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Create discussion prompts, rubrics, exit tickets, and more.

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No, you will not be able to grant students access to Khanmigo. At this time, classroom access for Khanmigo is only available through school or district implementations. We invite district and school administrators to contact our Districts Partnership team to learn about Khanmigo for Districts.

Khanmigo for teachers offers a lot of exciting features, such as standards-aligned lesson planning tied to Khan Academy's world-class content library; an on-demand summary of recent student work so you can quickly assess progress and identify areas where additional support is needed; and Khanmigo-crafted learning objectives, rubrics, and even exit tickets.

As a nonprofit educational organization, we see it as our responsibility to explore what AI could mean for the future of education. We believe that AI has the potential to transform learning in a positive way, but we are also keenly aware of the risks. Therefore, we are only offering access to learners who are under 18 years old when their parents give them access or when their school district is partnered with Khan Academy. This collaboration across school leaders, teachers, families, and Khan Academy staff makes it possible to provide the level of supervision and support children deserve. We believe these guidelines will help us responsibly adapt AI for an educational setting.

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Khanmigo: Free, AI-powered teacher assistant by Khan Academy

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Khan Academy's AI-powered teacher tool streamlines your prep giving you more energy for teaching and students. Khanmigo is free for educators.

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The Generative AI Product Tracker lists generative AI products that are either marketed specifically towards postsecondary faculty or students or appear to be actively in use by postsecondary faculty or students for teaching, learning, or research activities. The Tracker is a living document, which we update regularly as new products enter the market or new information about existing products becomes available. For more information, see our issue brief, Generative AI in Higher Ed: The Product Landscape.

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Artificial Intelligence In Higher Education with OneHE

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Artificial Intelligence In Higher Education with OneHE

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Intro to GenAI, how it can be used effectively with students and in your own practice and suggests useful resources on this topic.

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Artificial Intelligence in Higher Education - OneHE

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Resource What is GenAI? Examples of GenAI tools What's next? Useful Resources What is GenAI? Generative AI (GenAI) is a form of artificial intelligence that can be used to create (or generate) content, typically in the form of text, images, audio or video, with results that are very similar to those created by humans.

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A collection of assignments and materials inspired by the humanities,
for educators curious about how AI affects their students and their syllabi.

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Current policy that covers student use of AI

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CSU Institution-wide Policies Summer 2024 - CougarView Course Template

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As a coach, as a tutor . . ? We're all figuring this out together.

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Assigning AI: Seven Ways of Using AI in Class

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Also prompts! And things to watch out for!

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Includes detailed prompts for each part of backward course design (learning outcomes, assessments, daily lessons), plus ways to use AI for other faculty tasks. Curated by Cynthia Alby, GA Governor's Teaching Fellows staff and co-author of Learning That Matters.

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Guidance for Assignments and Policies

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Ideas for meaningful and innovative assessment

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Ideas for meaningful and innovative assessment

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Based on a synthesis of recently published papers and other web sources, this article offers practical suggestions on assessment methods that instructors can consider using in their courses. Jonna Lee is director for research in education innovation in the Center for 21st Century Universities at the Georgia Institute of Technology.

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ChatGPT can provide step-by-step solutions to mathematical problems, write code and generate lesson plans, draft emails and produce stories, music lyrics and social media posts. Therefore, it is not surprising that many in academia are excited about the potential positive influence of ChatGPT on productivity and efficiency.

On the other hand, ChatGPT has triggered a lot of debate around integrity issues in higher education, including concerns about plagiarism and cheating in and outside the classroom. AI models designed for plagiarism detection are being developed. However, this problem naturally leads to an increase in faculty workload to deal with challenges in detecting AI usage in their students' work. Also, beyond modifying their course syllabus, instructors will need to be trained on new assessment practices in response to students' exposure to and usage of ChatGPT.

Through self-assessment or reflective writing, students could discuss their writing or thinking process. Additionally, peer evaluations or interactive assessment activities could be integrated into grading by engaging students in group discussions or other activities such as research and analysis in which students are expected to co-construct knowledge and apply certain skills. Instructors may consider placing an emphasis on assessing the process of learning rather than the outcome. Instead of grading a final writing product alone, instructors could grade multiple drafts and assess how a student's writing improved.

Lastly, instructors can use ChatGPT as a supplemental tool for designing assessment activities. Instructors may ask detailed and specific prompts of ChatGPT to generate a rubric for grading students' knowledge and skills, create multiple-choice quiz questions or come up with group discussion questions about course topics. However, when using these sources, it would be important to ensure they are accurate and to check how the information presented can be interpreted or if it contains any biased views.

