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Data centre electricity needs are forecast to double between 2022 and 2026

There's a big problem with generative AI, says Sasha Luccioni at Hugging Face, a machine-learning company. Generative AI is an energy hog.
"Every time you query the model, the whole thing gets activated, so it's wildly inefficient from a computational perspective," she says.

Take the Large Language Models (LLMs) at the heart of many Generative AI systems. They have been trained on vast stores of written information, which helps them to churn out text in response to practically any query.

"When you use Generative AI... it's generating content from scratch, it's essentially making up answers," Dr Luccioni explains. That means the computer has to work pretty hard.

A Generative AI system might use around 33 times more energy than machines running task-specific software, according to a recent study by Dr Luccioni and colleagues. The work has been peer-reviewed but is yet to be published in a journal.

It's not your personal computer that uses all this energy, though. Or your smartphone. The computations we increasingly rely on happen in giant data centres that are, for most people, out of sight and out of mind.

"The cloud," says Dr Luccioni. "You don't think about these huge boxes of metal that heat up and use so much energy."

The world's data centres are using ever more electricity. In 2022, they gobbled up 460 terawatt hours of electricity, and the International Energy Agency (IEA) expects this to double in just four years. Data centres could be using a total of 1,000 terawatts hours annually by 2026. "This demand is roughly equivalent to the electricity consumption of Japan," says the IEA. Japan has a population of 125 million people.

At data centres, huge volumes of information are stored for retrieval anywhere in the world - everything from your emails to Hollywood movies. The computers in those faceless buildings also power AI and cryptocurrency. They underpin life as we know it.

AI can be "wildly inefficient" when using computing resources, says Sasha Luccioni

But some countries know all too well how energy hungry these facilities are. There is currently a moratorium preventing the construction of new data centres in Dublin. Nearly a fifth of Ireland's electricity is used up by data centres, and this figure is expected to grow significantly in the next few years - meanwhile Irish households are reducing their consumption.

The boss of National Grid said in a speech in March that data centre electricity demand in the UK will rise six-fold in just 10 years, fuelled largely by the rise of AI. National Grid expects that the energy required for electrifying transport and heat will be much larger in total, however.

Utilities firms in the US are beginning to feel the pressure, says Chris Seiple at Wood Mackenzie, a research firm.

"They're getting hit with data centre demands at the exact same time as we have a renaissance taking place - thanks to government policy - in domestic manufacturing," he explains. Lawmakers in some states are now rethinking tax breaks offered to data centre developers because of the sheer strain these facilities are putting on local energy infrastructure, according to reports in the US.

Mr Seiple says there is a "land grab" going on for data centre locations near to power stations or renewable energy hubs: "Iowa is a hotbed of data centre development, there's a lot of wind generation there."

Some data centres can afford to go to more remote locations these days because latency - the delay, usually measured in milliseconds, between sending information out from a data centre and the user receiving it - is not a major concern for increasingly popular Generative AI systems. In the past, data centres handling emergency communications or financial trading algorithms, for example, have been sited within or very near to large population centres, for the absolute best response times.

Jensen Huang, Nvidia chief executive, shows off the new Blackwell chip in March

There is little doubt that the energy demands of data centres will rise in the coming years, but there is huge uncertainty over how much, stresses Mr Seiple.

Part of that uncertainty is down to the fact that the hardware behind generative AI is evolving all the time.

Tony Grayson is general manager at Compass Quantum, a data-centre business, and he points to Nvidia's recently launched Grace Blackwell supercomputer chips (named after a computer scientist and a mathematician), which are designed specifically to power high-end processes including generative AI, quantum computing and computer-aided drug design.

Nvidia says that, in the future, a company could train AIs several times larger than those currently available in 90 days. It would require 8,000 of the previous generation of Nvidia chips and need a 15 megawatt electricity supply.

But the same work could be carried out in the same time by just 2,000 Grace Blackwell chips, and they would need a four megawatt supply, according to Nvidia.

That still ends up as 8.6 gigawatt hours of electricity consumed - roughly the same amount that the entire city of Belfast uses in a week.

"The performance is going up so much that your overall energy savings are big," says Mr Grayson. But he agrees that power demands are shaping where data centre operators site their facilities: "People are going to where cheap power's at."

Dr Luccioni notes that the energy and resources required to manufacture the latest computer chips are significant.

More technology of business

Still, it is true that data centres have got more energy efficient over time, argues Dale Sartor, a consultant and affiliate of Lawrence Berkeley National Laboratory in the US. Their efficiency is often measured in terms of power usage effectiveness, or PUE. The lower the number, the better. State-of-the-art data centres have a PUE of around 1.1, he notes.

These facilities do still create significant amounts of waste heat and Europe is ahead of the US in finding ways of using that waste heat - such as warming up swimming pools - says Mr Sartor.

Bruce Owen, UK managing director at Equinix, a data centre firm, says, "I still think that the demand is going to grow further than that efficiency gain that we see." He predicts that more data centres will be built with on-site power-generating facilities included. Equinix was denied planning permission for a gas-powered data centre in Dublin last year.

Mr Sartor adds that costs may ultimately determine whether Generative AI is worth it for certain applications: "If the old way is cheaper and easier then there's not going to be much of a market for the new way."

Dr Luccioni stresses, though, that people will need to clearly understand how the options in front of them differ in terms of energy efficiency. She is working on a project to develop energy ratings for AI.

"Instead of picking this GPT-derivative model that is very clunky and uses a lot of energy, you can pick this A+ energy star model that will be a lot more lightweight and efficient," she says.

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Electricity grids creak as AI demands soar

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Data centre electricity demand is forecast to double in four years, putting energy grids under pressure.

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With Spatial Intelligence, AI Will Understand the Real World | Fei-Fei Li | TED

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In the beginning of the universe, all was darkness - until the first organisms developed sight, which ushered in an explosion of life, learning and progress. AI pioneer Fei-Fei Li says a similar moment is about to happen for computers and robots.

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Inside Higher Ed | Higher Education News, Events and Jobs

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Since the launch of ChatGPT in November 2022, educators, administrators and other higher education stakeholders have grappled with the implications of generative artificial intelligence on intellectual property, academic integrity and ethical use, among other topics.

While the majority of U.S. students have yet to latch on to the trend, they're still outpacing their instructors, with a Tyton Partners report in October 2023 finding only 22 percent of faculty members utilize AI.

Among faculty who do use AI, 43 percent are running prompts to understand what their students might be using the tools for, 35 percent are using it to teach and 29 percent are using it for in-class activities, according to the report.

Survey Says

A November 2023 report from Anthology found 38 percent of students in the U.S. are using generative AI tools at least monthly. A November 2023 study from Chegg found only 20 percent of U.S. students have used generative AI for their coursework.

In the workforce, only 35 percent of U.S. adults say they are comfortable integrating these tools into work and school, with worries about the accuracy of information also a top concern, according to a University of Phoenix survey from September 2023.

