AI is becoming an integral part of everyday work and creating changes throughout nearly every industry. And learning and development is no exception. While discussions around AI have dominated conversations the past couple of years, the focus is rapidly moving from theoretical exploration to practical implementation. In the L&D realm, this evolution is not just about adopting new tools but about fundamentally rethinking how organisations approach the development, delivery, and measurement of knowledge. At the heart of this transition lies the critical role of eLearning standards—such as SCORM, LTI, xAPI, and cmi5—and their relevance in light of the rise of AI. Turns out, they serve as the foundation for AI-driven innovation and remain necessary for interoperability and data integrity.

 

Where AI fits: Training vs. learning

We are regularly asked ‘which standard should I use?’ and the answer is always to choose the right standard for the job to be done. Context matters. That is magnified when it comes to incorporating AI into your learning and training strategy.

As you are evaluating where AIt can enhance your learning program goals, it’s important to consider the context and intent." Traditionally, eLearning standards have focused heavily on the training side—assigned, high-stakes compliance work measured by standard metrics: Success (pass/fail), completion, time, and score. As compliance courses are often driven by precise, non-fungible content, organisations may not want to rely on AI tools for content generation, instead leveraging AI to analyse data to understand learner behavior better and enhance the learner experience. Examples here may include providing supplemental tools, such as an AI coach.

Learning, by contrast, is often unassigned and occurs more commonly "in the flow of work." It is driven by an individual's need to solve a problem or supplement their existing knowledge. AI's greatest potential lies in its ability to support true learning initiatives, such as delivering targeted, real-time microlearning experiences and identifying skill gaps with precision that adapts to a learner. Organisations focused on skills development and measurement will likely use AI tools to modify existing content, create new assets, and deliver more adaptable and dynamic experiences.

 

Why eLearning standards still matter in an AI world

AI is cutting-edge technology, but the SCORM standard is more than 20 years old and even xAPI was created over a decade ago. What role, if any, will eLearning standards play in the  digital learning ecosystem as AI capabilities reshape our approach to learning and training?

Standards provide the necessary structure for AI-enabled eLearning to function effectively across systems. All of the most common standards provide:

• Interoperability and portability: Allow content and data to move between disparate systems. Without these rules, organisations may find their data trapped in "walled gardens" or have to build and rebuild courses and integrations as their learning tech stack evolves. Working natively within a platform also makes it nearly impossible to leverage for an AI to provide a cohesive view of learning across the enterprise.
• Structured datasets: AI systems thrive on large amounts of structured data. While SCORM provides a baseline dataset (success, completion, score, duration), xAPI is particularly useful here, by design. It provides the ability to capture detailed experience data in a structured way—beyond the big four metrics SCORM inherently tracks—that AI can analyse to provide actionable insights and use to curate customised learning paths.
• Content discovery through metadata: Many standards have long included metadata capabilities that are often underutilised. AI can leverage this metadata to understand exactly what is in a content library, facilitating better searchability whether the training is required or not.

Even with more AI tools available, you will continue to need to employ eLearning standards for these reasons and choosing the right standard for your content and goals will become even more imperative.

In a compliance-driven environment where recording completions is critical, SCORM is still the most widely adopted and fits those needs perfectly. If your organisation is moving toward more learning experiences and wanting to track the data to see the impact on training and development, then a more modern standard, like xAPI or cmi5, may better fit those goals.

 

Breaking open the course package: AI needs context

Another crucial consideration is feeding your AI tools with relevant information. Your existing content library has a wealth of important content that can act as the trusted source of truth to power your AI initiatives. But it’s not uncommon for a content library to be missing key metadata, or clues, to inform what’s actually represented in that course. Extracting the text and content, however, isn’t as easy as just uploading a course into an AI tool. The SCORM standard, as well as other eLearning standards, don’t specify how an authoring tool should structure the content inside the package, and even different versions of the same authoring tool might handle this a little differently.

A content parsing tool that deconstructs your course catalog can help by breaking open your standards-based packages (like SCORM or xAPI) to unlock the valuable assets inside of them. From there, your catalog can become more dynamic to power your AI efforts, including:

• Automating metadata generation: AI can quickly generate topic lists, learning objectives, and keywords for large libraries of content and even align with your skills taxonomy to help admins and learners find the right content at the right time.
• Improving searchability: Moving beyond keyword matching to "semantic search," where the AI understands the ideas and context behind a query.
• Creating bespoke experiences: Once the AI understands the content, it can then more easily adapt outputs to fit a specific learner's role or skill level to close skill gaps.

 

AI in action: From microlearning to simulations

The practical applications of AI-infused eLearning are already being demonstrated. AI can simulate real-world scenarios far more nuanced than traditional multiple-choice formats. For example, a cybersecurity lab can use an AI chatbot to teach employees about the risks of prompt injection by allowing them to interact with a vulnerable shopping assistant.

The power of this approach is two-fold: The learner receives a highly interactive, realistic experience, while the organisation receives the cleaned-up data. The AI can translate a learner's free-form chat interactions into discrete xAPI statements mapped to specific learning objectives, such as "product reconnaissance" or "cart manipulation" like in the shopping assistant example.

Furthermore, AI excels at packaging existing training into microlearning bites that can be delivered through modalities, like Slack, reaching employees where they already work without needing to log into another system.

 

Navigating pitfalls and business impacts

While the potential is vast, organisations must remain wary of AI pitfalls. AI agents are only as good as the information they are given and can sometimes produce incorrect or "hallucinated" results. Human expertise still matters. Subject matter experts are essential for validating AI-generated quis questions and ensuring content accuracy.

Ultimately, the strategic use of AI tools offers a significant business impact by streamlining the "learning" side of the house. By using AI to reduce the time and cost of developing and managing training, organisations can reallocate those resources to improve learning experiences that truly drive employee growth and ROI. AI is not intended to replace instructional designers. Rather, it is a tool meant to help them focus on their true passion: Creating effective content that closes skill gaps and improves performance across the organisation.

 

eLearning standards in the future

While some may question the relevance of SCORM and xAPI with the introduction of AI, the reality is that the standards are far from becoming obsolete and are now more crucial than ever. They provide the fundamental, structured foundation—be it SCORM for critical compliance training or modern standards like xAPI and cmi5 for deep experience data—that AI systems require to function effectively and provide actionable insights.

AI should act as a strategic enabler. By streamlining the development and management of training and learning, organisations can focus on creating sophisticated, personalised learning experiences that leverage data to truly close skill gaps, measure clear business outcomes, and maximise the return on their investment in employee growth.

Attending Learning Technologies 2026 and want to learn more about content management strategies? Visit us at booth A30 and attend Chris Tompkins session at the Seminar Theatre 2, 30th April, 3:30 - 4:00.

 

 

Tammy Rutherford 

Managing Director at Rustici Software