As human capital professionals, we find ourselves in a new era where human-centered learning is at the intersection of technology and the consumption of knowledge. As I often say, we are in a time when humans are saturated with information and starved for insights. We are bombarded with surges of data and information, averaging the equivalent of 100,000 words heard or read, each day. This amount of information serves as a significant distraction and at the same time, provides few insights into our personal or professional lives. Signals get lost as noise clouds the most salient information to be extracted.

At this inflection point, learning leaders should aim to balance tools that accelerate the learning process and provide surgically precise information tailored to learners’ knowledge gaps. Attaining this level of precision creates complexity and downstream implications for stakeholders in every facet of the organization.

As artificial intelligence (AI) becomes more widely available and tested for use in learning organizations, leaders must develop models that enhance and improve learning rather than detract and distract from the experience. Specifically, the lure of using AI to design training, deliver customized microlearning or create knowledge checks can sway learning professionals to deploy tools that are fast and inexpensive. However, learning organizations must implement checks and balances that ensure the learner experience is relevant and ameliorated. Table 1 shows some of the pitfalls that learning organizations can experience, along with escape routes to help address them.

Table 1.

Effective vs. Efficient Training

Managing the intersection of effective versus efficient training is not a new problem for learning professionals. Tantamount to the challenges we all face today, our phones, televisions and computers are all pushing information to us in a constant deluge. There is a premium for our cognitive attention and learners can quickly become immune to relevant information among the confetti of data hurled at them through multiple mediums all day long. Attention is one of the most coveted resources in modern times.

Learning professionals must leverage tools and techniques (not all of which are technology) to keep focus on content at a steady pace. When learning professionals design content with too many stimulating sounds, games and graphics, the learner’s cognitive abilities can decline from fatigue. Attention must be attenuated, but not drowned to achieve the optimal level of learner performance.

Strategic Use Cases for AI

Leveraging the escape routes mentioned previously can help you strategically overcome some of the aforementioned challenges with AI. Integration of AI can enhance learner performance when considered one of many learning tools rather than the dominant tool. Here are the most anticipated strategic uses of AI that can ubiquitously transform how we engage learners in the art of knowledge acquisition:

• Engaging learners with AI conversation simulations that help master the dialogue and process skills on-screen simultaneously.
• Creating AI assessments based on the content for more accurate checks of learning retention.
• Aggregating cohort-level data on learning checks in simulations for a birds-eye view of individual and classroom gaps of knowledge transfer.
• Creating microlearning content from existing curriculum to address gaps without the intervention of a learning professional.
• Creating new content using AI by bringing in a developer for review while sequencing activities with lecture and curriculum.
• Scheduling training using an AI bot that scans the learner’s calendar for the accurate amount of capacity to take training.
• Automating learning intake requests and recommending learning solutions.

While many organizations are still vetting the infrastructure and security of AI within the firewall, learning professionals can begin to construct a strategy that is “just-in-time” as the utility becomes available. There are two parts to this strategy. First, learning teams must build use cases for content, quizzes, coaching and analytics that can accelerate readiness of AI use once the technology is in place. This requires a thoughtful approach to the investment of designing an AI model, as well as the anticipated returns in performance gains, reduced labor cost and training time.

The second part of AI preparation involves assimilating learning roles into existing technology. Learning leaders should strategize on how AI will extend the reach of design and delivery roles. Leaders can encourage designers and developers to become knowledgeable about the technology, prompts and capabilities expected to be used by the learning strategy. Sharing success stories and preparing teams can ensure teams understand how they should adapt to the evolving training technology to be successful. Early socialization and communication can defray concerns and resistance to embracing this pivotal technology.

Summary

Rather than blindly leverage the technology, leaders can create an AI strategy for each function in the learning and development function. Learning organizations can and should anticipate the new possibilities of emerging AI technology and become the architects of the next generation of modern learning, rather than being passive recipients. The intersection of human and AI learning marks an important time in learning history and leaders have a tremendous opportunity to write a new chapter in the annals of learning theory by thoughtfully crafting AI into the learning journey.