Across higher education, the main barriers to AI adoption are security concerns, a need for AI training programs and ethical implications, according to a Ellucian survey of administrators in the U.S. from summer 2023.

Here are four ways faculty members can employ artificial intelligence:

1. To improve student writing. Pamela Bourjaily, a business communication professor at the University of Iowa, created an assignment that requires students to experiment with ChatGPT and learn how to create the best prompts. Students learned to edit and revise prompts to produce more accurate and helpful outputs.

2. To prepare slide decks. Rodney B. Murray, host of the Pulse podcast, used AI-powered tools to put together a presentation for his podcasting course. Murray compares working with MagicSlides and SlidesGPT in design, images and prices.

1. To provide students with feedback. Harvard University's Computer Science 50: Introduction to Computer Science course uses an AI tool to highlight areas of code that could be improved, as well as provide solutions. The tool is designed to make the work of TAs and professors more efficient as well as provide students with a personalized learning experience.

2. To provide workforce tools. Following their time in college, today's learners will encounter AI in the workplace, and it is the responsibility of the institution to prepare them for this experience, Ray Schroeder wrote in a September 2023 blog post for Inside Higher Ed. Learning modules in senior capstone courses or certificates could credential students for their future jobs. Anand Rao, professor of communication at the University of Mary Washington, introduces students to low- and no-code options to build their own generative AI tools in his special topics course on digital studies.

Food for thought: For educators considering implementing AI into their classrooms, experts in the field share some considerations.

Create guidelines. A September 2023 report from Cornell University's Center for Teaching and Learning encourages faculty to explicitly set expectations for when and how students can employ generative AI into their work with proper attribution. Ohio University's Center for Teaching and Learning offers example AI policies and assignments from faculty members at the university to provide a reference point.

Teach ethical use. While some institutions are teaching about AI, many lack requirements around teaching students ethics and how they can be ethical users of the technology, particularly in the U.S.

Understand limitations. AI is not free from bias and sometimes hallucinates, so teaching students the shortcomings of tools such as ChatGPT can promote better learning and make them more prepared to engage with the resources.

Do you have an academic success tip that might help others encourage student success? Tell us about it.
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How college professors are using generative AI to teach

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Generative artificial intelligence tools continue to grow in popularity. Here are four ways faculty members in higher education are teaching about or with AI.

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How AI is revolutionizing the future of learning and how educators can adapt to this new era of human thinking.

Artificial Intelligence (AI) is revolutionizing the way we learn, work, and think. Its integration into classrooms and workplaces is already underway, impacting and challenging ideas about creativity, authorship, and education. In this groundbreaking and practical guide, teachers will discover how to harness and manage AI as a powerful teaching tool. José Antonio Bowen and C. Edward Watson present emerging and powerful research on the seismic changes AI is already creating in schools and the workplace, providing invaluable insights into what AI can accomplish in the classroom and beyond.

By learning how to use new AI tools and resources, educators will gain the confidence to navigate the challenges and seize the opportunities presented by AI. From interactive learning techniques to advanced assignment and assessment strategies, this comprehensive guide offers practical suggestions for integrating AI effectively into teaching and learning environments. Bowen and Watson tackle crucial questions related to academic integrity, cheating, and other emerging issues.

In the age of AI, critical thinking skills, information literacy, and a liberal arts education are more important than ever. As AI continues to reshape the nature of work and human thinking, educators can equip students with the skills they need to thrive in a rapidly evolving world. This book serves as a compass, guiding educators through the uncharted territory of AI-powered education and the future of teaching and learning.

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Navigating AI’s impact within an educational context is a formidable task, one that requires a collective effort. In Teaching with AI, authors Bowen and Watson equip educators to adapt to these changes with integrity, practical wisdom, and creative inspiration. This book serves as an invaluable resource for those seeking to harness the potential of AI to enhance their teaching and learning, while mitigating the challenges.

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Democratizing Education Through AI

As of November 2022, teachers and students gained access to a tool that can potentially act as the ultimate educational technology. This tool can create lessons that take advantage of the latest in pedagogical science while doing so in a way that matches both the students and the local context. This tool - AI, and specifically GPT-4 - is available for a fee via OpenAI's ChatGPT and free for Bing's Creative Mode. We believe this is a transformative moment in education.

Why does this matter?

Because, with just a paragraph of instructions, teachers can create tools to help their students learn that would have, in previous years, required buying specialized software. As of now, all teachers need is a prompt and some time to experiment. While it's important to remain vigilant for potential hallucinations, errors, and biases, GPT-4 enables teachers to craft personalized prompts, significantly bolstering their resources in the pursuit of quality education.

We find ourselves in an exhilarating era. Our goal is to actively democratize access to these cutting-edge tools by broadening their capabilities.

Our plan:

To combine our collective expertise about the science of learning, game, and narrative design and build AI-powered simulations that give everyone access to essential skills through tutoring, coaching, and immersive role-play.

To devise instructions that work for all students and foster a culture of ethical experimentation and collaboration, keeping the well-being of our students, schools, and societies at the forefront of our efforts.

To share our data and findings, advancing the state of teaching and learning with AI.

The Role of Simulations in a World of AI

Though the advent of generative AI challenges many kinds of instruction, Wharton Interactive simulations, harnessing games, stories, and the science of learning with in-class participation are especially relevant.

Our flagship simulations can integrate with AI exercises and are designed to provide educators with alternate pathways to assessment and instruction that integrate well and are robust to the challenges and opportunities of AI:

Saturn Parable Simulation: a fast-paced and engaging way to learn team leadership

Entrepreneurship Game: give learners the experience of running a high-growth startup

Machine Learning for Business Decisions: crack open the black box of Machine Learning

Organizational Leadership and Innovation: give students the experience of leading and managing an internal venture

As educators grapple with changing curricula in the Fall, our simulations are adaptable to this new world of teaching.

Resources & Research

Our Crash Course video series on AI is a practical guide for educators and students designed to provide an introduction to the world of Large Language Models. The series begins with an introduction to Large Language Models, with a focus on OpenAI's ChatGPT and Microsoft's Bing Chat, which includes an overview of how the models generate output and their transformative impact on our work and teaching. The second video delves deeper into the specifics of each model, providing practical examples and guidelines for effective use. The third video focuses on how to prompt the AI, teaching users how to guide the AI and integrate their own expertise into the interaction. The fourth and fifth videos are tailored for educators and students who can use AI to enhance their teaching and learning.

