bounds, GotTechED is best known for sessions on:

1:1 Chromebook Integration

EdTech Throwdown

TargetED Learning

Gamification (Badge Systems)

Game-Based Learning (Escape Rooms, Amazing Race, and more)

Google apps and extensions

Personalized learning and Choice Boards

Teacher productivity (Lesson Planning, Online Grading. and Feedback)

Digital content creation

Student Podcasting

Screencasting

Flipped Classroom

Student-Centered Learning

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ChatGPT and 2 Free Tools that Identify AI Written Text

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Welcome back to GotTechED the podcast this is Episode 129 called "ChatGPT and 2 Free Tools that Identify AI Written Text" In this episode, we'll share our take on ChatGPT and other AI apps that can write and think for you. We'll also share 2 edtech tools that help teachers identify when text has been AI generated.

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Joe Marquez: Embrace ChatGPT and Generative AI

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Listen to this episode from Tech Lasso on Spotify. Joe Marquez is the Director of Academic Innovation with the non-profit organization CUE, as well as the host of the Sons of Technology podcast. He believes that Chat GPT and generative AI will "truly change the trajectory of education."

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Episode #132: 12 AI Powered Tools that Make a Teachers Life Easier
Welcome back to GotTechED the podcast this is Episode 132 called "12 AI Powered Tools that Make a Teachers Life Easier". In this episode, we'll share 12 edtech tools that are powered by AI and can make your day a little simpler. We'll also share our thoughts on how these tools can be implemented in your classroom. This is another episode you don't want to miss, check it out.
Segment 1: Updates

1. Iste Philadelphia

2. NJECC Conference is March 7th (Tuesday in person) and March 8th (Wednesday remote)

AI is taking #education by storm. Is this a good or bad thing? Listen to episode 132 to see if one of these 12 AI #edtech tools can make your #teaching life easier! #ai #edtechchat #teachertwitter Click To Tweet

1. Beta.tome.app

1. Presentation Maker

2. https://tome.app/invite/ericguisehvrsdorg-cleim4ffn06iggs309g8b8ogl

2. -
Fast and easy to use tools for creating quizzes and formative assessments in a flash.

3. https://mindgrasp.ai/
Mindgrasp instantly creates accurate notes and answers questions from any Document, PDF, YouTube Video, Zoom Meeting, Webinar Recording, Podcast and much more!

Segment 3: Where to Find GotTechED

Do us 3 favors

Music Credits:

Subscribe to our Podcast

Connect with us on Social Media

Join the Conversation and our PLN

Our favorite part of recording a live podcast each week is participating in the great conversations that happen on our Facebook Group Page .

Need a Presenter?

As experienced presenters and content creators, you can contact Nick and Guise to speak at your school, event, or conference. They can customize a workshop that meets your organization's unique time and content needs. While no topic is out of bounds, GotTechED is best known for sessions on:

1:1 Chromebook Integration

EdTech Throwdown

TargetED Learning

Gamification (Badge Systems)

Game-Based Learning (Escape Rooms, Amazing Race, and more)

Google apps and extensions

Personalized learning and Choice Boards

Teacher productivity (Lesson Planning, Online Grading. and Feedback)

Digital content creation

Student Podcasting

Screencasting

Flipped Classroom

Student-Centered Learning

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Episode #132: 12 AI Powered Tools that Make a Teachers Life Easier

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Welcome back to GotTechED the podcast this is Episode 132 called "12 AI Powered Tools that Make a Teachers Life Easier". In this episode, we'll share 12 edtech tools that are powered by AI and can make your day a little simpler. We'll also share our thoughts on how these tools can be implemented in your classroom.

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ChatGPT: Artificial Intelligence, chatbots and a world of unknowns | 60 Minutes

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Lesley Stahl speaks with Brad Smith, president of Microsoft, and others about the emerging industry of artificial intelligence systems people can have conversations with.

