Emily Justin-Szopinski

Digital Learning Specialist and Educational Product Developer with over 15 years experience in creating high impact learning experiences for global audiences.

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Takeaways from CredSpark’s AI Strategy & New Features Webinar

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On September 10, 2025, our Customer Success Manager for L&D clients, Emily Justin-Szopinski, and our Head of Client Relations & Partnerships for Professional Communities, Gabi Murphy, hosted a joint webinar that discussed CredSpark’s philosophy and policies around artificial intelligence, as well as our current and upcoming features. 5 Key Takeaways on CredSpark’s Use of AI CredSpark makes sure that our clients are always in control of how their organization uses AI within our application. This approach ensures that teams using CredSpark are compliant with their organization’s specific policies on the use of AI. At their best, AI tools are used at the intersection of judiciously and efficiently. Quality and intentionality are more important than volume. Human-driven quality control is essential to ensure the best outcomes for businesses. No form of artificial intelligence understands the needs of your company or your clients better than the humans in your organization. CredSpark is rolling out new AI features designed to provide tailored, data-driven insights that assist with large-scale surveys, question phrasing, and feedback to strengthen your assessments and other interactions in real-time. CredSpark’s deployment of AI features adhered to a strict set of policies: Transparency: Continued communication from our Product Team via Client Success. Compliance: Clients have the right to turn on or off AI tools in order to align with your organization’s policies. CredSpark is SOC 2 Type 2 certified and compliant with our partners’ data policies. Model Management: AI Generated Questions uses OpenAI, which contractually does not use any data/content for training. CredSpark’s Approach to AI is Based on Trust. AI is a topic that seems to be everywhere all at once: in the news, in boardroom conversations, and in casual chats about productivity tools. But it’s worth slowing down and asking what AI really means for our work as learning professionals, publishers, and community builders. When we hosted our recent CredSpark AI Strategy & New Features webinar, our goal wasn’t just to provide a showcase; we wanted to share how we’re philosophically approaching AI at CredSpark: with transparency and with our clients’ needs front and center. Trust is the backbone of successful partnerships, and when it comes to AI, earning and keeping that trust is an essential part of our client relationships. Why Our Approach to AI Matters Like many of you, most of us here at CredSpark have had mixed experiences with AI on an individual level. Some tools save enormous amounts of time, effort, and money, and can greatly expand your capabilities. Others, frankly, can be frustrating. “I once spent four hours stuck in a loop with an AI agent associated with a WiFi provider. It was awful. But on the flip side, I’ve also seen AI used to deliver incredible customer support. The difference is thoughtful design and human  oversight.” — Gabi Murphy, Client Relations & Partnerships (Click here to watch the full recorded webinar.) Our perspective is simple: AI is powerful, but only if it’s implemented responsibly. At CredSpark we don’t want to chase every shiny, new feature. We want to design tools that make your work easier, safer, and more effective. We want to keep you firmly in control of how AI is used in your organization. CredSpark’s Guiding Principles on AI Everything we build with AI is shaped by three commitments: value, human oversight, and respect. AI features must create real value. For CredSpark, that means reducing the time and effort it takes to build high-quality interactions and freeing you up to focus on impactful outcomes and the more strategic and creative parts of your role. AI never replaces human judgment. You remain in the driver’s seat. Every feature we roll out is optional, editable, and transparent. We encourage you to review and refine whatever AI generates, because your critical thinking and professional expertise will always be irreplaceable. We “respect and protect” your data. Every organization has unique compliance requirements and policies around AI. We take those seriously. Our tools are built to respect existing agreements and safeguard client trust. Without that trust, no tool is worth using. “Anyone who’s worked in client success knows how hard trust is to earn, and how quickly it can be lost. That’s why our entire AI strategy is built around continuing to earn and keep that trust.” — Gabi Murphy, Client Relations & Partnerships at CredSpark Watch the recorded webinar here to learn more about CredSpark’s philosophy surrounding artificial intelligence. CredSpark & AI: Where We Are Today What does this look like in practice? Let’s start with the tools already available. One of the most common challenges we hear about from L&D professionals is knowledge checks. They’re essential for gauging whether learners understood material; but they’re also repetitive, time-consuming, and frankly not the highlight of anyone’s job. To help, we’ve built an AI-powered quiz generator into CredSpark. It is freely available to Enterprise, Premier, Ultimate, and CredSpark+ license tiers (this availability is subject to adjustment). You can generate a draft set of quiz questions in minutes by pasting in an article, transcript, or other learning content. From there, you can review, edit, and refine them to make sure they align with your goals. The demo can be found here, starting at the 19:14 mark. “We encourage you to keep human eyes on everything AI creates. Use your critical thinking to make sure the questions reflect what you want to measure. The AI does the heavy lifting, but you stay in control.” — Emily Justin-Szopinski, Customer Success Manager at CredSpark (Learn more by watching the webinar here.) The same functionality now extends to surveys. Whether you’re creating a quick pulse check or a more complex survey, you can use AI to draft a starting point. From there, you can adapt it to your specific audience and objectives. Flexibility is an important piece, too. Every organization has its own comfort level with AI, so we’ve built controls directly into your admin settings. You can choose to turn AI features on or off entirely, and you can decide which

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What risks come with using AI in skill assessments—and how can L&D teams stay ahead of them?

