AI is the shiny new power tool in the world of learning and development (L&D). Who would you trust more to use it to the best of its abilities: your neighbor who bought it just because it was on sale, or the carpenter who has 20+ years of experience and understands the real value the tool could provide?
The old adage: “the more things change, the more they stay the same,” is particularly applicable here. Technology advances, but it is only as effective as our understanding and use of it. While the potential of generative artificial intelligence (GenAI) in corporate L&D is immense, success demands that learning leaders approach learning experiences with the human skills and authentic intelligence required for successful AI implementation. Let’s explore why context, meaningful feedback, and the fusion of human expertise with AI are all crucial for effective learning experiences.
Can’t I just plug in a scenario and have a chat with ChatGPT?
You can… but is it the best use of your precious learning time? While ChatGPT and similar generative AI tools are revolutionizing the way we work and learn, they come with pitfalls that must be carefully considered when using them as learning resources.
We’ve tested our AI-augmented ExperienceBUILDER™ scenario sims against similar experiences running natively in ChatGPT, and the outcomes aren’t even close. ExperienceBUILDER’s GenAI responses, feedback, and scoring are based on custom parameters pre-programmed into the large language model (LLM) using sophisticated, coding-style prompt craft and parallel agents.
Let’s take a closer look by comparing an interaction in one of Blueline’s ExperienceBUILDER sims to the same scenario experienced natively in ChatGPT.
Prior to the interaction, you get briefed on the details of the challenge (e.g., how and why you’ve been assigned this task, critical information about previous product launches, background about the key members of your team, etc.). The ExperienceBUILDER sim is pre-programmed with explicit details about each character (e.g., personality, biases, strengths and weaknesses, etc.), as well as additional details specific to the culture of the organization.
Let’s get started:
As the sim opens, you are cornered by Jasmine, who asks about the project timeline:
You respond with:
“Hi Jasmine,
It’s great to have you on the team!
I hear that it’s been a taxing few months. But I’m excited about the new product, and I’m looking forward to getting a new strategy and processes in place to make the launch smoother this time around.
We’ll discuss my ideas and the new timelines in the meeting if that’s OK with you?”
Here’s how ExperienceBUILDER Jasmine responds to you:
Jasmine: Hey there! Thanks for the warm welcome! It’s really uplifting to hear that you’re diving into strategies and processes for a smoother launch. It’s exactly what the team needs right now. Your approach sounds like a breath of fresh air! I’m all ears for the ideas you’ve got and curious about those new timelines. Let’s definitely get into all the details in the meeting. We should head in now, don’t wanna keep everyone waiting.
In contrast, here’s how ChatGPT responds natively:
“Thanks for your understanding. I agree, it’s been quite hectic lately, but I appreciate your enthusiasm for the new product. I’m definitely open to hearing your ideas and discussing the timelines in the meeting. Let’s hope we can find a balance that works for everyone and ensures a successful launch. Looking forward to our discussion!”
At first glance, ChatGPT’s response isn’t bad. But when you take a closer look, you’ll see how the response basically rephrases and regurgitates the text you input. In contrast, “ExperienceBUILDER Jasmine” has been pre-programmed with personal traits and context about the organization and the scenario that enables her to respond like a member of your actual team.
Both responses have been generated by an LLM; one of them just has a lot more context, enabling it to form a very real response. And the more you interact with Jasmine the more divergent the two delivery mediums (ExperienceBUILDER vs. native ChatGPT) become.
Here’s another example comparing the responses of ExperienceBUILDER Jasmine and ChatGPT Jasmine:
Our input:
“Team, the new Eventide product launch has been moved from Q3 to Q1. I know many of you are feeling burned out after the last launch, but I am putting together a clear project plan to ensure that we are able to meet the new deadline without undue sacrifice or stress. I’m confident we can work together to pull this off!”