Ongoing discussions about assessment methods in light of the growing influence of generative AI such as ChatGPT remind us to adopt more student-centric assessments that can create meaningful learning opportunities for individual students. Design assessments that can provide students with ample scope to apply their skills to analyse ideas and integrate concepts in order to deepen their knowledge.

assessing these cognitive skills , how to incorporate assessments of non-cognitive skills such as collaboration, communication and leadership into classroom learning needs further discussion. That is, instead of emphasising skills that are traditionally measured by standardised or psychometric tests on which AI can perform very well, we should emphasise skills that are useful and meaningful for human learning.

Tools such as ChatGPT do not necessarily generate factual information. Rather, they produce pieces based on the probability of potential relationships between words. Depending on the questions asked, the outputs generated by ChatGPT may be incorrect, outdated or illogical. It has a limited understanding of context, can potentially generate offensive or harmful content, has limited ability to fact-check or verify information, lacks empathy and has limited knowledge on certain topics (especially if the subject matter is complex). In such case, instructors must caution students about the limitations of such tools and encourage them to compare the outputs with other reliable sources to prevent the spread of misinformation and misconceptions.

Jonna Leeisdirector for research in education innovation in theCenter for 21st Century Universities at the Georgia Institute of Technology.
If you found this interesting and want advice and insight from academics and university staff delivered direct to your inbox each week, sign up for the Campus newsletter.

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Effective assessment practices for a ChatGPT-enabled world

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Assessment methods that require students to produce authentic, novel and personalised responses can help educators stay ahead of the uncertainty and workload that AI writers create

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STUDENT USE CASES FOR AI: AN INSPIRING MINDS SERIES

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STUDENT USE CASES FOR AI: AN INSPIRING MINDS SERIES

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Prologue: Student Guidelines for AI Use

Part 1: AI as Feedback Generator

Part 2: AI as Personal Tutor

Part 3: AI as Team Coach

Part 4: AI as Learner

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Dive into this series of 4 student use cases for AI to discover how generative AI tools like ChatGPT can be used as a feedback generator, tutor, team coach, and learner. Get sample prompts and shareable guidelines to help students use AI tools effectively.

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Resources about AI for Teaching from Stanford

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Resources about AI for Teaching from Stanford

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CRAFT is an initiative in collaboration with the Graduate School of Education, Institute for Human-Centered AI, and Stanford Digital Education. We aim to provide support for teachers in high school and early college who want to teach their students about artificial intelligence, or AI. This website hosts a collection of resources to facilitate such instruction.

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Resources for teaching about artificial intelligence provided by the Stanford Graduate School of Education

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The age of AI is upon us; in many ways, it's engulfing us. We're overwhelmed with information, articles and opinions on AI. Experts and non-experts alike are attempting to envision a future driven by the rise of this exponential technology. Because of the constant flow of information on AI, it's becoming increasingly difficult to pinpoint what exactly AI is. A few of us are able to define artificial intelligence. Most of us make the mistake of using it synonymously with other buzzwords, like "robots".

Thanks to centuries worth of sci-fi books, movies, and speculations about the future, society has formed a fantasy of a world that's ruled (or served) by robots, aerocars, and jetpacks. Now that we're supposedly knee-deep in the AI era, we're bound to wonder: why don't our lives look anything like the Jetsons? Why haven't fallen in love with Operating Systems, like the moviedepicted?

The truth is that, despite being surrounded by intelligence that is not biological, few of us use the term "AI" in the right context. Misusing and misunderstanding the term can cause us to make fallacious statements and assumptions about what the future holds. As we know, the world is changing at an alarming pace, so thinking critically about the current state of technology is crucial if we want to thrive in the future. To adapt in a world driven by change, understand the implications of AI on society, and clarify where we stand today, we need to first distinguish between the various types of AI.

In basic terms, AI can be defined as:

a broad area of computer science that makes machines seem like they have human intelligence.

If a machine can solve problems, complete a task, or exhibit other cognitive functions that humans can, then we refer to it as having artificial intelligence.

The "broad" definition of AI is vague and can cause a misrepresentation of the type of AI that we interact with today.

Artificial Narrow Intelligence (ANI) also known as "Weak" AI is the AI that exists in our world today. Narrow AI is AI that is programmed to perform a single task - whether it's checking the weather, being able to play chess, or analyzing raw data to write journalistic reports.