Current papers include:
New Modes of Learning Enabled by AI Chatbots: Three Methods and Assignments.
In this paper, we discuss the opportunity provided by AI because it can help us teach in new ways. The AI's flaws -its tendency to make up facts, its lack of nuance, and its ability to make excellent student essays - can be used to improve education. This isn't for some future theoretical version of AI. Instructors can create assignments right now, using ChatGPT, that will help stretch students in new ways. These assignments:

Promote transfer

Break the illusion of explanatory depth

Challenge students to teach someone else

Using AI to Implement Effective Teaching Strategies in Classrooms: Five Strategies, Including Prompts
In the rush to deliver AI benefits directly to students, the role of teachers is often overlooked. AI tutors, as exciting as they are, do not replace the complex role of a teacher in front of a class. But not enough effort seems to be going toward applying AI to help instructors. We have a new paper that tries to remedy that gap by providing some research-backed approaches to pedagogy and the AI prompts (for GPT-4, GPT-3.5, and other AIs) to implement them. These strategies include:

AI-created examples

AI-created explanations

AI-created low-stakes tests

Assigning AI: Seven Approaches for Students, with Prompts
The incredible promise of AI as a way for students all over the world, of all ability levels, to learn is undeniable. Education is our most powerful system for increasing social mobility, unlocking potential, and improving lives. A tool that can help with this has tremendous implications. Plus, students are already using AI for direct help. Teaching them how to do it responsibly may alleviate some of the negative implications of our AI moment. In this paper, we tackle ways that students can be assigned to use AI directly. We don't shy away from the dangers but provide detailed instructions on how students and instructors can think about each of the tools we suggest.

AI Policy

As AI use grows, we are experimenting with and creating AI policies for classes. In this series of articles, Professor Ethan Mollick discusses his policy for AI use and includes guidance for teaching students how to learn with and about AI and for giving students assignments that ask them to use AI and then reflect on that use.

Coming soon: PitchQuest, an AI-powered simulation that teaches a critical entrepreneurship skill

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Wharton Interactive's goal is to improve teaching, learning, and scholarship by advancing research in AI and interactive pedagogy

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MIT Sloan Teaching & Learning Technologies

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Teaching with Generative AI Resource Hub - MIT Sloan Teaching & Learning Technologies

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Explore our Teaching with Generative AI Resource Hub to unlock AI’s potential for enhancing teaching and learning at MIT Sloan.

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A series of instructional web pages for instructors and teaching teams about using AI tools for teaching and learning.

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Teaching in the Age of AI | Notre Dame Learning

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By Amanda Leary, Steve Varela, Roberto Casarez, and Alex AmbroseSince ChatGPT burst onto the scene in December 2022, higher education has been buzzing with talk of, quite simply, what to do about it.

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Cite this guide: Coley, M., Snay, P., Bandy, J., Bradley, J., Molvig, O. (2023). Teaching in the Age of AI. Vanderbilt University Center for Teaching. https://cft.vanderbilt.edu/guides-sub-pages/

Much like the technologies it describes, this guide is always growing and changing.
Across higher education, there is increasing concern about the implications of generative artificial intelligence (AI) for teaching and learning. What dilemmas does generative AI pose for our courses and assignments? How might it change how students learn to write and learn through writing? How might we respond with effective educational strategies?

In February 2023, the Center for Teaching, the Writing Studio, and the A&S AI Grand Challenge Initiative co-hosted a workshop to begin a dialogue about the future of teaching writing in the age of AI. In this teaching guide, we will draw on video from that event and broaden the discussion to include resources and questions from a variety of sources. This guide will explore:

In this video, Joe Bandy gives an introduction to generative AI in the context of teaching and learning in higher education.

What is Generative AI and where can it be found?

Generative AI tools are the newest addition to the list of technologies that have impacted higher education and, for the most part, faculty are not panicking. The 1970s saw the advent of calculators in the classroom which changed the way people thought about math instruction. In the 1990s, spellcheck and autocorrect arrived to help us proofread, and Google added autocomplete in 2004. For most of us, these technologies are now an everyday part of our work lives. Language teachers have been working with and against Google Translate since 2006 with extensive research and advice published since then on how to best use the tool.

And now, as of November 2022, ChatGPT is the newest tool to come into the spotlight. The GPT in ChatGPT stands for "Generative Pre-trained Transformer", referring to a type of machine learning called a neural network (learn more here or here) in which a computer learns to perform some task by analyzing training examples. In this case, the training data used by ChatGPT 3.5 is a dataset that includes 570GB of data from sources like books, Wikipedia, articles, and other pieces of writing on the internet up to 2021. The training methods for ChatGPT 4.0 has not been revealed as of yet. ChatGPT is now a famous name because it is a public-facing tool that allows anyone to generate text based on prompts for free (although a paid version is also available). Fundamentally, tools like ChatGPT are designed to take user prompts and use them to generate a response it believes the user would like to see. The AI tool(s) have no concept of fact or accuracy. They simply look for patterns in their training data, then produce what they think is the next most likely word or phrase. It's also important to keep in mind, that giving the same prompt to the same generative AI tool does not always produce the same result. Each time a tool like ChatGPT is prompted, it creates a new output. While there will probably be similarities between outputs from the same prompt, they will not be the same every time. This is even more apparent when you use the same prompt across multiple AI products.

As Ole Molvig describes in this video from our January 2023 workshop on Writing in the Age of AI, ChatGPT differs from typical internet searches in one key way. Search engines like Google sift through internet data to find the information the user requests, whereas ChatGPT attempts to produce a plausible or coherent response without regard for whether it exists in the data. In Ole's example from the workshop, when he asked Google "Who is Ole Molvig?", his search results included various pages that directed him to university websites and other real information. When he asked ChatGPT "Who is Ole Molvig?", he received a plausible, but untrue and wholly fabricated response about how Ole Molvig was an 18th-century Danish artist, complete with a fabricated biography and notable works. This is just one example of how generative AI tools like ChatGPT can produce lots of good-looking but ultimately untrue text.

In addition to ChatGPT-like text generation tools, companies like Microsoft and Google are incorporating generative AI into their own search engines to produce experiences that attempt to take the best of Generative AI and live internet search to produce a richer response. This also allows these companies to build additional options or guardrails to their desired experience. For example, if you ask ChatGPT to write a five-paragraph essay on a topic, it will do it with no hesitation. Entering the same prompt into Bing Chat (Bing's generative AI interface) will usually produce a statement that it can't/won't write your essay, but it will offer an outline of topics you should cover. Since Bing Chat is powered by OpenAI (the same company that makes ChatGPT), this is an example of an added guardrail that Microsoft has added to its own product.

How can I harness Generative AI tools in my teaching to improve student learning?

In this video, Ole Molvig explains why instructors should work with AI to increase student learning and walks through some ways how instructors can do so.

Generative AI has a number of potentially helpful applications in teaching and learning environments. Some examples of this are as follows:

Formatting Changes & Proofreading

Generative AI is well suited to proofreading existing text or adjusting its formatting to suit a new presentation structure. This may be used to reformat text passages into step-by-step instructions, summarize information to make it more digestible, or ensure that the information in your writing is grammatically sound.

Note: Do not use generative AI tools to analyze student work. These tools have varied privacy policies and cannot be guaranteed to be FERPA compliant.

