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UNESCO Digital Learning Week 2023 Presentation: Generative AI and Education (UNESCO, 2023)

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UNESCO Digital Learning Week 2023 Presentation: Generative AI and Education (UNESCO, 2023)

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Digital Learning Week 2023: Generative AI and education

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Digital Learning Week 2023 - Speakers’ biographies

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Tech Explorer, Public Evaluator, Board Member, Future of Algorithms, Research & Policy

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Generative AI is a type of artificial intelligence technology that can produce various types of content, including text, imagery, audio and synthetic data. The recent buzz around generative AI has been driven by the simplicity of new user interfaces for creating high-quality text, graphics and videos in a matter of seconds.

The technology, it should be noted, is not brand-new. Generative AI was introduced in the 1960s in chatbots. But it was not until 2014, with the introduction of generative adversarial networks, or GANs -- a type of machine learning algorithm -- that generative AI could create convincingly authentic images, videos and audio of real people.

On the one hand, this newfound capability has opened up opportunities that include better movie dubbing and rich educational content. It also unlocked concerns about deepfakes -- digitally forged images or videos -- and harmful cybersecurity attacks on businesses, including nefarious requests that realistically mimic an employee's boss.

Two additional recent advances that will be discussed in more detail below have played a critical part in generative AI going mainstream: transformers and the breakthrough language models they enabled. Transformers are a type of machine learning that made it possible for researchers to train ever-larger models without having to label all of the data in advance. New models could thus be trained on billions of pages of text, resulting in answers with more depth. In addition, transformers unlocked a new notion called attention that enabled models to track the connections between words across pages, chapters and books rather than just in individual sentences. And not just words: Transformers could also use their ability to track connections to analyze code, proteins, chemicals and DNA.

The rapid advances in so-called large language models ( LLMs) -- i.e., models with billions or even trillions of parameters -- have opened a new era in which generative AI models can write engaging text, paint photorealistic images and even create somewhat entertaining sitcoms on the fly. Moreover, innovations in multimodal AI enable teams to generate content across multiple types of media, including text, graphics and video. This is the basis for tools like Dall-E that automatically create images from a text description or generate text captions from images.

These breakthroughs notwithstanding, we are still in the early days of using generative AI to create readable text and photorealistic stylized graphics. Early implementations have had issues with accuracy and bias, as well as being prone to hallucinations and spitting back weird answers. Still, progress thus far indicates that the inherent capabilities of this generative AI could fundamentally change enterprise technology how businesses operate. Going forward, this technology could help write code, design new drugs, develop products, redesign business processes and transform supply chains.

How does generative AI work?

Generative AI starts with a prompt that could be in the form of a text, an image, a video, a design, musical notes, or any input that the AI system can process. Various AI algorithms then return new content in response to the prompt. Content can include essays, solutions to problems, or realistic fakes created from pictures or audio of a person.

Early versions of generative AI required submitting data via an API or an otherwise complicated process. Developers had to familiarize themselves with special tools and write applications using languages such as Python.

Now, pioneers in generative AI are developing better user experiences that let you describe a request in plain language. After an initial response, you can also customize the results with feedback about the style, tone and other elements you want the generated content to reflect.

Generative AI models

Generative AI models combine various AI algorithms to represent and process content. For example, to generate text, various natural language processing techniques transform raw characters (e.g., letters, punctuation and words) into sentences, parts of speech, entities and actions, which are represented as vectors using multiple encoding techniques. Similarly, images are transformed into various visual elements, also expressed as vectors. One caution is that these techniques can also encode the biases, racism, deception and puffery contained in the training data.

Once developers settle on a way to represent the world, they apply a particular neural network to generate new content in response to a query or prompt. Techniques such as GANs and variational autoencoders (VAEs) -- neural networks with a decoder and encoder -- are suitable for generating realistic human faces, synthetic data for AI training or even facsimiles of particular humans.

Recent progress in transformers such as Google's Bidirectional Encoder Representations from Transformers ( BERT), OpenAI's GPT and Google AlphaFold have also resulted in neural networks that can not only encode language, images and proteins but also generate new content.

What are Dall-E, ChatGPT and Bard?

ChatGPT, Dall-E and Bard are popular generative AI interfaces.

