There’s a shift happening quietly in the background of every meeting, every dashboard, every “quick sync” that’s supposed to move work forward. You can feel it. Learning and Development (L&D) is no longer the department people come to when they need training—it’s the discipline organizations turn to when they need to reinvent.

As we step into 2026, L&D leaders face a paradox: We’re being asked to move faster than ever before while thinking more deeply about what it means to be human in the workplace. AI is changing how work gets done, but also who does the work, how people learn, and why they stay. The best L&D leaders won’t be those who simply keep up with technology; they’ll be the ones who help their organizations make sense of it.

Below are five priorities that will define the next era of Learning and Development—an era where adaptability, discernment, and empathy will matter as much as data and design.

1. From learning delivery to capability ecosystems

The term “training” is fading away. Employees want competence, not just a course. This competence must be built in real time, matching the speed of business.

The next frontier is not the learning management system (LMS), but the ecosystem—a dynamic network of learning experiences, data insights, AI-driven nudges, and communities of practice. The role of the L&D leader is no longer to create everything but to connect the pieces.

Ask yourself:

• Does your learning setup feel like a library or a vibrant network?
• Are employees wasting time searching for content, or are they finding what they need?
• Most importantly, do you know which skills your organization truly needs to compete in 2026, not just the ones you’ve always trained for?

Building capabilities must bridge strategy and execution. If your learning ecosystem doesn’t match your future operating model, you’re training people for the past.

2. AI is not the answer; it’s the amplifier

The buzz around AI can be overwhelming, but the reality is that AI isn’t taking over L&D; it’s coming through it. The leaders who succeed in 2026 won’t be the ones who gather the most tools; they’ll be the ones who ask tough questions.

AI can tailor learning paths, create adaptive simulations, and summarize insights quickly. But it can also increase bias, oversimplify complexities, and damage trust if misused. The real difference won’t be technical skill; it will be ethical awareness.

Consider:

• How are we using AI to support human judgment, not replace it?
• What hidden tradeoffs exist in our automation choices?
• Who is being excluded from the AI conversation because they don’t understand the technology yet?

AI can provide speed and scale, but meaning still comes from humans. The future L&D leader will be part technologist and part anthropologist, bridging machine intelligence and human experience.

3. Change management becomes culture management

In 2026, every organization is a change organization. Transformation isn’t an initiative, it’s a condition.

We’ve spent years teaching leaders how to “manage change,” but the term itself feels outdated. Change doesn’t need to be managed; people need to be guided through it.

This is where L&D becomes the cultural backbone of the enterprise. The best programs in 2026 will move beyond “coping with change” to cultivating change agility as a muscle, a practiced capability that helps teams adapt, learn, and reorient quickly.

The question shifts from “How do we train for change?” to “How do we normalize evolution?”

L&D should lead with practices that reinforce learning as identity, not event. Coaching, storytelling, reflection, and peer learning must sit alongside formal programs. When people see themselves as learners, not just employees, change stops feeling like something done to them and starts feeling like something done through them.

4. Rebuilding trust & belonging in hybrid cultures

The quiet crisis in today’s workplace is disconnection. Employees may work from anywhere, but they often feel they are learning from nowhere.

In hybrid settings, culture isn’t confined to buildings; it comes from behaviors. L&D is in a prime position to observe how people experience belonging, or lack thereof.

In 2026, L&D leaders must view engagement as an ecosystem built on trust. Learning experiences that foster psychological safety, shared language, and mutual understanding are essential for retention.

When employees feel ignored, they disengage. When they feel unappreciated, they leave. The future of learning will be measured more by connection rates than completion rates.

Ask:

• Are we designing learning that helps people feel recognized?
• Are we creating environments where questions are welcomed, not shamed?
• Are we valuing vulnerability as much as performance?

Learning can’t just be about transactions. It must be a conversation that reconnects people to purpose in this digital age.