AI

What we’ve learned so far about AI-powered functionalities for learning and assessment: Improves outcomes, increases engagement and knowledge retention, decreases training time, and more (learn more here) Increases ROI on learning initiatives, produces better business outcomes, and boosts employee engagement and retention (learn more here) These positive outcomes are a result of several components working together to form enhanced learning and assessment experiences: algorithms, machine learning, Gen AI, Large Language Models, Natural Language Processing, Data Learning Analytics (Learn more here) So, AI is good for learning assessment. It’s good for organizational performance. This is made possible because of its ability to analyze a lot of data, really fast, and use its analysis to produce unique, tailored learning paths, based on the needs of each employee. Great. AI seems like it presents a clear value add in the skills assessment arena. But like anything that sounds this good, it’s not without its challenges. Before jumping in, it’s worth pausing to look at the risks that come with using AI in learning assessment—and what we can do to manage them. What should L&D leaders be watching out for, and how can they put the right guardrails in place? AI Assessment Risks at a Glance The information below breaks down our research findings on key risks associated with AI-powered assessment—along with real-world examples of what those risks can look like in practice for L&D teams. AI Component Associated Risk (brief) Real-World Scenario Algorithms Bias & Discrimination – Models trained on skewed data can systematically disadvantage certain groups. An AI tool used to evaluate leadership potential consistently scores women lower than men due to biased historical data—resulting in fewer women being shortlisted for leadership development programs. Machine Learning Inaccurate or Unreliable Assessments – Systems can “hallucinate” or misinterpret inputs without human checks. In a manufacturing firm, a machine learning model misclassifies several proficient workers as “not qualified” during a safety certification assessment. As a result, those workers are pulled from emergency response teams and miss crucial hands-on training—leaving actual gaps in preparedness when an incident occurs. Learning Data Analytics   Data Privacy & Security – Collecting sensitive user data without airtight controls risks breaches and regulatory fines. A financial organization’s analytics platform leaks assessment scores in a misconfigured cloud repository—triggering a GDPR investigation. GenAI Overreliance & Reduced Oversight – Blind faith in AI decisions can miss context only humans catch. An L&D team uses a GenAI tool to automatically generate assessment questions and grade responses for a technical upskilling program. Without sufficient human review, the AI produces several poorly framed questions with ambiguous wording and inaccurate answer keys. Learners become frustrated when correct responses are marked wrong, undermining their confidence and trust in the assessment process. The issue goes unnoticed for weeks, impacting completion rates and learner satisfaction. NLPs + LLMs Compliance & Regulatory Risk – Failing to keep pace with evolving fairness/transparency laws can incur big penalties. A financial services firm uses an NLP tool to score written responses in its FINRA-mandated compliance training. The tool flags certain responses as non-compliant but lacks a transparent audit trail for how those decisions were made. During a FINRA audit, the firm is unable to demonstrate consistent, explainable evaluation criteria—raising red flags about fairness and regulatory adherence. AI Component Algorithms Associated Risk (brief) Bias & Discrimination – Models trained on skewed data can systematically disadvantage certain groups. Real-World Scenario An AI tool used to evaluate leadership potential consistently scores women lower than men due to biased historical data—resulting in fewer women being shortlisted for leadership development programs. Machine Learning Associated Risk (brief) Inaccurate or Unreliable Assessments – Systems can “hallucinate” or misinterpret inputs without human checks. Real-World Scenario In a manufacturing firm, a machine learning model misclassifies several proficient workers as “not qualified” during a safety certification assessment. As a result, those workers are pulled from emergency response teams and miss crucial hands-on training—leaving actual gaps in preparedness when an incident occurs. Learning Data Analytics Associated Risk (brief) Data Privacy & Security – Collecting sensitive user data without airtight controls risks breaches and regulatory fines.  Real-World Scenario A financial organization’s analytics platform leaks assessment scores in a misconfigured cloud repository—triggering a GDPR investigation. GenAI Associated Risk (brief) Overreliance & Reduced Oversight – Blind faith in AI decisions can miss context only humans catch. Real-World Scenario An L&D team uses a GenAI tool to automatically generate assessment questions and grade responses for a technical upskilling program. Without sufficient human review, the AI produces several poorly framed questions with ambiguous wording and inaccurate answer keys. Learners become frustrated when correct responses are marked wrong, undermining their confidence and trust in the assessment process. The issue goes unnoticed for weeks, impacting completion rates and learner satisfaction.  NLPs + LLMs Associated Risk (brief) Compliance & Regulatory Risk – Failing to keep pace with evolving fairness/transparency laws can incur big penalties.  Real-World Scenario A financial services firm uses an NLP tool to score written responses in its FINRA-mandated compliance training. The tool flags certain responses as non-compliant but lacks a transparent audit trail for how those decisions were made. During a FINRA audit, the firm is unable to demonstrate consistent, explainable evaluation criteria—raising red flags about fairness and regulatory adherence. Associated Risk (brief) Bias & Discrimination – Models trained on skewed data can systematically disadvantage certain groups. Real-World Scenario An AI tool used to evaluate leadership potential consistently scores women lower than men due to biased historical data—resulting in fewer women being shortlisted for leadership development programs. Associated Risk (brief) Inaccurate or Unreliable Assessments – Systems can “hallucinate” or misinterpret inputs without human checks. Real-World Scenario In a manufacturing firm, a machine learning model misclassifies several proficient workers as “not qualified” during a safety certification assessment. As a result, those workers are pulled from emergency response teams and miss crucial hands-on training—leaving actual gaps in preparedness when an incident occurs. Associated Risk (brief) Data Privacy & Security – Collecting sensitive user data without airtight controls risks breaches and regulatory fines. 

