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AI can be a useful tool for educational materials at work and in school, but it should be used alongside human interaction.
For many companies, schools, and organizations, delivering information to large groups can be an uphill challenge. While this is in part due to people’s ability to understand, retain, and apply that information, there can be barriers to understanding, especially if learning tools are not tailored to individual learners.
Low engagement, fragmented information, and repetitive administrative work are only a few of the things that can prevent large-scale learning programs from producing meaningful results. Today, however, several organizations and businesses can harness the power of AI to help not only personalize learning and organize information, but also remove operational bottlenecks.
Moving Beyond Generic AI Content
While many organizations initially adopted AI as a fast content-generation tool, in part due to its ease of use and rapid output, producing more content does not automatically create better learning or even a better learning environment. Iryna Kurkina of Academy Smart understands this firsthand.
“A lot of shallow content has been produced right now, so this is one of the most popular ways I see the companies and learning institutions utilizing AI at the moment,” Kurkina says.
Companies utilizing AI may adopt it into a larger workflow. Still, the strategy behind using it should be intentional, according to Kurkina, who says the tools should “make a bigger impact.”
“This is, I think, another sphere where AI could be very, very good, because it can analyze big amounts of data, it can spot some trends that you probably, as an L&D professional or HR professional, may not be able to, you know, to identify,” the Academy Smart professional shares.
Personalization and Engagement at Scale With AI
Engagement remains one of the largest barriers to effective corporate and educational training, in part because many training materials are designed to cater to groups rather than individuals. By incorporating artificial intelligence into a workflow, the technology can observe learner behavior and incorporate scenario-based courses, simulations, and gamified learning to make training more relevant and memorable.
However, AI isn’t a one-size-fits-all approach. This is because, as companies expand, valuable information can become trapped in an individual’s head, scattered across teams, or even buried in unstructured feedback.
Tyler Zanini, the founder of the senior caregiving app Memoryboard, shares that he thinks a lot about “the ways of working are fundamentally changing across the board, whether you’re at a small company, big company, and if you’re not experimenting with AI, you’re almost certainly going to be left behind at some point.”
Despite this, Zanini says that AI needs to be a “support, and not a replacement for genuinely doing the work.”
“What we do is we use a lot of AI agents and automation tools to make sure that we’re capturing feedback that we’re getting from customers, and then we’re able to kind of structure that in a really organized way.”
Memoryboard’s workflow, for example, provides an initial customer-support draft, while a human reviews and adjusts the response before it is sent. This model allows a company to support more people while preserving the hands-on, relationship-focused experience that many crave.
Improving Reliability With Trusted Information
Despite advances in AI, it often produces different answers to the same prompt. They also lack specific knowledge of events and information beyond their training data, which makes it harder for people to fully utilize the technology on its own.
Yousef Ali, of J. Servo, shares that “AI is a non-deterministic system. So it doesn’t necessarily mean that the inputs define the outputs.”
For this reason, an organization using AI should rely on an approved knowledge base before allowing it to retrieve relevant information. This can give context to the AI before it generates an answer. Ali is clear, however, that one needs to “make sure that it retrieves its knowledge from a trusted source, a reliable source.”
“You collect all the knowledge, sources and information that you have, that you may use in this learning process, and you start feeding them to the model, which will eventually help it in providing the most accurate, the most factual, the most deterministic responses.”
Automating Repetitive Tasks Can Create More Time for Learning
Sandra Vives of J&Y Law understands that targeted AI adoption is crucial for certain workflows, especially those involving sensitive data. Rather than simply using a single AI across the business, Vives says that they review workflows department by department. This is especially important for automated client communications and AI-assisted case reviews.
“They’re here to build relationships with the clients,” Vives shares. “They are at the forefront. They are the first person that a client will speak to when they call our firm. So we wanted to give them the tools to be able to focus on that.”
Despite the use of AI agents, Vives says AI will not replace people. Instead, she explains, “It removes competitive work, it saves business hours, [and] improves workflows and processes.”
“If you don’t have those and they’re not a solid foundation, whatever tool you’re deciding to implement is only going to create a larger scaling inconsistency, because that’s what you’re scaling if your processes and your workflows are not aligned.”
The Future of AI
As AI works to scaffold intelligence, thereby allowing generalists to bypass foundational knowledge required in major industries, platforms such as Emra expect AI performance to increase by 20x in 5 years.
Emra seeks to democratize software creation so that anyone can build custom tools without code. This moves away from using SaaS to creating personalized software by solving the hindrance of resource-constrained development.
“At the end of the day, I think the reason I’m working here is that I care so much about actually solving problems for people,” says Jarren Rocks of Emra.
Human oversight remains vital when using AI in order to verify output and guide tools to achieve visions. The challenge comes when trying to retain workers whose careers center around intermediary tasks that can be automated by AI.
Rocks emphasizes instead that AI can scaffold intelligence, being an aid to workers rather than a replacement for them. It can be used to bypass foundational intelligence so people can focus on high-level problem solving.
“If you’re able to use it to ask the right questions and then filter through that noise, kind of like a Google search, I think you actually are going to have a massive leg up in terms of being able to understand things,” says Rocks.
“But if you are really good at asking the right questions, you don’t have to spend so much time in the sum total of what we already know, in some ways you as a person can jump all the way to the end of the problem space and start focusing on new problems,” he continues.
What Effective AI-Powered Learning Looks Like
Utilizing AI-powered learning ultimately revolves around catering knowledge to the individual rather than to a group as a whole. Using AI agents and tools to personalize experiences may improve individual retention and help automate repetitive work, allowing employees to focus on building their judgment and relationship skills in the workplace.
While the future of learning will not be defined by how much content AI can produce, it does rely heavily on personal data and technology, guided by trusted data, clear processes, and human judgment.
As the future of AI continues to evolve and the technologies behind it advance, organizations, schools, and businesses may be better equipped to help their clients, students, and workers succeed.
