Does anyone remember being told in grade school that you had to learn various calculation techniques because “you won’t be walking around with a calculator in your pocket when you grow up”? I may have dated myself a bit, but this line of reasoning turned out to be dead wrong. I not only have a calculator, but a translation device, phone, camera, entertainment hub, and comprehensive worldwide library in my pocket. New technology disrupts how things are done. Learning is no different. As tech evolves, so should the way we teach and learn.
It comes as no surprise that 63% of companies said they plan to increase or maintain artificial intelligence (AI) and machine learning (ML) spending in 2023. Learning leaders now have powerful tools at their disposal to transform corporate training. At the outset, however, I think it’s prudent to mention that AI is not about replacing human expertise but enhancing it, freeing up time for more strategic initiatives and personal connections.
While it offers so much potential, for many industries, a conservative approach to AI (particularly generative AI) is prudent. Remember, many new technologies have open-source origins and broad end-user agreements that could introduce undue risk.
Here are five ways learning leaders can leverage AI:
#1 To offer more personalized learning content
We all know what it’s like to receive a marketing email that isn’t relevant to us; it goes straight to the trash. If, however, we get an offer tailored to our current needs and desires, we are much more likely to engage. In the same way, learners are motivated by content that meets them where they are.
In a survey by Brightwave, 77% of L&D professionals highlighted personalized learning as vital to employee engagement. AI has the potential to deliver highly personalized learning experiences to individual learners in real-time. AI models can dynamically adjust difficulty levels by analyzing learner behavior and preferences, inserting relevant examples and scenarios, and tailoring course versions for each learner. For example, an AI model may adapt to a learner by reiterating questions around a subject area in which they scored poorly in the past. That could simply be in the current session, or in future sessions as technology advances. This level of personalization boosts learner motivation and engagement—key factors in creating learning that can deliver a positive ROI.
#2 To provide detailed, continuous feedback
Learning is an active process that involves an individual trying new things, failing forward, and, crucially, receiving constructive feedback. However, traditional methods often fall short in providing timely and valuable feedback. Immersive scenario simulations replicate the challenges people are likely to encounter in their roles, allowing them to apply their knowledge in context, receive immediate feedback, and iterate their strategies for optimal outcomes, thus enabling them to build confidence and proficiency.
AI-powered models can take this a step further by tracking multiple data points, analyzing learner responses and sentiment, and providing real-time, hyper-personalized feedback. By leveraging these techniques, we have the potential to transform routine feedback into dynamic conversations:
- Delivering personalized critiques
- Responding to clarifying questions
- Offering follow-up assessments until the learner achieves competence
This type of feedback system is the ideal state. To achieve it, organizations must prioritize collecting, formatting, and analyzing learner records to provide integrated AI systems with the training data they require to fuel these kinds of solutions. More on that in a moment.
#3 To empower learner-driven exploration
Learners’ questions and curiosities don’t fit neatly within the confines of a prescribed curriculum. AI-powered tools allow learners to explore and interact with course content in a more flexible and personalized way. For example, learners can move seamlessly from core content to related support material to tangential topics that pique their interests. This learner-driven approach empowers individuals to delve deeper into their specific areas of curiosity and enhances their overall learning experience.
One additional bonus of dynamically offering tangential learning opportunities is that we may discover areas of interest or expertise that fall outside an employee’s traditional assignments. By indulging such interests, employees may find ways to add more value through cross-training, or perhaps by shifting their career path entirely, bringing with them the tribal knowledge they have collected in their current department into another.
#4 To automate learning analytics
While the first three points were aimed at learners, these final two are focused on strategies and techniques that support learning leaders.
In my experience, the data organizations capture through their digital learning programs is vastly underutilized. After engaging in hundreds of client consultations with LMS or LRS ‘experts’ within their respective organizations, I have found precious few that have the requisite data analytics or modeling capabilities needed to tease out insights from the raw information collected. While general reporting features are available, these usually only provide basic details on completion percentages, average scores, and time spent in training. None of those figures will help us understand how learners engage with the material, what subtopics they are struggling with, or if there are trends of excellence or deficiency across departments for specific performance areas. To access and assess these types of performance metrics, a deeper look into our captured learner data is required. Enter AI.
AI’s ability to analyze and process large amounts of data may meaningfully reduce the burden of data wrangling and analysis and put the results directly in the hands of leaders. For example, reporting on learners’ performance can quickly be reorganized by group or region, existing skill gaps can be more easily identified, and trends become more apparent when all the data is at our fingertips. Organizations may use AI’s predictive analytics to forecast future skill gaps, prepare for potential shortages, and future-proof their operations. The applications of proper data analytics are exciting and promising. I believe these tools will be a key part of L&D leadership’s toolbox for the foreseeable future.
#5 To get your hands dirty
Given the speed at which tools and techniques leveraging AI are coming to market, it is a challenge to stay abreast of the latest and greatest given the plethora of responsibilities and commitments learning leaders face daily. It may be tempting to simply lean on reports or summaries provided by trusted advisors or articles from your favorite periodical; however, I would encourage you to explore some of these capabilities for yourself.
While there are many ways in which AI and ML can be implemented, the most recent incarnation of AI in Large Language Models (LLMs) is able to provide valuable insight from direct questions without the need for jargon or departmental expertise. You may be surprised how quickly you can get up to speed on any number of challenges using this method. Do keep in mind that generative AI outputs must be validated as the technology is still relatively new and prone to errors. Due diligence is still required, but the spark of innovation and inspiration that can come from exploration is well worth it.
Learning leaders should embrace AI as a valuable tool
I urge learning leaders not to be fearful or overly skeptical of AI, but to embrace it (responsibly) as a co-pilot and unlock its true potential in shaping the future of corporate training. The technology is ever evolving, and our approach will need to do the same. Although AI’s possible use cases are still being discovered (let alone perfected), L&D teams that do not begin to at least explore these new tools may be left behind.
We can’t predict the future, but I don’t see it being a dystopian one. There will always be a place for human-led learning design that picks up on the cues and nuances that AI can’t interpret and understand. The goal is for AI to amplify the learning experience and pave the way for continuous learning across all organizations.