Frequently Asked Questions about Immersive Learning with AR, VR, & AI

March 11, 2025April 2, 2025 by Tiffany Vojnovski

Your guide to hardware, use cases, research, AI-powered coaching, and more

As L&D innovators explore opportunities to leverage immersive learning for just-in-time, flow-of-work solutions, they’ve got more questions than ever about the design, development, implementation, and effectiveness of these solutions.

Below are some of the most frequently asked questions we’ve been hearing from L&D leaders in a wide range of industries and organizations. We’ve mixed in audience questions from our live Tech Talk with Training Industry on January 13th and a few FAQs from our innovative client-partners.

1. How might we overcome hygiene and cost concerns about headsets?

Let’s first level-set and clarify that when we’re talking about headsets, we mean a virtual reality (VR) headset that immerses the wearer in a fully digital, 360-degree 3D environment that they experience instead of the real world.

Some ways to help with hygiene concerns include:

Disposable covers, similar to face masks, which create a barrier between the face and the headset.
Hand sanitizer stations, which are often offered at conferences and events.
Sanitization stations for larger deployments, allowing for quick and easy disinfection of headsets.
The use of augmented reality (AR) as an alternative, where participants scan a code on their smartphones or tablets to experience the solution without utilizing a shared device. They then experience digital content as a layer on top of their real environment. (The “view in room” feature on many shopping websites is a great example of AR!)

Because participants use their own devices to enter the experience, an AR solution can also help alleviate cost concerns associated with VR hardware. So can WebXR, a browser-based immersive technology that can be experienced via headset or via browsers on a laptop, tablet, or smartphone. WebXR can be a great option for any organization to alleviate cost or hygiene concerns—or simply to offer their people options for how to consume the experience.

2. How do you create a 3D environment, and what is the experience like for learners?

Here’s an answer you’ll hear a lot: It depends on the client’s needs!

We have a skilled team of 3D technical artists who create realistic 3D environments by collaborating closely with clients to accurately depict machinery, equipment, and workspaces.

For clients who need a hyperrealistic, extremely precise 3D digital twin of their workspace, our team visits the space with specialized equipment to measure each item and the space itself.

For client projects that don’t need a fully custom environment, we can leverage stock 3D assets that can be adjusted, edited, and customized. This option helps us increase efficiency while reducing cost.

The results? A fully interactive 3D environment where learners can:

Manipulate objects: Open valves, pick up items, and interact with the environment.
Experience full-body immersion: Practice skills in an authentic, immediate 3D environment.
Enjoy a safe practice space: For skills that learners can’t afford to get wrong—yet need to practice—a 3D environment provides a safe environment to try, receive feedback, and try again.

WebXR, the browser-based immersive technology we mentioned in Question 1, can be a great way to scale these benefits to a geographically distributed audience. If learners enter via headsets, they can enjoy the fully immersive digital experience; if they enter via their laptop or tablet, they experience a deeply engaging, realistic experience much like a first-person video game.

3. Are there any efficacy studies showing that VR training outcomes meet or exceed those of job shadowing, classroom training, or traditional eLearning?

There are a few! Below are some findings that have caught our attention.

A comprehensive study of VR training outcomes by PwC found that:

Learners trained in VR develop soft skills 4x faster than learners trained in the classroom and feel 275% more confident applying what they learned.
Learners trained in VR are 3.75 times more emotionally connected to the content than classroom learners and 4x more focused than eLearning participants.

The Imperial College of London used VR to train surgeons and discovered that 83% of learners were ready to perform procedures with little guidance after training—compared with an astounding 0% using traditional methods.

A randomized clinical trial focusing on pathology recognition and complex procedural skills found that orthopedic surgeons trained in VR demonstrated “superior learning efficiency, knowledge, and skill transfer” compared with those trained via traditional methods.

The Miami Children’s Health System found that medical staff who learned new procedures in an immersive environment had an 80% retention rate one year later, compared to 20% one week later using traditional training methods.

Why is VR so effective for learning?

Consistency: VR provides a standardized learning experience, unlike shadowing, where individual mentors may have different teaching styles and levels of experience.
Repetition: VR allows for repeated practice, which is often impractical with shadowing due to time constraints and mentor availability.
Safety: VR allows for training on dangerous or rare situations that cannot be replicated through shadowing.
Scalability: VR enables a global workforce to be trained quickly and efficiently without scheduling conflicts or mentor availability issues. (Browser-based immersive technology WebXR, which learners can access via either a headset or their laptop, tablet, or smartphone, extends the benefits of scalability even further.)

True, an immersive learning strategy is an investment, but it’s a sound one: VR training has been shown to be more cost-effective at scale than traditional classroom or eLearning experiences.

At 375 learners, VR training achieves cost parity with classroom learning.
At 1,950 learners, VR training achieves cost parity with eLearning.
At 3,000 learners, VR training becomes 52% more cost-effective than classroom learning.

4. How do bandwidth and internet connectivity impact the usability of an immersive learning experience?

Native VR applications are pre-downloaded and installed on a headset, making them great for offline access. Of course, you need connectivity for the initial download and any updates but, after that, the experience can run without any internet connectivity.

Because it’s stored and accessed from a server, AR content depends on continuous cellular or internet connectivity. The same goes for WebXR content: Because it’s accessed either via headset or web browser, it relies heavily on internet connection and speed.

Striking a balance between scalability and bandwidth can be complex—and we consult closely with our client-partners to understand their audience, their technical constraints, and their performance objectives.

If you’re curious what an immersive learning solution could look like for your organization, we’ll work with you to design a strategy that works for you. We have extensive experience creating native VR, AR, and WebXR experiences, and even dual-delivery solutions that offer the best of both worlds.

5. What tools do you use to build your immersive simulations?

We’re technology-agnostic, which means that we recommend solutions built with the software, game engines, hardware, and platforms that best fit your needs and technology ecosystems. For example, we leverage game engines like Unity to enable interactions with 3D objects and environments in VR and AR experiences; we often use PlayCanvas for WebXR solutions. These tools offer us the flexibility to create highly immersive, game-like simulations rather than linear eLearning-style experiences.

6. Can accessibility features be built into immersive learning experiences to accommodate learners with different visual, auditory, and mobility abilities? What are some best practices for accessible immersive learning?

We often hear questions about designing XR experiences to meet or exceed WCAG standards; however, these standards speak more to webpages and eLearning. By prioritizing inclusive design and integrating assistive technologies, XR can better serve users with diverse abilities, ensuring a more accessible and beneficial experience for neurodiverse and differently abled learners.

To ensure that we’re designing an XR program or experience that welcomes all learners, we consider a range of accessibility needs across three main areas:

Visual Accessibility: XR typically relies heavily on visual elements, but incorporating audio cues, haptic feedback (such as vibrating hand controls), and robust voice control—which allows learners to talk their way through the experience—can make an immersive experience more inclusive.

Mobility and Interaction Accessibility: Customized controllers, gesture recognition, spatial mapping, and more accessible interactions—such as single-click actions instead of double-clicks—can facilitate smoother interaction and movement.

Cognitive and Linguistic Accessibility: Simplified interfaces, customizable settings (for speed, difficulty, and content complexity), gesture recognition, haptic feedback to offer more guidance on what to do next, and guided tutorials can help improve understanding and engagement with immersive content.

