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2024 Learning Trends | Back to the Future: Revisiting the Fundamentals of Learning

As members of the world of L&D, we don’t have a choice but to prepare for an increasingly technology-based and AI-enabled future in 2024. But to do so, we must “go back to the future” by revisiting the foundations that our learning strategies have and will be built on, including how we skill our people and train our leadership.

We can successfully adapt to disruptions like AI by more profoundly understanding our work, our organizations, and our goals.

Learning Trend 1: Creating a Skill-Oriented Infrastructure

In a world anchored to work and its outputs, organizations need to transcend traditional competency models so that we can ensure our workforce remains skilled, agile, and relevant. It’s not enough to merely list out the skills we need; we must have a profound insight into our current organizational readiness and chart a clear course for growth through skill-oriented infrastructures. Creating these skill-oriented infrastructures requires deep coordination between business units, human resources, and L&D.

Clean, accurate data is the bedrock upon which we can build our recommendations and personalization efforts. And it’s not just about gathering data; it’s about obtaining a comprehensive understanding of our skill landscape. We must strive for a crystal-clear view of the skills needed and those currently used within our organization. Cultivating a skill-oriented infrastructure not only equips us to meet the challenges of today but sets the stage for continued success in an ever-evolving learning environment.

Learning Trend 2: Embracing Data-Driven Mindsets

Gartner has predicted that 80% of organizational skills will need to be reprioritized because of digital business transformation by 2024. The McKinsey Global Institute found that 82% of executives view reskilling as critical for business success. It’s evident that the future demands a profound shift in our approach to learning. Data, the lifeblood of AI solutions, becomes central in this paradigm.

The challenge lies in gathering vast amounts of data and ensuring its cleanliness and usability. At GP, we’ve been utilizing AI for our RFP process. We recognized quickly that having lots of data doesn’t mean you have lots of usable data. It’s imperative to structure and prepare our data to be truly useful to AI and future business performance.

This commitment to clean data will remain pivotal as AI emerges as a key player in creating scalable and responsive learning systems. By embracing a data-driven mindset, we adapt to the digital era’s demands and unlock the potential for better-designed learning experiences through actionable insights.

Learning Trend 3: Enabling All Employees to Achieve Better Work Outputs Through AI

According to IBM, a staggering 40% of the global workforce is set to acquire new skills within the next three years due to the implementation of AI, and 87% of leaders believe that most positions will be augmented rather than replaced by AI. As we navigate this shift, we must emphasize crafting a harmonious human intelligence (HI) + AI relationship.

Critical thinking and problem-solving are pivotal as we discern when, where, and why AI will be beneficial. To achieve better work outputs through AI, we must focus on nurturing the distinct qualities that make us human.

As we integrate AI into our workflows, we need to create space for meaningful human connections and cherish uniquely human characteristics like curiosity and innovation. As we explore AI and attach our work to it, we must constantly ensure that our use of it enhances, not diminishes, our human experiences.

Learning Trend 4: Integrating Technologies with AI

The explosive growth of the AI landscape means tools are constantly emerging that could fundamentally transform how we approach L&D. In this environment of constant growth and integration, transparency becomes paramount, and we must be mindful of what, why, and how we are integrating these AI tools.

To make this influx of AI tools more manageable, we should develop rubrics for use cases. We can do this by grouping tools together by shared characteristics and highlighting characteristics that might make one tool distinct from another. Grouping like tools together and thinking strategically about when one tool is appropriate (and when another is not) allows us to establish a robust rubric for each tool—including guidelines for when, how, and why we should deploy a specific tool. This can help ensure our AI integration efforts align seamlessly with our organizational values and objectives. It also ensures that we are not embracing innovation for the sake of it but that we are doing so responsibly and to solve real challenges.

At the same time, we must advocate for a deep understanding of how our data is handled—including who manages it and where they store it—for all AI use cases in both professional and personal settings.