Dall-E. Trained on a large data set of images and their associated text descriptions, Dall-E is an example of a multimodal AI application that identifies connections across multiple media, such as vision, text and audio. In this case, it connects the meaning of words to visual elements. It was built using OpenAI's GPT implementation in 2021. Dall-E 2, a second, more capable version, was released in 2022. It enables users to generate imagery in multiple styles driven by user prompts.

ChatGPT. The AI-powered chatbot that took the world by storm in November 2022 was built on OpenAI's GPT-3.5 implementation. OpenAI has provided a way to interact and fine-tune text responses via a chat interface with interactive feedback. Earlier versions of GPT were only accessible via an API. GPT-4 was released March 14, 2023. ChatGPT incorporates the history of its conversation with a user into its results, simulating a real conversation. After the incredible popularity of the new GPT interface, Microsoft announced a significant new investment into OpenAI and integrated a version of GPT into its Bing search engine.

Here is a snapshot of the differences between ChatGPT and Bard.

Bard. Google was another early leader in pioneering transformer AI techniques for processing language, proteins and other types of content. It open sourced some of these models for researchers. However, it never released a public interface for these models. Microsoft's decision to implement GPT into Bing drove Google to rush to market a public-facing chatbot, Google Bard, built on a lightweight version of its LaMDA family of large language models. Google suffered a significant loss in stock price following Bard's rushed debut after the language model incorrectly said the Webb telescope was the first to discover a planet in a foreign solar system. Meanwhile, Microsoft and ChatGPT implementations also lost face in their early outings due to inaccurate results and erratic behavior. Google has since unveiled a new version of Bard built on its most advanced LLM, PaLM 2, which allows Bard to be more efficient and visual in its response to user queries.

What are use cases for generative AI?

Generative AI can be applied in various use cases to generate virtually any kind of content. The technology is becoming more accessible to users of all kinds thanks to cutting-edge breakthroughs like GPT that can be tuned for different applications. Some of the use cases for generative AI include the following:

Implementing chatbots for customer service and technical support.

Deploying deepfakes for mimicking people or even specific individuals.

Improving dubbing for movies and educational content in different languages.

Writing email responses, dating profiles, resumes and term papers.

Creating photorealistic art in a particular style.

Improving product demonstration videos.

Suggesting new drug compounds to test.

Designing physical products and buildings.

Optimizing new chip designs.

Writing music in a specific style or tone.

What are the benefits of generative AI?

Generative AI can be applied extensively across many areas of the business. It can make it easier to interpret and understand existing content and automatically create new content. Developers are exploring ways that generative AI can improve existing workflows, with an eye to adapting workflows entirely to take advantage of the technology. Some of the potential benefits of implementing generative AI include the following:

Automating the manual process of writing content.

Reducing the effort of responding to emails.

Improving the response to specific technical queries.

Creating realistic representations of people.

Summarizing complex information into a coherent narrative.

Simplifying the process of creating content in a particular style.

What are the limitations of generative AI?

Early implementations of generative AI vividly illustrate its many limitations. Some of the challenges generative AI presents result from the specific approaches used to implement particular use cases. For example, a summary of a complex topic is easier to read than an explanation that includes various sources supporting key points. The readability of the summary, however, comes at the expense of a user being able to vet where the information comes from.

Here are some of the limitations to consider when implementing or using a generative AI app:

It does not always identify the source of content.

It can be challenging to assess the bias of original sources.

Realistic-sounding content makes it harder to identify inaccurate information.

It can be difficult to understand how to tune for new circumstances.

Results can gloss over bias, prejudice and hatred.

What are the concerns surrounding generative AI?

The rise of generative AI is also fueling various concerns. These relate to the quality of results, potential for misuse and abuse, and the potential to disrupt existing business models. Here are some of the specific types of problematic issues posed by the current state of generative AI:

It can provide inaccurate and misleading information.

It is more difficult to trust without knowing the source and provenance of information.

It can promote new kinds of plagiarism that ignore the rights of content creators and artists of original content.

It might disrupt existing business models built around search engine optimization and advertising.

It makes it easier to generate fake news.

It makes it easier to claim that real photographic evidence of a wrongdoing was just an AI-generated fake.