7. How would you incorporate hand movements into an immersive learning experience?

Adding hand movements helps to create a more immersive narrative experience. Hand agency spans a wide range of gestures, including pointing, swiping, pinching, and other refined motor movements that require learners to use their hands instead of controllers.

From simpler tools, such as paintbrushes or wrenches, to highly sophisticated surgical or mechanical tools, hand agency is a great element to leverage for highly specialized, finely detailed interactions. We’ve leveraged hand movements to help learners practice installing and repairing sophisticated machinery and performing specialized medical procedures (See Question 3 for more on why this is effective), to name a few.

That said, hand agency isn’t limited to highly technical use cases: It’s also great in soft skills training to help learners shake hands with an avatar, hold documents, and de-escalate emotionally charged situations.

8. What are some of the most common industries and use cases you see for immersive learning experiences?

Once upon a time, technical training was the dominant use case for immersive learning. Imagine a digital twin of a manufacturing environment where learners could get the hang of specialized equipment, machinery, and procedures—all without harming people or property.

This safe, immersive practice environment speeds learners’ time to proficiency, reduces errors, and enhances retention.

But as we’ve already touched on, as workplaces and job roles continue to evolve and increase in complexity, L&D leaders are embracing XR solutions for a wider spectrum of use cases, including nuanced interpersonal skills like leadership, empathy, and de-escalation. In fact, we’re now seeing a 50-50 split between hands-on technical training and soft skills training use cases.

9. I’ve been hearing a lot about incorporating AI into immersive scenarios. Could you share more about how that works and what some use cases might be?

Incorporating generative AI (genAI) into any and all of the immersive learning use cases (see Question 8) helps to increase the emotional immediacy of the learning experience, on-the-job skill transfer, and long-term skill retention…while decreasing learners’ time to competence.

As learners interact with genAI-powered characters, coaches, and scenarios, situations evolve in response to what they say and do. There’s no “script”: Any scenario or interaction can—and does!—unfold in myriad possible ways.

Caveat: Like any team member, genAI works best when it has a clear understanding of its role and purpose.

It’s not a substitute for humans, but it can, with careful oversight, help us extend human expertise and intelligence. (Think of it as a bright, eager, but extremely literal robot assistant that needs constant training and supervision.)

Thanks to its coachability and bottomless energy, genAI can be an effective and engaging addition to our immersive learning solutions. To help learners practice the skills they need, we can train it to serve in any of the following roles:

Just like our human team members, genAI also needs to be trained: Context, role, and goals are key. Equally, it needs a personality, backstory, and communication style that feels authentic, not mechanical. Getting our genAI evaluator, coach, assimilator, curator, or simulator to perform well in its role requires plenty of training—complete with trial, error, feedback, and refinement.

GenAI should never stand alone: It’s part of a much larger digital strategy that comprises data security, ethics, privacy, value, authenticity, and commitment to unwavering human oversight. (Discover the foundations and blueprinting of a sound genAI strategy with our comprehensive playbook.)

(Wondering what a genAI-powered coaching experience might look, sound, and feel like? Check out Hilton’s genAI-powered, immersive guest service training experience for its global family of hotel team members.)

10. I’d love to add immersive experiences to my learning portfolio, but I need to work within my organization’s LMS. What options do I have?

This is a very common constraint, and we’ve got a fix for it! It’s called the WebXR LMS Integration Tool, or LIT. Its superpower is integrating easily with your LMS platform to allow WebXR experiences to launch directly from your LMS—helping you create a one-stop shop for all of your learning solutions.

Thanks to LIT, WebXR experiences can launch directly from existing eLearning courses. At launch, LIT cues learners to choose their device: headset or computer.

After selecting their devices, learners proceed seamlessly into the WebXR experience. While they’re in the immersive experience, LIT “recognizes” them and brings their performance metrics back to your LMS, creating a continuous, seamless experience on the front and back ends.

Which data does it collect? That depends on your needs! We work with you to determine the learner performance data that matter most to you—including complex performance data that your LMS may not be able to process. Using our LIT Analytics Dashboard, we can capture and display such detailed learner metrics as tone-of-voice analytics, gaze tracking, and reaction time.

If you’re wondering whether LIT is compatible with your organization’s LMS, our working answer would be “yes”! We’ve deployed WebXR content on hundreds of LMSs, all with their own complexities, quirks, and security protocols.

Got an LMS challenge, immersive or otherwise? Our in-house Director of Systemic Solutioning, John Cleave, would love to hear more about your current learning and IT ecosystem and how we might help you build, in the words of one client-partner, “with, through, over, around, and under” your existing environment.

11. Questions about immersive learning and AI strategy?

Wondering how immersive learning solutions for a variety of use cases might look, sound, and feel? Check out the webisode featuring no fewer than eight real-life immersive simulations we’ve created with some of our most innovative client-partners. (Yes, AI is involved!)

If you’ve got a question of your own about immersive learning and AI strategies, technologies, or scaling, we’re all ears! We’re incredibly excited about fresh opportunities to bring these effective, engaging, and high-quality experiences to new learner audiences, and we’d love to chat about the possibilities.

AI and the Future of Work: A Leader’s Guide to Roles and Skills

December 19, 2024January 7, 2025 by Emma Klosson

As AI becomes increasingly integrated into the workplace, it is up to talent development, organization design, and L&D leaders to partner closely with the business and each other to identify the specialist roles and skills necessary for success.

As we contemplate AI and the future of work, what might these specialist AI roles and skills look like?

I asked John Cleave to share his insights with us. As SweetRush’s Senior Learning Engineer, John has been working with AI in L&D for decades, beginning with his graduate work in symbolic AI at the Northwestern’s Institute for the Learning Sciences. Here’s his take on the roles and skills leaders should consider as they partner with the business to develop an enterprise-wide strategy.

Bonus content!
As a bonus, John also shares the roles and skills that L&D teams should consider developing or adding to enable, enhance, and accelerate the implementation of the AI strategy.

What are the Specialist AI Roles and Skills Leaders Should Consider?

AI Strategy and Governance Roles

AI initiatives require careful oversight and a strong ethical framework. The following roles are crucial for any organization implementing AI, regardless of specific applications:

Governance and Regulation Specialist: Formulates and revises AI policies and practices, devises management protocols, develops and institutes controls, and advises on potential risks. This role ensures responsible AI usage across the organization.
Security and Policies Specialist: Creates security protocols (e.g., approval process), identifies potential risks, handles breaches and violations, and provides leadership with knowledge of consequences, safeguarding both data and ethical practices.
AI Generalist with HR Focus: Identifies applications of AI in HR (talent acquisition, employee development, etc.), works to integrate AI into HR practice, and stays abreast of emerging trends, ensuring alignment between AI and HR goals.

AI Data Analysis

Data analysis and model training are fundamental to any successful AI implementation. The following roles are essential for extracting meaningful insights and driving data-informed decisions:

Business (Statistical and Data Visualization) Analyst: Applies AI methodologies to evaluate data in order to gain insight into the business, visualizes data, identifies data sources, and generates data via AI. This role makes the connection between data and actionable business intelligence.
Data Analytics Expert (backend collection, reporting): Creates data handling and analysis protocols, constructs data lakes, and applies statistical analysis to operations, ensuring data integrity and efficient data management.
Machine Learning and Big Data Manager: Devises machine learning models, collects/cleanses data, evaluates results (statistically, against business norms, etc.), detects patterns, and spots new opportunities to apply AI, driving innovation through data-driven insights.

