Practical Strategies To Integrate AI In eLearning
Integrating AI into eLearning can solve real challenges, but starting without a clear purpose often leads to wasted effort. AI is powerful, but like any tool, its impact depends on how well it is aligned with specific learning goals. Too many organizations fall into the trap of chasing buzzwords, launching flashy pilots that never address real needs.
AI-generated image / dominknow.com
In a recent episode of the IDIODC (Instructional Designers in Offices Drinking Coffee) podcast, Tim Handley, Chief Product and Technology Officer at Welbee, shared practical insights on integrating AI meaningfully into training experiences. Welbee, a UK-based company focused on improving well-being in educational institutions, has successfully implemented AI across their curriculum. Their journey offers valuable lessons for training teams aiming to enhance content development, learner engagement, and measurable impact.
Begin With Real Problems, Not Technology
Tim emphasized avoiding the trap of adopting AI just because it's trendy or demanded by leadership. "We wanted to make sure that we were incorporating AI in a way that made sense, but also wasn't just a buzzword," he noted.
The most successful implementations start with clarity. Instead of asking, "How do we use AI?" ask, "What specific problems are we struggling to solve?"
For Welbee, the key issues were:
- Helping users navigate vast well-being resources quickly.
- Offering round-the-clock support across global time zones.
These challenges led to their first AI application: a chatbot trained on course content. The chatbot acted as an always-available support system, helping learners find the right information without requiring human intervention.
This approach reflects a broader industry trend: implementing AI through targeted pilot programs that address genuine needs. Smart Instructional Designers use AI as a co-pilot to speed content creation and tackle the "blank page problem," not as a replacement for expertise.
Building Upon Success
Once Welbee proved the value of their chatbot, they expanded AI integration to other areas of eLearning. Today, their AI-enhanced experiences include:
- Course-specific chatbots: Trained on relevant content for targeted answers.
- Interactive knowledge checks: Deliver immediate, contextual feedback instead of generic responses.
- Dynamic role-playing scenarios: Allow learners to practice coaching conversations in safe, simulated environments.
- Personalized learning paths: Suggest custom routes based on learner progress, goals, and areas of difficulty.
One standout example was their coaching exercises. Initially offered as static handouts, the activities intimidated learners and were underutilized. By embedding AI, Welbee transformed them into interactive role-play sessions. The chatbot plays the role of a teacher seeking guidance, while learners practice multiple conversations. The system provides structured feedback based on coaching models, reinforcing skills through safe repetition.
This shift highlights a critical lesson: AI doesn't just automate tasks; it can reimagine learning experiences in ways that are more engaging, personalized, and impactful.
Technical Considerations That Matter
Behind the scenes, several technical decisions shaped Welbee's success:
- Content control: AI should be grounded in curated, course-specific materials to ensure accuracy and relevance. This reduces the risk of "hallucinations" or inappropriate responses.
- Temperature settings: Temperature influences creativity. Low for reliability, high for variety—but riskier. Welbee lowered it for most scenarios, ensuring responses adhere to provided content. Settings range from 0 to 2, with lower values yielding predictable output.
AI-generated image / dominknow.com
- Prompt engineering: Crafting clear prompts is now a core skill for instructional designers. At Welbee, prompts defined the AI's persona, tone, context, and boundaries. Example:
- Persona: "You are an expert instructor specializing in leadership skills using the GROW model."
- Context: "Use UK English and terms for UK education; reference provided course content."
- Rules: "Always respond supportively, using British English and focusing on coaching."
- Quality assurance: Every AI feature was tested rigorously, including edge cases and sensitive topics. Creative scenarios required deeper validation to prevent missteps.
These details might seem technical, but they determine whether learners have a smooth, trustworthy experience—or a frustrating one.
Managing Bias And Ensuring Safety
AI can unintentionally perpetuate biases hidden in training data, engine design, or even prompt rules. Welbee mitigated this by:
- Restricting AI to vetted, course-specific content.
- Using low-temperature settings for predictable output.
- Building in guardrails through precise prompting.
- Conducting regular monitoring and updates.
- Clearly labeling AI features, such as their "Nebula AI" chatbot.
Sensitive subjects, like mental health, required extra safeguards. For example, conversations involving well-being automatically included hotline numbers and links to official resources. Transparency was essential: when learners know they're interacting with AI, trust and comfort rise.
As the Brookings Institute notes, bias can emerge at three levels: the data, the engine, and the human rules applied. [1] Addressing all three is essential for fair, ethical AI deployment.
Measuring Real Impact
Adding AI for novelty adds little value. The real test is whether learners are more engaged, successful, and supported. Welbee tracked tangible results, including:
- Higher course completion rates.
- Increased engagement compared to passive modules.
- Positive learner feedback, especially about chatbot support.
- Longer time spent exploring resources.
- Stronger retention of key concepts.
Picture this: learners not just finishing courses but staying longer, practicing more, and reporting better outcomes. That's meaningful impact.
And Welbee isn't alone. Companies like IBM, Amazon, and Walmart report similar results when AI supports personalization, real-time feedback, and adaptive learning. [2]
Practical Implementation Guidance
Tim offered clear guidance for teams exploring AI:
- Start small: Identify one problem AI can fix today.
- Maintain transparency: Tell learners when and why AI is being used.
- Enhance, don't replace: Keep human expertise central.
- Limit frequency: Aim for 3–4 AI touchpoints per two-hour course.
- Test thoroughly: Cover edge cases and stress-test for sensitive topics.
This disciplined approach prevents AI from overwhelming learners or becoming a gimmick.
The Path Forward
As AI evolves, so do eLearning opportunities. Welbee's story shows that the most effective applications are purpose-driven, carefully controlled, and learner-focused.
Instructional Designers should view AI not as a shortcut but as a partner in creating richer experiences. Emerging possibilities include:
- AI-driven emotional intelligence tools that adapt responses based on learner sentiment.
- AR/VR integration for immersive practice environments.
- Predictive analytics that flag learners at risk of disengagement before it happens.
For L&D teams, the message is clear: thoughtful AI adoption strengthens training impact and positions learning as a driver of organizational success.
Building Better Learning Experiences
At the foundation of any great learning organization are systems, tools, and teams that empower learning curricula and drive continuous improvement. It's not just about delivering courses—it's about building a sustainable structure for collaboration, efficiency, and innovation across the entire learning lifecycle.
dominKnow | ONE provides an all-in-one LCMS and authoring solution that unites these elements. By enabling Instructional Designers, Subject Matter Experts, and learning leaders to collaborate seamlessly, dominKnow | ONE ensures that creativity and expertise are shared rather than siloed. Combined with the thoughtful integration of AI, this foundation allows organizations to create engaging, accessible, and future-ready learning experiences that scale with evolving needs.
References:
[1] Artificial intelligence and bias: Four key challenges
[2] Case Studies: Successful AI Adoption In Corporate Training
Read More:
- How AI Is Revolutionizing eLearning: The Future Of Personalized Education
- Prompting With Purpose: Why Collaborative Prompting Creates Scalable eLearnings