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Components of AI that make improved outcomes possible

AI

Part II What are the key components of AI that enable it to produce improved learning and organizational outcomes in skill assessment? In our last installment, we dug into the impact that AI-powered functionalities have on learning and assessments within organizations. Our research showed that AI’s ability to personalize assessment experiences and interpret data from multiple sources at scale improve efficiency and outcomes of learning strategies and business goals. Learn more here. Having established that the overall impact of AI-driven capabilities on skill development and measurement is net positive, we moved on to our next question: What are the key components of AI that enable it to produce these improved outcomes for skills assessment? The Scaffolding that Supports AI for Learning Assessment There are many functionalities that make AI impactful for assessing skills. Here are some of the most common that we found in our research: Component What it is What it enables Algorithms A step-by-step logic process programmed by humans that serves as instructions for solving problems or completing tasks. Think: if X, then Y Adaptive assessments that adjust according to learner performance and preferences Personalized suggestions for learning content Personalized feedback Machine Learning Algorithms A type of algorithm that can train and refine itself to learn patterns or relationships from learning, performance, and market data. Identify knowledge gaps Predict future performance and suggest preventive learning measures Analyze learner interaction patterns Automate adjustments in question difficulty Automate adjustments in question difficulty level according to learner performance and preferences Generative AI Any AI system that can create new content. Creation of customized learning content like industry- or role-specific case studies or scenarios Personalized assessment questions Automatically generated summary materials and study guides Automatically created practice exercises that target specific skill gaps Creation of context- or industry-specific images Large Language Models (LLMs) and Natural Language Processing (NLP) Essentially LLMs and NLP are the motors behind GenAI. They are able to understand, analyze, and recognize patterns in text, which then allows GenAI to produce relevant output. Automated grading for open response questions Automated, personalized feedback within assessments Simulated conversations, for scenario-based assessments Summarize large amounts of text and create suggestions, like open-ended survey questions Data Learning Analytics (DLA) DLA focuses specifically on measuring, collecting, analyzing and reporting on learning data, with the explicit goal of improving training, learning outcomes, and organizational decision-making. Track response times on assessment questions or chat interactions – info that can then be used to adjust difficulty level Sentiment analysis of open responses – which can then trigger personalized learning recommendations Predictive analytics for early intervention with employees that may be at higher risk of violating compliance regulations, for example Informing organizational decision making on focus for training programs and resource allocation Examples from the Field How are organizations leveraging these tools to solve real challenges—like speeding up content development, increasing relevance, or enhancing outcomes at scale? The following use cases offer a glimpse into how platforms are building on this technology to meet the evolving demands of modern learning ecosystems. CredSpark leverages GenerativeAI to create assessment questions based on text-based inputs. Our product roadmap for 2025 includes the use of DLA, LLMs, and NLPs to analyze open responses for sentiment analysis, summary, and custom recommendations. Degreed’s Maestro – a personal coach for skill development – uses DLA and machine learning algorithms to analyze individual learning behaviors and preferences. It uses this information to offer personalized content recommendations and practice opportunities.  Finetune uses machine learning algorithms to analyze and classify educational content. They “tag” learning and assessment materials accurately to align with different methodological standards and taxonomies. How does this look in real life? Now it’s time to put this into practice. You’ll look at real-life examples of skills assessments being used today and decide which AI components—like NLP, machine learning, or generative AI—are behind them. Just read each case and pick out the tech you think is at work. Key Takeaways: Applying AI in Skills Assessment Personalize Learning with AI-powered adaptive assessments and Generative AI-created content tailored to roles and skills. Automate Feedback & Insights using LLMs & NLP for instant grading and personalized coaching, while DLA tracks performance trends. Make Data-Driven Decisions by leveraging predictive analytics to address skill gaps, improve compliance, and optimize training investments. Interested in this topic? Check out our previous article in this series

