AI in modern eLearning development: Opportunities, limitations, and the role of human expertise
How can companies create learning experiences that grow with their business, bring together knowledge from different experts, and reach employees in different countries without losing quality and consistency? Increasingly, the answer lies at the intersection of Learning and Development, educational technology (EdTech), and artificial intelligence.
AI creates new opportunities to develop digital learning content, translate and adapt materials, produce videos, and analyse learning data. Its real value becomes clear when it is part of a well-designed learning system in which strategy, methodology, technology, and business goals work together.
Modern eLearning is much more than creating online courses. It is a structured process that connects business needs with the knowledge and skills employees need to develop. It includes understanding performance challenges, defining learning objectives, choosing the right learning methods, developing content, selecting technology, and measuring results. AI can support many of these activities, helping L&D specialists work with information and content more efficiently.
The process starts with understanding the business. Before developing a course, L&D specialists need to understand what the organisation wants to achieve, what employees need to learn, and what prevents them from performing at their best. Sometimes, training is the right solution. In other cases, employees may need clearer processes, better tools, or more support from their managers. A careful analysis helps identify the real need and prevents companies from investing in training that does not solve the underlying problem.
The next step is to design the learning experience. This involves defining learning objectives, organising the content, choosing suitable activities, and deciding how learners will practise and apply their knowledge. Different audiences may need different learning paths, even when they are working towards the same business goal. Learning design must also consider how adults learn, how much information they can process at one time, and how to keep them engaged throughout the experience.
AI can help develop different versions and prototypes of a learning solution. This makes it easier to test ideas, compare approaches, and improve content before full production begins. Instructional design expertise guides this process, ensuring that the learning experience follows adult learning principles, considers how people process information, and supports the specific objectives of the programme.
Several established approaches can support this work. ADDIE provides a structured process covering analysis, design, development, implementation, and evaluation. SAM supports a more iterative approach, with repeated testing and improvement throughout development. The right method depends on the project, its complexity, the available time, and the need for flexibility. AI can support these processes, while experienced L&D professionals ensure that the chosen approach fits the learning objectives and the organisation’s needs.
Working with subject matter experts is another important part of eLearning development. These experts provide the knowledge, experience, and practical examples needed to make training accurate and relevant. L&D specialists help turn this knowledge into a clear learning experience that employees can understand and use.
AI can support the summarising of documents, the organisation of information, and the preparation of working versions of learning materials. This makes collaboration with subject matter experts easier and helps organise content more efficiently. It also gives the team more time to check the accuracy of the information, improve the learning approach, and refine the final solution.
This work requires several different areas of expertise. Effective L&D functions bring together strategic consulting, needs analysis, learning architecture, instructional design, digital content development, project management, learning platform administration, localisation, learning analytics, and quality assurance. Each area contributes a different part of the process. Coordinating them helps ensure that business goals, learning methods, technology, and results remain connected. AI can support activities across these areas, increasing the capacity of specialists and making it easier to work with information, content, and data.
For many organisations, maintaining an internal team with all these skills is a significant organisational and financial challenge. It requires ongoing investment in specialised roles and enough projects to make full use of their expertise. This is one reason why outsourcing the full L&D function can be a valuable business model. Instead of building and managing every role internally, companies can work with an external partner that coordinates strategy, consulting, development, implementation, and evaluation. This gives them access to the right expertise and production capacity based on their actual needs. It also helps create a more flexible L&D function that can adapt to changing priorities while maintaining quality over time.
AI is also changing the production of digital learning content. It can help teams prepare scripts, structure information, develop initial visual concepts, and create different versions of content. These capabilities can support faster production and make it easier to update materials when processes, products, or internal requirements change. Professional review remains essential to ensure that the final content is accurate, clear, appropriate for its audience, and aligned with the learning objectives.
Video production is another area where AI can provide practical support. Synthetic voices, AI-assisted video tools, and digital presenters can help organisations create learning materials in different formats and languages. These options can be especially useful when companies need to communicate information regularly across large teams or several markets.
AI avatars deserve particular attention because they can support consistent communication across an organisation. In a global company, for example, a functional leader responsible for HR, occupational health and safety, information security, compliance, or business transformation may need to communicate the same standards across several countries. A digital version of that leader can help present the same core message in different languages and formats. This can make communication more consistent and reduce the need to record every version separately.
An avatar can also help organisations update content more efficiently. When a policy or process changes, teams may be able to revise the script and produce an updated version without organising a completely new recording session. The benefits depend on the quality of the technology, the complexity of the message, and how well the avatar fits the audience and the purpose of the training.
Using a real employee’s image and voice in an avatar requires careful planning. The organisation should clearly agree how the avatar will be used, where it will appear, how long it may be used, and what will happen if the employee changes roles or leaves the company. It is also important to consider whether the use of the avatar remains appropriate as the content changes. A message presented by a recognisable leader can carry significant weight, so the content must remain accurate and credible. Clear approval processes, version control, and regular reviews help keep the material trustworthy and up to date.
Localisation is another area where AI can support eLearning teams. Translating a course is not simply a matter of changing words from one language to another. The content must also make sense in the local business environment and reflect relevant terminology, cultural expectations, and operational requirements. AI translation tools can provide useful first versions and help speed up the process. Language specialists and subject matter experts then review the result to ensure that the meaning, tone, and technical details remain correct. When a course includes video, narration, or avatars, the team must also check pronunciation, timing, subtitles, and the consistency of the message across languages.
Technology selection and implementation are equally important. A learning solution must work within the organisation’s existing systems and be accessible to its intended audience. This may involve choosing a learning management system (LMS), preparing content for different devices, testing navigation, and checking accessibility. Standards such as SCORM and xAPI can support the recording and exchange of learning activity data, depending on the technical setup and the organisation’s requirements. The team must test the complete experience before launch to identify technical issues and confirm that the content works as intended.
Measuring results is another essential part of the process. Completing a course does not automatically mean that employees have learned something or changed how they work. Evaluation should consider what learners understood, whether they can apply their knowledge, and whether the training contributes to the intended business outcomes. The Kirkpatrick model offers a framework for evaluating different levels of learning impact, from learner reactions to changes in workplace behaviour and business results. Return on investment (ROI) can also be considered when the available data allows a meaningful comparison between the costs of the learning initiative and the value it creates.
AI can help analyse feedback, identify patterns in learning data, and prepare reports that support decision-making. However, these insights are useful only when the data is reliable and the questions being asked are clear. L&D specialists need to interpret the findings in context, understand the limitations of the available evidence, and decide what changes are needed. The goal is not simply to collect more data, but to use it to improve learning and support business performance.
Successful eLearning development therefore depends on the coordination of many connected activities. Strategy defines the direction. Needs analysis identifies the real challenges. Learning design turns objectives into meaningful experiences. Technology supports delivery, while production and localisation make content accessible to different audiences. Quality assurance protects accuracy and usability, and evaluation helps determine whether the solution delivers value.
AI expands what teams can achieve in digital learning development, localisation, video production, and data analysis. Sustainable results depend on integrating these capabilities into a professionally managed L&D system. Strategy, learning architecture, methodology, project management, technology, and evaluation each require specific but connected skills. Bringing these capabilities together is what turns AI from a useful technology into real business value.
