The Intersection of AI and Neuroscience in Learning and Development

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Artificial intelligence (AI) and neuroscience have long been intertwined in the field of Learning and Development (L&D), with both disciplines borrowing from each other. In L&D, the goal is to improve performance by facilitating learning and behavior change. Three common challenges faced by learning designers and deliverers include the lack of time, aligning objectives with business requirements, and addressing company-specific needs.

Current design tools often struggle to keep up with the pace of change, resulting in ineffective behavior change. Additionally, stakeholders find it difficult to define their needs and often request courses without clear objectives. Subject matter experts tend to overload learners with information, lacking time to validate or evaluate. These challenges have persisted for years, but AI alone cannot solve them.

Thankfully, AI is not yet advanced enough to completely replace the vital role of human skills in creating enjoyable and impactful learning experiences. The key is to incorporate strong learning science principles into AI prompts, rather than relying solely on AI-generated content that may contribute to cognitive overload. AI can generate content faster, but tools like ChatGPT tend to produce generic content without understanding its usefulness. The responsibility lies with learning designers to use AI as a supportive tool and provide the appropriate guidance, experimentation, practice, and support needed for behavior change.

While AI can accelerate content creation, it is important to remember that content alone is insufficient for behavior change. AI can be trained to deliver additional resources like spaced repetition questions, which help combat the forgetting curve. Moreover, AI can streamline laborious processes, allowing for quicker iteration and optimization of learning journeys to stay relevant to business needs. It also facilitates alignment with stakeholders by providing clear examples of expected behaviors and prototype activities, allowing for faster consensus. This enables learning designers to spend more time evaluating and leveraging meaningful learning data.

The combination of AI and learning science empowers non-specialists, such as subject matter experts, to design effective learning interventions. By avoiding cognitive overload and focusing on behavior change, these interventions can be more impactful.

Personalization is another benefit of combining AI and neuroscience. Learners no longer have to follow a standardized training program; instead, they can demonstrate their existing knowledge and skills to the smart system and access relevant content. Ultimately, AI and neuroscience augment learning design rather than render it obsolete.

By leveraging AI’s time-saving capabilities and the solid framework provided by neuroscience, learning designers can enhance their work in several ways. They can devote more time to engage with learners, replace traditional evaluation methods (such as ‘happy sheets’) with valuable data-driven insights, influence the application of learning at organizational levels, upskill themselves and their teams, and gain a deeper understanding of the technologies that support performance. The combination of AI and neuroscience enables the achievement of quality learning outcomes at an accelerated pace.

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