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Do AI-powered functionalities make learning better?

AI

A Client Inspired Journey Into AI For Assessments Part I As AI continues to reshape industries, L&D professionals are under increasing pressure to not only incorporate these advanced technologies but to do so in a manner that is both strategic and impactful. When analyzing the applicability of AI-powered enhancements to the CredSpark product, we have to ask ourselves: do these techno-wonders actually make learning better or are they just the latest trend we’re all pressured to embrace? With the need to quickly upskill a diverse workforce and engage learners more effectively, understanding the potential of AI in learning assessments is more critical than ever – for us, and for you. In this installment of our AI for assessments research, we’ll explore what the data says about how this tool is influencing learning experiences, and dive into the how and why. In our exploration into the impact of AI on learning assessments, we started at the most fundamental place – is AI really adding value to the learning process? How? Why? So, does AI add value to the learning process? The simple answer is yes. Overall, our research showed that AI-powered functionalities in learning assessments were found to produce positive impacts in these areas: Improved learning outcomes A recent study found that AI-powered functionalities in diverse e-learning based applications enhanced personalized learning by adapting to learners’ cognitive styles and emotional states, using advanced feedback and predictive analytics, all of which contributed to improved learner engagement and performance. A related paper out of the University of Dehradun in India, found that learners that used AI-powered adaptive learning performed better in assessments, compared with the control group. They found that leveraging AI-powered algorithms to customize learning content led to increased interest, personal relevance, interaction, and a deeper understanding of the subject matter, which then translated into higher levels of proficiency and ownership over learning. Enhanced organizational outcomes Improved learning outcomes and learner engagement translate into positive organizational impact, particularly for ROI and operational efficiency in learning initiatives (because personalized training is more efficient and effective), better organizational performance (because of the improved learning outcomes and learner motivation), and increased retention and employee engagement (employees that are experiencing professional growth are happy and want to stay). (Sources: 1 2 3 4) Deeper, more actionable insights AI’s ability to pull in data from multiple sources and apply it to algorithms to give feedback and suggest learning actions in real time makes it highly effective for learners, compared to traditional methods of instruction. Assessment data, learning preferences, and performance measurement, coupled with contextual industry information can also help companies to detect skill gaps and learning needs, allowing them to stay ahead of the curve. (Sources: 1 2) The How and Why of Improved Learning and Organizational Outcomes Personalization at scale What it does: Analyzes user data from multiple sources and suggests content / remediation according to emotional states, learning style, proficiency, learning habits, predictive analytics (see below). Why it works: Ensures content is relevant for the learner, increasing attention, motivation, ownership, self-efficacy, engagement, and reduces cognitive load, which, in turn, helps learners develop skills faster and retain them for longer. Examples:A learning assessment that adapts question / content delivery based on individual performance, providing immediate remediation for those that need it, and increased complexity, for those that need it.Options for content delivery formats (ie. audio, text-based, scenario-based) based on user preferences. Real time feedback What it does: Provides performance / proficiency evaluations and remediation at the moment of occurrence. Why it works: Redirects erroneous thinking and keeps habits from becoming internalized, building confidence and self-awareness and increasing capability to internalize complex concepts and learner motivation. Examples:Learning assessment platform that provides instant correction for inaccurate responses in compliance training, redirecting the learner immediately to complementary remediation (content-, learning style-, emotional state-based) and retesting until the learner has demonstrated understanding. Predictive analytics What it does: Analyzes learner / organization / market past behavior (using multiple data sources) to predict future behavior, and suggests actions or content based on those predictions. Why it works: Allows learning platforms to predict skill gaps / performance issues on an individual OR organizational level, and target them for training. Examples:Past data shows that 90% of sales people that scored under 60% on the knowledge check for a training on Active Listening, were likely to close 40% less sales. This group of learners is then targeted for coaching / increased training to preemptively prevent underperformance in sales targets. At the end of the day, these 3 pillars – all powered by AI – have been shown to improve individual and group performance because learners are more engaged and are receiving the content and instruction that they need, when and how they need it; desired behavior is being constantly reinforced; and these tools provide an increased capacity to anticipate future problems so they can be solved before they impact the learner (or organization). Key takeaways: AI-powered capabilities have been shown to have an overall positive impact on learning AND organizational outcomes. (Stay tuned for our upcoming installment on what to watch out for when implementing AI-powered learning tools.) Learners tend to experience increased engagement, motivation, ownership and awareness of the learning process, as well as increased skill / knowledge retention and shorter training time to achieve key skill milestones. Organizations tend to experience increased satisfaction, employee engagement / retention, improved organizational outcomes, and improved ROI on training programs. These results are most directly influenced by the scalability that AI offers in the areas of: personalization, real-time feedback, and predictive analytics, which increase learner motivation and relevance, reinforce desired behavior, and help to predict and avoid future problems.

