How Small eLearning Teams Use AI For Accessibility
Accessible design, inclusive design, adaptive learning, accessibility, and AI are often discussed together, even if the connections between them are still evolving. However, most of these conversations assume a certain context: mature Learning and Development (L&D) teams, enterprise platforms, dedicated accessibility expertise, and the time and budget to implement complex systems, which makes sense as many of these ideas were developed for large organizations.
Many learning products don't live in that world. I've worked on eLearning products and the realities of small teams are different: limited resources, competing priorities, and the constant pressure to ship and iterate. From that perspective, what do AI-driven accessibility and adaptive learning actually look like for learning products designed by small teams? And can they realistically help without becoming another layer of complexity?
This article doesn't aim to provide definitive answers. Instead, it explores a simple question: can AI help eLearning products designed by small teams become more accessible and adaptive in practical, meaningful ways without enterprise budgets or infrastructure?
In this article...
- eLearning Products Designed By Small Teams Operate Under Different Constraints
- Where AI Seems Genuinely Useful For Small Teams
- Trade-Offs, Risks, And Open Questions
- Accessibility As An Ongoing Responsibility
- How This Applies To Learning Product Design
- AI Can Change What's Possible For eLearning Products Designed By Small Teams
eLearning Products Designed By Small Teams Operate Under Different Constraints
eLearning products designed by small teams rarely ignore accessibility on purpose. Accessibility more often competes with other urgent needs: fixing bugs, shipping new features, updating content, or responding to customer feedback. Such products, whether they're built by start-ups, small companies, or internal teams, operate under a very different set of constraints than large corporate training environments.
The same applies to adaptive learning. Different things feel out of reach for small teams who are trying to improve an existing product. Even though adaptive learning is an appealing concept, it's often associated with complex systems, large datasets, and long implementation cycles. Many small teams don't have in-house accessibility specialists. Moreover, learning about accessibility standards and best practices happens alongside everything else. This creates a tension for small teams.
From a product perspective, teams want flexibility, personalization and learning experiences to work for more people, but they also need solutions that are realistic to build, maintain, and scale. This is where AI becomes interesting for small teams, but not a silver bullet. The question isn't whether AI can fully solve accessibility or adaptation, but whether it can lower the barrier to doing something better than before.
Where AI Seems Genuinely Useful For Small Teams
The value that AI brings seems much more specific for small eLearning teams. Rather than solving accessibility outright, AI appears most useful when it helps teams save time on repetitive work, reduce friction, and lower the barrier to making incremental improvements.
Reducing Repetitive Content Work
When teams are maintaining or updating existing learning materials, AI seems genuinely helpful in reducing repetitive, low-leverage content work. AI can help:
- Summarize long lessons into quick-reference versions.
- Simplify text for different reading levels.
- Generate practice questions from existing content.
These benefits don't require a full adaptive learning engine. Offering a shorter summary, an audio version or providing alternative representations aligns with Universal Design for Learning principles, will improve usability for a broader audience and can reduce cognitive load and improve learner engagement, especially for learners with diverse needs. In practice, AI can act as a content multiplier:
- One lesson becomes multiple usable formats.
- One update can be reflected across versions more quickly.
- Teams spend less time rewriting and more time refining.
The limitation however is quality. Automatically generated summaries can oversimplify or remove nuance, particularly in complex or compliance-sensitive topics. But for small teams, the trade-off is often acceptable if AI output is treated as a draft rather than a finished asset.
Automating The "First Layer" Of Accessibility Work
Small teams might often get stuck with the first layer of accessibility. This work is necessary, time-consuming, and often deprioritized simply because of limited capacity. AI can make the difference between:
- No captions vs. usable captions.
- No alternative text vs. something reviewable.
- Inaccessible content vs. content that can be improved.
Having accessibility features available by default, rather than added later, shows improved usability and engagement. Moreover, automation helps provide alternative representations of learning materials, especially for learners with sensory or language barriers, while reducing manual workload for educators and designers [1]. AI can handle:
- Generating captions and transcripts for video.
- Suggesting alt text for images.
- Converting content into different formats (text, audio, summaries)
The limitation is that AI output still needs review. Automated captions and descriptions can be inaccurate or context-poor, especially for domain-specific learning content. Still, for small teams, AI can turn accessibility from an overwhelming task into a manageable starting point.
Supporting Small-Scale Adaptation Without Heavy Infrastructure
Adaptation doesn't need to be complex to be effective. Even when adaptation is relatively simple, such as adjusting pacing, providing targeted feedback, or offering alternative explanations, there are improvements in engagement and learning outcomes. From a product lens, this opens up more realistic possibilities:
- Letting learners choose between formats.
- Offering optional explanations or examples.
- Adjusting content depth based on interaction, not prediction.
These kinds of adaptations don't require predictive models or deep learner profiling. They can be implemented as responsive features, supported by AI, rather than full adaptive systems.
The limitation here is over-automation. Research [2] consistently warns that adaptive systems, which rely heavily on learner data, can introduce bias, misinterpret intent, or reduce learner agency if not carefully designed. For small teams, this reinforces an important idea: AI works best as a layer, not a decision-maker.