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"So when people get lazy and [say], 'Hey, write this thing for me,' and then take it and use it, there could be errors in it," said Schneider. This makes it a valuable tool for generating ideas and writing rough drafts, but a risky option when using it for final assignments. Students who decide to use ChatGPT will likely need to double check that the information it provides is correct either by knowing the information in the first place or confirming with other dependable sources.

ChatGPT can support teachers, not replace them

For some educators, ChatGPT also raises alarm that the widespread adoption of AI could lead to , particularly in areas such as tutoring and teaching languages. Schneider said that's unlikely. "I can't imagine a school system that has no teachers in it," he said. Numerous studies show a correlation between strong student-teacher connections and increased student involvement, attendance and academic performance.

As people explore how AI will support teaching and learning, teachers' roles may change as these tech tools become more widely used. "Teachers are going to have to evolve and figure out how to harness the power of this tool to improve instruction," said Schneider. For example, the AI Institute for Transforming Education for Children with Speech and Language Processing Challenges, which was awarded $20 million in funding from IES and the National Science Foundation, is exploring how ChatGPT can support speech pathologists. According to a recent survey by the American Speech-Language-Hearing Association , the median number of students served by one speech pathologist is 48. "There are simply not enough pathologists in schools," said Schneider. ChatGPT has the potential to help speech pathologists complete paperwork, which takes up almost six hours each week , and build personalized treatment plans for students with cognitive disabilities, such as dyslexia.

"We need to rethink what we can do to free up teachers to do the work that they are really good at and how to help them individualize their interventions and provide instruction and support," said Schneider.

When you use ChatGPT, your data is not secure

ChatGPT is convincing because it references a massive amount of data and identifies patterns to generate text that seems like it is written by a human. It can even mimic the writing style and tone of the person who uses it. "The more data they have, the better the model," said Schneider, referring to ChatGPT's ability to generate responses. "And there's tons of data floating around."

The information that users put into ChatGPT to make it generate a response - also known as the input - can take the form of a question, a statement or even a partial text that the user wants ChatGPT to complete. But when students use ChatGPT they may be putting their data at risk.

According to Open AI's privacy policy , inputs - including ones with personal information, such as names, addresses, phone numbers or other sensitive content - may be reviewed and shared with third parties. Also, there is the ever present risk that if ChatGPT is hacked, a bad actor can access users' data.

Schneider acknowledged that if ChatGPT will be used to support teaching and learning, privacy is a major concern. "We are developing much better methods for preserving privacy than we have in the past," he said. "We have to remember it's a bit of a cost analysis. Using all this data has many benefits. It also has some risks. We have to balance those." He added that ChatGPT is similar to wearing an Apple Watch or talking to an Amazon Alexa, because those tools also rely on data from users.

Banning ChatGPT isn't a long-term solution

Because students can input original prompts into ChatGPT and get unique answers, it raises the question: Is using ChatGPT plagiarism? And how much does AI-generated text need to be edited until it is considered a students' own work ? In lieu of answering these questions, some schools, including districts in Los Angeles, New York City and , have opted to ban use of ChatGPT outright.

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Worried about ChatGPT and cheating? Here are 4 things teachers should know

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"So when people get lazy and [say], 'Hey, write this thing for me,' and then take it and use it, there could be errors in it," said Schneider. This makes it a valuable tool for generating ideas and writing rough drafts, but a risky option when using it for final assignments.

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Too Long; Didn't Read

Everybody has heard of ChatGPT, which has gained tremendous popularity to enhance productivity. We'll showcase the top 10 AI tools that can help you boost your productivity. From open-source data analysis software to cloud-based platforms for deploying AI solutions, this article is a must-read for anyone looking to utilize the power of AI.

Hackernoon hq - po box 2206, edwards, colorado 81632, usa

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Top 10 AI Tools to Check Out If You're Bored With ChatGPT | HackerNoon

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Everybody has heard of ChatGPT, which has gained tremendous popularity to enhance productivity. We'll showcase the top 10 AI tools that can help you boost your productivity. From open-source data analysis software to cloud-based platforms for deploying AI solutions, this article is a must-read for anyone looking to utilize the power of AI.