It could impersonate people for more effective social engineering cyber attacks.

Implementing generative AI is not just about technology. Businesses must also consider its impact on people and processes.

What are some examples of generative AI tools?

Generative AI tools exist for various modalities, such as text, imagery, music, code and voices. Some popular AI content generators to explore include the following:

Text generation tools include GPT, Jasper, AI-Writer and Lex.

Image generation tools include Dall-E 2, Midjourney and Stable Diffusion.

Music generation tools include Amper, Dadabots and MuseNet.

Code generation tools include CodeStarter, Codex, GitHub Copilot and Tabnine.

Voice synthesis tools include Descript, Listnr and Podcast.ai.

AI chip design tool companies include Synopsys, Cadence, Google and Nvidia.

Use cases for generative AI, by industry

New generative AI technologies have sometimes been described as general-purpose technologies akin to steam power, electricity and computing because they can profoundly affect many industries and use cases. It's essential to keep in mind that, like previous general-purpose technologies, it often took decades for people to find the best way to organize workflows to take advantage of the new approach rather than speeding up small portions of existing workflows. Here are some ways generative AI applications could impact different industries:

Finance can watch transactions in the context of an individual's history to build better fraud detection systems.

Legal firms can use generative AI to design and interpret contracts, analyze evidence and suggest arguments.

Manufacturers can use generative AI to combine data from cameras, X-ray and other metrics to identify defective parts and the root causes more accurately and economically.

Film and media companies can use generative AI to produce content more economically and translate it into other languages with the actors' own voices.

The medical industry can use generative AI to identify promising drug candidates more efficiently.

Architectural firms can use generative AI to design and adapt prototypes more quickly.

Gaming companies can use generative AI to design game content and levels.

Ethics and bias in generative AI

Despite their promise, the new generative AI tools open a can of worms regarding accuracy, trustworthiness, bias, hallucination and plagiarism -- ethical issues that likely will take years to sort out. None of the issues are particularly new to AI. Microsoft's first foray into chatbots in 2016, called Tay, for example, had to be turned off after it started spewing inflammatory rhetoric on Twitter.

What is new is that the latest crop of generative AI apps sounds more coherent on the surface. But this combination of humanlike language and coherence is not synonymous with human intelligence, and there currently is great debate about whether generative AI models can be trained to have reasoning ability. One Google engineer was even fired after publicly declaring the company's generative AI app, Language Models for Dialog Applications (LaMDA), was sentient.

The convincing realism of generative AI content introduces a new set of AI risks. It makes it harder to detect AI-generated content and, more importantly, makes it more difficult to detect when things are wrong. This can be a big problem when we rely on generative AI results to write code or provide medical advice. Many results of generative AI are not transparent, so it is hard to determine if, for example, they infringe on copyrights or if there is problem with the original sources from which they draw results. If you don't know how the AI came to a conclusion, you cannot reason about why it might be wrong.

Generative AI vs. AI

Generative AI focuses on creating new and original content, chat responses, designs, synthetic data or even deepfakes. It's particularly valuable in creative fields and for novel problem-solving, as it can autonomously generate many types of new outputs.

Generative AI, as noted above, relies on neural network techniques such as transformers, GANs and VAEs. Other kinds of AI, in distinction, use techniques including convolutional neural networks, recurrent neural networks and reinforcement learning.

Generative AI often starts with a prompt that lets a user or data source submit a starting query or data set to guide content generation. This can be an iterative process to explore content variations. Traditional AI algorithms, on the other hand, often follow a predefined set of rules to process data and produce a result.

Both approaches have their strengths and weaknesses depending on the problem to be solved, with generative AI being well-suited for tasks involving NLP and calling for the creation of new content, and traditional algorithms more effective for tasks involving rule-based processing and predetermined outcomes.

Generative AI vs. predictive AI vs. conversational AI

Predictive AI, in distinction to generative AI, uses patterns in historical data to forecast outcomes, classify events and actionable insights. Organizations use predictive AI to sharpen decision-making and develop data-driven strategies.

Conversational AI helps AI systems like virtual assistants, chatbots and customer service apps interact and engage with humans in a natural way. It uses techniques from NLP and machine learning to understand language and provide human-like text or speech responses.

