The Future of Learning and Development: Harnessing the Power of Generative AI

The world of Learning and Development is on the verge of a revolution, with generative AI taking center stage. From creating highly engaging and personalized learning experiences to reducing the cost and time of producing content, generative AI is transforming the way we approach L&D. As the founder and CEO of an AI startup that helps L&D teams utilize AI video as a tool for effective learning content creation, I’ve spent a lot of time investigating the learning and development sector, as well as generative AI and its capabilities. 

As a result, I strongly believe that whether or not you’re planning to use generative AI in your workflow, it is important to understand what it is, its advantages, and how to overcome the potential challenges that it may impose — and that’s exactly what I’m planning to explore through this article, with practical examples of how Colossyan utilizes the potential of generative AI to empower creators.

The future is already here, so being informed is the first step of embracing it; however, before we explore ways of harnessing the power of generative AI in L&D, let’s begin by answering this simple question: what even is generative AI?

What is Generative AI?

Let me give you a simple explanation — generative AI is a branch of artificial intelligence techniques that can generate various types of content based on a user's prompt. These generative AI systems use neural networks, deep learning, and natural language processing techniques to learn from existing data sets and produce new data that resembles the original data. Let’s look at some examples of content can be created using generative AI:

Text. GPT-4, Bard and similar language models have the ability to generate high-quality text in various writing styles, and can be used for content creation, translation, summarization, and conversation. They have the potential to revolutionize the way we interact with technology by providing contextually appropriate and virtually indistinguishable human-like text.

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ChatGPT: One of the most well-known examples of Generative AI, with over 100 million users, ChatGPT has already achieved the title of the fastest-growing application in history.

Images. Text-to-image platforms such as Dall-E and Midjourney generate images from prompts — they learn from large datasets and generate high-quality images that resemble real-world objects. This technology has a wide range of applications, including content creation, prototyping, and visual storytelling.

Audio. TTS (Text-to-Speech) such as ElevenLabs and other audio generators can create voice overs in various voices, languages and accents, as well as music and sound effects for all kinds of purposes, from video production to e-commerce and gaming.

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Video. AI video platforms like Colossyan provide creators with an opportunity to produce high-quality videos for learning & development, communications and even marketing, all in a fraction of the time it would take with traditional video production.

How are AI videos generated? Let me share some insider information about the generative AI in Colossyan with you: as explained in one of our previous articles, Colossyan uses neural rendering, a technique that involves training neural networks to understand and simulate the physical properties of real-world objects and environments. To generate an AI video, Colossyan first captures real-world video footage of the desired scene, along with depth and motion information. This data is then fed into a neural network, which uses it to generate a 3D model of the scene.

The neural rendering techniques are employed to produce a textured and photorealistic image out of the 3D model. The networks are trained to produce realistic textures and studio quality lighting. The underlying 3D model can be manipulated and animated with the help of conditional generative networks and with the help of neural rendering, a photorealistic image can be generated based on the new scene parameters, allowing for the creation of dynamic, engaging AI videos. Colossyan's use of neural rendering enables the creation of AI videos that can easily be used for professional video-making, while also providing flexibility and customization options that traditional video production methods cannot match.

To summarize my point, generative AI has numerous applications, from content creation all the way to personalized chatbots that can engage in human-like conversations,  capable of taking learning programs and even customer service to a whole new level. Overall, it is clear that the possibilities for generative AI are vast and continue to expand as the technology advances. 

The Limitations of Traditional L&D Approaches

From having conversations with leading learning professionals and actively investigating the sector, I can see how corporate training has evolved rapidly over the past two decades. The rise of the LMS, as well as rapid authoring tools has led to a profusion of elearning in many forms. This content is often paired with classroom training to create blended, or flipped learning experience. Organizations recognize the value of training their people, with the global L&D market estimated to be worth 357 billion U.S. dollars.  But for all the investment, L&D teams often face criticism that the learning experiences they offer are flawed, and fail to meet the individual needs of employees. Specifically, learning experiences face limitations such as:

Lack of personalization. One-size-fits-all training programs tend to be the norm, but this can make it difficult for organizations to address the specific needs and skill levels of each employee. Without personalized training, employees may struggle to reach their full potential, and organizations may fail to achieve their desired results. Furthermore, when designing learning at scale, it is very difficult to produce content that is optimally designed for each individual, and so learning designers tend to design experiences that will work best ‘for most people’. 

Time and cost. In addition to the issue of personalization, traditional L&D methods can also be time-consuming and costly. Scaling L&D efforts across large organizations can be a daunting task, especially when it involves delivering training in-person or through other traditional means like filming video, or designing complex elearning. This gets even more complicated when multiple languages are involved, or updates to content are required..

Limited interactivity. Finally, these methods often have limited interactivity and do not provide opportunities for learners to apply new skills in real-world situations. Interactions need to be pre-determined by learning designers, for instance, in a branching scenario, even though learners are given the opportunity to make a decision, outcomes are predetermined, and there are only so many decisions they can make. As a result, employees may not fully engage with the material or retain the knowledge they learn, which can have a negative impact on the organization as a whole. 