AI Construction

Building and implementing AI solutions requires specialized technical expertise. The following roles are crucial for developing and deploying AI tools and systems:

Machine Learning Engineer: Constructs machine learning models, trains and evaluates models, selects algorithms appropriate for solving business problems, and works with data analysts to refine and distill data, enabling the creation of powerful AI applications.
Natural Language Processing (NLP) Specialist: Uses NLP engines (e.g., Siri) to process NLP inputs, connects inputs to actions, and creates inputs and outputs, facilitating human-computer interaction and automating language-based tasks.
Large Language Model (LLM) Expert: Sets up an LLM for a purpose(s), creates retrieval-augmented generations (RAGs), tests outputs, manages costs, and creates application programming interfaces (APIs) and overlays, harnessing the power of LLMs for advanced language-based applications.
AI Tools Implementor: Advises an organization on tools (ChatGPT, Exemplary AI, Dall-E, Claude, Paragraph Generator, Midjourney, Writesonic, Canva, Grammarly, Podcastle, Synthesia) useful for solving business problems, conducts experiments and R&D, evaluates options, addresses challenges, and trains on tools, facilitating the effective adoption of AI tools across the organization.

AI & L&D: Specialist Skills and Roles for AI-Powered Learning Creation

This section focuses specifically on roles that leverage AI to enhance learning experiences and drive L&D innovation.

Instructional Design/Learning Experience Creator: Uses AI to support and enhance learning, incorporates AI into LX, provides guidance on best practices and techniques, and uses AI to generate content for training, creating more engaging and effective learning experiences.
HR/L&D Strategy and Change-Management Consultant: Provides guidance in the use of AI to bring about organizational improvement and transformation, explores use of AI to automate processes and create efficiencies, and guides the organization through the changes associated with AI adoption.
RAG Creator: Applies symbolic AI to guide and focus LLMs and deep learning (e.g., skills definition) and pairs symbolic AI and deep learning to improve AI efficacy, enabling more accurate and contextually relevant learning experiences.
Reinforcement Learning/Advanced AI Developer: Creates, evaluates, and trains AI-infused devices, constructs and shapes environments, shapes and manipulates AI models, and experiments with advanced AI techniques to create adaptive and personalized learning environments.
Expert AI Tool User: Steps in and uses AI tools (ChatGPT, Dall-E, Claude, etc.) in order to bring about organization improvement (e.g., generates content), provides guidance on best tools (evaluates alternatives), and maximizes the value of AI tools for L&D.
Asset Creators: Creates videos, audio, and/or animations using AI-based tools, streamlining the production of multimedia learning assets.

As AI continues to reshape the workplace, talent, OD, and L&D teams must work together to identify the skills and expertise needed to help organizations meet their goals for the future. Understanding the roles outlined here is a crucial first step!

Need Help Building YOUR Workplace of the Future?

As an award-winning custom learning solution provider with more than two decades of experience in digital and immersive learning technologies and over a decade of experience sourcing temporary talent for L&D, SweetRush is uniquely positioned to help you navigate this new landscape. We provide comprehensive support in the following ways:

AI Strategy and Implementation: SweetRush’s AI strategy and consulting services empower your organization to navigate the complexities of AI adoption, offering not only cutting-edge learning experiences and programs, but also holistic roadmapping, foundational assets, and ongoing support to ensure your AI initiatives are human-centered, future-proofed, and drive lasting value.

AI Training: We create custom training programs to upskill your workforce quickly and comprehensively.

AI Talent Sourcing: We can source and place the ideal candidates for the AI roles you need, whether for temporary staffing or permanent positions. We have access to a deep bench of AI experts across diverse fields who can support your needs.

How AI Is Shaping the Future of Recruitment

December 13, 2024December 13, 2024 by Rodrigo Salazar

AI has reshaped how we approach recruitment. But to use this technology wisely, we must pair its capabilities with the uniquely human qualities that make recruitment personal and impactful.

In this article, I share my experience and advice for leveraging AI in a way that helps to maintain and elevate the human experience.

AI: A New Chapter in Talent Acquisition

AI is revolutionizing recruitment. From automating job postings to pre-screening résumés, AI is eliminating time-consuming tasks, freeing up recruiters to focus on building relationships.

However, as powerful as AI is, it should not overshadow the human aspects of hiring. Instead, it should complement them, serving as a tool to streamline processes while amplifying empathy and connection and validating intuition.

Hung Lee captures this balance perfectly in his talent acquisition forecasts for 2025: “Our attention will be reserved for a) known callers and b) conspicuously human. Recruiters who have network, community standing and profile—and are obviously human—will win in a world dominated by AI composed information.”

This insight highlights a critical truth: In a landscape increasingly influenced by AI, human authenticity remains a competitive advantage.

Human-Centered Recruitment in the Age of AI

While AI can analyze vast amounts of data and match skills to job requirements with remarkable precision, it cannot replicate the nuanced judgment and relational expertise of a skilled recruiter. People connect with people. As Lee suggests, the recruiters who thrive in an AI-dominated world will be those who bring visible humanity, trusted networks, and community influence to the table. Recruiters must now do more than just fill roles; they need to cultivate meaningful relationships and establish themselves as trusted advisors within their industries.

Candidates are more likely to respond to someone who is “conspicuously human” than to an automated outreach, no matter how well-tailored it might be.

AI and Recruitment Strategy: Prioritizing Skills and Potential

One of AI’s most transformative impacts on recruitment is the shift away from traditional credentials. The résumé, long the gold standard of hiring, is losing relevance in an era where AI can assess candidates based on skills, experiences, and even potential. By focusing on capabilities rather than titles, AI helps recruiters identify talent that might have otherwise been overlooked.

This evolution encourages a more inclusive hiring approach, opening the door to candidates from diverse backgrounds and non-linear career paths. With the support of AI, we can prioritize potential over pedigree, building teams that are innovative and adaptable.

Recruiting with AI: An Ally for Human Recruiters

The integration of AI into recruitment is not about replacing humans but empowering them. This requires intentionality. Organizations must continuously evaluate AI tools to ensure they promote fairness, eliminate bias, and respect candidates’ individuality. Similarly, recruiters must be trained to interpret AI-generated insights critically and thoughtfully.

To succeed, recruiters must embrace a dual role: leveraging AI to manage the technical aspects of talent acquisition while doubling down on the human elements—relationship building, empathy, and authenticity—that no machine can replicate (at least not yet).

The Future of Staff Augmentation: AI and Human Collaboration

By combining AI’s efficiency with human creativity and connection, we can move beyond the outdated mindset of a “war for talent.” The recruiters who excel in this new era will be those who embody Lee’s vision—professionals with genuine networks, a strong sense of community, and the unmistakable presence of humanity.

AI has the potential to enhance recruitment in profound ways, but its success depends on how we wield it. Together, we can use AI not as a weapon of war, but as a tool for collaboration, creating a hiring process that is more inclusive, efficient, and human-centric. The war for talent is over. Now, let’s build the future.