Generative AI history

The Eliza chatbot created by Joseph Weizenbaum in the 1960s was one of the earliest examples of generative AI. These early implementations used a rules-based approach that broke easily due to a limited vocabulary, lack of context and overreliance on patterns, among other shortcomings. Early chatbots were also difficult to customize and extend.

The field saw a resurgence in the wake of advances in neural networks and deep learning in 2010 that enabled the technology to automatically learn to parse existing text, classify image elements and transcribe audio.

Ian Goodfellow introduced GANs in 2014. This deep learning technique provided a novel approach for organizing competing neural networks to generate and then rate content variations. These could generate realistic people, voices, music and text. This inspired interest in -- and fear of -- how generative AI could be used to create realistic deepfakes that impersonate voices and people in videos.

Since then, progress in other neural network techniques and architectures has helped expand generative AI capabilities. Techniques include VAEs, long short-term memory, transformers, diffusion models and neural radiance fields.

Best practices for using generative AI

The best practices for using generative AI will vary depending on the modalities, workflow and desired goals. That said, it is important to consider essential factors such as accuracy, transparency and ease of use in working with generative AI. The following practices help achieve these factors:

Clearly label all generative AI content for users and consumers.

Vet the accuracy of generated content using primary sources where applicable.

Consider how bias might get woven into generated AI results.

Double-check the quality of AI-generated code and content using other tools.

Learn the strengths and limitations of each generative AI tool.

Familiarize yourself with common failure modes in results and work around these.

The future of generative AI

The incredible depth and ease of ChatGPT spurred widespread adoption of generative AI. To be sure, the speedy adoption of generative AI applications has also demonstrated some of the difficulties in rolling out this technology safely and responsibly. But these early implementation issues have inspired research into better tools for detecting AI-generated text, images and video.

Indeed, the popularity of generative AI tools such as ChatGPT, Midjourney, Stable Diffusion and Bard has also fueled an endless variety of training courses at all levels of expertise. Many are aimed at helping developers create AI applications. Others focus more on business users looking to apply the new technology across the enterprise. At some point, industry and society will also build better tools for tracking the provenance of information to create more trustworthy AI.

Generative AI will continue to evolve, making advancements in translation, drug discovery, anomaly detection and the generation of new content, from text and video to fashion design and music. As good as these new one-off tools are, the most significant impact of generative AI in the future will come from integrating these capabilities directly into the tools we already use.

Grammar checkers, for example, will get better. Design tools will seamlessly embed more useful recommendations directly into our workflows. Training tools will be able to automatically identify best practices in one part of an organization to help train other employees more efficiently. These are just a fraction of the ways generative AI will change what we do in the near-term.

What the impact of generative AI will be in the future is hard to say. But as we continue to harness these tools to automate and augment human tasks, we will inevitably find ourselves having to reevaluate the nature and value of human expertise.

Generative AI will find its way into many business functions.

Generative AI FAQs

Below are some frequently asked questions people have about generative AI.

Who created generative AI?
Joseph Weizenbaum created the first generative AI in the 1960s as part of the Eliza chatbot.

Ian Goodfellow demonstrated generative adversarial networks for generating realistic-looking and -sounding people in 2014.

Subsequent research into LLMs from Open AI and Google ignited the recent enthusiasm that has evolved into tools like ChatGPT, Google Bard and Dall-E.

How could generative AI replace jobs?
Generative AI has the potential to replace a variety of jobs, including the following:

Writing product descriptions.

Creating marketing copy.

Generating basic web content.

Initiating interactive sales outreach.

Answering customer questions.

Making graphics for webpages.

Some companies will look for opportunities to replace humans where possible, while others will use generative AI to augment and enhance their existing workforce.

How do you build a generative AI model?
A generative AI model starts by efficiently encoding a representation of what you want to generate. For example, a generative AI model for text might begin by finding a way to represent the words as vectors that characterize the similarity between words often used in the same sentence or that mean similar things.

Recent progress in LLM research has helped the industry implement the same process to represent patterns found in images, sounds, proteins, DNA, drugs and 3D designs. This generative AI model provides an efficient way of representing the desired type of content and efficiently iterating on useful variations.