How Generative AI Can Transform L&D

Generative AI is already known to enhance L&D efforts in many ways, and I’m a strong believer that it has the transformative power to make L&D more effective than ever before. It can help boost course content ideation and creation by generating new ideas and producing relevant, engaging text, as well as audio and visual content. However, the benefits don’t stop there, as generative AI can also elevate L&D experiences with:

Personalization. Generative AI can be used to make learning much more personal and engaging: a good example would be KhanAcademy, where they utilize generative AI to power their Khanmigo assistant, which provides personalized help and guidance to students. The assistant is designed to understand the user's specific learning needs and provide tailored feedback and support, allowing learners to not only have access to information, but also to receive excellent guidance on how to effectively engage with it.

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Planning. Generative AI can be a helpful tool for L&D strategy planning, as explored in the video below. Just like using Google to look up information, effective prompting can help you utilize generative AI to structure information, design a strategy and even apply relevant data (just make sure not to share any sensitive data!) to maximize the relevance of the output. 

Content creation. A good example of this is AI video, which is a great tool for increasing engagement and providing valuable learning experiences, while reducing the cost and time needed to produce learning videos. Another benefit of AI video is its flexibility — while updating traditional videos can be challenging and time-consuming, creating AI videos is as simple as creating presentations, which makes it very easy to update and adapt them whenever needed. Generative AI can also help with localization, which allows creators to adapt videos to different languages and cultural contexts, making content accessible to a global audience.

Overcoming the Challenges of Implementing
Generative AI in L&D

Implementing generative AI in L&D requires addressing ethical considerations, such as ensuring that AI-generated content is unbiased and transparent. While generative AI can produce some very impressive content, I’d like to highlight that it still requires human input and review in order to produce truly effective results:  AI is most effective when used as an assistant and a collaborator, not a replacement. Here are some other challenges to consider:

Decision-making. People are more than data, and even with the most accurate input, you could still get AI-generated results that do not necessarily reflect what’s best for your specific learner or team. For that reason, I believe that it is important to work together with the AI, and not rely on it to do all the work for you: it is always a good practice to review the decisions made and the content produced with the AI, and where needed, adapt them using your own knowledge and expertise. 

Credibility. Reviewing the credibility of AI-generated content is a crucial aspect of using generative AI — and this is where human efforts are most required. All generative AI models clearly state that there is always a chance of misinformation with AI-generated content, which is why it is important to ensure that you prompt the generative AI to only draw on credible sources for learning related materials, as well as carefully review the content generated. 

Privacy and intellectual property rights. Using AI-generated videos ethically involves respecting privacy and intellectual property rights. It is important to ensure that personal information is not disclosed, and that the content does not violate copyright laws or infringe on someone's privacy. For that reason, Colossyan only allows creating custom avatars when given full consent from the person requesting their digital actor, with creators only being able to request a digital actor of themselves, and not of other people. 

As the founder of a platform that provides creators with powerful AI tools, I understand that with great technology comes great responsibility, and therefore place a lot of importance on ensuring that the AI content generated in Colossyan Creator is used respectfully and for the right reasons.

My Prediction for the Next 5-10 Years

While the phenomenal speed of AI progress makes it difficult to predict when and how AI will affect various aspects of L&D, as well as life in general, I have some ideas about what could happen in the near future. Some of my predictions include:

Full democratization of video creation. In my opinion, the next 5-10 years will see the end-to-end creation of video content by anyone, enabling full democratization of video creation. Similarly to how Colossyan’s current AI Prompt-to-Video feature allows creators to produce video drafts by simply entering a prompt, this technology is likely to advance much further, with AI-generated videos going way beyond “talking heads'', and creators likely being able to generate complex, fully AI-generated videos.   

Learning content personalization. Additionally, personalization of learning content will match AI-generated learning strategies to specific viewers, while the development of custom video avatars will make learning much more immersive and engaging. With personalized learning content, generative AI will completely transform the way we approach learning, as anyone will be able to engage with information in ways that work best for them. It will be much easier to track individual progress, as well as identify how knowledge gaps can be tackled. 

Interactivity. Interactivity will also become a major component of learning, allowing viewers to engage in real-time experiences, and resulting in increased engagement, retention, and application of knowledge. AI-powered interactive learning tools like simulations, games, and virtual reality environments will enable learners to practice skills and apply knowledge in a risk-free and immersive way. This will lead to more effective and efficient L&D strategies and better learning outcomes for individuals and organizations. 

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The Future for Colossyan

The potential of generative AI goes beyond L&D, and here at Colossyan, providing a powerful tool for learning teams is only the beginning. Our vision is to revolutionize the learning, commercial and entertainment industries, as we continue to enable creators all over the world to embrace the future by harnessing the power of generative AI.  

Thank you for reading this article, and I hope that you’ve found it useful! If you want to have a practical overview of how generative AI works, I encourage you to try out Colossyan — our free trial allows anyone to experience the power of AI video for themselves. Feel free to reach out to our team if you have any questions and connect with me on LinkedIn — I’d be very interested in hearing your thoughts on this topic. Happy creating!

Best,

Dominik from Colossyan