Rodrigo Salazar-Kawer is the Director of Talent Solutions at SweetRush, where he and his team work with clients to help them find high-performing talent and augment their teams in L&D, a people-centric field that impacts all lines of business within the enterprise. Connect with Rodrigo.

Special Edition: Top Learning & Development Articles for Your Toolkit

November 26, 2024November 27, 2024 by Tiffany Vojnovski

Seven topics to quench your curiosity and ignite your learning strategy

Ever since our hunter-gatherer days, we humans have been collectors.

Though most of us have given up stockpiling grains, some of us collect stamps, others coins, and still others Star Trek, superhero, or historical memorabilia.

We love all of these enthusiasts, but our rainbow heart has always belonged to a special group of superfans: L&D leaders and professionals. These aficionados simply can’t get enough freshly minted thought leadership on learning strategies, emerging technologies, or design philosophies.

They’re the inspiration for this limited-edition set of our Top Learning and Development articles on the seven topics that are keeping you and your fellow L&D devotees up at night (We see you Googling!).

1. Always in Style: L&D Trends

Like time, tide, and technology, L&D is always in flux…and always interesting. Our industry’s most recent trends have landed us at the intersection of AI, skills, learning impact, and, yes, AI, which merits its own headline. (See No. 3.)

Learning is–or should be–ultimately a part of a larger change management initiative. Learning in the flow of work offers opportunities to meet learners at their moments of need while respecting their limited time and bandwidth. When they’re met with training that meets the seven learner-centered design criteria, they just might come back for more.

Want the unabridged version of the latest learning trends? Download our eBook for a look at the top priorities, challenges, and aspirations of L&D leaders and professionals like you.

2. Diagnosis Before Design: The Value of Learning Needs Analysis

What seems to be the problem?

What are the symptoms?

What does recovery look like for you?

If these three questions remind you of a trip to the doctor, you’re right! These are exactly the kinds of needs analysis questions L&D leaders should be asking stakeholders long before they launch into a plan for “treatment.” (Spoiler: Training won’t be the right prescription for every case!)

Dive into this vital diagnostic stage and discover how to put your business and learning needs under a microscope at the strategic and project levels.

3. AI-Powered Solutions and Strategies for L&D

Will AI make L&D departments (and professionals) obsolete? We’re inclined to agree with our Magic 8 Ball: Very doubtful.

We’re optimistic about our fellow learning innovators’ ability to adapt and upskill to this rapidly changing technology–and with 3 out of 4 knowledge workers across generations using genAI at work, many of us have at least been dabbling.

AI can be a great tool to engage learners and offer more value, flexibility, and scalability to our stakeholders and learners. But first, we need to have strong AI governance and ethics in place.

Once that foundation is laid, you’re ready to open the door to three types of AI implementation,..and observe all of these in action with a rapid-fire video tour of no less than seven real-life AI projects from the SweetRush portfolio.

Curious to listen in on your peers’ conversations about AI? Discover their top five AI priorities and how they’re partnering with us for end-to-end mapping of their AI learning and business strategy. (Spoiler: Needs analysis shows up again here!)

But remember, AI is the sidekick, not the superhero: it’s a partner for human intelligence (HI); a digital Alfred to your Batman.

Where to go from here to deepen your AI knowledge? We’d recommend diving into a good book: our comprehensive guide to genAI for L&D innovators.

If you’d prefer to begin with the basics, our four-part AI Glossary for L&D has you covered! Simply choose your topic and watch your AI vocabulary grow:

4. Adaptive Learning + Online Training: A How-to Guide

There’s been some debate about the differences between “adaptive learning” and “personalized learning.”

At SweetRush, we believe these are one and the same. (Cue the meme of Spider-Man pointing at his identical twin.)

Here are just a few of the benefits of adaptive learning:

Greater learning and retention
Less time spent in training
Happier employees

You don’t need superpowers (or a massive budget) to create adaptive learning. Get inspired by six pro tips for applying adaptive learning to your online training, using off-the-shelf technology and your own ingenuity.

Prefer a more comprehensive look into adaptive learning? Our eBook, Hats Off to Adaptive Learning, will outfit you with a deep understanding of adaptive learning techniques and plenty of examples.

5. Polymath or Partner? How SweetRush Creates Custom Learning Solutions for Clients in Every Industry

From finance to pharma, and from transportation to technology, we’ve partnered with clients in a diverse range of industries to create award-winning, impactful custom learning solutions.

It’s not that we’re polymaths with expertise in every industry (though we are proud of our deep bench of SMEs!)…we’re partners who learn your language, represent your brand, and rise to your industry’s scrupulous regulations.

Explore how a custom learning partnership unfolds across industries, topics, and business challenges.

6. Consult, Shift, Align, Repeat: Securing Leadership Buy-in for L&D

Historically, L&D has been viewed by stakeholders as a cost center that is tapped into on an as-needed basis: The organization identifies a need and then goes to L&D for “help.” This cycle creates an order-taker mentality that keeps L&D leaders and teams in reactive mode.

To break this cycle and be seen as a trusted partner that adds value, L&D leaders need to turn the conversation around. That means engaging in ongoing strategic-level needs analysis (See No. 2) but, above all, it entails a mindset shift.

Discover how to earn your team a seat at the table with SweetRush Co-Founder Andrei Hedstrom’s open letter to L&D leaders.

7. Did It Work? How Do You Know? Learning Analytics and Evaluation

Every facet of business is becoming more data-driven, and L&D is no exception. Yet more than one-third of L&D leaders and teams are struggling to put efficient measurement, evaluation, and learning analytics programs into practice.

Where does the problem begin? With needs analysis (No. 2 strikes again!), or rather, the lack thereof. Learn why this well-placed prevention helps us define the problem and the symptoms up front—and sets us up for easy evaluation of the cure.

In the market for a new LMS or LXP with powerful data analytics and reporting features? This comprehensive list of frequently asked questions will help you pinpoint the best LMS for your needs.

If your needs analysis points you toward an immersive solution–for example, super-scalable, browser-based WebXR content–you may be wondering about the care, feeding, and collection of learner metrics in these data-rich experiences. Learn how the metrics of your choice can live and thrive within your LMS or LXP.

Wondering how to get the best data at the assessment level? Discover how a psychometrician ensures that every question is valid, reliable, free from bias, and absolutely airtight.

We hope you enjoyed unpacking this collection of top L&D articles much as we enjoyed curating it!

We hope you’ll share the wealth with other superfans…and reach out and talk shop with us about anything that’s captured your curiosity.

AI Glossary for L&D, Part 4: Architecture and Algorithms

November 14, 2024November 18, 2024 by Emma Klosson

Welcome to the final installment of our AI Glossary for L&D! We round out our exploration of all things artificial intelligence with a peek behind the AI curtain at the algorithms and architecture that enable AI technologies and applications to perform, including:

Graphs
Cluster analysis
Backpropagation
Symbolic AI

Catch up on the first three installments here:

Peeking Behind the Curtain: The algorithms and architecture that enable AI to perform

What are graphs?

A graph is a visual way to represent relationships between different things. It consists of “nodes” (like dots) connected by “edges” (like lines). In AI, graphs are used to organize and analyze complex data, such as social networks, knowledge maps, or relationships between concepts in a learning curriculum.