5. Redefining talent: From roles to potential

Job descriptions are becoming outdated. Rapid changes mean roles shift faster than organizational charts. The smartest companies are now mapping skills rather than titles and focusing on potential instead of background.

In 2026, L&D leaders must become curators of potential. This means identifying hidden skills, promoting them through meaningful work, and removing obstacles that hinder personal growth.

Upskilling and reskilling will continue to be crucial, but the key differentiator will be building resilience. Technical skills help you land a job. Human skills; like empathy, curiosity, and adaptability—keep you relevant when roles change.

Learning leaders need to think long-term: designing programs that develop not just tasks but future-ready identities.

Examine:

• Do your programs promote adaptability, or just expertise?
• Do your managers know how to identify and nurture potential?
• Are your learning investments building skills or developing people?

The competition for talent isn’t just about hiring anymore. It’s about unlocking the potential already present in your organization.

The meta-skill for 2026: Sense-making

The most crucial skill for any L&D leader in 2026 won’t be instructional design, data analysis, or even AI literacy, it will be sense-making.

We have plenty of information, but we lack meaning. Leaders need to help interpret noise, find clarity, and create coherence in contradiction. That’s the real role of L&D today, to aid people in navigating complexity so they can act with confidence.

This means:

• Turning insights into compelling stories people can rally around.
• Translating organizational strategy into actions people can take.
• Positioning technology as a resource to enhance our humanity, not as a threat.

In a world driven by optimization, learning leaders have the courage to ask a different question: What is worth preserving?

A final thought: The human dividend

As AI takes over routine tasks and the pace of change quickens, the human dividend, our ability to connect, empathize, and adapt, becomes the unique factor that no machine can replicate.

The irony of 2026 is that the more digital we become, the more human we need to be. L&D exists at this crossroads. As you plan for the year ahead, keep this in mind: Technology will continue to progress. Roles will keep changing. But people; their fears, hopes, and desire to grow, will stay the same.

The leaders who thrive in 2026 won’t be the ones who master every new tool. They’ll be the ones who remind their organizations of what it means to learn, grow, and be human.

Image credit: Parradee Kietsirikul

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During this session, you will learn:

• How to deliver hands-on labs across AI, data, and software stacks without infrastructure setup.
• How to drop learners directly into a coding or data project so they can start learning by doing immediately.
• How Codio’s Sandboxes replicate production-like setups for real-world relevance.
• How to manage access, costs, and scale while giving learners a seamless browser-based experience.
• How to package and reuse labs across cohorts to save time and improve consistency.
• How instructors can integrate both AI and data tools alongside traditional developer stacks.

The post Scalable, Hands-On Labs for Any Tech Stack appeared first on Learning Guild.

Through interactive examples and real-world patterns, we’ll explore how to:

• Reframe fear into forward motion by building shared understanding of AI’s strengths and limits
• Equip teams with lasting capabilities through role-based upskilling, hands-on AI playgrounds, and gamified learning
• Redesign daily work with embedded copilots, practical templates, and lightweight SOP updates
• Use the 70/20/10 rule to reinvest in talent and scale what works

Attendees will leave with clear steps to start small, scale fast, and build a roadmap that aligns leaders and teams around measurable AI value.

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So why do so many leaders hesitate to move on? It’s rarely because they think the system is great. It’s because change feels messy. The sunk costs, the thought of migrating data, the worry that the next platform will be just as disappointing. It’s easier to limp along than to risk a big switch. Until it isn’t.

This white paper dissects all these issues. It looks at common complaints and issues and offers remedies to help you. And if you decide to move it in gives a practical step-by-step guide in involving your whole team, getting management approval, and how to run an RFP. Real practical advice written by industry leaders who have been through it and come out the other side. And the grass can be a lot greener!

The post LMS Fail? How to Recognize a Failing LMS and Remedy It appeared first on Learning Guild.