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As we all know, search engines are an integral part of our daily lives. We rely on them to help us find information, products, and services online. However, sometimes the search results we get can be overwhelming or even irrelevant. Some have quickly touted that ChatGPT will put Google out of business. While I think that is an overstatement, ChatGPT does offer a tool that many might find refreshing and intriguing when used alongside a search engine: ChatGPT for Google.

What Is GPT?

Let's back up just a tad and define "GPT," what the Chrome extension is based on. GPT (Generative Pre-trained Transformer) is a state-of-the-art language generation model developed by OpenAI. It has been trained on a large dataset and can generate human-like text in a variety of languages and formats.

GPT can be fine-tuned for specific tasks and languages with training on additional data, which can help it generate more accurate and relevant responses for a given application. It is a powerful tool and has been used in a wide range of applications, including chatbots, translation, summarization, and more.

What Is ChatGPT?

As it relates to searching, ChatGPT focuses on giving you a defined answer based on the dataset that it has. Google search results, on the other hand, give you an extensive listing of items that have been designated (most likely through good SEO optimization, using LSI keywords in your page, building backlinks to your site, and more), which in reality boils down to "good marketing." And marketing isn't bad if it is giving you what you are searching for instead of what others want you to have.

What's the Difference Between Search Engines and ChatGPT?

One analogy for explaining the difference between Google's search engine (or Bing, Yahoo!, etc.) and ChatGPT is to put it in the context of going to a restaurant. Sometimes you want a menu of items to look at and choose from. And, if you've ever gone to Cheesecake Factory, you are basically given a book of menu items, much like getting 423.2 million search results in 1.3 seconds.

On the other hand, you might have a conversation with your server and say something like, "I'm not super hungry, but I would like a good chicken dish that is lower in carbs. What would you recommend?" What the server tells you is more in line with what ChatGPT offers. It's based on their knowledge of the "menu items," what others have tried, and possibly what they like themselves (if they can relate to your request). The server may give you a couple of options, but they most likely won't say to look between pages 5 and 12 for good options.

What Is ChatGPT for Google?

ChatGPT for Google is a free Chrome extension that uses artificial intelligence to provide more informative search results. It will send your search query to the GPT servers and pull back the response. Unfortunately, you aren't able to dive down deeper with the response as you would if you were on the ChatGPT website. You can, however, tweak your search query and have it provide more focused information.

How Does ChatGPT for Google Work?

After you have installed the ChatGPT for Google Chrome extension, just go to Google and search as you normally would. Your browser will now show results in two panels. The panel on the left will show your usual Google search results. The panel on the right will provide the results from ChatGPT as if you went directly to OpenAI's site and typed your search prompt there.

What Are the Benefits of Using ChatGPT?

There are several benefits to using ChatGPT for your search needs:

More targeted results: By asking you clarifying questions, ChatGPT can provide more targeted search results that are more relevant to what you are looking for.

Fewer search results: Because ChatGPT provides more targeted results, you will likely see fewer search results overall. This can save you time and make it easier to find what you are looking for.

Improved search experience: The interactive nature of ChatGPT can make the search process more enjoyable and engaging.

Tips for Using ChatGPT for Google

Be specific in your search query. The better you are at asking questions or telling it what you want, the better results you'll get.

Be patient. OpenAI is scaling, but many folks are finding that it may not be quite as fast as they'd like it to be, and there are times when it is unavailable due to exceedingly high user demands.

Be aware of the time limitations. Currently, according to ChatGPT, "I have access to information up until my knowledge cutoff in 2021, but I do not have access to real-time information or updates beyond that."

Provide feedback. Give a thumbs up or down based on how well the ChatGPT's info was in your opinion and experience.

Consider asking for more detailed information. You can jump over to ChatGPT's site to ask for more detailed information, but you will need to enter your search query again.

If you are tired of sifting through irrelevant search results and want a more targeted and personalized search experience, give ChatGPT for Google Chrome extension a try. Try ChatGPT for yourself and see how it can improve your search experience. Its artificial intelligence technology and interactive approach can help you find what you are looking for more quickly and easily.