Generating unique visuals or images for presentations
Instead of searching for the perfect image or visual for a presentation, AI tools like DALL-E, Stable Diffusion, or Midjourney can generate visuals based on given text prompts. For example, this image was created by Bing's AI tool (powered by DALL-E) based on the prompt "Please generate an illustration of a teacher in front of a college classroom. This should be in the art style of Microsoft Office clipart."

What are some limitations of Generative AI?

Generative AI tools can be very helpful in supporting teaching and learning, but it's exceptionally important that those using these tools understand the inherent limitations and biases they have.

How can I craft assignments that deter unauthorized AI use?

Faculty may want to find strategies to mitigate student usage of generative AI tools in class content. There are several strategies that may allow instructors to do so. We strongly encourage faculty to work to balance their mitigation strategies with an understanding of the potential benefits of these tools.

In this video, John Bradley explores how to create a safe, welcoming learning environment that teaches students to use their own voice rather than relying on AI.

Localize Assignments

Requiring students to use information discussed in class, specific texts, and other information that would not be readily available online can ensure that generative AI tools are not able to generate text that would meet assignment requirements.

Given the training data limitations of some generative AI tools, requiring information from recent events (after 2021) can prevent some tools from being able to speak to them. Please note that this does not impact tools with access to the internet, including Bing's Chat tool and Google Bard/Docs.

How does academic integrity relate to Generative AI tools?

At Vanderbilt, instructors determine what kinds of outside assistance are allowed or not allowed in their courses. Be clear in your syllabus what tools you expect students to use and how students should engage them. For more information about crafting syllabus policies, check out our teaching guide on the topic.

For suggestions and examples of syllabus language specific to generative AI, you can refer to this collection of ideas, policies, and syllabus passages here.

Detecting AI writing can be challenging. Like plagiarism detectors, there are tools that can find similarities or patterns but have no foolproof way to detect AI-written work. Traditional plagiarism checkers work by comparing text in a student's paper to other existing text it has in the program's database. These tools are looking for the similarity of exact, specific pieces of text from students' papers compared to its source data. This method of comparing similarity does not work with AI-generated text because, fundamentally, this AI text is being newly generated. Furthermore, AI tools like ChatGPT are designed to predict the next most likely word or phrase and are not-and were never-designed to detect AI text. Entering suspected text into an AI tool like ChatGPT and asking if that text is AI-generated is not an accurate way of determining if text is AI-generated. Due to these issues, and many others, we do not feel that AI detection is a useful tool, and we do not recommend any AI detector at this time. For more on this, you can read our blog post about Guidance on AI Detection here.

While there is no foolproof way to know for sure, if you are concerned about how to tell if students have submitted AI-generated text, there are some things you can look for in their writings.

What are some Generative AI Tools?

Text-based tools

ChatGPT is a text generator based on training data through 2021.
Regular access is free, but ChatGPT Plus is a paid subscription that includes access to the newer/better GPT-4 language model, faster response times, and priority access to new features like the new Code Interpreter plugin.

Bing Chat (search with GPT-4) is a collaboration between Microsoft's search engine and OpenAI. Bing Chat performs similarly to ChatGPT, but it also includes up-to-date search information from Bing. This results in more timely information. This tool also has three options for the tone of its responses (More Creative, More Balanced, or More Precise) that can help focus your prompts.

Google Bard is Google's AI chatbot. The underlying language model is also used in Google Drive as Duet AI. One unique feature of Bard is that every output gives you three versions that you can review. While these are usually mostly the same, they can vary slightly in tone or content.

Claude is an AI text generator from a company called Anthropic AI that takes more of a careful approach to text generation. Claude also allows for the upload of files to use in prompts without having to pay for advanced features.

Productivity-based tools

Image-based tools

DALL-E is an image generator based on a text prompt and is built by the same company (OpenAI) that makes ChatGPT.

Stable Diffusion is another image generation tool based on data from Stability.AI.
DreamStudio is a web app based on Stable Diffusion that allows for more advanced prompting and input.

AutoDraw is a basic drawing tool (think MS Paint) that uses AI to change user drawings into higher-quality visuals.

Other Generative AI tools

GitHub Copilot is a tool that helps programmers write code and translate existing code into other programming languages.

Character.AI allows users to chat with chatbots based on historical, fictional, or contemporary figures.

State of the Union Presidential Rebuttals allows users to pick any two Presidents in history to offer rebuttals to each others' State of the Union speeches.

Other Sources for Learning More about AI

This teaching guide is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

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by Michael Coley, Paige Snay, Joe Bandy, John Bradley, and Ole Molvig Print Version Cite this guide: Coley, M., Snay, P., Bandy, J., Bradley, J., Molvig, O. (2023). Teaching in the Age of AI. Vanderbilt University Center for Teaching. https://cft.vanderbilt.edu/guides-sub-pages/ Much like the technologies it describes, this guide is always growing and changing.

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AI is transforming how students learn to engage with the world around them and use new technologies to create solutions to real problems.

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Today, many priorities for improvements to teaching and learning are unmet. Educators seek technology-enhanced approaches addressing these priorities that would be safe, effective, and scalable. Like all of us, educators use AI-powered services in their everyday lives, such as voice assistants in their homes; tools that can correct grammar, complete sentences, and write essays; and automated trip planning on their phones. As a result, educators see opportunities to use AI-powered capabilities like speech recognition to increase the support available to students with disabilities, multilingual learners, and others who could benefit from greater adaptivity and personalization in digital tools for learning. They are further exploring how AI can enable writing or improving lessons, as well as their process for finding, choosing, and adapting material for use in their lessons.

Educators are also aware of new risks. Useful, powerful functionality can also be accompanied with new data privacy and security risks. Educators recognize that AI can automatically produce output that is inappropriate or wrong. They are wary that the associations or automations created by AI may amplify unwanted biases. They have noted new ways in which students may represent others' work as their own. They are well-aware of "teachable moments" and pedagogical strategies that a human teacher can address but are undetected or misunderstood by AI models. They worry whether recommendations suggested by an algorithm would be fair. Educators' concerns are manifold.

Everyone in education has a responsibility to harness the good to serve educational priorities while also protecting against the dangers that may arise as a result of AI being integrated in educational technology.

NEW!! Report on AI and the Future of Teaching & Learning

Key Insights

AI enables new forms of interaction. Students and teachers can speak, gesture, sketch, and use other natural human modes of communication to interact with a computational resource and each other. AI can generate human-like responses, as well. These new forms of action may provide supports to students with disabilities.

AI can help educators address variability in student learning. With AI, designers can anticipate and address the long tail of variations in how students can successfully learn-whereas traditional curricular resources were designed to teach to the middle or most common learning pathways. For example, AI-enabled educational technology may be deployed to adapt to each student's English language abilities with greater support for the range of skills and needs among English learners.

AI supports powerful forms of adaptivity. Conventional technologies adapt based upon the correctness of student answers. AI enables adapting to a student's learning process as it unfolds step-by-step, not simply providing feedback on right or wrong answers. Specific adaptations may enable students to continue strong progress in a curriculum by working with their strengths and working around obstacles.