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Partners in Innovation: A Client-inspired journey into AI for Assessments

AI, Learning & Training

First, A Pulse Check on AI-Powered Learning & Assessment. Your organization is likely looking into how AI can help to produce better outcomes and work more efficiently, and CredSpark is right there with you. Over the past few months, we´ve been looking into how AI impacts skill assessment in the workplace and have uncovered exciting opportunities to harness this technology. We’ve combined our findings in a 4-part report that we´ll be sharing with you over the next several weeks to help our L&D community thrive and enhance their skills assessment strategies and practices. Each part of the report will focus on a crucial aspect of AI for learning and development, providing actionable insights, best practices, and innovative approaches by exploring topics such as:  The impact of AI-driven assessment on learner engagement / outcomes  Emerging trends / technologies Ethical and privacy considerations Best practices for integrating AI capabilities to create, deliver, and manage learning evaluations in the workplace.  Are you thinking about AI applications for assessment yet? We aim to empower our community with the knowledge and tools needed to adapt and excel in effectively assessing and developing skills in the workplace. Stay tuned for each interactive installment and join us for an expert problem-solving panel in April that will respond to our audience’s biggest AI learning challenges. Stay tuned!  And now, a little quiz… Dive deeper into this research Do AI-powered functionalities make learning better?

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Unlock the Power of Skill Assessment and Data Analytics

Learning & Training

As business environments become more data-driven, the ability to measure employee skills and tie learning outcomes directly to business performance is critical for learning and development (L&D) professionals. But how do you connect the dots between skills assessments, data analytics, and true business impact?  On October 9, 2024, at 1:00 PM EST, we’re bringing together top industry experts to explore exactly that in a live panel discussion: *Mastering Skill Assessment for Business Growth*. This insight-filled event will delve into the intersection of skill assessments and data analytics to help you unlock actionable strategies and approaches that drive business results and demonstrate ROI on your L&D initiatives. Why This Event Matters Now In today’s competitive business landscape, it’s not enough to simply train employees—you need measurable results. Organizations that can assess skills effectively and leverage learning data are better positioned to make strategic decisions that impact both employee performance and overall business growth. However, many struggle to move beyond basic training metrics and link learning to tangible business outcomes. This event will show you how. We’ll explore how combining advanced skills assessments with powerful data analytics can help you: Identify and address skills gaps more effectively Align learning initiatives with your company’s strategic goals Measure the ROI of your L&D efforts and make informed, data-driven decisions For leaders in L&D, HR, and talent development, this session offers the tools and insights you need to elevate your workforce while proving the value of your programs. What You’ll Learn Our expert panel will cover the following key topics: Leveraging Skills Assessments for Business Impact: Gain actionable insights into how skill assessments can help measure employee development and align training efforts with business goals. Harnessing Data Analytics to Prove ROI: Discover how data analytics can provide a clearer picture of your organization’s learning landscape, showing the real impact of training on business outcomes. Linking Learning Data to Strategic Decisions: Learn best practices for capturing and analyzing learning data, and how to use it to influence organizational strategy and demonstrate a return on investment. Key Takeaways: Best practices for creating data-driven skill assessments that yield actionable insights How to connect learning data with key business metrics to demonstrate ROI Real-world examples of companies that have used skill assessments and learning analytics to drive growth Future trends in data analytics and skill assessments, and what they mean for your L&D strategy Meet the Speakers This event brings together seasoned experts who have successfully helped organizations harness the power of both skills assessment and data analytics to fuel business success. Here’s who you’ll hear from: Chris Tompkins | VP of Business Development, Watershed Chris leads the teams behind Watershed, the world’s leading learning analytics platform. With years of experience in the L&D sector, he’ll share insights on how organizations are using learning data to drive business outcomes and why data is critical to showing ROI. Casey Cornelius | Head of Content and Client Services, CredSpark Casey is a key strategist at CredSpark, specializing in helping organizations design interactive assessments that both engage learners and generate essential data. She’ll discuss how to construct assessments that offer meaningful insights and align with organizational goals. Jennifer Kriksciun | Enterprise Learning Professional With a background in designing and implementing effective L&D programs, Jennifer will discuss how assessment data can be used to create strategies that not only foster employee growth but also drive measurable business results. Stephanie McCurdy | Learning Analytics Professional Stephanie has years of experience in corporate training and learning data analytics. She’ll share her expertise in using data to enhance learning strategies and improve overall performance. Emily Szopinski | Customer Success Manager, CredSpark (Host) As a learning and development expert, Emily will guide the discussion, focusing on how skill assessments and learning data can be applied to achieve organizational impact and long-term success. Why You Should Attend If you’re responsible for ensuring the success of your L&D initiatives and proving their value to your organization, this event will provide immeasurable value. You’ll gain a deeper understanding of how skill assessments and data analytics work together to drive real business results—and walk away with the knowledge to implement these strategies in your own organization. Take your L&D strategy to the next level with practical insights and data-driven tools that make a measurable difference. Reserve your seat today. Ready to unlock the full potential of skills assessment and data analytics to measure ROI? and secure your spot for this exciting event. Event Details 📅 **Date**: October 9, 2024   🕐 **Time**: 1:00 PM EST   ⏳ **Duration**: 45 minutes   📍 **Location**: Online (link to be provided upon registration) Register Here About Watershed How can organizations turn vast amounts of learning data into actionable insights that drive success? Watershed, the world’s first xAPI-conformant Learning Record Store (LRS), offers a powerful platform for collecting, storing, and analyzing learning data from various sources across an organization’s entire learning ecosystem. By integrating data from tools like CredSpark, which collects robust learning data through interactive assessments, Watershed enables organizations to aggregate this data using xAPI and other connectors, creating a unified view of learning and performance. About CredSpark What if you could instantly gauge your learners’ understanding and adapt your training programs in real-time? CredSpark’s interactive assessment tool is designed to elevate the learning experience by conducting engaging polls, knowledge checks, and simple to sophisticated learning assessments. With capabilities that range from real-time audience engagement to the creation of complex assessments, CredSpark empowers organizations to gain immediate insights into learner performance and understanding.