AI In eLearning Products Designed By Small Teams Has A More Grounded Role
AI doesn't replace accessibility expertise, it doesn't magically create adaptive learning or remove the need for thoughtful design. What it can do is:
- Lower the cost of getting started.
- Reduce repetitive effort.
- Help teams ship something better than before.
For small teams trying to improve learning products incrementally, that's often enough to make AI worth exploring cautiously, critically, and with clear boundaries. So, rather than asking: "How do we build adaptive learning?", a more grounded question for small teams might be: "Where do learners need flexibility, and how can we offer it without adding complexity?"
AI can help answer that question by making it easier to experiment with variations, respond to common friction points, and iterate based on real usage, but adaptation remains a design choice, not a technical one.
Trade-Offs, Risks, And Open Questions
Many of the risks and trade-offs show up later, once tools are already in use, and for small teams in particular they affect trust, product quality, and long-term maintainability.
The Risk Of Over-Automation
Automation is not a substitute for design judgment. Automated accessibility and personalization tools can create a false sense of completeness where content technically meets certain criteria but still fails learners in practice. Thus, automation can save time but only if it's paired with review and iteration.
Quality, Accuracy, And Context Still Matter
AI performs best on patterns, but learning is often about nuance. AI-generated learning content can introduce inaccuracies, oversimplifications, or subtle distortions, particularly in technical, regulated, or concept-heavy domains. For small teams, the challenge isn't just correcting errors. It's knowing where errors are likely to matter. And here is an open question for many teams: "How much review is 'enough' when AI is part of the content workflow?" Without clear review practices, AI can quietly erode content quality over time.
Bias And Representation
Another recurring concern in the research [3] is bias. AI systems trained on limited or homogeneous data can reinforce dominant language styles, cultural norms, or learning expectations, potentially excluding the very learners accessibility efforts aim to support. Small teams may not have the resources to audit models or retrain systems, which makes it especially important to treat AI output as suggestive, not authoritative, and:
- Test with real users whenever possible.
- Remain cautious about "one-size-fits-all" adaptations
Data, Privacy, And Trust
Research on AI in education [4] highlights ongoing concerns around transparency and data misuse. Adaptive and AI-supported learning often relies on learner data such as engagement signals, interaction patterns, and sometimes personal information. Thus, another question for the team emerges: "How much adaptation is helpful before it becomes uncomfortable?" For products designed by small teams, trust is fragile and even well-intentioned data use can feel invasive if it's not clearly communicated.
Accessibility As An Ongoing Responsibility
Research [4] consistently emphasizes that meaningful accessibility requires continuous attention, not one-time intervention. Content changes, interfaces evolve, and the learner's needs shift. AI works best as a support mechanism, not a replacement for responsibility. What questions remain open when/if you are navigating this space without clear playbooks:
- Where does AI meaningfully reduce effort, and where does it add hidden complexity?
- How can teams balance speed with accountability?
- What does "good enough" accessibility look like when perfection isn't feasible?
- How do we design adaptation that feels supportive, not opaque?
Asking them is often the difference between thoughtful progress and accidental harm.
How This Applies To Learning Product Design
If there's one takeaway from exploring AI, accessibility, and adaptive learning through the lens of eLearning products desigbed by small teams, it's this: progress doesn't come from doing everything at once. It comes from making a series of small, intentional decisions.
For small teams, the challenge is rarely a lack of ambition. It's deciding what to address now, what to defer, and what not to build at all. Accessibility and adaptability often surface the same tension: teams want learning experiences to work for more people, but they also need solutions that are realistic to ship, maintain, and evolve.
In this context, AI is most useful when it supports existing product decisions rather than driving them. It can help reduce repetitive work, surface friction, and expand options for learners, but it can't replace design judgment or clarify priorities on its own. Practically, this means focusing on:
- Building for iteration rather than completion.
- Starting where learner friction is already visible.
- Using AI to expand options, not to make decisions.
- Treating AI output as a draft, not a deliverable.
- Being explicit about what the product is not trying to solve yet.
Instead of asking, "How do we add AI-driven accessibility or adaptive learning?" a more grounded question for small teams is: "Where can AI help make this learning experience clearer, more flexible, or less frustrating than it is today?"
Framed this way, accessibility and adaptation become part of ongoing product improvement, not separate initiatives competing for attention. In this context, accessibility becomes a signal of product quality rather than a standalone compliance requirement.
AI Can Change What's Possible For eLearning Products Designed By Small Teams
The more I look at how AI is being applied to accessible and adaptive learning, the more questions emerge: about scale and trade-offs, and about what "good enough" really means.
What do we know at this moment? AI can help small teams with limited time, budget, or expertise. AI can support the process, but it can't replace judgment, empathy, or reflection. Quality still matters. Context still matters. Learners still experience products in ways that tools can't fully predict. And accessibility remains an ongoing responsibility rather than a one-time feature. For many learning products, especially ones designed by smaller teams, progress doesn't come from having all the answers. It comes from being willing to ask better questions and to keep improving.
References
[1] The Impact of Artificial Intelligence on Inclusive Education: A Systematic Review
[3] Exploring Artificial Intelligence in Inclusive Education: A Systematic Review of Empirical Studies
[4] Digital Accessibility for Students with Disabilities and Inclusive Learning in Education