ChatGPT Jasmine’s response:
“Thanks for the update. While it’s definitely a shift in our timeline, I appreciate your proactive approach in putting together a clear project plan. It’s important that we ensure the success of this launch while also taking care of our team’s well-being. Let’s collaborate closely on the project plan and make sure we allocate resources effectively to meet the new deadline. I’m confident that with our collective efforts, we can indeed pull this off successfully!”
ExperienceBUILDER Jasmine’s response:
“Thanks for the heads up. I gotta say, hearing the timeline has been pushed up initially made me a bit wary, especially considering how the last launch went. But hearing there’s a clear project plan in the works? That’s definitely a relief. We’re all about rolling up our sleeves and getting the job done. Just wanna make sure we keep an eye on the team’s energy levels this time around. We’re ready to give it our all, just hoping we can find that sweet spot between hard work and well-being.”
Again, you can see how ExperienceBUILDER Jasmine’s pre-programmed personality and context create richer, more nuanced responses that make it possible to create a more powerful learning experience.
Context is key
Sure, AI can help with the delivery of rapid, real-time feedback and responses, but who decides what the right and relevant content is—for the individual and the organization? ChatGPT lacks the ability to understand the specific context of a conversation or scenario. It generates responses based on statistical patterns learned from vast amounts of data but doesn’t comprehend the nuances of the situation, which can lead to irrelevant or inappropriate responses, especially in complex or specialized domains. While interacting with ChatGPT might provide some insights, it lacks the personalization that is essential for real-world application.
Blueline’s ExperienceBUILDER simulations, on the other hand, immerse learners in scenarios tailored to their organization’s unique milieu, specific learning objectives, organizational needs, unique performance models, standards, and industry regulations. By incorporating custom parameters, ExperienceBUILDER simulations mirror real-life situations, enabling learners to practice within familiar contexts and making the learning experience more relevant and meaningful.
Significant feedback matters
ChatGPT can provide generic feedback, but it lacks context and specificity. Because of its generality, it’s unable to offer nuanced feedback tailored to individual learners’ needs or organizational goals. As a result, learners won’t receive the coaching needed to facilitate meaningful improvement.
In contrast, Blueline’s simulations are programmed to offer real-time feedback grounded in organization-specific rubrics. Targeted feedback enables learners to identify strengths, areas for improvement, and actionable insights to enhance their performance.
A fully immersive experience
While ChatGPT and other LLMs can simulate conversations, creating truly realistic and immersive scenarios requires so much more. Taken out of context, these interactions can feel impressive, but upon closer inspection, they lack the quality needed to keep learners fully motivated and engaged.
Blueline’s simulations use sophisticated story arcs and advanced, coding-style prompt craft with parallel agents and rich media to create a fully immersive experience.
Security and ethics concerns
Using native LLMs often raises concerns about data security, privacy and ethics. Since these tools operate by processing user input and generating responses, many fear that sensitive information could be exposed or mishandled, especially in environments where confidentiality is paramount. Or that, left unchecked, it may inadvertently perpetuate biases or inaccuracies.
Blueline’s use of LLM technology is self-contained and protected. And our simulations are carefully programmed and tested to ensure they are accurate and unbiased.
You may also be interested in: Here’s when not to use AI
The human touch: where creativity meets technology
At the heart of our work lies the fusion of human expertise with advanced AI technology. Our team of skilled professionals combines deep simulation design experience with cutting-edge AI tools to craft immersive learning experiences that drive tangible results. By leveraging authentic intelligence and creativity, we ensure that every simulation is tailored to meet the unique needs of our clients and delivers meaningful learning outcomes.
So, the next time you’re looking to sharpen your skills, remember to choose the carpenter over the novice. AI is a powerful tool, but it’s the human touch that adds context, empathy, and understanding to the learning process.
How can I learn more about ExperienceBUILDER?
We’d love to show you a demo of our next-gen learning technology using the power of generative AI to provide quick, lower-cost learning experiences that accelerate skill development, behavior change, and business transformation.