Think of a map of a city. The locations are the nodes, and the roads connecting them are the edges. Graphs in AI are similar—they show how different pieces of information are linked.

How do graphs relate to other AI concepts?

Graphs are fundamental data structures used in many AI applications. They can be used to represent knowledge in a way that AI systems can understand and reason with. For example, knowledge graphs are used in natural language processing (NLP) and some generative AI (genAI) models to provide context and improve understanding.

How might graphs be applied in L&D applications?

Skill mapping: Visualizing the relationships between different skills in a learning pathway.
Knowledge representation: Creating a network of interconnected concepts to help learners understand complex topics.
Personalized learning paths: Recommending learning resources based on a learner’s current knowledge and goals.

What is cluster analysis?

Cluster analysis is a technique used to group similar items together. It’s like sorting a box of toys, putting all the cars in one pile, all the blocks in another, and so on. In AI, cluster analysis helps find hidden patterns and structures in data.

Imagine you’re organizing a library. You might group books by genre, author, or topic. Cluster analysis does something similar with data, finding natural groupings based on shared characteristics.

How does cluster analysis relate to other AI concepts?

Cluster analysis is an unsupervised learning technique that can be used to analyze data without predefined labels. It can be applied to various data types, including text, images, and numerical data.

How might cluster analysis be applied in L&D applications?

Learner segmentation: Grouping learners with similar learning styles, preferences, or needs.
Content analysis: Identifying clusters of related topics within a large collection of learning materials.
Personalized recommendations: Suggesting learning resources based on the clusters a learner belongs to.

What is backpropagation?

Backpropagation is a key algorithm used to train neural networks. It’s like a feedback mechanism that helps the network learn from its mistakes. It works by calculating the error in the network’s output and then adjusting the connections between the neurons to reduce that error.

Imagine you’re learning to throw a ball at a target. If you miss, you adjust your aim based on where the ball landed. Backpropagation is similar—it helps the AI adjust its “aim” to improve its accuracy.

How does backpropagation relate to other AI concepts?

Backpropagation is essential for training deep learning models. It allows the network to learn complex patterns and relationships in data by iteratively adjusting its internal parameters.

How might backpropagation be used in L&D applications?

Improving the accuracy of AI models: Backpropagation is used to train AI models for tasks like automated essay grading or personalized feedback.
Optimizing learning algorithms: It can be used to fine-tune the performance of adaptive learning systems.

What is symbolic AI?

Symbolic AI is a type of AI that uses symbols and rules to represent knowledge and solve problems. It’s like using a set of instructions or a logical formula to arrive at a conclusion. It focuses on manipulating symbols to perform logical reasoning and decision-making.

Think of a computer program that follows a set of if-then rules. Symbolic AI works in a similar way, using predefined rules and logic to process information.

How does symbolic AI relate to other AI concepts?

Symbolic AI is a different approach to AI compared to machine learning and deep learning. It relies on explicit knowledge representation and logical reasoning rather than learning from data.

How might symbolic AI be used in L&D applications?

Intelligent tutoring systems: Creating systems that can provide step-by-step guidance and feedback based on predefined rules.
Knowledge-based expert systems: Developing systems that can answer questions and provide explanations based on a knowledge base.
Automated curriculum design: Using symbolic AI to generate learning pathways based on predefined learning objectives and rules.

The Journey Continues: Embracing the Evolving Landscape of AI in L&D

As we conclude our travels through the AI Glossary for L&D, remember, the journey doesn’t end here. New terms, techniques, and technologies are constantly emerging.

And while It can feel like a lot to keep up with, don’t worry—you don’t need to become an AI expert to leverage its power in L&D. The key is to build a foundational understanding of the core concepts and stay informed about the potential applications in learning. By grasping the basics and recognizing the possibilities, you can make informed decisions about how to integrate AI into your L&D strategy.

Remember, the AI landscape is dynamic. What seems cutting-edge today might be commonplace tomorrow! Embrace the learning process, stay curious, and remain open to the transformative potential of AI and you will do just fine!

AI Glossary for L&D, Part 3: Advanced Techniques and Applications

November 14, 2024November 14, 2024 by Emma Klosson

In Part 3 of our AI Glossary for L&D, we venture into the realm of the more specialized AI techniques and applications that are pushing the boundaries of what’s possible, including:

Deep learning
Supervised, unsupervised, and reinforcement learning
Transfer learning
Fuzzy logic
Explainable AI

Advanced Techniques and Applications Pushing the Boundaries of AI

What is deep learning?

Deep learning is a subset of machine learning in which artificial neural networks with multiple layers (hence “deep”) analyze data and solve complex problems. It’s like giving your AI a more intricate and powerful brain, so it can learn from vast amounts of information and discover patterns that would be impossible for humans to identify.

Imagine a child learning to identify different animals. Initially, they might just focus on basic features like size and color. But as they learn more, they start to recognize subtle differences in shapes, patterns, and behaviors. Deep learning works in a similar way—it allows AI to go beyond surface-level features and understand the nuances within data.

How does deep learning relate to other AI concepts?

Deep learning is the engine behind many advanced AI applications, including large language models (LLMs) and some types of generative AI (genAI).

What are supervised, unsupervised, and reinforcement learning?

Supervised, unsupervised, and reinforcement learning describe three fundamental types of machine learning.

Recall from Part 1 of the AI Glossary for L&D where we explained that, in machine learning, “Systems are fed vast amounts of data and use it to recognize patterns, make predictions, and take actions using the data, all without human intervention.” Supervised, unsupervised, and reinforcement learning refer to the methods in which the data is fed and how the system is trained to recognize the data.

In supervised learning, AI learns from labeled data and is given examples with known inputs and outputs.
In unsupervised learning, AI explores unlabeled data to discover hidden patterns and relationships.
In reinforcement learning, AI learns through trial and error, receiving rewards for correct actions and penalties for incorrect ones.

Here’s a simple analogy to illustrate this concept.

Imagine teaching a child to recognize fruits:

Applying a supervised learning approach, you show them pictures of apples, bananas, and oranges, labeling each one.

Applying an unsupervised learning approach, you give them a basket of different fruits and let them group them based on similarities.

Applying a reinforcement learning approach, you give them a fruit and ask them to name it, rewarding them when they’re right.

How do supervised, unsupervised, and reinforcement learning relate to other AI concepts?

These learning paradigms are core concepts in machine learning. They are used to train various AI systems, including deep learning models.

What is transfer learning?

Transfer learning is a technique where an AI model trained for one task is used as a starting point for a new, related task. This allows the AI to leverage its existing knowledge and learn the new task more efficiently. It’s like a chef who knows how to bake cakes using their skills to learn how to make pastries.

How does transfer learning relate to other AI concepts?

Transfer learning is a technique often used in deep learning to accelerate the training process and improve performance on new tasks.

What is fuzzy logic?

Fuzzy logic is a way of dealing with uncertainty and imprecise information. It allows AI to make decisions based on degrees of truth rather than strict true or false values. It’s like saying “somewhat likely” or “mostly true” instead of just “yes” or “no.”

Think of a thermostat. Instead of just turning on or off at a specific temperature, it can gradually adjust the heating or cooling based on a range of temperatures.

How does fuzzy logic relate to other AI concepts?