How do you train a generative AI model?
The generative AI model needs to be trained for a particular use case. The recent progress in LLMs provides an ideal starting point for customizing applications for different use cases. For example, the popular GPT model developed by OpenAI has been used to write text, generate code and create imagery based on written descriptions.

Training involves tuning the model's parameters for different use cases and then fine-tuning results on a given set of training data. For example, a call center might train a chatbot against the kinds of questions service agents get from various customer types and the responses that service agents give in return. An image-generating app, in distinction to text, might start with labels that describe content and style of images to train the model to generate new images.

How is generative AI changing creative work?
Generative AI promises to help creative workers explore variations of ideas. Artists might start with a basic design concept and then explore variations. Industrial designers could explore product variations. Architects could explore different building layouts and visualize them as a starting point for further refinement.

It could also help democratize some aspects of creative work. For example, business users could explore product marketing imagery using text descriptions. They could further refine these results using simple commands or suggestions.

What's next for generative AI?
ChatGPT's ability to generate humanlike text has sparked widespread curiosity about generative AI's potential. It also shined a light on the many problems and challenges ahead.

In the short term, work will focus on improving the user experience and workflows using generative AI tools. It will also be essential to build trust in generative AI results.

Many companies will also customize generative AI on their own data to help improve branding and communication. Programming teams will use generative AI to enforce company-specific best practices for writing and formatting more readable and consistent code.

Vendors will integrate generative AI capabilities into their additional tools to streamline content generation workflows. This will drive innovation in how these new capabilities can increase productivity.

Generative AI could also play a role in various aspects of data processing, transformation, labeling and vetting as part of augmented analytics workflows. Semantic web applications could use generative AI to automatically map internal taxonomies describing job skills to different taxonomies on skills training and recruitment sites. Similarly, business teams will use these models to transform and label third-party data for more sophisticated risk assessments and opportunity analysis capabilities.

In the future, generative AI models will be extended to support 3D modeling, product design, drug development, digital twins, supply chains and business processes. This will make it easier to generate new product ideas, experiment with different organizational models and explore various business ideas.

What are some generative models for natural language processing?

Some generative models for natural language processing include the following:

Carnegie Mellon University's XLNet

OpenAI's GPT (Generative Pre-trained Transformer)

Google's ALBERT ("A Lite" BERT)

Google BERT

Google LaMDA

Will AI ever gain consciousness?

Some AI proponents believe that generative AI is an essential step toward general-purpose AI and even consciousness. One early tester of Google's LaMDA chatbot even created a stir when he publicly declared it was sentient. Then he was let go from the company.

In 1993, the American science fiction writer and computer scientist Vernor Vinge posited that in 30 years, we would have the technological ability to create a "superhuman intelligence" -- an AI that is more intelligent than humans -- after which the human era would end. AI pioneer Ray Kurzweil predicted such a "singularity" by 2045.

Many other AI experts think it could be much further off. Robot pioneer Rodney Brooks predicted that AI will not gain the sentience of a 6-year-old in his lifetime but could seem as intelligent and attentive as a dog by 2048.
George Lawton is a journalist based in London. Over the last 30 years, he has written more than 3,000 stories about computers, communications, knowledge management, business, health and other areas that interest him. Editor's note: This guide was updated to reflect new developments in the fast-evolving field of generative AI technologies.

Continue Reading About What is generative AI? Everything you need to know

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What is Generative AI? Everything You Need to Know

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Generative AI is a type of artificial intelligence technology that can produce various types of content. Find out how it works and why it's a hot commodity.

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Evolution of Generative AI (Koury, Chomsky, Bettmann, 2023). 

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Evolution of Generative AI (Koury, Chomsky, Bettmann, 2023). 

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Role of Generative AI in Learning and Education (Kulik, 2024)

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Role of Generative AI in Learning and Education (Kulik, 2024)

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In a world where technology and education intertwine more closely every day, generative AI in learning and education is writing even this chapter. From the depths of neural networks and the vastness of data, generative AI emerges not just as a tool but as a visionary in the field of education. Join us on a journey through eight fascinating examples where AI is not merely a supporting character but the hero, reshaping the narrative of learning and development.

As we turn the pages, we'll uncover the role of artificial intelligence in education, from personalized learning paths to newfound freedom for teachers and trainers. Welcome to a story of innovation, inspiration, and insight - a glimpse into the present (and the near future) of education with generative AI.