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Beyond the Buzzword: Elevating Skills Assessment for Real-World Impact

Learning & Training

In today’s rapidly evolving corporate landscape, “skills assessment” has become more than just a buzzword. The goal is to understand the nuanced abilities employees need to succeed in their roles and how organizations can effectively assess and develop these skills. Recently, we had an insightful conversation with Casey Cornelius from CredSpark on the intricacies of skills assessment, its challenges, and practical strategies for implementation. Here are the key takeaways and how learning and development (L&D) professionals can apply these insights to create impactful skills-based assessments. Understanding Skills in Professional Development Skills aren’t just about what people know; they’re about what people can do. This shift from knowledge to application requires a different approach to assessment. In traditional models, emphasis was often placed on the information held in one’s mind—think of the multiple-choice exams many of us are familiar with. These assessments tested recall and sometimes basic application but rarely delved deeper. In contrast, skills-based assessments explore how well employees can apply their knowledge in real-world scenarios. For instance, in customer service, awareness of company policies is one thing, but effectively communicating with customers, understanding their needs, and providing accurate solutions requires another level of understanding to effectively apply those policies in the real world. Breaking down roles into component skills helps identify areas for development, ensuring employees are ready to perform their duties effectively. Moving Beyond Traditional Assessments Although they have their purpose and place, the limitations of multiple-choice tests are evident—they primarily measure recognition rather than the ability to generate responses independently. To truly assess skills, organizations need to employ more sophisticated methods. Scenario-based assessments are a prime example, where employees engage in realistic simulations that mirror their actual work environment. This method allows for a more accurate evaluation of their abilities, providing a clearer picture of their readiness and identifying specific areas for improvement. Practical Tips for Implementing Skills-Based Assessments: Start with Real-World Scenarios: Develop assessments that mimic the actual challenges employees face. For example, if assessing a customer support agent’s skills, create a simulation where they must handle a difficult customer interaction from start to finish. Use Technology Wisely: Leverage AI to help scale assessments. These technologies—with human review—can help to create impactful assessments in a fraction of the time. As they become more sophisticated, the potential for AI for skills assessment will only increase. Incorporate Peer Review: Encourage peer assessments where employees review each other’s work against a clear rubric. This not only promotes a collaborative learning environment but also helps in scaling the feedback process. The Importance of Personalized Feedback Personalized feedback is crucial in skills development. It ensures that learners understand not just what they got wrong, but why. This approach allows for deeper reflection and promotes a growth mindset. To deliver personalized feedback at scale, consider using technology such as AI-driven sentiment analysis or branching logic that tailors feedback based on the learner’s responses. Strategies for Effective Feedback: Use Personalized, Human Feedback: Leverage technology to offer personalized feedback and guidance at scale, while developing a deep understanding of employee skill levels, gaps, and motivation. Implement Branching Scenarios: Design assessments that adapt based on the learner’s performance. If someone is struggling, they might receive more foundational questions, while those excelling can move on to more complex tasks. Encourage Self-Assessment: Ask learners to evaluate their own performance and confidence levels. This metacognitive approach can provide valuable insights into their self-awareness and readiness for further development. Overcoming Common Obstacles in Skills Assessment Defining what skills—and levels of competency—are necessary can be one of the biggest challenges organizations face. It’s essential to involve the employees who perform these roles to ensure the assessments are grounded in reality. Another common challenge is determining ownership of the skills strategy. Is it the responsibility of L&D, recruitment, or another team? Successful organizations take an organization-wide approach, breaking down silos and fostering collaboration across departments. Tips for Addressing Skills Assessment Challenges: Collaborate Across Departments: Involve various stakeholders from the outset. This ensures that the skills being assessed are relevant and comprehensive. Adopt a Continuous Improvement Mindset: Skills assessments should be dynamic. Regularly revisit and refine them based on feedback from both employees and assessors to keep them relevant and effective. Encourage an Open Dialogue: Keep the lines of communication open with employees about the skills they need and the competencies expected of them. This transparency helps align their development goals with organizational needs. Conclusion Skills-based assessment is more than just the latest trend in L&D; it’s a critical component of preparing employees for success in an increasingly complex and continuously evolving work environment. By moving beyond traditional assessment models and embracing more sophisticated, real-world methods, organizations can better understand their workforce’s abilities, drive more targeted development efforts, and ultimately achieve greater business outcomes. Start by building a collaborative skills strategy, leverage technology for scalability, and prioritize personalized feedback to meet each learner where they are. With these strategies, your organization will be well-equipped to navigate the future of skills development.