What You’ll Learn:

• Learning intelligence 101: Why data fluency is the new leadership currency in L&D to evolve from activity tracking to outcome prediction.
• The hidden costs of disconnected systems: How platform sprawl, manual exports, and mismatched taxonomies quietly erode insight, speed, and credibility.
• Metrics that matter (right now): The critical few you should track to link learning to performance, productivity, time-to-competency, and business outcomes.
• Architecture and consolidation strategies: Practical ways to connect LMS/LXP, HRIS, CRM, and performance data—without boiling the ocean.

The post Data Without the Drama: How to Consolidate and Visualize Learning Metrics That Matter appeared first on Learning Guild.

For years, we’ve heard, “Data is the new oil.” However, for EdTech companies, that analogy falls short. Oil gains value when refined—so does data. Its worth doesn’t come from mere collection but from thoughtful design, integration, and application. That’s the essence of Data as a Product (DaaP).

Data is no longer a passive byproduct in the evolving EdTech landscape—from self-paced apps to corporate learning platforms—it’s a core, designable asset. Treating data as a product means managing it with the same intentionality as curriculum or code: structured design, clear ownership, and continuous iteration.

The EdTech market is booming—projected to reach $158 billion in 2024 and $430 billion by 2030—driven by AI, VR, and hybrid learning. But so is competition. Traditional institutions have gone digital; simply digitizing textbooks or layering quizzes onto videos isn’t enough. Today’s stakeholders—schools, parents, employers—demand proof. Does it work? Can you show the results? What’s the ROI?

Answering those questions often hinges on how well a company can productize its data.

What ‘Data as a Product’ really means

DaaP means treating data like software—deliberately designed, rigorously maintained, and continuously improved. It’s not just about gathering data but about making it:

• Clean: structured and standardized
• Contextualized: tied to user behavior and outcomes
• Accessible: easy to use across teams and systems
• Actionable: supporting real decisions, not just reports

Most EdTech companies collect plenty of data—from test scores to clickstreams—but few turn it into intelligence that drives outcomes. The leaders don’t just store data; they build systems that learn from learners.

Turning data into a learning advantage

Treating data as a product unlocks continuous improvement and deeper personalization across the educational experience. Here’s how:

Real-time course optimization

Behavioral data—where learners disengage, which videos are rewatched, where assessments fail—can surface friction points instantly. Teams can then adjust courses dynamically, not months later.

Personalization at scale

True personalization means more than greeting users by name. DaaP supports adaptive pacing, targeted content, and just-in-time nudges. In corporate settings, learning paths can automatically adapt by role, region, or performance pattern.

Smarter strategic decisions

One constant question for CTOs, instructional designers, or L&D leads is: What’s working? With DaaP, dashboards replace static reports, offering real-time insights across cohorts, geographies, and content types.

Data as a differentiator in sales and accountability

While courses are often the core product, data is increasingly part of the value proposition—especially in B2B and institutional contexts.

• For enterprise training, clients want proof of learning. DaaP powers insight delivery, not just content delivery.
• In K–12 and higher education, real-time reporting builds trust with administrators and families. It’s not surveillance—it’s clarity.

Integration is key for large customers. If your learning data can’t be integrated into their HR systems, ERPs, or internal dashboards, you’re not just lagging—you’re invisible.

AI that works starts with data that’s ready

Everyone’s talking about becoming “AI-first,” but most EdTech efforts start at the wrong end: building flashy features instead of solid infrastructure. Chatbots and generative interfaces are exciting, but their impact is shallow without high-quality, structured data. Instead, treat foundational models as infrastructure. Then, build intelligent, context-aware layers on top—informed by real user data.

Enter model-context protocols (MCPs): frameworks that define how an AI agent behaves in educational settings. They pull in learner history, lesson goals, performance patterns, and misconceptions. The result? An AI tutor that doesn’t just answer—it teaches. Smart AI starts with smart data.

Rethinking business models with data as a product

Subscription models have limits—especially in crowded markets like language learning. DaaP opens doors to new models:

• Freemium content in exchange for anonymized data used to generate benchmarks or research partnerships.
• Data-backed outcomes are a premium service, particularly valuable in workforce training and compliance.