AI in Learning And Development: A Brief History

The history of generative AI in education began in the early 2000s. Then, computer scientists began to use basic AI algorithms in adaptive learning systems, laying the foundation for personalized learning. The advancement of machine learning and neural networks in the late 2000s significantly enhanced AI's capabilities in education, allowing for more sophisticated educational tools and insights into student learning patterns.

A major breakthrough was the introduction of natural language processing (NLP), enabling AI-powered educational assistants to interact with students in natural language.

The late 2010s saw the emergence of generative AI models like GPT, revolutionizing the field by generating educational content, providing instant feedback, and creating interactive learning experiences. Today, generative AI plays a dynamic role in education, continually evolving to offer more immersive and personalized learning experiences.

Role Of Artificial Intelligence In Education

Let's first emphasize that the education and training sector is among those most eager to adopt generative AI. Artificial intelligence in learning uses the latest technology of deep neural networks, huge data sets, and enormous computing power to revolutionize information processing. This potential creates enormous opportunities for schools, universities, educational institutions, and training companies. What are the most popular use cases for generative AI in learning and education?

Here, we can distinguish 8 use cases of generative AI in learning and educational development, including content creation or identifying knowledge gaps. Now, let's briefly describe each use case:

Content Creation

As for content creation, generative AI in learning and education provides unique, tailored materials that cater to various subjects and learning styles. For instance, AI tools can now automatically generate interactive quizzes and educational games, making learning more engaging for students. AI can create diverse and culturally rich language exercises that mimic real-life conversations, enhancing language acquisition in language classes.

Moreover, AI creates realistic simulations in science education, allowing students to conduct virtual experiments and explore concepts in a safe, controlled environment. This enriches the curriculum and brings interactivity and practicality previously challenging to achieve. Such innovations are invaluable in creating a more dynamic, inclusive, and effective educational experience, paving the way for a future where learning is deeply personalized and universally accessible.

Educators and learners most often use a LLM ChatGPT for this purpose ( source). Other commonly used generative AI tools include Google Bard, Midjourney, Bing, Claude, Dall-E, and Canva.

Identifying Knowledge Gaps

The ability to identify a student's areas of difficulty is important in the teaching profession, and that makes the role of artificial intelligence in education extremely crucial.

For instance, in the field of mathematics education, AI-based diagnostic tools can examine student answers to identify certain misconceptions in subjects like algebra or geometry. Algorithms that use artificial intelligence are also great at finding patterns in students' work and finding common grammatical or lexical mistakes.

Teachers can modify their instruction to fill identified gaps when receiving targeted feedback. Generative AI can improve learning experiences and help students with their challenges. With this approach, education gets a more effective and flexible system adapted to each student's needs.

Personalized Learning

Custom learning is another use case of generative AI in education and learning, making it more adaptive and effective. For instance, different AI tools can adjust math problem difficulty and type based on individual student performance, providing a tailored learning experience.

Another example is the widely-known Duolingo, a language learning tool that uses AI to customize lessons to each learner's pace and skill level. In reading, various applications utilize AI to select texts that match and challenge each student's reading level, adapting as they improve.

These examples demonstrate how generative AI tools make education more inclusive and effective, allowing students to progress at their own pace and ensuring a deeper understanding of the material.

Instant Feedback And Support

Following the trend of personalized learning, the role of AI in providing instant feedback and support is equally transformative. Platforms utilizing AI for immediate grading of assignments and quizzes offer learners quick, actionable insights into their performance. This instant feedback is vital for students to understand and correct their mistakes promptly.

In specialized areas like coding, for example, generative AI in learning can analyze and provide real-time feedback on student-written code. This speeds up the learning process and builds students' confidence in mastering new skills. Especially in distant learning environments where in-person teacher interaction is less common, this AI-enabled rapid feedback is essential to sustaining student engagement and guaranteeing ongoing, interactive learning.

Social-Emotional Development

The socio-emotional development of learners is related to the personal development of learning. Here, too, is the generative AI trained to bring advancements. Examples? Innovative AI-driven tools can now monitor students' emotional states and behaviors, providing crucial insights for a more empathetic learning approach. Additionally, this type of software can detect signs of frustration or disengagement in students by analyzing their responses and behaviors. Such detection enables educators to provide timely emotional support or motivational strategies.