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Unveiling the Future of Learning: Insights from LENS 2024

Learning & Training

Knowledge is constantly evolving and the demand for upskilling and reskilling is ever-increasing, events like Degreed’s LENS 2024 are important spaces to interact with industry leaders around innovation and inspiration in L&D. This year’s edition of LENS, where CredSpark was a proud sponsor, brought together business leaders, technology innovators, and L&D professionals from diverse fields to explore the future of organizational learning. As the dust settles and participants return to their respective domains, it’s imperative to reflect on the insights gleaned and contemplate their implications for the evolving landscape of education and professional development. Here are our key takeaways from attending LENS 2024: Skills based hiring and training is taking center stage as the go-to for finding and nurturing talent for organizations. What does this mean?  More and more companies are basing hiring decisions on seeking out people that are able to demonstrate the knowledge and skills necessary to do their job, rather than using traditional skill-signals such as degrees. Companies are rethinking their L&D strategies to focus on broader skills, such as leadership, critical thinking, creativity, and problem solving, rather than on role-specific tasks. Some changes to traditional L&D approaches that we may see as a result of this shift are: An increase in priority for technology upskilling – especially around AI and related areas A push towards personalization and interest-based learning An uptick in training around power skills (formerly recognized as “soft skills”) across all roles and organizational levels. A decrease in linear training models, with organizations opting instead for an ecosystem paradigm around learning How does CredSpark help?  As Degreed´s assessment partner since 2016, hundreds of their  clients incorporate CredSpark’s tools into Degreed’s learning pathways to engage their learners and provide an objective diagnostic or certification of their skill sets. This information is then used to inform business decisions, individual and group learning pathways, and directions for further L&D initiatives. Learn more about CredSpark’s partnership with Degreed here. Lev Kaye, CEO, Casey Cornelius, Head of Content and Client Service, and Emily Justin-Szopinski, Customer Success Manager attended LENS 2024 on CredSpark’s behalf. Aligning skills data with business goals is key. What does this mean?  L&D’s role in an organization is to make sure that the workforce has the necessary skills, knowledge, and tools for the company to achieve its strategic goals. However, it is often the case that there is a disconnect between data around skill levels and their impact on the larger company goals. Several speakers highlighted the importance of connecting organizational stakeholders with L&D through effective communication and data sharing to increase visibility and impact. This goes not just for strategic organizational goals, but also for employee engagement and satisfaction. As a result, it is likely that we will see more and more companies collecting more data around skills and integrating that information with existing business analytics. This may take the form of a skills database or skills assessment tools that feed into BI software, for example. This allows for a real time, clear visualization of where the business wants to go and how their workforce is equipped to get them there. How does CredSpark help?  CredSpark+ harnesses the power of data by providing API / xAPI endpoints that can be tapped to safely share robust information on employee engagement and skill levels, allowing for more effective communication and understanding around how L&D initiatives are impacting our clients’ organizations. Technology, and especially AI, are changing the way we learn and train. What does this mean?  It’s impossible to ignore the capabilities and potential impact of AI on work. Noelle Russell, Chief AI Officer at the AI Leadership Institute, invited LENS attendees to not get caught up in the fear of being replaced by technology, but rather to decide how they (and their organizations) want to use it and can benefit from it. What this means for learning is that: organizations need to develop skills that help us to adapt to AI; we need to reflect on the value that each contributor brings to organizations, and world, as human beings and focus on deepening the skills related to that; there is a need to focus on new skill areas that arise as a result of an increase in usage of AI and related technologies in the workplace – like creative thinking, AI / digital literacy, adaptability, self-learning / self-awareness, and more. This represents a focal shift for L&D strategies at many organizations and the early-adapters – many of them present at LENS – are already well on their way. How does CredSpark help?  Apart from CredSpark’s ability to serve as a skills assessment tool, the app is also integrating Gen AI-based functionalities into its repertoire to give our users assistance when creating assessment questions. By simply pasting a URL or text into our tool, users can quickly create and edit question sets that before may have taken hours to create. Harnessing the power of this technology helps our users to spend more time on creative tasks, like learning, assessment, and business strategy. LENS 2024 was more than just a conference; it was a glimpse into the future of learning. From the transformative potential of technology to the enduring value of human connections, the event offered a wealth of insights and inspiration. As we chart a course into the unknown terrain of tomorrow, let us carry forward the lessons learned at LENS 2024 and continue to innovate, collaborate, and empower learners on their journey of lifelong discovery and growth. Together, we can shape a future where education knows no bounds and every individual has the opportunity to thrive. Learn more about how CredSpark can support your organization’s assessment strategy! Book a call with one of our experts today.