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How to Improve Your Search Results with ChatGPT * TechNotes Blog

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As we all know, search engines are an integral part of our daily lives. We rely on them to help us find information, products, and services online. However, sometimes the search results we get can be overwhelming or even irrelevant. Some have quickly touted that ChatGPT will put Google out of business.

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computer science and information technology

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Real estate agents say they can't imagine working without ChatGPT now | CNN Business

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If you came across a four bedroom, 3.5 bathroom home listed for sale recently on a quiet cul-de-sac in Cedar Rapids, Iowa, you might not think twice about the online listing. It included typical real estate descriptions like "ideal for entertaining" and "ample space for relaxation."

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Attendee - 2023 OETC Revolutionizing Education with A.I.

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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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How to Level Up Your Teaching with ChatGPT and AI - HoET218

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Ethan Mollick has a message for the humans and the machines: can't we all just get along?

After all, we are now officially in an A.I. world and we're going to have to share it, reasons the associate professor at the University of Pennsylvania's prestigious Wharton School.

"This was a sudden change, right? There is a lot of good stuff that we are going to have to do differently, but I think we could solve the problems of how we teach people to write in a world with ChatGPT," Mollick told NPR.

Ever since the chatbot ChatGPT launched in November, educators have raised concerns it could facilitate cheating.

Some school districts have banned access to the bot, and not without reason. The artificial intelligence tool from the company OpenAI can compose poetry. It can write computer code. It can maybe even pass an MBA exam.

One Wharton professor recently fed the chatbot the final exam questions for a core MBA course and found that, despite some surprising math errors, he would have given it a B or a B-minus in the class.

And yet, not all educators are shying away from the bot.

This year, Mollick is not only allowing his students to use ChatGPT, they are required to. And he has formally adopted an A.I. policy into his syllabus for the first time.

He teaches classes in entrepreneurship and innovation, and said the early indications were the move was going great.

"The truth is, I probably couldn't have stopped them even if I didn't require it," Mollick said.

This week he ran a session where students were asked to come up with ideas for their class project. Almost everyone had ChatGPT running and were asking it to generate projects, and then they interrogated the bot's ideas with further prompts.

"And the ideas so far are great, partially as a result of that set of interactions," Mollick said.

He readily admits he alternates between enthusiasm and anxiety about how artificial intelligence can change assessments in the classroom, but he believes educators need to move with the times.

"We taught people how to do math in a world with calculators," he said. Now the challenge is for educators to teach students how the world has changed again, and how they can adapt to that.

Mollick's new policy states that using A.I. is an "emerging skill"; that it can be wrong and students should check its results against other sources; and that they will be responsible for any errors or omissions provided by the tool.

And, perhaps most importantly, students need to acknowledge when and how they have used it.

"Failure to do so is in violation of academic honesty policies," the policy reads.

Mollick isn't the first to try to put guardrails in place for a post-ChatGPT world.

Earlier this month, 22-year-old Princeton student Edward Tian created an app to detect if something had been written by a machine. Named GPTZero, it was so popular that when he launched it, the app crashed from overuse.

"Humans deserve to know when something is written by a human or written by a machine," Tian told NPR of his motivation.

Mollick agrees, but isn't convinced that educators can ever truly stop cheating.

He cites a survey of Stanford students that found many had already used ChatGPT in their final exams, and he points to estimates that thousands of people in places like Kenya are writing essays on behalf of students abroad.

"I think everybody is cheating ... I mean, it's happening. So what I'm asking students to do is just be honest with me," he said. "Tell me what they use ChatGPT for, tell me what they used as prompts to get it to do what they want, and that's all I'm asking from them. We're in a world where this is happening, but now it's just going to be at an even grander scale."

"I don't think human nature changes as a result of ChatGPT. I think capability did."
The radio interview with Ethan Mollick was produced by Gabe O'Connor and edited by Christopher Intagliata.

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'Everybody is cheating': Why this teacher has adopted an open ChatGPT policy

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An associate professor at the prestigious Wharton School is not only allowing his students to use ChatGPT, they are required to.