Let's add that AI-driven chatbots and virtual assistants create safe spaces for students to express emotions and seek advice, fostering emotional resilience. This broad application of generative AI in learning and education extends beyond academic achievement, focusing on nurturing students' emotional well-being for overall success.

Increasing Learners' Engagement

The role of artificial intelligence in education also involves boosting learners' engagement.. Tools like gamified learning platforms use AI to create dynamic, personalized challenges that motivate students. AI can generate immersive virtual and augmented reality experiences, transforming traditional training into an exciting adventure, especially in industry-related subjects like supply chain management, safety management, or general productivity.

Let's not forget that this enhanced engagement through AI is particularly effective in maintaining student interest, which is vital for remote or hybrid learning models where keeping students focused can be challenging. This shift towards a more engaging learning environment showcases AI's potential to make education more effective and enjoyable for students.

Getting Insights About Learners

The use of generative artificial intelligence in learning and education also means extracting important insights about the way students and employees learn during training. The impact of these insights is particularly valuable in identifying trends and gaps in learning, which is crucial for curriculum development and improvement. Generative AI in learning assesses student performance data, offering educators a deeper understanding of class dynamics and individual learning needs.

In mathematics, AI systems can widely analyze student responses to identify common misunderstandings, guiding teachers in focus on challenging areas. Language learning apps use AI to track progress in grammar and vocabulary, providing data to refine teaching approaches.

In science, AI tools analyze lab experiment results, offering insights into students' experimental skills and conceptual grasp. This data is invaluable for educators in customizing lab activities and theory sessions.

Generating More Free Time For Teachers And Trainers

The last use case of generative artificial intelligence in education is humanistic. Thanks to AI solutions, students and educators simply have more free time left. By automating administrative tasks like grading and attendance tracking, AI frees educators from time-consuming paperwork.

For instance, various platforms use generative AI in learning to grade assignments, allowing teachers to focus more on student interaction and personalized teaching. In professional training scenarios, AI-driven platforms stream content creation and assessment processes, enabling trainers to dedicate more time to developing innovative training modules and engaging directly with trainees.

Ethical Implications of AI in Learning and Education

While generative AI in learning and education brings exciting advancements, it's essential to consider its potential challenges and ethical dilemmas. One major concern is data privacy. With AI systems processing vast amounts of personal student data, there's a risk of data breaches or misuse. For instance, in the US, regulations like the Children's Online Privacy Protection Act (COPPA) are being scrutinized to ensure they encompass AI's evolving capabilities in safeguarding minors' data.

Another issue is the potential for AI to reinforce biases. AI algorithms can perpetuate prejudices, leading to unfair or biased educational outcomes if not correctly designed. This has prompted regions like the European Union to propose regulations under the Digital Services Act and the Artificial Intelligence Act, focusing on transparent and non-discriminatory AI systems.
In Asia-Pacific, countries like Singapore and Japan are exploring ethical AI frameworks, emphasizing the responsible use of AI in education to prevent issues like over-reliance on technology, which could impact critical thinking skills.

As we embrace AI's potential in transforming education, it's crucial to navigate these challenges thoughtfully, ensuring that AI is used in a way that's fair, and ethical, and enhances the learning experience for all students.

Generative AI in Learning: A conclusion

As you can see, generative AI in learning and education is changing methods and the essence of teaching and learning. The wide range of applications of generative AI, from creating engaging content to customizing individual learning experiences and streamlining administrative tasks, has ushered in a new era in education. Professional AI consulting can provide more information about the possibilities of generative artificial intelligence in education and learning.

Technological progress revolving around AI capabilities certainly brings many benefits to educators and learners, and there are many examples of this in current life. However, navigating this new landscape requires a certain balance. The balance between harnessing the innovative potential of artificial intelligence and protecting copyright, creativity, independent thinking, and the integrity of education. Let's not forget this.