This approach isn’t for everyone—it demands strong governance and ethical boundaries—but it view data as a strategic asset, not just operational exhaust.

Laying the groundwork for DaaP maturity

Real DaaP capability doesn’t come from exporting CSVs. It comes from connecting your entire tech stack—LMS, CMS, CRM, and assessments—and intentionally designing data flows. And like any product, data needs an owner. A data product manager should oversee the following:

• Data quality
• Privacy compliance
• Governance
• API accessibility
• Cross-team usability

Without ownership, data remains fragmented. With it, it becomes foundational.

Motivation is the metric

Ultimately, motivation is the single greatest predictor of learning success. But motivation can’t be left to chance. Data and AI should be used to reduce friction, personalize experiences, and nudge learners forward. EdTech should be more than content delivery—a dynamic system that learns from every learner interaction. We need a new mindset to make that possible: data isn’t just something we collect. We design, refine, and deliver it—just like any product.

Image credit: IR_Stone

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The title might be what grabbed your attention—something about whether tech is making us… well, less sharp. It’s a provocative question, certainly designed to spark thought. And it touches on a genuine anxiety many feel as technology, particularly AI, becomes more integrated into our lives and our learning environments.

Today, however, we’re not here just to debate the potential downsides. We’re here to flip the script. We’re going to explore how, with intention and smart design, these very tools—calculators, computers, the internet, and especially AI—can become powerful allies, not adversaries, in making our students and employees more capable, more creative, and more critically minded than ever before.

Think back: People worried that calculators would kill math skills, the internet would kill research skills. What actually happened? The tools changed how we apply those skills, freeing us up for higher-level thinking if we adapted strategically. We are at a similar inflection point with AI. Let’s explore a couple of solutions that might alleviate someone’s fear of technology, particularly AI.  

Tech as an amplifier, not a replacement

Let’s explore how you can use technology strategically to amplify learning design. The key word here is “strategically.” It’s not about using tech for tech’s sake, but to achieve learning outcomes that are difficult or impossible with traditional methods alone.

Think about the evolution: from chalkboards to projectors, to computers in labs, to laptops, smartphones, and immersive realities. Each step offered new possibilities.

Instead of asking, “How can I put this lecture online?” ask, “How can technology enable learners to experience this concept? How can it provide personalized practice? How can it connect learners?”

The core principle is putting pedagogy first. What learning outcome do you want? Then, identify the technology that can best help you achieve it in a powerful, engaging, or efficient way. This moves us beyond basic information delivery to creating rich, interactive learning experiences.

Think about how you can use data from learning platforms. Analytics can show you where learners are struggling collectively or individually, allowing you to intervene effectively. This is data-driven instructional design in action.

Leveraging AI to cultivate essential future-ready skills

What are five of the critical job skills that will always be needed but are currently lacking in most learning programs?

• Creativity
• Problem-solving
• Teamwork
• Communication
• Resilience

Now let’s focus on AI, which brings unprecedented capabilities. The fear sometimes is that AI will do the skills for learners, making them passive. Our goal is the opposite: Use AI to create situations where learners actively develop these skills.

Let’s break down how AI can help cultivate those crucial future-ready skills:

Creative problem-solving:

• AI can act as a relentless brainstorming partner, offering diverse perspectives or challenging assumptions you might not have considered.
• Use AI to quickly generate complex scenarios or data sets for learners to analyze and find novel solutions.
• AI simulations can allow learners to test out creative solutions in a safe environment and see potential outcomes.

Example: Provide an AI with a complex business challenge or a scientific problem and ask learners to query the AI for different angles, potential roadblocks, or unusual approaches, then synthesize this into a proposed solution.