AI can enhance feedback loops. AI can increase the quality and quantity of feedback provided to students and teachers, as well as suggesting resources to advance their teaching and learning.

AI can support educators. Educators can be involved in designing AI-enabled tools to make their jobs better and to enable them to better engage and support their students.

Recommendations: Call to Action for Education Leaders

Recommendation #1: Emphasize Humans in the Loop

We reject the notion of AI as replacing teachers. Teachers and other people must be "in the loop" whenever AI is applied in order to notice patterns and automate educational processes. We call upon all constituents to adopt Humans-in-the-Loop as a key criteria.

Recommendation #2: Align AI Models to a Shared Vision for Education

We call upon educational decision makers, researchers, and evaluators to determine the quality of an educational technology based not only on outcomes, but also based on the degree to which the models at the heart of the AI tools and systems align to a shared vision for teaching and learning. The figure to the right describes the important qualities of AI models for education leaders to consider.

Recommendation #3: Design Using Modern Learning Principles

Achieving effective systems requires more than processing "big data"-it requires more than data science. Applications of AI must be based on established, modern learning principles, the wisdom of educational practitioners, and should leverage the expertise in the educational assessment community around detecting bias and improving fairness. Going forward, we also must seek to create AI systems that are culturally responsive and culturally sustaining, leveraging the growth of published techniques for doing so. Further, most early AI systems had few specific supports for students with disabilities and English learners and we must ensure that AI-enabled learning resources are intentionally inclusive of these students.

Recommendation #4: Prioritize Strengthening Trust

Technology can only help us to achieve educational objectives when we trust it. Yet, we learned through a series of public listening sessions that distrust of educational technology and AI is commonplace. Because trust develops as people meet and relate to each other, we call for a focus on building trust and establishing criteria for trustworthiness of emerging educational technologies within the associations, convenings, and professional organizations that bring educators, innovators, researchers, and policymakers together.

Recommendation #5: Inform and Involve Educators

We call on educational leaders to prioritize informing and involving educational constituents so they are prepared to investigate how and when AI fits specific teaching and learning needs, and what risks may rise. Now is the time to show the respect and value we hold for educators by informing and involving them in every step of the process of designing, developing, testing, improving, adopting, and managing AI-enabled educational technology. This includes involving educators in reviewing existing AI-enabled systems, tools, and data use in schools, designing new applications of AI based on teacher input, carrying out pilot evaluations of proposed new instructional tools, collaborating with developers to increase the trustworthiness of the deployed system, and raising issues about risks and unexpected consequences as the system is implemented.

Recommendation #6: Focus R&D on Addressing Context and Enhancing Trust and Safety

Research that focuses on how AI-enabled systems can adapt to context (diversity among learners, variability in instructional approaches, differences in educational settings) is essential to answering the question "Do specific applications of AI work in education, and if so, for whom and under what conditions?" We call upon researchers and their funders to prioritize investigations of how AI can address the long tail of learning variability and to seek advances in how AI can incorporate contextual considerations when detecting patterns and recommending options to students and teachers. Further, researchers should accelerate their attention to how to enhance trust and safety in AI-enabled systems for education.

Recommendation #7: Develop Education-Specific Guidelines and Guardrails

Data privacy regulation already covers educational technology; further, data security is already a priority of school educational technology leaders. Modifications and enhancements to the status quo will be required to address the new capabilities alongside the risks of AI. We call for involvement of all perspectives in the ecosystem to define a set of guidelines (such as voluntary disclosures and technology procurement checklists) and guardrails (such as enhancements to existing regulations or additional requirements) so that we can achieve safe and effective AI for education.

Listening Sessions

The U.S. Department of Education's Office of Educational Technology, with support from Digital Promise, held listening sessions about Artificial Intelligence (AI). We connected with all constituents involved in making decisions about technology in education, including but not limited to teachers, educational leaders, students, parents, technologists, researchers, and policy makers.

The goal of these listening sessions were to gather input, ideas, and engage in conversations that will help the Department shape a vision for AI policy that is inclusive of cutting-edge research and practices while also informed by the opportunities and risks.

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Contact ed.tech@ed.gov with questions.

Please Include "OET AI" in the Subject Line

For a print-out and summary of the Artificial Intelligence Initiative, please review our AI One-pager

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Artificial Intelligence - Office of Educational Technology

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OET is working to develop policies and supports focused on the effective, safe, and fair use of AI-enabled educational technology.

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Even though it's been more than a year since ChatGPT made a big splash in the K-12 world, many teachers say they are still not receiving any training on using artificial intelligence tools in the classroom.

More than 7 in 10 teachers said they haven't received any professional development on using AI in the classroom, according to a nationally representative EdWeek Research Center survey of 953 educators, including 553 teachers, conducted between Jan. 31 and March 4.

The survey data shows that teachers who are in urban districts, those in districts with free/reduced-price meal rates of more than 75 percent, and those who teach elementary grades are more likely than their peers to say they haven't received any AI training.

A previous EdWeek Research Center survey found that one of the major reasons teachers aren't using AI in their work is because of a lack of knowledge and support for how to use AI tools effectively and appropriately.

Some experts say that educators can't ignore this technology that is predicted to be a huge force in the world. They say that it's important for teachers to learn more about AI, not just so they can use it responsibly in their work, but also to help model that use for students who are already interacting with this technology and will need to become smart AI consumers.

Districts have 'competing priorities'

For Justin Sealand, a math teacher for Woodford County High School in Versailles, Ky., getting training on AI is important because "the kids are going to be looking for" it, and "I would personally like to be able to provide that."

While his district has provided two professional learning sessions focused on AI so far, Sealand still wants more training.

"I want it to be way more tailored to what I'm trying to accomplish," he said.

For instance, for his class, he writes a lot of code for graphics or manipulatives and curates datasets for statistics examples, but he would like to "figure out how to better leverage AI for those content-specific things."

But it's not surprising that many districts have yet to provide AI-specific training, said Tara Nattrass, the managing director of innovation strategy at the International Society for Technology in Education and the Association for Supervision and Curriculum Development.

"Districts are focusing on a lot of different areas right now," Nattrass said. "There's a focus on the science of reading. There's a focus on social-emotional learning. There are a lot of competing priorities when we look across school districts, and AI is one piece of that."

Looking for PD opportunities elsewhere

Sealand said he understands why his district hasn't had any other training on AI beyond the basics.

"I might be the only person in my entire district interested in that hyper-specific type of prompt writing," he said. So in his own time, he's been researching ways to leverage AI tools to create graphics, visualizations, and manipulatives to use for demonstrations with his students.

Other educators are also looking for professional development opportunities outside of the district. More than 3,500 educators have signed up for ISTE's professional learning course on AI, according to the organization. Others are also participating in the organization's webinar sessions on AI, Nattrass said.

This isn't a bad thing, according to Nattrass. Teachers should have "a variety of ways" to engage in AI learning, such as district-led professional development opportunities, courses from other organizations, and networking with other educators.