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7 Things Teachers Can Do With ChatGPT Tomorrow - Easy EdTech Podcast 201 - Class Tech Tips

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Learn seven things teachers can do with ChatGPT tomorrow to save time completing your daily tasks inside and outside of the classroom.

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Sal Khan believes the impact of AI technologies such as ChatGPT will be greater in education and beyond than the advent of search engines or smartphones.

"It is a new epoch and it is the most epoch of the epochs, I really believe, and I wouldn't have said that six months ago or a year ago," Khan says. "This is a moment we're in. I don't think this is hype. And education is really at the center of this hurricane. It's the first field that is getting a shock."

In 2008, Khan founded the nonprofit Khan Academy, a free online resource for students with lessons in math, sciences, and the humanities. Today, it has more than 145 million registered users.

Khan shares his thoughts on the ways AI and chatbot technology will change education and how teachers can update their educational practices to embrace it.

A Potential Boon To Writing Instruction

(Image credit: Sal Khan)

Khan believes there is tremendous potential for ChatGPT and similar AI technology to help teach students to write better. Currently, despite the best efforts of K-12 educators, many students still struggle to write well when they enter college.

"Most students can't write a five-paragraph essay. They can't write a cogent paragraph with a thesis statement," he says. AI could potentially help with this by providing students with an encouraging writing coach that provides more feedback and in a much more timely manner than even the most dedicated teacher ever could.

"Writing, infamously, has a very slow feedback loop," Khan says. "Even old-school math, pre-Khan Academy had a faster feedback loop. With things like Khan Academy you get immediate feedback, which is one of the things we've always said: 'Imagine having to shoot free throws and not knowing if you made them until the next morning.'"

AI has the potential to extend this immediate feedback from math to writing, reducing the response time for essays from a week or two to a few seconds. The feedback will also be more consistent, Khan says.

Other Learning Opportunities

Teachers can use AI programs to create lesson plans, and ChatGPT is already being used in this way. "That experience is only going to get better as people create applications around it," Khan says.

Educators can already use AI to grade papers and other work, and there are numerous apps that leverage AI to support teaching (opens in new tab). Khan believes any initial reluctance educators might have about using ChatGPT-type tools as a grading assistant will dissolve after they see how fast and good AI tools will become at it. "If you're a university professor teaching a 300-student English 101 with eight TAs, you're not grading, those TAs are grading," Khan says. "And now every teacher is going to have an army of TAs."

Khan also believes in the future almost every job will require working with AI technology in some capacity and there will be specific jobs geared toward writing AI prompts. So having students learn how to interact with AI early on makes sense.

For example, Khan says a teacher might require students to use ChatGPT-style technology to write multiple chapters in a novel. "It's not easy to do a multi-chapter novel, you're gonna have to think about how it fits together, etc. It's still gonna take you a few hours to do that, or probably more," he says. "But it's very fun because you get a lot of output very quickly."

What About Those Who Use ChatGPT to Cheat?

Khan acknowledges the challenges educators face this current school year adjusting to ChatGPT (opens in new tab) on the fly. One simple way he suggests educators can prevent this misuse of ChatGPT is to have students work on shorter essays during class time. "I actually think this is a much better use of class time, definitely better than lecturing," he says.

He also doesn't agree with school districts that ban the technology both because such bans are unenforceable, and even if these were enforceable, it would take away potential benefits to students from using the technology.

And not all the cases of supposed ChatGPT "cheating" are clear cut in Khan's mind. For instance, students have begun to use ChatGPT to write their college admission essays, and while it may be questionably ethical to do so, the use of the technology in this way is raising important questions about the college admission process overall.

"It's really healthy because it just puts a spotlight on what's already been going on," he says. "If you're an affluent student, these people hire $5,000, $10,000 admission coaches, who aren't AI, they're 'I.' That's already happening and so to some degree, this levels the playing field, and it puts it in everyone's face that this was not really an equitable way to evaluate who gets in and who doesn't."