Fuzzy logic is a different approach to AI that can be used in conjunction with other techniques, such as machine learning, to handle situations where data is imprecise or ambiguous.

How and where might fuzzy logic appear in L&D applications?

Fuzzy logic can be leveraged to:

Design adaptive assessments: Tailor assessments to individual learner needs and performance levels, providing more nuanced feedback.
Model complex decision-making scenarios: Simulate real-world situations where there are no clear-cut answers, helping learners develop critical thinking and problem-solving skills.
Create personalized learning pathways: Adapt learning pathways based on learners’ preferences and interests, offering a more customized experience

What is explainable AI (XAI)?

Explainable AI aims to make AI’s decision-making processes more transparent and understandable to humans. It’s like having the AI show its work so we can understand how it arrived at a conclusion.

Imagine a doctor diagnosing a patient. They don’t just say “you’re sick”—they explain the symptoms, test results, and reasoning behind their diagnosis.

How does XAI relate to other AI concepts?

XAI is crucial for building trust and accountability in AI systems, especially in sensitive areas like education, healthcare, finance, and legal domains. It can be applied to various AI techniques, including deep learning and machine learning.

How can L&D leverage or apply XAI?

In L&D, XAI can help us understand why an AI model made a particular recommendation or assessment, fostering trust and transparency in AI-driven learning solutions.

Continue expanding your AI vocabulary

There is one more chapter in our AI Glossary for L&D to explore. Click on the link to Part 4 (below) to learn about the algorithms and architecture that form the basis of many of today’s AI platforms. Need a refresher? Visit Parts 1 or 2 to brush up on the basics.

Part 1: Foundational Concepts explores the basics of artificial intelligence including machine learning and neural networks.
Part 2: Key Technologies explores the technologies that put AI into action including natural language processing (NLP), large language models (LLMs), generative AI (GenAI), and retrieval-augmented generation (RAG).
Part 4: Algorithms and Architecture offers a peek behind the AI curtain at the algorithms and architecture that enable AI technologies and applications to perform.

Our Consultative Approach to Building Your GenAI Learning Strategy

October 29, 2024October 30, 2024 by Tiffany Vojnovski

In our GenAI Playbook and recent articles, we talked about the three doors of genAI and how to approach this rapidly evolving technology with a builder’s mindset and scrupulous attention to security, human oversight, and ethics. Here’s how that looks when the SweetRush team shows up to help you craft your GenAI learning strategy.

We’re learning partners first.

We love to build—but there’s more to us than bricks and mortar.

We start with curiosity about your business and learning needs and explore those with you to find the right solution. The best fit may turn out to be an exciting genAI learning strategy…or it might be something else entirely.

If, for example, a podcast, video, or an eLearning course would work better, we’ll say so! Some of our best friends are custom audio and learning creators—and we’d love to introduce you.

Our team has a wide and wonderful range of capabilities, and we’re united by our passion for creating engaging, effective ways to help learners meet the rapidly unfolding future (and present!) of work.

We’d love to build with you and promise you’ll be in good hands. Check out our industry awards and reviews from current client-partners).

We can consult with you on your genAI learning strategy, L&D strategy, and broader business strategy.

We’re committed to needs analysis, and it shows.

We want to understand your specific needs, use cases, learners, and work environments so that we can recommend the optimal learning experience.

Whether you choose to use learner-facing genAI, want to leverage it for your own workflow, or both, we’re ready to help! Together, we’ll explore your AI infrastructure and help integrate your learning solutions with your organization’s existing tools, workflows, and policies.We call this beginning-to-end consulting process strategy mapping, and it’s just one way we help enhance your workflow and learning experiences with genAI.

We’re technology-agnostic.

When a vendor-partner is a hammer purveyor, they’re bound to see your learning needs as a nail. You might get lucky, and they might truly be a nail!

But your partner’s immediate response to your needs shouldn’t be, “I have a great hammer for that!” It should be, “Let’s identify the right tools for the job—together.”

Working with a more varied genAI toolbox helps us recommend the most effective solutions for your organization’s needs and existing infrastructure. We’ll work—and evolve—with the genAI infrastructure, tools, and policies you have in place at your organization.

If that sounds complicated, don’t worry—we love a challenge! In fact, we’re known, in the words of one client-partner, for building leading-edge learning experiences that work “with, through, over, around, and under” existing technology ecosystems and infrastructures.

We’re dedicated to research, development, and human oversight.

Some things are best out of the box—like pizza, video games, and Thin Mints.

You’ve probably noticed that genAI learning tools haven’t made the list. That’s not by accident: As learning researchers and scientists first, we believe that no genAI tool is ready to be used right out of the box.

As much as we love putting these bright, eager, but extremely literal robot assistants to work, we don’t send any genAI learning strategies or solutions out into the world without a chaperone.

Regular Turing Tests² one of the ways we ensure the quality, consistency, and accuracy of genAI coaching and training solutions.

In these studies, we:

Compare responses to prompts from human subject matter experts (SMEs) and genAI models trained to mimic them.
Involve human judges to assess the origin of each response (human or genAI)³
Identify areas where genAI can augment human expertise and opportunities for further development.

This rigorous evaluation process ensures that our AI solutions:

Enhance human expertise rather than replace it.
Maintain a human-centered and ethical approach.
Develop culturally attuned and responsible AI training that reflects our values—and those of our client-partners.

We share our expertise (literally!).

Rome wasn’t built in a day, and neither was a genAI learning strategy.

To add genAI to your learning portfolio, you need experts who can help you answer mission-critical questions to help you decide:

Whether to use genAI.
What genAI approach to pursue, and how to pursue it.
Policies and processes to protect data and intellectual property.
A plan for continuous human oversight to ensure that AI-generated content is accurate, high-quality, and free of bias.

Given the race to attract new talent with genAI skills⁴ you may not have access to these experts at your organization…yet.

That’s where our talent pool of AI consultants comes in. The same SMEs who make your projects shine are also available on a contract, temporary, or long-term basis. See below for our areas of expertise.

Got an AI need all your own? Let’s talk.

How do you know whether you need a SME to augment your team or a full project team to support you?

SweetRush Director of Talent Solutions Rodrigo Salazar describes the difference in builder’s terms:

Whether you need an individual expert or the whole crew, we’ve got you covered.

Custom AI Workshops

If you’re curious about what AI could look like for your organization, we can help you explore! Our dynamic, interactive workshops are designed to empower you to develop a holistic, ethical, and human-centered AI practice and genAI learning strategy. (Learn more about the workshop experience in this in-depth article.) We cover critical topics like prompt engineering, data security, and bias mitigation, and we’ll help you transform your vision into an actionable roadmap.

Curious to learn more? Share your needs with us, and we’ll craft a workshop to help you find your own AI journey.

The L&D Call to Action

The people have spoken: Working professionals want training on how to use genAI⁶ but only 25% of organizations are answering this need⁷.

Yet the number of jobs requiring genAI skills is growing exponentially⁴, and most leaders won’t consider candidates without AI skills⁷.

The risk is clear: By leaving the need for genAI training unmet, our organizations are setting themselves up for a severe skill deficit.

Friends, this is our Rubicon: It’s the critical moment to invite our leaders and stakeholders to bridge this ever-widening skills gap to ensure the survival and growth of our organizations.