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Teachers Desperately Need AI Training. How Many Are Getting It?

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Teachers' lack of AI knowledge and support hinders their use of the technology.

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“Crafting Effective an AI Prompts: A Step-by-Step Guide to Getting the Results You Want from AI”

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In the realm of AI, an AI prompt refers to a question or instruction that’s given to an AI model, particularly large language models (LLMs), to get a specific response. It’s essentially the bridge between what a human wants and what the AI delivers.

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The Art of AI Prompt Crafting: A Comprehensive Guide for Enthusiasts - Prompting - OpenAI Developer Forum

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The Art of AI Prompt Crafting: A Comprehensive Guide for Enthusiasts Introduction Welcome to the fascinating world of AI and the art of crafting effective prompts! A Brief History of AI Development: Artificial Intelligence has come a long way since its inception. The journey began in the mid-20th century with the theoretical groundwork laid by pioneers like Alan Turing. Over the decades, AI evolved through various stages, from simple rule-based systems to the more complex neural networks…

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MIT Sloan Teaching & Learning Technologies

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Let's look at the foliage season example above. Once AI generates the response tailored to the kindergarten audience, you can simply add a follow-up. For example, instruct it to "make it funnier," or "explain it to college students who are English majors using analogies they will understand." There is no need to repeat the context and other parameters. You can continue to build upon AI's responses just by adding other prompts. This reiterative process unlocks more potential from AI (Neil, 2023). Additionally, most AI systems will allow you to generate a new response if you'd like to see a different version without entering another prompt.

Limitations

While prompt engineering can improve the outputs from AI, there are some limitations to bear in mind.

Focus More on Problems, Less on Prompts

AI platforms and the models they are based upon are rapidly evolving, reaching more advanced levels of sophistication. For that reason, some experts doubt whether the importance of prompt engineering will be long lasting. Smith (2023) predicts that AI models may be able to write prompts themselves.

Acar (2023) foresees a future where advanced AI systems will be able to intuit our intentions without deliberate prompts. He calls for our attention the difference between problems and prompts. "Prompt engineering focuses on crafting the optimal textual input by selecting the appropriate words, phrases, sentence structures, and punctuation. In contrast, problem formulation emphasizes defining the problem by delineating its focus, scope, and boundaries." (Acar, 2023). In the long run, it may be more important to develop skills in crafting problems as compared to mastering prompt engineering (Acar, 2023).

Entering a prompt and receiving an output is much like the process of having a conversation with another person. But just like a conversation between two humans, interacting with AI can sometimes be complicated and AI may forget where it was in the conversation. This is another reason focusing more on the problem may be a more helpful approach than repeated attempts at crafting perfect prompts.

Be Aware of AI's Flaws

Despite rapid advancements, AI isn't flawless. Concerns around factual accuracy persist, as highlighted by a CNET incident in 2023 where AI-generated content was found factually incorrect (Thorbecke, 2023). Similar cases where AI generated factually false information can easily be found in many different settings, too, including academia. AI's production of content that is inaccurate, misleading, and even completely made up (even if it may seem perfectly coherent and believable on the surface) is called AI hallucination (Weise & Metz, 2023). It is important to keep in mind AI's limitations when formulating your prompts and always look at results with a critical eye.

Avoid AI's Potential Harms

AI can perpetuate biases. Many at MIT may be familiar with a controversy where an MIT student of Asian heritage asked AI to turn her photo into a professional looking headshot (Buell, 2023) only to find that it generated an image of her with bright blue eyes and a lighter skin tone. Sam Altman, CEO of ChatGPT, recognizes that AI falls short of removing biases and producing non-inclusive language, and advocates for iterative development that solicits broader public feedback to counter such challenges (Yu, 2023).

Conclusion

As users seek to harness the power of AI, crafting the right prompt becomes an essential skill, guiding AI towards desired outcomes and ensuring optimal results. The promise of AI systems like ChatGPT, Claude, and others lies in their ability to adapt and learn from your carefully crafted inputs, mimicking human conversation and generating pertinent outputs. Yet, we must remain vigilant about potential flaws, biases, and the implications of over-relying on these systems without critical scrutiny.

References

Acar, O. A. (2023, June 8). AI prompt engineering isn't the future. Harvard Business Review. https://hbr.org/2023/06/ai-prompt-engineering-isnt-the-future

Buell, S. (2023, August 24). Do AI generated images have racial blind spots? See an example. The Boston Globe. https://www.bostonglobe.com/2023/07/19/business/an-mit-student-asked-ai-make-her-headshot-more-professional-it-gave-her-lighter-skin-blue-eyes

Cook, J. (2023, June 26). How to write effective prompts for ChatGPT: 7 Essential steps for best results. Forbes. https://www.forbes.com/sites/jodiecook/2023/06/26/how-to-write-effective-prompts-for-chatgpt-7-essential-steps-for-best-results/?sh=5a76b51e2a18

Johnmaeda. (2023, May 23). Prompt engineering overview. Microsoft Learn. https://learn.microsoft.com/en-us/semantic-kernel/prompt-engineering

Liu, D. (2023, June 8). Prompt engineering for educators: Making generative AI work for you. LinkedIn. https://www.linkedin.com/pulse/prompt-engineering-educators-making-generative-ai-work-danny-liu

Mollick, E. (2023, January 10). How to. . . use ChatGPT to boost your writing. One Useful Thing. https://www.oneusefulthing.org/p/how-to-use-chatgpt-to-boost-your

Mollick, E. (2023, March 29). How to use AI to do practical stuff: A new guide. One Useful Thing. https://www.oneusefulthing.org/p/how-to-use-ai-to-do-practical-stuff

Neill, J. (2023). Enhance your ChatGPT prompts. Dr. Jeffrey Neill, Ed.D. https://www.jeffreyneill.com/blog/2023/6/30/prompt-magic-enhancingprompts

Smith, C. S. (2023, April 5). Mom, Dad, I want to be a prompt engineer. Forbes. https://www.forbes.com/sites/craigsmith/2023/04/05/mom-dad-i-want-to-be-a-prompt-engineer/?sh=206f254359c8

Thorbecke, C. (2023, January 25). Plagued with errors: A news outlet's decision to write stories with AI backfires. CNN Business. https://www.cnn.com/2023/01/25/tech/cnet-ai-tool-news-stories/index.html

Urban, E. (2023, July 18). What is intent recognition? Microsoft Learn. https://learn.microsoft.com/en-us/azure/ai-services/speech-service/intent-recognition

Weise, K., & Metz, C. (2023, May 9). When AI chatbots hallucinate. The New York Times. https://www.nytimes.com/2023/05/01/business/ai-chatbots-hallucination.html

Wu, Y., Mao, W., & Feng, J. (2022). AI for online customer service: Intent recognition and slot filling based on deep learning technology. Mobile Networks and Applications, 27(6), 2305-2317. https://doi.org/10.1007/s11036-021-01795-5

Yu, E. (2023, June 19). Generative AI should be more inclusive as it evolves, according to OpenAI's CEO. ZDNET. https://www.zdnet.com/article/generative-ai-should-be-more-inclusive-as-it-evolves

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Effective Prompts for AI: The Essentials - MIT Sloan Teaching & Learning Technologies

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Unlock the potential of AI by crafting effective prompts. Learn how prompt engineering can optimize your AI interactions, enhance output quality, and understand its limitations.

