We have been suffering from learning content overload of late. In an earnest attempt to bridge the skills gap and develop talent, organizations have been purchasing off-the-shelf content to populate content libraries, designing custom learning solutions, curating videos and podcasts and, with the emergence of generative artificial intelligence (AI) tools, creating more content faster and in large quantity. However, with more content comes the paradox of choice — a term coined by Barry Schwartz, an American psychologist. When consumers are confronted with too many choices, Schwartz stated that they will end up making sub-optimal choices or not able to make any decisions at all — a decision-making paralysis.

In the context of learning, the consequences of content overload can be severe — it can overwhelm and demotivate learners, reduce learning outcomes and lower engagement with learning materials, essentially the opposite of what we are striving to achieve.

To mitigate these negative impacts, it is important to develop a unifying content strategy that empowers learners and encourages exploration. Below are a few considerations to get started:

Better UX

User experience (UX) design can play a critical role in mitigating content overload and supporting a better content strategy. When evaluating and configuring a content library or resource repository platform, make sure that it is user-friendly, with a clean and clearly labeled user interface and intuitive navigation, and that the search and filter functions are robust. Organize your content in a way that is logical to your learners and uses language that is consistent across the company. Think about how and where your learners access the content. Context and environment matter.

Learner-directed Personalization

Personalized learning services such as adaptive learning platforms and recommender systems offer a chance to reduce content overload by curating, suggesting and tailoring content to the individual’s needs, interests and abilities. However, there are challenges associated with this approach, particularly how the adaptive and filtering process is mediated: who makes decisions on what to personalize, personalized by whom and for whom, why is one option recommended over another option and most importantly, who will have ultimate control over this process?

When implementing personalized learning, we ought to ensure that these platforms allow for learner control and direction. For example, some recommender systems allow for learner feedback and editing. When presented with each new piece of content, learners can accept, reject or update the recommendation, making future recommendations more accurate and more closely aligned with learning goals. Look for personalized approaches/systems that balance content filtering and learner control.

Human Oversight is Needed

Generative AI can produce a vast amount of content quickly, and increasingly, learning professionals are utilizing generative AI to aggregate, curate and create learning material. However, the quality of the generated content can vary widely, depending on the quality of the input data, the complexity of the algorithm and the types of guardrails that are in place to vet out problematic or misleading information. As such, human oversight, quality control and prioritization are more urgently needed than ever.

While it can be time-consuming, we need to critically evaluate and fact-check AI-generated content to make sure it is correct, relevant, of good quality and up-to-date. Learning professionals need to shift their skill sets to take on the roles of AI-generated content reviewers, critics, analysts and mentors, particularly in the case of dealing with content that may be misinformed or biased.

All in all, an effective content strategy that considers good user experience, learner-directed personalization and human oversight can all play important roles in mitigating the negative impacts of learning content overload and helping learners to access the most relevant and useful content.