ANI systems can attend to a task in real-time, but they pull information from a specific data-set. As a result, these systems don't perform outside of the single task that they are designed to perform.

Unlike General or "Strong" AI, which I'll discuss further below, Narrow AI is not conscious, sentient, or driven by emotion the way that humans are. Narrow AI operates within a pre-determined, pre-defined range, even if it appears to be much more sophisticated than that.

Every sort of machine intelligence that surrounds us today is Narrow AI. Google Assistant, Google Translate, Siri and other natural language processing tools are examples of Narrow AI. Some might assume that these tools aren't "weak" because of their ability to interact with us and process human language, but the reason that we call it "Weak" AI is because these machines are nowhere close to having human-like intelligence. They lack the self-awareness, consciousness, and genuine intelligence to match human intelligence. In other words, they can't think for themselves.

When we converse with Siri, for example, Siri isn't a conscious machine responding to our queries. Instead, what Siri is able to do - what it is designed to do - is process the human language, enter it into a search engine (Google), and return to us with results.

This explains why when we pose abstract questions about things like the meaning of life or how to approach a personal problem to Siri or Google Assistant, we get vague responses that often don't make sense, or we get links to existing articles from the Internet that address these questions. On the other hand, when we ask Siri what the weather outside is, we get an accurate response. That's because answering basic questions about the whether outside is within the range of intelligence that Siri is designed to operate in.

As humans, we have the capacity to assess our surroundings, to be sentient creatures, and to have emotionally-driven responses to situations. The AI that exists around us doesn't have the fluidity or flexibility to think like we do. Even something as complex as a self-driving car is considered Weak AI, except that a self-driving car is made up of multiple ANI systems.

Though we refer to existing AI and intelligent machines as "weak" AI, we shouldn't take it for granted. Narrow AI by itself is a great feat in human innovation and intelligence.

ANI systems are able to process data and complete tasks at a significantly quicker pace than any human being can, which has enabled us to improve our overall productivity, efficiency, and quality of life. ANI systems like IBM's Watson, for example, is able to harness the power of AI to assist doctors to make data-driven decisions, making healthcare better, quicker, and safer.

Additionally, Narrow AI has relieved us of a lot of the boring, routine, mundane tasks that we don't want to do. From increasing efficiency in our personal lives, like Siri ordering a pizza for us online, to rifting through mounds of data and analyzing it to produce results, Narrow AI has made our lives significantly better, which is why we shouldn't underestimate it. With the advent of advanced technologies like self-driving cars, ANI systems will also relieve us of frustrating realities like being stuck in traffic, and instead provide us with more leisure time.

ANI systems also act as the building blocks of more intelligent AI that we might encounter in the near future.

As this article in the Singularity Hub points out,

"We're slowly building a library of narrow AI talents that are becoming more impressive. Speech recognition and processing allows computers to convert sounds to text with greater accuracy.

Google is using AI to caption millions of videos on YouTube. Likewise, computer vision is improving so that programs like Vitamin D Video can recognize objects, classify them, and understand how they move. Narrow AI isn't just getting better at processing its environment it's also understanding the difference between what a human says and what a human wants."

Artificial General intelligence or "Strong" AI refers to machines that exhibit human intelligence. In other words, AGI can successfully perform any intellectual task that a human being can. This is the sort of AI that we see in movies like "Her" or other sci-fi movies in which humans interact with machines and operating systems that are conscious, sentient, and driven by emotion and self-awareness.

Currently, machines are able to process data faster than we can. But as human beings, we have the ability to think abstractly, strategize, and tap into our thoughts and memories to make informed decisions or come up with creative ideas. This type of intelligence makes us superior to machines, but it's hard to define because it's primarily driven by our ability to be sentient creatures. Therefore, it's something that is very difficult to replicate in machines.

AGI is expected to be able to reason, solve problems, make judgements under uncertainty, plan, learn, integrate prior knowledge in decision-making, and be innovative, imaginative and creative.

But for machines to achieve true human-like intelligence, they will need to be capable of experiencing consciousness.

Oxford philosopher Nick Bostrom defines superintelligence as

"any intellect that greatly exceeds the cognitive performance of humans in virtually all domains of interest"

Artificial Super Intelligence (ASI) will surpass human intelligence in all aspects - from creativity, to general wisdom, to problem-solving. Machines will be capable of exhibiting intelligence that we haven't seen in the brightest amongst us. This is the type of AI that many people are worried about, and the type of AI that people like Elon Musk think will lead to the extinction of the human race.