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One Best Thing - Prompt Crafting for Educators

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One Best Thing - Prompt Crafting for Educators

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Data centre electricity needs are forecast to double between 2022 and 2026

There's a big problem with generative AI, says Sasha Luccioni at Hugging Face, a machine-learning company. Generative AI is an energy hog.
"Every time you query the model, the whole thing gets activated, so it's wildly inefficient from a computational perspective," she says.

Take the Large Language Models (LLMs) at the heart of many Generative AI systems. They have been trained on vast stores of written information, which helps them to churn out text in response to practically any query.

"When you use Generative AI... it's generating content from scratch, it's essentially making up answers," Dr Luccioni explains. That means the computer has to work pretty hard.

A Generative AI system might use around 33 times more energy than machines running task-specific software, according to a recent study by Dr Luccioni and colleagues. The work has been peer-reviewed but is yet to be published in a journal.

It's not your personal computer that uses all this energy, though. Or your smartphone. The computations we increasingly rely on happen in giant data centres that are, for most people, out of sight and out of mind.

"The cloud," says Dr Luccioni. "You don't think about these huge boxes of metal that heat up and use so much energy."

The world's data centres are using ever more electricity. In 2022, they gobbled up 460 terawatt hours of electricity, and the International Energy Agency (IEA) expects this to double in just four years. Data centres could be using a total of 1,000 terawatts hours annually by 2026. "This demand is roughly equivalent to the electricity consumption of Japan," says the IEA. Japan has a population of 125 million people.

At data centres, huge volumes of information are stored for retrieval anywhere in the world - everything from your emails to Hollywood movies. The computers in those faceless buildings also power AI and cryptocurrency. They underpin life as we know it.

AI can be "wildly inefficient" when using computing resources, says Sasha Luccioni

But some countries know all too well how energy hungry these facilities are. There is currently a moratorium preventing the construction of new data centres in Dublin. Nearly a fifth of Ireland's electricity is used up by data centres, and this figure is expected to grow significantly in the next few years - meanwhile Irish households are reducing their consumption.

The boss of National Grid said in a speech in March that data centre electricity demand in the UK will rise six-fold in just 10 years, fuelled largely by the rise of AI. National Grid expects that the energy required for electrifying transport and heat will be much larger in total, however.

Utilities firms in the US are beginning to feel the pressure, says Chris Seiple at Wood Mackenzie, a research firm.

"They're getting hit with data centre demands at the exact same time as we have a renaissance taking place - thanks to government policy - in domestic manufacturing," he explains. Lawmakers in some states are now rethinking tax breaks offered to data centre developers because of the sheer strain these facilities are putting on local energy infrastructure, according to reports in the US.

Mr Seiple says there is a "land grab" going on for data centre locations near to power stations or renewable energy hubs: "Iowa is a hotbed of data centre development, there's a lot of wind generation there."

Some data centres can afford to go to more remote locations these days because latency - the delay, usually measured in milliseconds, between sending information out from a data centre and the user receiving it - is not a major concern for increasingly popular Generative AI systems. In the past, data centres handling emergency communications or financial trading algorithms, for example, have been sited within or very near to large population centres, for the absolute best response times.

Jensen Huang, Nvidia chief executive, shows off the new Blackwell chip in March

There is little doubt that the energy demands of data centres will rise in the coming years, but there is huge uncertainty over how much, stresses Mr Seiple.

Part of that uncertainty is down to the fact that the hardware behind generative AI is evolving all the time.

Tony Grayson is general manager at Compass Quantum, a data-centre business, and he points to Nvidia's recently launched Grace Blackwell supercomputer chips (named after a computer scientist and a mathematician), which are designed specifically to power high-end processes including generative AI, quantum computing and computer-aided drug design.

Nvidia says that, in the future, a company could train AIs several times larger than those currently available in 90 days. It would require 8,000 of the previous generation of Nvidia chips and need a 15 megawatt electricity supply.

But the same work could be carried out in the same time by just 2,000 Grace Blackwell chips, and they would need a four megawatt supply, according to Nvidia.

That still ends up as 8.6 gigawatt hours of electricity consumed - roughly the same amount that the entire city of Belfast uses in a week.

"The performance is going up so much that your overall energy savings are big," says Mr Grayson. But he agrees that power demands are shaping where data centre operators site their facilities: "People are going to where cheap power's at."

Dr Luccioni notes that the energy and resources required to manufacture the latest computer chips are significant.

More technology of business

Still, it is true that data centres have got more energy efficient over time, argues Dale Sartor, a consultant and affiliate of Lawrence Berkeley National Laboratory in the US. Their efficiency is often measured in terms of power usage effectiveness, or PUE. The lower the number, the better. State-of-the-art data centres have a PUE of around 1.1, he notes.

These facilities do still create significant amounts of waste heat and Europe is ahead of the US in finding ways of using that waste heat - such as warming up swimming pools - says Mr Sartor.

Bruce Owen, UK managing director at Equinix, a data centre firm, says, "I still think that the demand is going to grow further than that efficiency gain that we see." He predicts that more data centres will be built with on-site power-generating facilities included. Equinix was denied planning permission for a gas-powered data centre in Dublin last year.

Mr Sartor adds that costs may ultimately determine whether Generative AI is worth it for certain applications: "If the old way is cheaper and easier then there's not going to be much of a market for the new way."

Dr Luccioni stresses, though, that people will need to clearly understand how the options in front of them differ in terms of energy efficiency. She is working on a project to develop energy ratings for AI.

"Instead of picking this GPT-derivative model that is very clunky and uses a lot of energy, you can pick this A+ energy star model that will be a lot more lightweight and efficient," she says.

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Electricity grids creak as AI demands soar

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Data centre electricity demand is forecast to double in four years, putting energy grids under pressure.

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

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

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

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

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How can AI help us become better writers? | Stanford CRAFT

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

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IU International University of Applied Sciences

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AI Tools for Education

Understanding the Role of AI Tools for Education

AI has revolutionized the field of education, transforming the way students acquire knowledge and enhancing their learning experiences.

By using machine learning algorithms, AI has the ability to adapt to individual student needs, providing personalized learning experiences that cater to their unique requirements.

In addition to personalized instruction, AI tools have also made education more accessible than ever before. Students with disabilities or diverse learning styles can now benefit from an AI powered assistive technology that provides tailored support and accommodations.