We’ve talked about approaching genAI with curiosity and a builder’s mindset characterized by agility, adaptability, resilience⁸—and, yes, optimism. A recent study by BetterUp + Stanford describes this approach as a pilot mindset⁹.

Whatever you choose to call it, this mindset is an absolute must-have for every organization.

As we invite our learners to upskill in genAI ethics, usage, and tools, we model a healthy partnership between humans and genAI…and allay our people’s fears of being replaced⁷.

If used responsibly, a genAI learning strategy can help us scale high-quality, high-touch learning experiences—and bring about more positive change than ever before.

When paired with immersive learning technology, genAI can help us distill life’s most teachable moments and develop competence and insight that normally take months or years to acquire.¹⁰

We hope you’re feeling inspired by this transformative moment and the opportunity for L&D innovators to lead a responsible, practical, and optimistic approach to AI technologies and tools. Whether you think of it as breaking ground or taking off, we’d love to talk strategy and next steps!

¹ Cleave, J. (2024, February 12). Unlocking The Potential Of AI Coaching In Learning And Development. eLearning Industry.
² Oppy, G., & Dowe, D. (2021, October 4). The Turing Test. In E. N. Zalta & U. Nodelman (Eds.), The Stanford Encyclopedia of Philosophy (Winter 2021 Edition). Metaphysics Research Lab, Stanford University.
³ Cleave, J., Dale, E., & Hedstrom, A. (2024). AI-generated versus human-generated training content: A SweetRush Turing Test exploratory study [Unpublished manuscript].
⁴ Nawrat, A. (2024, March 11). Randstad CHRO: Demand for gen AI skills grew by 2,000% in 2023.
⁵UnleashSalazar, R. (2015, April 9). Hire a Temporary Learning Consultant or Outsource to a Team? How to Make the Right Choice. SweetRush.
⁶ LinkedIn Learning. (2024). 2024 Workplace Learning Report.
⁷ Microsoft and LinkedIn. (2024, May 8). AI at Work is Here. Now Comes the Hard Part: How to make it work for you. 2024 Work Trend Index Annual Report.
⁸Vojnovski, T. (2022, August 8). The L&D Trifecta: Why Agility, Adaptability, And Resilience Top The List Of In-Demand Skills. eLearning Industry.
⁹ Hancock, J., et al. (2024, June 20).The Pilot Mindset: Leading Your Team to Thrive with AI [Handout]. The Pilot Mindset Virtual Event, BetterUp.
¹⁰ Vojnovski, T. (2024, February 5). Experience Required: How Virtual Reality Supports Learning and Skilling in a VUCA World. SweetRush.

AI Glossary for L&D, Part 2: Key Technologies

October 11, 2024February 26, 2025 by Emma Klosson

Welcome to Part 2 of the AI Glossary for L&D! In Part 1, we laid the foundations by defining what artificial intelligence is and exploring some of the fundamental concepts, such as machine learning and neural networks. In this second installment, we build on those foundations by examining the following key technologies that put AI into action:

Natural language processing (NLP)
Large language models (LLMs)
Generative AI (GenAI)
Retrieval-augmented generation (RAG)

If you’re enjoying this exploration of AI, stay tuned for Part 3, where we’ll explore advanced AI technologies and applications, and Part 4, where we’ll take a peek behind the AI curtain at the architecture and algorithms that make everything work

The Key Technologies Putting AI into Action

What is natural language processing (NLP)?

Imagine teaching a computer to understand human language, not just as symbols or even individual words, but as meaningful sentences and stories filled with emotions and intentions. This is essentially what NLP is. It’s like giving computers the ability to read between the lines, grasp the nuances of language, and even respond in a way that makes sense to us humans.

How and where is NLP used?

NLP is the technology that enables you to have a natural conversation with your smartphone’s voice assistant. You can ask it questions, give it commands, or even just chat about your day, and it will understand what you mean, while taking into account slang, accents, or incomplete sentences.

If you’ve ever used an email spam filter, had your text automatically corrected by your phone, engaged with a chatbot, or used a language translation app, you’ve experienced NLP in action.

How can L&D leverage or apply NLP?

NLP technology could be applied to analyze learner feedback from surveys and comments to identify areas where training programs can be improved.

What are large language models (LLMs)?

Now imagine a vast library containing every book, article, and website ever written. Large language models (LLMs) are like superpowered librarians who have read and memorized everything in the library. They can understand the patterns and relationships between words, ideas, and even emotions expressed in the text. This enables them to not only answer questions accurately but also write different types of creative text formats and translate languages.

How and where are LLMs used?

With a remarkable ability to understand and generate human-like language, LLMs are incredibly versatile. LLMs are the driving force behind chatbots, language translation tools, and content-generation platforms.

If you’ve ever used a chatbot on a website to ask a question and received a helpful response, you’ve likely interacted with an LLM.

How can L&D leverage or apply LLMs?

LLMs can be used to analyze and summarize content, create personalized learning content, and develop realistic simulations for performance-based training.

What is generative AI (genAI)?

Generative AI takes things a step further. Instead of simply understanding and processing language, it can create something entirely new. Think of it like a musician who can play any instrument and compose entirely original symphonies based on their deep understanding of music theory and different musical styles, or a chef who’s sampled every ingredient and explored every cooking technique and can create new and exciting dishes. GenAI can create original text, images, audio, and more.

How and where is genAI used?

GenAI is behind tools like ChatGPT for text generation, DALL-E for image creation, and numerous music and code generation platforms.

If you’ve ever used an app to generate art from a text prompt, or seen AI-generated images in your social media feed, you’ve witnessed the power of genAI.

How can L&D leverage or apply genAI?

GenAI can be used to design engaging eLearning scenarios and simulations, creating immersive learning experiences for employees. (Download a free copy of our GenAI Playbook for more L&D use cases, tips, and inspiration!)

What is retrieval-augmented generation (RAG)?

Finally, imagine combining the vast knowledge of LLMs with the ability to instantly access and retrieve specific information. That’s the power of RAG. It’s a bit like having a detective who uses their knowledge and experience (LLM) to investigate a case, but also relies on databases and records (information retrieval) to gather evidence and verify facts.

How and where is RAG used?

RAG is used to improve the performance of LLM-powered applications, especially in domains where specific knowledge is crucial. If you’ve ever used a search engine like Google to research a topic and received a concise summary with links to relevant sources, you’ve benefited from RAG technology.

How can L&D leverage or apply RAG?

RAG technology can be used to develop AI-powered learning assistants that can answer employee questions about company policies, procedures, or training materials by pulling information from internal data sources.

Continue expanding your AI vocabulary

Click on the links to view the rest of the AI Glossary for L&D series.

Part 1: Foundational Concepts explores the basics of artificial intelligence including machine learning and neural networks.
Part 3: Advanced Techniques and Applications examines the specialized AI techniques and applications that are pushing the boundaries of what’s possible.
Part 4: Algorithms and Architecture offers a peek behind the AI curtain at the algorithms and architecture that enable AI technologies and applications to perform.

AI Glossary for L&D, Part 1: Foundational Concepts

September 27, 2024February 26, 2025 by Emma Klosson

As L&D professionals, we’re no strangers to jargon. Remember when “LIFOW” was just a bunch of letters? Now, “learning in the flow of work” is a cornerstone of our learning experience design (LXD) strategies. But AI has thrown us a curveball. Suddenly, we need to be fluent in a whole new language, complete with its own set of jargon and acronyms.