But like any other technology, AI is a double-edged sword. According to futurist Ray Kurzweil, if the technological singularity happens, then there won't be a machine takeover. Instead, we'll be able to co-exist with AI in a world where machines reinforce human abilities.

Kurzweil predicts that by 2045, we will be able to multiply our intelligence a billionfold by linking wirelessly from our neocortex to a synthetic neocortex in the cloud. This will essentially cause a melding of humans and machines. Not only will we be able to connect with machines via the cloud, we'll be able to connect to another person's neocortex. This could enhance the overall human experience and allow us to discover various unexplored aspects of humanity.

Though we're years away from ASI, researchers predict that the leap from AGI to ASI will be a short one. No one really knows when the first sentient computer life form is going to arrive. But as Narrow AI gets increasingly sophisticated and capable, we can begin to envision a future that is driven by both machines and humans; one in which we are much more intelligent, conscious, and self-aware.

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Distinguishing between Narrow AI, General AI and Super AI

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The age of AI is upon us; in many ways, it's engulfing us. We're overwhelmed with information, articles and opinions on AI. Experts and non-experts alike are attempting to envision a future driven by...

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Syllabus language for 3 levels of AI permission/prohibition

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Syllabus language for 3 levels of AI permission/prohibition

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Regardless of the approach selected, faculty should explicitly discuss with students the approach and its underlying rationale.

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From the author of _Small Teaching Online_ Flower Darby

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Perhaps the most damaging result of people using these inaccurate and imperfect tools is the personal cost of false accusations.

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If you feed America's most important legal document-the US Constitution-into a tool designed to detect text written by AI models like ChatGPT, it will tell you that the document was almost certainly written by AI. But unless James Madison was a time traveler, that can't be the case. Why do AI writing detection tools give false positives? We spoke to several experts-and the creator of AI writing detector GPTZero-to find out.

Among news stories of overzealous professors flunking an entire class due to the suspicion of AI writing tool use and kids falsely accused of using ChatGPT, generative AI has education in a tizzy. Some think it represents an existential crisis. Teachers relying on educational methods developed over the past century have been scrambling for ways to keep the status quo-the tradition of relying on the essay as a tool to gauge student mastery of a topic.

As tempting as it is to rely on AI tools to detect AI-generated writing, evidence so far has shown that they are not reliable. Due to false positives, AI writing detectors such as GPTZero, ZeroGPT, and OpenAI's Text Classifier cannot be trusted to detect text composed by large language models (LLMs) like ChatGPT.

If you feed GPTZero a section of the US Constitution, it says the text is "likely to be written entirely by AI." Several times over the past six months, screenshots of other AI detectors showing similar results have gone viral on social media, inspiring confusion and plenty of jokes about the founding fathers being robots. It turns out the same thing happens with selections from The Bible, which also show up as being AI-generated.

To explain why these tools make such obvious mistakes (and otherwise often return false positives), we first need to understand how they work.

Understanding the concepts behind AI detection

Different AI writing detectors use slightly different methods of detection but with a similar premise: There's an AI model that has been trained on a large body of text (consisting of millions of writing examples) and a set of surmised rules that determine whether the writing is more likely to be human- or AI-generated.

For example, at the heart of GPTZero is a neural network trained on "a large, diverse corpus of human-written and AI-generated text, with a focus on English prose," according to the service's FAQ. Next, the system uses properties like "perplexity" and burstiness" to evaluate the text and make its classification.

In machine learning, perplexity is a measurement of how much a piece of text deviates from what an AI model has learned during its training. As Dr. Margaret Mitchell of AI company Hugging Face told Ars, "Perplexity is a function of 'how surprising is this language based on what I've seen?'"

So the thinking behind measuring perplexity is that when they're writing text, AI models like ChatGPT will naturally reach for what they know best, which comes from their training data. The closer the output is to the training data, the lower the perplexity rating. Humans are much more chaotic writers-or at least that's the theory-but humans can write with low perplexity, too, especially when imitating a formal style used in law or certain types of academic writing. Also, many of the phrases we use are surprisingly common.

Let's say we're guessing the next word in the phrase "I'd like a cup of _____." Most people would fill in the blank with "water," "coffee," or "tea." A language model trained on a lot of English text would do the same because those phrases occur frequently in English writing. The perplexity of any of those three results would be quite low because the prediction is fairly certain.

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Why AI detectors think the US Constitution was written by AI

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Can AI writing detectors be trusted? We dig into the theory behind them.

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60+ Ways to Use ChatGPT in your classroom today

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60+ Ways to Use ChatGPT in your classroom today