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Assessment Data Integration: Bridging learning outcomes and organizational goals

Learning & Training

Tapping into the dataverse In the realm of L&D, unlocking organizational impact is dependent on integrating assessment data with learning insights, offering L&D professionals an opportunity to align workforce skills with broader company goals.  Integrating assessment data with learning analytics, performance data, and business insight tools allow companies to visualize where their workforce is at in terms of key skill development, and quickly identify areas for upskilling to drive organizational goals. But, how well are businesses harnessing this integrated knowledge to meet the demands of their own organization and workforce?  This chart illustrates the iterative process of skill assessment, training, progress evaluation, and integration with broader business data that enables organizations to effectively identify skill gaps, address training needs, and develop actionable strategies that respond to the demands of both their workforce and their business. You may happen to notice that assessment is key in several parts of the process. Is your dataverse thriving, or just surviving? What does data integration look like in the context of skill-based L&D initiatives? It takes on a few different forms. It may be an Information hub that centralizes insights on workforce abilities / knowledge / experience; it could include data-point collection during learning experiences, assessment results, and integration with data analytics within the larger organization. According to our research, an effective L&D data integration plan will include at least some element of all of these.  Sounds a bit overwhelming if you ask me. So, why put the effort in? Beyond the obvious value around leveraging data to identify skill gaps and inform training programs / progress, this data is also important in strategic decision making and forecasting, helping to inform questions like: What skills will your workforce need for your company to reach your strategic goals? What kind of L&D investment is necessary in order to achieve those goals? How do our workforce skill sets line up with company goals (and how are they progressing over time)?  However, L&D has experienced a decline in alignment with organizational goals over the past year. The 2024 HowNow L&D report noted a substantial decrease in alignment of L&D strategy with overall organizational goals, leadership recognition of L&D impact, and clarity on L&D contribution to business value within the organization.  Implementing a comprehensive data integration, with assessment data as a key figure, may help to improve these numbers. Here are a few examples of how this might play out in a business: Collecting & integrating data with CredSpark+ CS+ enables organizations to tap into the dataverse with:  API / xAPI access to CredSpark data easily integrate insights with your organization’s analysis and tracking tools Data & analytics features that allow you to easily visualize time spent on assessments, question results, and confidence levels, as well as individual engagement with each interaction.  Content & data governance for multiple teams/departments in your organization, making sure that information is securely managed, easily accessible, and compliant with regulatory standards.  Own Your Data with CredSpark Learn more about how CredSpark can support your organization’s data analytics strategy! Book a call with one of our experts today.

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