These tools level the playing field, ensuring that every student has equal opportunities to succeed. For example, an AI powered speech recognition software can assist students with speech impairments, enabling them to actively participate in classroom discussions.

Moreover, AI has the potential to enhance student engagement and motivation. Virtual tutors powered by AI can interact with students, offering personalized feedback and guidance.

However, it is important to note that while AI has tremendous potential in education, it should not replace human teachers. The role of educators remains crucial in guiding and mentoring students, fostering critical thinking skills, and providing emotional support.

AI should be seen as a valuable tool that complements and enhances the teaching and learning process, rather than a substitute for human interaction.

AI tools have revolutionized education by providing personalized learning experiences, making education more accessible, and enhancing student engagement.

With continued advancements in AI technology, the future of education holds great promise, as it continues to evolve and adapt to the changing needs of students.

AI tools for students

Best AI Tools for Students

Now, let's have a look at what AI tools for students do, and which ones you should consider using.

One of the key advantages of AI in education is its targeted approach to teaching. Traditional classroom settings often struggle to cater to the diverse learning needs of students. However, with AI, students can now grasp complex concepts more effectively and at their own pace.

The adaptive nature of AI algorithms allows students to receive tailored instruction, ensuring that they fully understand the material before moving on. This personalized approach has been shown to significantly improve academic performance and retention rates.

In addition, AI has the potential to enhance student engagement and motivation. Virtual tutors powered by AI can interact with students, offering personalized feedback and guidance.

An AI powered tutoring tool for students can adapt its teaching methods to suit individual learning styles and preferences, making the learning process more engaging and enjoyable.

By incorporating interactive elements, such as gamification and virtual reality, AI can create immersive learning experiences that captivate students' attention and foster a love for learning.

Types of AI Tools for Students

When it comes to educational journeys, students now have access to a wide range of AI tools that are specifically designed to assist them.

These AI tools for students not only enhance the learning experience but also provide valuable insights and personalized support. Let's explore some of the most popular types of AI tools for students, so you can find the best AI tools for your needs:

Aside from these popular AI tools, there are many other innovative applications of AI in education. For example, AI chatbots provide instant support and answers to common student queries, reducing response times and improving accessibility.

Additionally, AI algorithms can analyze vast amounts of educational data to identify patterns and trends, helping educators make data-driven decisions to enhance teaching strategies.

As technology continues to evolve, the potential for AI tools for students is limitless. These tools not only assist students in their learning journeys but also empower educators to create more personalized and effective learning environments.

But even what's available now can completely transform your learning experience, and help you get better results faster and with more ease.

Here are our recommendations for 10 of the best AI tools for students:

Grammarly is an AI powered writing assistant that helps students improve their writing skills. It can detect and correct grammar, spelling, punctuation, and style mistakes in real time. This tool is especially useful for students when writing essays, term papers, and other academic assignments.

Notion is an all-in-one workspace that allows students to organize and manage their tasks, notes, and projects. It combines the features of note-taking, task management, and project planning into a single platform.

Students can use Notion to:

Create to-do lists

Take class notes

Collaborate with classmates

Stay organized throughout their studies.

Syntea is a cutting-edge AI powered tool developed by IU International University of Applied Sciences, specifically for our students. It's available in English in all of our courses, and it's a groundbreaking development in online education.

With Syntea, the AI teaching assistant, students can optimise their studies so they match their needs at any given time.

As an IU student, you'll be able to leverage the benefits of state-of-the-art technological advancements before everyone else, so you'll not only learn better but also be more prepared for working with AI tools in the future, in whatever career you choose.

Syntea currently offers two features to help you ace your studies, with more features coming soon.

First, you can ask Syntea any question related to the course content, get an immediate answer, and be directed to the course book element where the answer was taken from, so you can have more context and further develop your understanding.

Second, you can use the PreAssessment feature to assess your skills and knowledge before you start a course, to understand which units you should focus on, and set up the right study plan for your needs.

You can also use it to test your knowledge before you take the final exam, to understand what you need to improve and how you should optimise your exam preparation.

Want to learn more about our AI teaching assistant? Request your free information material at the top of this page.

Gradescope is an AI powered grading tool that streamlines the grading process for students and instructors. It allows students to submit their assignments online and automatically grades multiple-choice, fill-in-the-blank, and coding questions.

With Gradescope, students can receive instant feedback on their work and track their progress throughout the semester.

Chat-GPT needs no introduction (but we'll do it anyway). This game-changing web application is not only one of the best AI tools for students - it's one of the best AI tools for everyone.

This AI powered chatbot was developed by Open AI. It can simulate human-like conversations and answer questions on a wide range of topics.

Students can use Chat-GPT to get instant answers to their queries, seek clarification on complex concepts, or have interactive study sessions with the chatbot. It's definitely a leader in terms of AI tools for students, helping them to enhance their learning experience and access information quickly.

Tutor.ai is an AI based tutoring platform that connects students with qualified tutors. Through the platform, students can schedule virtual tutoring sessions, receive personalized assistance, and get help with difficult subjects or assignments.

Tutor.ai can provide valuable guidance and support to students, helping them improve their understanding of various topics.

Copyscape is an AI powered plagiarism detection tool that helps students ensure their work is original and properly sourced. It scans documents for duplicate content and provides a report showing any matches found on the web.

By using Copyscape, students can avoid unintentional plagiarism and maintain academic integrity in their writing.

This handy transcription tool converts spoken language into written text. It is particularly helpful for students who want to transcribe lectures, interviews, or class discussions.

With Otter.ai, students can easily review and search through recorded audio files, making it easier to study and reference important information.

This tool isn't meant for every student - though we certainly recommend it - but it is without a doubt one of the best AI tools for students of graphic design or the visual arts.

DALL-E is a version of the Open AI machine learning model, GPT (the same model Chat-GPT runs on). It's specifically designed to generate images from textual descriptions, meaning that if you provide it with a text prompt, it can create an image accordingly.

For example, if you ask DALL-E to generate "an elephant riding a unicycle," it would use its trained AI capabilities to generate images that match your request as closely as possible.

DALL-E's impressive ability to create unique, creative images from text descriptions is a significant leap forward in the field of AI and machine learning algorithms, opening up fun possibilities for fields like art, design, and more.

Mendeley is a reference management tool that helps students organize and annotate their research papers and articles. It allows students to:

Easily import and organize references

Create citations and bibliographies

Collaborate with peers on research projects.

Mendeley can save students valuable time in managing their research materials and ensure proper citation practices.

These are some of the best AI tools for students currently available, and they offer a wide range of benefits, including:

Improving writing skills

Enhancing organization and productivity

Providing personalized tutoring and study recommendations

Detecting plagiarism

Converting speech to text

Optimizing study habits

Managing research papers effectively.

Incorporating these tools into your study routine can help you become more efficient, confident, and successful in your academic pursuits.

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The Best AI Tools for Students | IU International

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Use AI to ace your studies! Discover the top 10 AI tools for writing papers, exam preparation, memorising new material, and more.

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