Khan has told his employees to start experimenting with ways in which they can utilize ChatGPT and other AI technologies to enhance their work.

Ultimately, he believes that AI technology will help teachers and students everywhere. "If I were to guess, I think this is going to be a major accelerant and benefit for education, in the not-too-far future," he says.

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Sal Khan: ChatGPT and Other AI Technology Herald "New Epoch"

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Sal Khan believes the impact of AI technologies such as ChatGPT will be greater in education and beyond than the advent of search engines or smartphones. "It is a new epoch and it is the most epoch of the epochs, I really believe, and I wouldn't have said that six months ago or a year ago," Khan says.

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Journalist says he had a creepy encounter with new tech that left him unable to sleep

New York Times technology columnist Kevin Roose has early access to new features in Microsoft's search engine Bing that incorporates artificial intelligence. Roose says the new chatbot tried to get him to leave his wife.

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computer science and information technology

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Journalist says he had a creepy encounter with new tech that left him unable to sleep | CNN Business

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Journalist says he had a creepy encounter with new tech that left him unable to sleep New York Times technology columnist Kevin Roose has early access to new features in Microsoft's search engine Bing that incorporates artificial intelligence. Roose says the new chatbot tried to get him to leave his wife.

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New York City schools have banned ChatGPT, the artificial intelligence chatbot that generates human-like writing including essays, amid fears that students could use it to cheat.

According to the city's education department, the tool will be forbidden across all devices and networks in New York's public schools. Jenna Lyle, a department spokesperson, said the decision stems from "concerns about negative impacts on student learning, and concerns regarding the safety and accuracy of contents".

ChatGPT was created by OpenAI, an independent artificial intelligence research foundation co-founded by Elon Musk in 2015. Released last November, OpenAI's chatbot is able to create stunningly human-like responses to a wide range of questions and various writing prompts. ChatGPT is trained on a large sample of text taken from the internet and interacts with users in a dialogue format.

According to OpenAI, the conversation format allows ChatGPT "to answer follow-up questions, admit its mistakes, challenge incorrect premises, and reject inappropriate requests". Users can request rephrasings, summaries and expansions on the texts that it churns out.

The decision to ban the chatbot in New York schools comes amid widespread fears that it could encourage students to plagiarize.

"While the tool may be able to provide quick and easy answers to questions, it does not build critical-thinking and problem-solving skills, which are essential for academic and lifelong success," Lyle said.

Nevertheless, individual schools are still able to request access to ChatPGT for "purposes of AI and technology-related education", she added.

Since New York's announcement, OpenAI has tried to reassure teachers. The company told the Washington Post: "We don't want ChatGPT to be used for misleading purposes in schools or anywhere else, so we're already developing mitigations to help anyone identify text generated by that system.

"We look forward to working with educators on useful solutions, and other ways to help teachers and students benefit from artificial intelligence," it added.

Last month, OpenAI's CEO, Sam Altman, tweeted that ChatGPT is "incredibly limited, but good enough at some things to create a misleading impression of greatness".

"It's a mistake to be relying on it for anything important right now. It's a preview of progress; we have lots of work to do on robustness and truthfulness," he said, adding, "Fun, creative inspiration; great! Reliance for factual queries; not such a good idea."

The chatbot has so far proved to be divisive among educators.

"The robots are here and they're going to be doing our students' homework," warned educator Dan Lewer on TikTok.

Lewer advises teachers to ask students who submit their essays at home to also submit a "short and sweet" video response in which they "restate their thesis ...review some of their best evidence, their best arguments, their reasoning and then at the end I would have them reflect ... What did they learn from the essay ... what did they struggle with, where did they think they grew.

"This will help students develop better communication skills while helping you ensure they're really learning the material," said Lewer.

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New York City schools ban AI chatbot that writes essays and answers prompts

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New York City schools have banned ChatGPT, the artificial intelligence chatbot that generates human-like writing including essays, amid fears that students could use it to cheat. According to the city's education department, the tool will be forbidden across all devices and networks in New York's public schools.

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