Are There AI Hallucinations In Your L&D Strategy?
More and more often, businesses are turning to Artificial Intelligence to meet the complex needs of their Learning and Development strategies. There is no wonder why they are doing that, considering the amount of content that needs to be created for an audience that keeps becoming more diverse and demanding. Using AI for L&D can streamline repetitive tasks, provide learners with enhanced personalization, and empower L&D teams to focus on creative and strategic thinking. However, the many benefits of AI come with some risks. One common risk is flawed AI output. When unchecked, AI hallucinations in L&D can significantly impact the quality of your content and create mistrust between your company and its audience. In this article, we will explore what AI hallucinations are, how they can manifest in your L&D content, and the reasons behind them.
What Are AI Hallucinations?
Simply speaking, AI hallucinations are errors in the output of an AI-powered system. When AI hallucinates, it can create information that is completely or partly inaccurate. At times, these AI hallucinations are completely nonsensical and therefore easy for users to detect and dismiss. But what happens when the answer sounds plausible and the user asking the question has limited knowledge on the subject? In such cases, they are very likely to take the AI output at face value, as it is often presented in a manner and language that exudes eloquence, confidence, and authority. That’s when these errors can make their way into the final content, whether it is an article, video, or full-fledged course, impacting your credibility and thought leadership.
Examples Of AI Hallucinations In L&D
AI hallucinations can take various forms and can result in different consequences when they make their way into your L&D content. Let’s explore the main types of AI hallucinations and how they can manifest in your L&D strategy.
Factual Errors
These errors occur when the AI produces an answer that includes a historical or mathematical mistake. Even if your L&D strategy doesn’t involve math problems, factual errors can still occur. For instance, your AI-powered onboarding assistant might list company benefits that don’t exist, leading to confusion and frustration for a new hire.
Fabricated Content
In this hallucination, the AI system may produce completely fabricated content, such as fake research papers, books, or news events. This usually happens when the AI doesn’t have the correct answer to a question, which is why it most often appears on questions that are either super specific or on an obscure topic. Now imagine you include in your L&D content a certain Harvard study that AI “found,” only for it to have never existed. This can seriously harm your credibility.
Nonsensical Output
Finally, some AI answers don’t make particular sense, either because they contradict the prompt inserted by the user or because the output is self-contradictory. An example of the former is an AI-powered chatbot explaining how to submit a PTO request when the employee asks how to find out their remaining PTO. In the second case, the AI system might give different instructions each time it is asked, leaving the user confused about what the correct course of action is.
Data Lag Errors
Most AI tools that learners, professionals, and everyday people use operate on historical data and don’t have immediate access to current information. New data is entered only through periodic system updates. However, if a learner is unaware of this limitation, they might ask a question about a recent event or study, only to come up empty-handed. Although many AI systems will inform the user about their lack of access to real-time data, thus preventing any confusion or misinformation, this situation can still be frustrating for the user.
What Are The Causes Of AI Hallucinations?
But how do AI hallucinations come to be? Of course, they are not intentional, as Artificial Intelligence systems are not conscious (at least not yet). These mistakes are a result of the way the systems were designed, the data that was used to train them, or simply user error. Let’s delve a little deeper into the causes.
Inaccurate Or Biased Training Data
The mistakes we observe when using AI tools often originate from the datasets used to train them. These datasets form the complete foundation that AI systems rely on to “think” and generate answers to our questions. Training datasets can be incomplete, inaccurate, or biased, providing a flawed source of information for AI. In most cases, datasets contain only a limited amount of information on each topic, leaving the AI to fill in the gaps on its own, sometimes with less than ideal results.
Faulty Model Design
Understanding users and generating responses is a complex process that Large Language Models (LLMs) perform by using Natural Language Processing and producing plausible text based on patterns. Yet, the design of the AI system may cause it to struggle with understanding the intricacies of phrasing, or it might lack in-depth knowledge on the topic. When this happens, the AI output may be either short and surface-level (oversimplification) or lengthy and nonsensical, as the AI attempts to fill in the gaps (overgeneralization). These AI hallucinations can lead to learner frustration, as their questions receive flawed or inadequate answers, reducing the overall learning experience.
Overfitting
This phenomenon describes an AI system that has learned its training material to the point of memorization. While this sounds like a positive thing, when an AI model is “overfitted,” it might struggle to adapt to information that is new or simply different from what it knows. For example, if the system only recognizes a specific way of phrasing for each topic, it might misunderstand questions that don’t match the training data, leading to answers that are slightly or completely inaccurate. As with most hallucinations, this issue is more common with specialized, niche topics for which the AI system lacks sufficient information.
Complex Prompts
Let’s remember that no matter how advanced and powerful AI technology is, it can still be confused by user prompts that don’t follow spelling, grammar, syntax, or coherence rules. Overly detailed, nuanced, or poorly structured questions can cause misinterpretations and misunderstandings. And since AI always tries to respond to the user, its effort to guess what the user meant might result in answers that are irrelevant or incorrect.
Conclusion
Professionals in eLearning and L&D should not fear using Artificial Intelligence for their content and overall strategies. On the contrary, this revolutionary technology can be extremely useful, saving time and making processes more efficient. However, they must still keep in mind that AI is not infallible, and its errors can make their way into L&D content if they are not cautious. In this article, we explored common AI errors that L&D professionals and learners might encounter and the reasons behind them. Knowing what to expect will help you avoid being caught off guard by AI hallucinations in L&D and allow you to make the most of these tools.