Predicting Learner Needs: The Future of AI in Learning

It’s no secret that big businesses have been mining and storing personal data to target consumers for years. The advertisements on our social media feeds, the emails in our inboxes, and the coupons that land in our mailboxes are all curated for us as individuals. Modern marketing isn’t just about providing coupons to consumers for things companies think we’re interested in. They’re also predictive, and they have been for quite some time. 

Over a decade ago, Target came under fire for practicing this personalized and predictive marketing a little too well. The father of a 16-year-old girl furiously approached Target for sending his daughter coupons for baby clothes and cribs, accusing them of encouraging his high school-aged daughter to get pregnant. But Target wasn’t encouraging her to become a teen mom. She actually was pregnant. The company’s predictive marketing algorithm correctly assumed her pregnancy based on her purchasing trends and responded in kind. 

Amazon likewise attempts to predict consumer needs. The online retail giant has been practicing “anticipatory shipping” for ten years—packing and preparing items for shipment from a local warehouse before a customer even purchases them. This type of predictive behavior isn’t just reserved for retail; it’s rapidly entering learning and development (L&D).

AI in Learning: Delivering Insights at Scale

AI can deliver meaningful insights at scale, offering powerful insights that humans can then validate, refine, and act upon.

So how can AI “predict” needs in the world of L&D? Well, while it can’t exactly predict the future, AI can deliver meaningful insights at scale—and fast. Gone are the days when learning professionals are left sifting through mountains of data to uncover learner preferences, skills gaps, or development needs. Today, AI can analyze these vast datasets with unprecedented speed and accuracy, offering powerful insights that humans can then validate, refine, and act upon.

Streamline Content Creation Process

For example, AI systems like large language models (LLMs) can dramatically streamline the content creation process. One of the ways GP Strategies uses AI is for the production of requests for proposals (RFPs) for potential client projects. RFPs can be exceptionally labor-intensive, with 300–400 questions, and they can take several days to complete. Now, we can upload the RFP questions into our LLM. AI will then analyze our previous RFP responses—stored in our secure repository—and generate tailored answers for an entire RFP in a matter of hours.

This shift saves us significant time and allows us to focus on customizing and fine-tuning responses to meet each client’s unique challenges and deliver more value. This efficiency doesn’t just apply to content generation—it also extends to data analysis. Beyond the time AI can save us, it can also help us glean insights into how our responses to clients are evolving over time. AI may detect a trend in the direction the market is taking before we do. 

AI tools can also sift through learner data to summarize trends, identify knowledge gaps, and suggest areas for future development. The role of the human expert is then elevated. Freed from laborious or repetitive tasks, people can focus on interpreting these insights, validating the findings, and making strategic decisions.

AI provides intelligence, but humans bring wisdom. Knowing that a tomato is a fruit is intelligence—knowing not to put it in a fruit salad is wisdom.

Anticipating Needs: The Next Generation of AI-Driven L&D

Just as companies like Target and Amazon use data to predict consumer behavior, L&D organizations can potentially use AI to analyze vast amounts of data to forecast the learning needs of individuals and organizations in the future. This shift from reactive to proactive learning strategies is set to transform the way we approach skill development and knowledge acquisition. 

As this technology evolves, AI won’t replace the human touch in learning and development—it will enhance it. Leaders and L&D professionals can leverage AI to make more informed, strategic decisions while focusing on what truly matters: mentoring, coaching, and fostering a culture of continuous learning. 

AI empowers us to work smarter, not harder, and its potential to transform L&D is only just beginning.

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.