Teamwork & collaboration:

• AI can facilitate large-scale collaboration platforms, helping organize ideas or identify potential conflicts early (though use with caution regarding privacy and interpretation).
• Use AI to create simulated team members with different personalities or expertise for learners to practice collaborating with diverse perspectives.
• AI tools can analyze communication patterns in group text discussions (anonymously, with consent!) and provide feedback to the group on balance of participation or clarity, helping them improve their collaborative process.

Example: Learners work in groups on a project, using an AI tool that helps organize shared research notes and provides prompts to ensure all team members are contributing equally or addressing different facets of the problem.

Communication:

• AI chatbots or virtual agents can provide infinite practice partners for practicing presentations, sales pitches, difficult conversations (e.g., performance reviews in corporate, or debates in academic).
• Learners can write emails, reports, or essays and use AI tools for feedback on clarity, tone, structure, or conciseness before submitting or sending.
• AI can simulate conversations with diverse audiences, helping learners adapt their communication style.

Example: A corporate learner practices delivering feedback to a difficult “virtual employee” AI, receiving feedback on their language and approach. An academic student practices explaining a complex scientific concept to an AI programmed to act like a layperson.

Resilience:

• AI can power simulations where learners face setbacks, failures, or unexpected challenges in a low-stakes environment. The AI provides consistent feedback, prompting reflection on why something didn’t work and encouraging iteration.
• AI can help learners identify patterns in their own work habits or emotional responses based on data they choose to share (e.g., calendar data + self-reported stress levels), providing personalized insights and suggesting coping strategies or resources.
• AI tools can offer access to mindfulness exercises or motivational prompts based on user preference. (Important: Frame AI as a tool for practice and access to resources, not a therapist).

Example: In a project simulation, a learner’s proposed solution fails. The AI guides them through analyzing the failure points (“Tell me why you think that didn’t work.” “What variable did you not account for?”) and encourages them to try a modified approach.

The key is that AI provides personalized, scalable, and often immediate opportunities for practice and feedback in complex skill areas that were previously hard to train effectively. It allows learners to do, fail safely, reflect, and try again.

AI as your Socratic partner

AI can facilitate deep critical thinking, specifically through Socratic techniques. The Socratic method, at its heart, is about asking probing questions to stimulate thought, expose assumptions, and guide someone towards their own understanding.

Traditionally, this requires a skilled human facilitator and is difficult to scale. AI changes this.

How can we implement AI-powered Socratic approaches?

• AI as a Persistent Questioner: Design AI prompts or chatbots that are programmed to always respond with a question that pushes the learner’s thinking. “Okay, you said X. What evidence supports X?” “What are the potential counterarguments to that?” “How would that apply in a different context?”
• AI for Exploring Multiple Perspectives: Present a case study or problem to an AI and ask it to articulate the viewpoints of different stakeholders. Learners then have to analyze and critique these AI-generated perspectives.
• AI for Challenging Assumptions: Design AI interactions that identify implicit assumptions in a learner’s statement and directly challenge them, forcing the learner to articulate their underlying beliefs.
• AI for Justifying Reasoning: Ask learners to explain their step-by-step reasoning to an AI, which is programmed to ask “Why?” at each step, demanding explicit justification.
• Simulating Debates: AI can be programmed to argue a specific side of an issue, requiring learners to engage in a simulated debate, constructing arguments and refuting points.
• Focus on the Meta Level: After an AI-Socratic interaction, the human facilitator brings the learners together to discuss how the AI questioning worked, what they found challenging, and how they could apply similar questioning to their own thinking in the future. The AI facilitates the practice; the human facilitates the learning about critical thinking.

This isn’t about the AI having critical thinking; it’s about the AI being a tool that prompts and requires critical thinking from the learner. The AI provides the stimulus—the challenging question, the alternative perspective, the demand for justification. The learner provides the cognitive effort.

This approach helps learners move beyond surface-level understanding to analyzing, evaluating, synthesizing, and applying knowledge—the hallmarks of deep critical thinking.

From fear to empowerment

So, looping back to our opening question: Does technology make people dumb? Our exploration today suggests that the answer isn’t a simple yes or no based on the technology itself. It depends on how we choose to use it.