So, if you’re unsure what “NLP” and “LLMs” are—and how they differ—or are curious about “Deep Learning” and “Backpropagation,” help is at hand! Our AI Glossary for L&D is your go-to guide for simple, straightforward explanations of the concepts, tools, technologies, and methodologies that fall under the umbrella of “AI.”

To make things even easier, we’ve broken the glossary into four parts. In this first part, we introduce the foundational concepts of AI. Over the coming weeks, we’ll continue adding items to the glossary as we expand on the concepts and dive deeper into the “AI-verse.”

The Building Blocks of AI

Let’s begin by laying a solid foundation with the core concepts that underpin the field of AI. In this foundational section, we’ll look at the building blocks of AI, specifically:

AI
Machine Learning
Neural Networks

For each glossary term, you can find a high-level description as well as examples of its common uses and how and where it can be leveraged by L&D teams.

What is AI?

AI is the umbrella term for a wide range of technologies and approaches that are designed to mimic human reasoning and problem-solving abilities.

How and where is AI used?

AI is all around us, powering applications like social media, facial recognition, recommendation systems, and chatbots.

How can L&D leverage and apply AI?

Here are two ways L&D can leverage AI:

To enhance the learner experience via AI-powered learning solutions that provide opportunities for personalized and adaptive learning.
To enhance learning design practices and processes. Learning experience designers can leverage AI throughout their learning solution design workflow. From needs analysis to design, development to implementation and evaluation, there are dozens of tools and technologies to support this work.

Where applicable, we’ll share specific examples of these applications.

What is machine learning?

Machine learning is a subset of AI where systems learn from data and improve their performance on a task without being explicitly programmed. Systems are fed vast amounts of data and use it to recognize patterns, make predictions, and take actions using the data, all without human intervention. There are three main categories of machine learning: supervised learning, unsupervised learning, and reinforcement learning. We’ll explain these advanced concepts in a later addition to the AI Glossary for L&D.

How and where is machine learning used?

Machine learning is the engine behind dozens of everyday applications such as fraud detection, image recognition, and recommendation systems. If you’ve ever used streaming platforms like Netflix or Spotify, you’ve experienced machine learning in action. These platforms analyze your viewing and listening habits to identify patterns and make recommendations tailored to your preferences.

How can L&D leverage and apply machine learning?

Machine learning can be used to analyze learners’ behavior and create personalized learning pathways. Machine learning might also be used to analyze and predict learner performance, recommend relevant content, and even automate the analysis of learner data.

What are neural networks?

Neural networks are computational models inspired by the human brain. Neural networks used in machine learning recognize patterns in data. Think of them as interconnected networks of artificial neurons that process information and learn from it, much like our own brains do.

How and where are neural networks used?

Neural networks are the backbone of technologies like image recognition, natural language processing, and speech recognition.

How can L&D leverage and apply neural networks?

Neural networks can be configured to power personalized learning experiences, generate adaptive assessments, and provide intelligent feedback.

Ready to dive deeper? Continue exploring the AI Glossary for L&D

Now that you have some of the basic concepts of AI down, why not take a deeper dive into the technologies and applications that power AI.

Click on the links to view the rest of the AI Glossary for L&D series.

Part 2: Key Technologies explores the technologies that put AI into action including natural language processing (NLP), large language models (LLMs), generative AI (GenAI), and retrieval-augmented generation (RAG)
Part 3: Advanced Techniques and Applications examines the specialized AI techniques and applications that are pushing the boundaries of what’s possible.
Part 4: Algorithms and Architecture offers a peek behind the AI curtain at the algorithms and architecture that enable AI technologies and applications to perform.

Training Industry Honors SweetRush for 3 Consecutive Years: A Leader in Experiential Learning

September 11, 2024October 30, 2024 by Carolina Montero

San Francisco, Calif., September 11, 2024 — Good things happen in threes for SweetRush’s Emerging Tech team, who are currently celebrating their third consecutive year on Training Industry’s list of Top Experiential Learning Technologies Companies.

Experiential learning technologies include modalities such as virtual reality (VR), extended reality (XR), simulations, and serious games, which can be leveraged to meet such diverse learning needs as medical, compliance, manufacturing, and soft skills training.

Training Industry uses the following criteria to evaluate the Top 20 Experiential Learning Technologies Companies:

Breadth, quality, and advancement of features, capabilities, and analytics
Industry visibility, innovation, and impact in the learning technologies training market
Client and user representation
Business performance and growth

SweetRush Solution Architect Danielle Silver characterizes the honor as “a testament to the brilliant and forward-thinking client-partners who inspire us to push the boundaries of what’s possible. Crafting immersive and AI-powered learning experiences is not just our job, it’s our passion.”

Indeed, 2024 has been a year of innovation for SweetRush and its world-class client-partners. Leveraging the power duo of generative AI (genAI) and web-based extended reality (WebXR) technologies, the SweetRush XR team has helped L&D innovators in a wide range of industries bring high-impact learning, coaching, and skilling experiences to a wider audience than ever before.

Director Adrián Soto and team have also been engaged in developing custom enterprise AI training programs to help forward-thinking organizations future-prep their people. These programs model responsible, ethical AI usage via AI coaching, gamified branching simulations, and other experiential learning modalities.

In addition to earning the interest of learners, these solutions have also garnered attention from an industry audience. The SweetRush Emerging Tech team is enthusiastic about the AI opportunity and sharing their innovations in AI-powered and immersive learning via webinars, presentations, and publications. (Visit the SweetRush YouTube Channel for recordings of these live events.)

Most recently, the team authored a comprehensive eBook, titled The GenAI Playbook: An L&D Innovator’s Guide Leading-Edge Learning Transformation. “The Playbook doesn’t just talk about possibilities, it gives you a roadmap. The use cases and project lookbook show you exactly how genAI can transform L&D, from creating better learning experiences to getting your teams ready for the AI-driven workplace”, describes Annie Hodson, Chief Client Solutions and Marketing Officer.

SweetRush’s consultative approach to custom experiential learning design, which one client describes as “second to none,” addresses organization-wide business and learning needs through beginning-to-end strategy mapping. Leading organizations such as Hilton, Coursera, Bridgestone, and the American Academy of Family Physicians (AAFP) are pioneering leading-edge experiential learning solutions that engage diverse, globally distributed learner audiences in skill acquisition and practice.

These partnerships result in creative, compelling solutions that make learning an experience to remember—and have been recognized by 157 Gold Brandon Hall Awards and 10 Gold CLO Learning in Practice Awards to date.

If you’d like to create rich, resonant, and award-winning experiential learning of your own, please reach out to be connected with an expert learning consultant.

About SweetRush

SweetRush is trusted by many of the world’s most successful companies to help them improve the performance of their employees and extended enterprise. SweetRush is known for exceptionally creative and effective solutions that combine the best of learning experience design with highly engaging delivery.

SweetRush services include custom L&D solution design and development, high-performing staff-augmentation talent, certification development, and innovative learning technologies such as VR, AR, and AI. SweetRush’s work has earned a long list of awards and accolades in collaboration with its world-class clients. Discover more at www.sweetrush.com.