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Generative AI's Positive Impact on Education (TED, 2023)

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Generative AI's Positive Impact on Education (TED, 2023)

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How AI Could Save (Not Destroy) Education | Sal Khan | TED

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Debate: Weighing the Impact: Does AI Do More Harm Than Good? (Al Sharq Youth, 2019)

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Debate: Weighing the Impact: Does AI Do More Harm Than Good? (Al Sharq Youth, 2019)

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Debate: Artificial Intelligence Brings More Harm than Good

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How can Generative AI Support Education?(Kloos, et al., 2024)

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How can Generative AI Support Education?(Kloos, et al., 2024)

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Educational Generative AI Applications (Poth, 2023)

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Educational Generative AI Applications (Poth, 2023)

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These apps and websites can help teachers boost their productivity, personalize learning, and create lesson content.

Over the past five years, I've explored ways to integrate AI into my teaching practice-even before ChatGPT and other generative AI became some of the most talked-about topics in education. Every educator needs to learn about AI and how we can leverage this technology to benefit our students and enhance our own work. To best provide for our students, we need to understand how this technology will impact them and us. What better way than to explore new tools via AI in our teaching practice?

Why Educators Need to Understand AI Tools

Our roles as educators have continued to change over the years. With technology comes a bit of hesitancy, especially with something as powerful as AI. In our schools, we have to provide opportunities for students to learn about changing technology because of the impact it may have on their future. Not only can AI tools enhance creativity and productivity, but also they can provide educators with valuable insights into student learning and assist with some of the time-consuming tasks that educators have.

Even with all of the promises of AI, it is important that we take time to talk about artificial intelligence in our classrooms. Not only do we teach the content, but we serve as mentors, facilitators of learning and co-learners with our students, especially as we embrace these emerging powerful technologies. It's important that we help our students learn about the benefits of them and also show how to use these tools properly, responsibly, and ethically.

How AI Can Improve A Teacher's Job

Personalized learning: Educators can provide tailored learning experiences based on AI-driven analytics that provide valuable insights into student performance and learning trends. Using this data, AI can instantly adapt student learning materials. Teachers can then use this information to provide personalized learning experiences, adapting to each student's strengths, weaknesses, and learning pace.

Productivity and efficiency: Greater efficiency comes with AI as well. Educators are responsible for a variety of clerical tasks, such as communicating with students and their families, grading assessments, and providing feedback. Educators may find they spend more time on these clerical tasks rather than on teaching and working directly with students. The right AI tools can help to automate or streamline these tasks, which allows teachers to have additional time with their students.

Creating and supplementing content: Through AI-powered platforms, teachers can curate a range of educational resources. With generative AI in particular, teachers are able to create lessons, activities, assessments, prompts for discussion, and presentations simply by providing a short prompt with keywords.

7 education Tools that work

Here are seven AI-powered tools that will help teachers with personalized learning that enables them to become more efficient and save time that can then be spent with students. I have used each of these for my own personal writing and creating of presentations, and the amount of time they save by generating the slides alone helps me to focus more closely on the content. I also appreciate that the tools offer translation options and a variety of templates and other resources that are commonly used by educators.

The recently released Teaching with AI guide for teachers was created to help educators use ChatGPT in their classroom. The guide comes with several suggested prompts and includes explanations that clarify exactly how ChatGPT works and what its limitations are, and it provides reminders of the importance of verifying information and checking for bias. With ChatGPT 4, which is a paid version, there is greater accuracy and reliability of information than with the original version.

Creating and sharing these resources with our students leads to rich conversations about the benefits of AI and proper use of this technology for creating and learning. There are many more tools available for teachers to explore that can help with each of the key areas that I mentioned. Most important is selecting a tool to start with and reflecting on how it impacted your practice.

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7 AI Tools That Help Teachers Work More Efficiently

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These apps and websites can help teachers boost their productivity, personalize learning, and create lesson content.

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Guidance document published by UNESCO providing recommendations and strategies for the ethical and responsible use of Generative AI (GenAI) in education and research. (UNESCO,2023)

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Guidance document published by UNESCO providing recommendations and strategies for the ethical and responsible use of Generative AI (GenAI) in education and research. (UNESCO,2023)

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Provide feedback on UNESCO's 2023 Mobile Learning Week Resource Pack: "Generative AI and Education"

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Provide feedback on UNESCO's 2023 Mobile Learning Week Resource Pack: "Generative AI and Education"