If we use it passively, merely consuming information or letting tools do our thinking for us, there’s a risk of cognitive atrophy. But if we use it actively, strategically, and pedagogically—as an amplifier for experience, a partner in skill-building, and a tireless Socratic questioner—then technology, including AI, becomes a powerful engine for enhanced learning, future-ready skills, and deep critical thinking.

Your role, as learning professionals, is more critical than ever. You are the architects who design these interactions, the guides who facilitate the learning journey alongside the technology. Embrace these tools, experiment ethically, and focus on designing for the human skills that technology can help us elevate.

Using technology provides an unprecedented opportunity to make learning smarter, deeper, and more human-centric than ever before. Embrace the challenge!

Image credit: AndreyPopov

The post Is Technology Making Us Dumber? Leveraging Tech & AI for Supercharged Learning appeared first on Learning Guild.

Learning platforms have evolved at a significant pace by integrating adaptive learning, AI-driven recommendations, and immersive experiences to engage learners. However, there is one critical issue that goes unnoticed often: quality assurance (QA) and reliability testing in learning technology.

When L&D (learning and development) systems exhibit broken interactions, slow content or failures in accessibility, user engagement plummets. Research has shown that adoption of digital learning drops by up to 60% when platforms exhibit usability issues. Despite this study, a lot of organizations still tend to focus more on the content creation aspect rather than ensuring that the learning experience is of high quality to the end users.

Organizations can leverage test automation, which is widely used in software engineering, to accelerate development of high-quality L&D systems for the customer base. Using this, companies can improve usability, performance, reliability and user engagement. Organizations can create learning experiences that are seamless by embedding QA automation into development life cycles which creates products of best quality.

Why traditional QA fails in learning platforms

Many learning platforms today still rely on manual testing techniques. This comes with several limitations such as limited coverage. It may be impractical to test every possible user flow across different devices. In addition to that, manual testing comes with high maintenance costs. Frequent updates to the application require retesting the content repeatedly, leading to feedback loops that are delayed due to the amount of time it takes to test. Bugs surface only after deployment. This causes user frustration and disengagement leading to decreased user satisfaction.

A modern approach to quality assurance is needed if organizations want to deliver reliable, high quality learning experiences. This can be possible using test automation, AI-driven testing, and continuous integration/DevOps practices.

The role of test automation in learning technology

Test automation enables organizations to identify issues and fix them before users encounter problems. It also ensures that the application is working across devices and browsers. Several aspects of test automation play a vital role in L&D applications success.

Functional testing: Ensuring seamless learning interactions

Functional testing verifies that all the features in the learning platform work as expected. These can be navigation and progress tracking aspects—for which automated scripts can be leveraged—to test if learners can resume courses where they left off without any issues. It can validate interactive assessments, quizzes, and ensure the accuracy of real time scoring accuracy. Functional testing also validates additional workflows such as enrollment and certification, ensuring users receive completion certificates without any failures.

How test automation helps: Thousands of automated test cases can be run in minutes. This ensures that the new platform updates do not break existing workflows.

Performance testing: Can your learning platform handle real world usage?

Thousands of learners can log in simultaneously to attend live training sessions. When a system is not able to handle this performance, bottlenecks occur, and cripple user engagement and completion rates. Scalability testing—in which thousands of concurrent users are simulated into the system—ensures the platform can handle peak traffic. To validate that videos and interactive content is loading without buffering, latency tests can be performed. In addition, cloud based performance monitoring can be implemented to test across different geo-locations and network profiles.

How test automation helps: Learning organizations can simulate large scale traffic by using tools such as JMeter or Gatling. These would help in identifying performance bottlenecks before learners experience delays.

Accessibility testing: Making learning inclusive

Organizations need to comply with 508 and WCAG (Web Content Accessibility Guidelines) to support inclusive learning. Screen reader compatibility for learning content using technologies such as NVDA or JAWS can be automated. Tests can also be created to validate color contrast and keyboard navigation without manual intervention. In addition, features such as closed captions and transcripts can be automated.

