Exploring AI in eLearning Content Creation

We don’t need to tell you about the profound impact of Artificial Intelligence (AI) on how we live and work. The evidence is all around. And when it comes to eLearning, AI is transforming the digital learning landscape. In particular, the emergence of generative AI like ChatGPT is a game-changer for content creation. Generative AI tools produce unique content, streamlining the content creation process.  

This latest instalment in our series exploring the powerful capabilities of AI in digital learning focuses on content creation. We explore the challenges in digital learning content development and how AI platforms can add value. Finally, we share some of the exciting AI-driven tools that are revolutionising eLearning content creation.  

Content development is the process of creating course materials to help people acquire knowledge and develop new skills. In digital learning, content takes many forms, including eLearning modules, interactive simulations, quizzes, video lessons, and much more.   

The bottom line is that content development is critical to digital learning. Get it right, and learners can engage with content in dynamic, interactive ways, taking the learning experience to a whole new level.  

Currently, eLearning content development is a collaborative effort. It involves several players, including instructional designers, subject matter experts, multimedia specialists and technology professionals. In short, it’s a time-consuming, involved process.  

While popular content development platforms like Articulate and Adobe Captivate have helped streamline the process, several steps are still involved. A typical content creation workflow looks something like the following:  

• Needs analysis: This step involves analysing the learning content and learner profiles and setting the learning objectives.  

• Creating an instructional design storyboard: A design storyboard is a high-level overview of the content. It sets out how the eLearning will be broken down into modules. And it maps out all the different elements, including videos, simulations, infographics, and animations.  

• Prototype development: A few slides or screens are completed with multimedia elements, colours, branding and animations. Revisions and rewrites may be needed before all stakeholders, including subject matter experts, sign off on the prototype. You now have the green light to complete the whole program.  

• Measure and review: Once the eLearning goes live, user analytics help you monitor its effectiveness. Digital learning is adjusted in light of the data. Content may also be refined to reflect changing industry or educational standards.   

The time and resource-intensive nature of content development is one of many challenges. Here are some other issues involved in the development process: 

• Engagement: Designing content that keeps learners engaged and motivated in an online world full of distractions is an ongoing challenge.  

• Personalisation: Learners are now used to personalised news and social media feeds. And entertainment platforms like Netflix and Spotify offer customised recommendations. Increasingly, learners expect the same from digital learning. Not personalising content based on preferences, interests and learning styles can result in disengagement, poor retention and an underwhelming learning experience.  

• Updating content: Ensuring course content remains current and relevant in today's fast-changing world is an issue. It's hard to keep on top of curriculum changes and industry standards. 

• Assessment: Another challenge is developing accurate tools to measure learners' progress and understanding. If digital learning provides timely and constructive feedback, it can help learners' progress and motivation.  

• Accessibility: Learners are diverse. Ensuring content is universally accessible, including those with disabilities, is a continuing issue. If digital learning is not accessible, it can create barriers to participation.  

AI offers a range of solutions to these challenges. Let’s break down just how AI adds value to eLearning content development: 

• Adaptive learning: Use AI tools to create adaptive learning experiences that adjust to the learner's progress and performance in real time. For example, if a learner understands audio content better than text, AI tools can tweak eLearning to provide more audio resources. Adaptive learning helps ensure that participants are challenged appropriately and receive the support they need.  

• Data-driven insights: AI tools can analyse learner performance, engagement and behaviour data. The data helps you understand what content types resonate most with learners. And it also highlights areas where participants may be struggling. Instructors and designers can use these data-driven insights to adjust their programs accordingly.  

• Automated content creation: AI adds value by generating content for digital learning programs. Content examples include quizzes, flashcards, simulations and case studies. Using AI reduces the time and effort required to create high-quality learning materials, speeding up the entire process.  

• Voice assistance and translation: These AI-powered tools help make digital learning more accessible to learners with disabilities, such as visual or hearing impairments. Furthermore, they can provide real-time translations of spoken and written content, opening eLearning to global markets.  

• Intelligent tutoring systems: These powerful platforms combine AI with machine learning to interact directly with learners. Intelligent tutoring systems can provide the feedback and coaching previously reserved for human instructors and educators.  

While the transformative potential of AI in eLearning content development is undoubtedly exciting, there are some pitfalls. Before diving in headfirst, consider the following downsides:  

• Over-automation: Rely too much on AI tools, and you run the risk of losing the human touch that’s so critical to effective learning. AI can lack authenticity. So, over-automation could lead to learner disengagement, a breakdown in trust and an increase in drop-offs.  

• Bias: AI tools are trained on data sets. And if this data is biased, AI may inadvertently continue or even worsen existing biases in learning content. These could lead to unfair outcomes for some learners.  

• Data privacy: As we’ve seen, data is the lifeblood of AI, but with that comes data privacy responsibilities. Content creators and instructional designers must ensure AI tools comply with education and data protection standards.  

• Depersonalisation: One of the significant advantages of AI is its ability to create personalised learning pathways. However, it’s a balancing act. Too much personalisation can come across as inauthentic and mechanised. Instead, the learner can feel as though their learning experience is the opposite.  

• Quality control: AI-generated content may not align perfectly with educational standards and local curriculums. Furthermore, it’s not foolproof and may contain inaccuracies or inadvertently generate content similar to existing copyrighted materials. Plagiarism or, even worse, violation of copyright laws are the unintended outcomes.  

Despite the watchouts, there are several exciting tools for you to explore. Here’s our pick of the best AI-powered digital learning content development tools.  

Adaptive Platforms 

Examples here include ALEKS and DreamBox Learning. These adaptive platforms continuously assess learner data and adjust content to suit the individual’s style and performance better, enhancing the effectiveness of eLearning.   

Content Generation 

ChatGPT is the best-known tool, but others include LearningStudioAI and Quillonz. LearningStudioAI is a course creation tool that uses AI to create engaging and interactive digital learning. Quillonz is an AI-powered tool that generates quiz questions, summaries, and more based on content.  

Data Analytics  

These tools help instructional designers gain valuable insights from data to make informed decisions about content creation and delivery. A top choice here is Tableau. This powerful data visualisation tool will help you analyse learner performance, track engagement, and evaluate the effectiveness of eLearning. BrightBytes does a similar job and is specifically designed for educational settings.  

Intelligent Tutoring 

Knewton is perhaps the market leader when it comes to intelligent tutoring platforms. It uses AI to analyse learners’ responses and behaviour and adjusts content to suit individual learning needs. SmartSparrow is also worth checking out. It uses AI algorithms to analyse interactions with learning content, tweaking the difficulty level and providing targeted feedback to enhance learning outcomes. 

Translation and Voice 

Google Translate and Microsoft Translator are readily accessible platforms, while IBM Watson Language Translator is probably a step up. Amazon Polly is a text-to-speech service and captioning and transcription tools like Otter.ai or Caption.Ed are also must-haves.  

AI’s integration into digital learning content development offers exciting opportunities. Leveraging the power of AI can create more personalised, engaging and inclusive learning experiences for all.  

While these are thrilling times in digital learning, it’s vital that the human element is not lost. A balanced approach, merging the expertise of designers and instructors with AI’s capabilities, is essential.  

The potential is there to reshape the future of digital learning, making it more effective and accessible than ever. Instructional designers and educators should embrace the transformative potential and pave the way for a more inclusive future.  

Want to learn more about AI's powerful potential in digital learning? The Digital Learning Institute’s Professional Diploma in Digital Learning Design is a great place to start. This university-accredited program involves lots of hands-on practice using the latest AI tools, including generative AI.