Do you have AI hallucinations in your L&D strategy?
In many cases, companies look to artificial intelligence to meet the complex needs of their learning and development strategies. It’s no wonder why they’re doing that. It continues to become more diverse and demanding, taking into account the amount of content that needs to be created for the audience. Using AI for L&D will streamline repetitive tasks, provide learners with enhanced personalization, and enable L&D teams to focus on creative and strategic thinking. However, many of the benefits of AI come with risk. One common risk is the defects in AI output. If not checked, AI hallucinations in L&D can have a significant impact on the quality of the content and create mistrust between the company and its audience. In this article, we explore what AI hallucinations are, how they can manifest themselves in L&D content, and the reasons behind them.
What are AI hallucinations?
Simply put, AI hallucinations are errors in the output of AI-equipped systems. When AI hallucinates, it can create completely or partially inaccurate information. Sometimes these AI hallucinations are completely meaningless, making them easier for users to detect and reject. But what if the answer sounds plausible and the user asking the question has limited knowledge of the subject? In such cases, it is very likely that you will get AI output at face value, as it is often presented in ways and language that exudes eloquence, confidence and authority. At that time, these errors can proceed to the final content whether it is an article, video, or a full-fledged course or whether it affects your reliability and thought leadership.
Examples of AI hallucinations in L&D
AI hallucinations can take a variety of forms and can have different results when they enter L&D content. Let’s explore the main types of AI hallucinations and how they can manifest themselves in L&D strategies.
A de facto mistake
These errors occur when AI generates answers that contain historical or mathematical errors. Even if your L&D strategy does not include mathematical problems, there can be de facto errors. For example, an AI-powered onboarding assistant could list the profits of non-existent companies, leading to confusion and frustration of new hires.
Manufactured content
In this hallucination, the AI system may generate fully manufactured content, such as fake research papers, books, news events, and more. This usually happens when the AI doesn’t have the correct answer to the question. Therefore, it is best displayed on questions either on very specific or vague topics. Now imagine that in your L&D content, AI will include Harvard research “discovered.” This can seriously hurt your credibility.
No meaningful output
Finally, some AI answers are meaningless because they are inconsistent with the prompts inserted by the user, or because the output is self-contradictory. The former example is an AI-powered chatbot that explains how to send a PTO request when an employee asks how to find the remaining PTO. In the second case, the AI system may give different instructions whenever they are asked, causing the user to confuse what the correct course of action is.
Data delay error
Most AI tools used by learners, professionals, and everyday people work with historical data and do not have immediate access to current information. New data is only entered through periodic system updates. However, if learners are unaware of this limitation, they may ask questions about recent events and research, but they will only come out empty-handed. Many AI systems notify users that they have no access to real-time data, preventing confusion and misinformation, but this situation can be frustrating for users.
What causes AI hallucinations?
But what happens to AI hallucinations? Of course, I’m not conscious of artificial intelligence systems, so it’s not intentional (at least not yet). These mistakes are the result of how the system is designed, the data used to train them, or simply user errors. Let’s dig a little deeper into the cause.
Inaccurate or biased training data
The mistakes you observe when using AI tools often come from the data sets used to train them. These datasets form the complete foundation on which AI systems rely on to “think” answers to questions. The training dataset may be incomplete, inaccurate or biased, providing a flawed source of AI. In most cases, the dataset contains only a limited amount of information about each topic, so AI can fill the gap on its own.
Faulty model design
Understanding the user and generating responses is a complex process performed by a large-scale language model (LLM) by using natural language processing and creating plausible text based on patterns. However, the design of AI systems may lead to difficulties understanding the complexity of phrasing and lacking detailed knowledge of the topic. When this occurs, AI output can be short and surface levels (too simplified) or long-term and meaningless, as AI attempts to fill the gap (overgeneralization). These AI hallucinations can lead to learners’ frustration as their questions are flawed or inadequate answers and reduce the overall learning experience.
Overfitting
This phenomenon explains an AI system that has learned training materials up to the point of memory. This sounds like a positive thing, but if the AI model is “overfitting” it may have a hard time adapting to information that is different from the new one. For example, if your system is aware of only specific phrasing for each topic, it can misunderstand questions that do not match the training data, leading to slight or completely inaccurate answers. Like most hallucinations, this issue is more common in specialized niche topics where AI systems lack sufficient information.
Complex prompts
Remember that advanced and powerful AI technologies, no matter how advanced and powerful AI technologies are, can be confused by user prompts that do not follow spelling, grammar, syntax, or coherence rules. Questions that are overly detailed, subtle, or poorly structured can cause misunderstandings or misunderstandings. Also, AI is always trying to respond to users, so any effort to guess what users mean can lead to unrelated or incorrect answers.
Conclusion
E-Learning and L&D experts should not be afraid to use artificial intelligence for their content and overall strategy. On the contrary, this innovative technology is extremely useful, saving time and makes the process more efficient. However, it should be noted that AI can definitely enter L&D content if the error is not careful. In this article, we investigated the common AI errors that L&D experts and learners may encounter and the reasons behind them. Knowing what to expect will help you avoid being caught off guard by L&D’s AI hallucinations and make sure you get the most out of these tools.