How test automation helps: Accessibility tests can be automated. These can be triggered automatically and report compliance issues, reducing manual effort.

API & data integrity testing: Ensuring learning analytics are accurate

Learning analytics are leveraged by organizations to measure customer engagement, course progress, and effectiveness. However, if data is inconsistent, it can mislead decision makers. Tests can be created to automatically ensure that learner progress, quiz scores, and completion rates are recorded correctly in both learning management systems (LMS) and learning record stores (LRS). Using automated tests, data displayed in analytics dashboards can be validated against backend records. In addition, the important aspect of role-based testing can be automated, which is critical to ensuring learner privacy.

How test automation helps: Using automated API testing, organizations can ensure that learning data is able to flow seamlessly across integrated systems without any discrepancies.

How learning organizations can implement test automation

Organizations do not have to perform a complete overhaul in order to integrate test automation into L&D. Organizations can start by selecting the right automation frameworks such as Selenium (for UI testing), JMeter (for performance testing), and Axe (accessibility). Automating critical workflows first would be a great step. In addition, running these tests seamlessly against CICD pipelines will help identify defects early. Organizations can also leverage AI powered automation such as self-healing test automation to reduce script maintenance.

By taking a gradual and strategic approach, organizations can improve their software quality while optimizing test automation efforts in parallel.

The future of QA in learning technology

Learning technology is evolving at a rapid pace using AI-powered adaptive learning and immersive VR training. This requires an equally innovative approach to quality assurance and testing.

Emerging trends in test automation that can help with improving the quality of learning systems include AI driven test generation—where tools can automatically generate test cases—reducing manual effort. Self-healing automation scripts reduce maintenance time and effort. When the tests are seamlessly integrated into DevOps pipelines, learning platforms can be released to end users in full confidence. When organizations prioritize automation in their day-to-day stack, it will create a seamless high quality digital learning experience.

Conclusion: Prioritize quality for impactful learning

The digital learning revolution is not just about the published content. The system needs to be of high quality, accessible and engaging for users. By adopting test automation and the different types of tests suggested in this article, organizations can:

• Reduce technical issues that disrupt end user learning experience
• Ensure learning environments are seamless, inclusive, and scalable
• Improve end user engagement and retention

Remember that the future of L&D does not depend on just what people learn, but how well they experience it. Is your organization ready to accelerate its success by integrating test automation into its strategy?

 Image credit: NicoElNino

The post Beyond Click-Next: How Test Automation Can Revolutionize Learning Technology & Improve User Engagement appeared first on Learning Guild.

Embrace technology at your organization

Attendees were wowed by the scope of technology that exists today and how much of it they were unaware of before the event. One attendee even said, “There’s a lot that I don’t know and a lot that I can know.”  

Another attendee encouraged organizations to embrace this new technology and incorporate it into training without fear. “That’s where we are right now in this industry,” the attendee stated as evidence that more companies need to be using the latest innovative technology in their learning.

How attendees are embracing technology

While there are plenty of examples of how DevLearn attendees are using technology, here are a few that we heard from the attendees we interviewed:

• One attendee we spoke to was in the early stages of developing a Learning Management System (LMS) and was looking to vet out a system and choose the correct one for corporate strategy. Aligning learning technology with business goals, as this attendee did, is always key to gaining stakeholder buy-in for purchasing a new technology.
• Another was looking to make Storyline courses more interactive. This attendee was on the right track, as making courses more interactive helps with learner engagement and retention.
• The final attendee we talked to was exploring how to integrate artificial intelligence (AI) into current workflows to optimize current tasks. There were a lot more attendees using AI as well, and this article has the details on how they’re leveraging it.

We hope that you’ll take our attendees’ advice to heart and brainstorm how your organization can embrace technology this year. See the full interview:

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