Trend 5: Embracing Scenario-Based Learning Labs

In 2022, Gartner found that only 20% of employees have the necessary skills for their current roles and future careers, and more recently, Resume Builder reported that 30% of job seekers exaggerate their AI skills. We’ve always been aware of the profound limitations of self-reported skills inventories, but verifying and improving skills has never been more critical than it is now.

Scenario-based learning labs or skill labs are far better at proving and improving employee efficiency or mastery of tasks than self-reported skill inventories, and skill labs will become increasingly crucial as skill gaps become more apparent in the workforce and AI adoption accelerates. Skill labs:

  • Provide objective assessment by moving from self-reporting to objective skill evaluation.
  • Embrace hands-on learning and foster proficiency.
  • Enable your workforce to be more adaptable.

These labs can objectively validate skills and empower our workforce to excel in a rapidly changing work environment.

Trend 6: Infusing AI Into Extended Reality-Enabled Learning

AI-based avatars in extended reality (XR)-enabled learning are rapidly advancing, and the era of generic avatars is giving way to creating specific personalities with contextual awareness. AI is dramatically enhancing XR avatars, making them more responsive and attuned to individual learner needs.

As we further infuse AI into XR learning experiences, we redefine the fabric of personalized and culturally inclusive educational experiences. These AI avatars can be dynamic agents capable of adapting to learners’ unique preferences and learning pace, and they can connect with users in their local language, breaking down barriers and fostering a more immersive and inclusive learning environment.

Trend 7: Preparing Leadership to Make Deeper Human Connections

Because of all this new technology, we must recalibrate our understanding of a leader’s role. The primary function of our advancing technology should be to carve out space and time for genuine human connections.

Leadership’s importance lies in navigating our evolving landscape and empowering individuals to inspire and grow. To that end, leaders must play a key role in supporting the delicate balance of the HI + AI relationship, ensuring that technological advancements catalyze deeper human connections.

It’s about capturing the essence of what a good leader looks like in this new era—leaders who understand that the true impact of their role transcends technological proficiency and centers on fostering meaningful connections and growth within their teams.

More on 2024 Learning Trends

For a deeper dive into these learning trends for 2024, check out my latest webinar with
Training Industry, 2024 Learning Trends | Back to the Future: Revisiting the Fundamentals of Learning.

About the Authors

Matt Donovan
Chief Learning & Innovation Officer
Early in life, I found that I had a natural curiosity that not only led to a passion for learning and sharing with others, but it also got me into trouble. Although not a bad kid, I often found overly structured classrooms a challenge. I could be a bit disruptive as I would explore the content and activities in a manner that made sense to me. I found that classes and teachers that nurtured a personalized approach really resonated with me, while those that did not were demotivating and affected my relationship with the content. Too often, the conversation would come to a head where the teacher would ask, “Why can’t you learn it this way?” I would push back with, “Why can’t you teach it in a variety of ways?” The only path for success was when I would deconstruct and reconstruct the lessons in a meaningful way for myself. I would say that this early experience has shaped my career. I have been blessed with a range of opportunities to work with innovative organizations that advocate for the learner, endeavor to deliver relevance, and look to bend technology to further these goals. For example, while working at Unext.com, I had the opportunity to experience over 3,000 hours of “learnability” testing on my blended learning designs. I could see for my own eyes how learners would react to my designs and how they made meaning of it. Learners asked two common questions: Is it relevant to me? Is it authentic? Through observations of and conversations with learners, I began to sharpen my skills and designed for inclusion and relevance rather than control. This lesson has served me well. In our industry, we have become overly focused on the volume and arrangement of content, instead of its value. Not surprising—content is static and easier to define. Value (relevance), on the other hand, is fluid and much harder to describe. The real insight is that you can’t really design relevance; you can only design the environment or systems that promote it. Relevance ultimately is in the eye of the learner—not the designer. So, this is why, when asked for an elevator pitch, I share my passion of being an advocate for the learner and a warrior for relevance.

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