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7 Mistakes To Avoid When Training AI Models For Corporate ELearning

Artificial intelligence has been doing miracles for years, and in recent times, it has found itself simplifying learning. Particularly, corporate learning is where people from various disciplines and backgrounds are involved in attaining a common organizational goal.

Author:Hajra Shannon
Reviewer:Paula M. Graham
Feb 28, 2024977 Shares54.2K Views
Artificial intelligence has been doing miracles for years, and in recent times, it has found itself simplifying learning. Particularly, corporate learning is where people from various disciplines and backgrounds are involved in attaining a common organizational goal.
As a matter of fact, AI in eLearninghas made it possible for organizations to implement learning quicker and with more efficiency. It has made it incredibly easy and more time-efficient to train employees to take up challenges and prepare them for future demand changes. However, AI, if not used smartly to create learning modules, can end up turning less impactful and engaging.
7 Mistakes To Avoid When Training AI Models For Corporate eLearning

Not Keeping An Eye On The Quality Of Data You Are Using

Data sets are diverse, and it is essential to ensure that you are using only high-quality data sets to train your artificial intelligence models. Using data sets randomly can get challenging as the intent and quality of output being generated can be severely compromised and would need additional manhours to get fixed.
This is especially a problem if you are looking to dispatch your product as soon as possible. Hence, a quick resolution is to label the data properly. Incorporating the data monitoring process includes identifying sources that produce unreliable, erroneous data and clearing out records that are no longer relevant or serving a purpose.

Not Using Data Sets That Align With Today’s Demands

While historical data is required to analyze and understand how the market has evolved over time, using such data to produce eLearning content might not be a good idea. The information and value these data sets offer can be used to understand changing market trends, behavior & potential corresponding needs.
Still, the same data cannot be used to teach those specific, new needs. Instead, it is essential to ensure that the data you are using is new and has been sourced after extensive research and analysis.
This will ensure the generation of content for learning purposes is more focused on preparing the candidate for future challenges. Furthermore, using advanced and current data sets also makes it easier for candidates not unquestioningly to jump on trends but rather learn long-lasting skills.
It works best for organizations that have a vast range of talents spread across departments that eventually work together.

Not Customizing the AI-Generated Coursework

AI is incredibly powerful in analyzing, computing, and generating high-quality learning content. However, directly using this generated content without customizing it for the target audience can potentially dilute your efforts. Therefore, paying close attention to customizing your AI-generated learning content is highly recommended. This will ensure the required information is delivered with utmost precision and clarity, no irrelevant information is being added, the data has been utilized to its best potential, and the generated content aligns with the organization’s goals.

Add a Layer of Judgment

Artificial intelligence and machine learning often have a tendency to hallucinate information. What might seem right could be wrong, and vice versa. It is, therefore, crucial to cross-check the content that has been generated to ascertain that the facts, data, and information presented in the course content are accurate and contain no discrepancies.

Narrowing Down Content Generation

When generating content for modules that cover lengthy and complex topics, the inputs being given can become monotonous and similar in nature. This can narrow down the nature and quality of content being generated.
Furthermore, overtraining an AI model with a specific type of input can create brittleness and might have difficulties including vast data information in course material.
Additionally, it can also restrict your creative and innovative abilities, turning the coursework into a monotonous and not-so-engaging task that must be completed.

Not Defining Objectives With AI

Defining objectives goes a long way in ensuring that your AI model is able to generate relevant, latest, and intent-centric content. All this while not missing out on the engaging and innovation part of the equation.
Defining the purpose can be as basic as educating the learners to develop a new skill, promote logical thinking, or be able to solve complex problems with simple and pragmatic solutions.
A purpose helps the AI model innovate assignment questions, quizzes, and course content so that it meets the objectives without overdoing it. Additionally, defining an objective for the AI model will also work as a parameter for you to identify if the model is able to generate the desired format and type of content.
Using this parameter while analyzing results will ensure that you are making necessary adjustments and customizations to make the coursework align with your goals better.

Not Updating It According to the Feedback

Once the content has been consumed and responded to by the learners, taking their feedback and acting on it becomes a crucial part of ensuring the content is meeting the needs and requirements of the learners. These feedbacks do not have to be in the form of experience shared as testimonials alone.
They can also include individual learner performance analysis, challenges faced, areas where difficulty was most faced, and what made the course less interesting. Being open to feedbackis essential as it allows learners to share their genuine concerns and struggles while using the platform.
As you collect feedback, be sure to maintain a labeled library that can be incorporated into training the AI model. This way, you will be able to have your own organized data library that will constantly be used to improve and customize the coursework.

Parting Words

Artificial intelligence and machine learning have significantly changed the face of learning as we know it. These technological advances have made it possible to create innovative and creative coursework that will be enjoyed and taken seriously by your learners.
However, with the corporate world always running to compete, it has become essential to create diverse learning opportunities in sync with current market trends and be ready for future challenges.
Combining AI, data sets, and feedback will ensure you are offering the value, information, and confidence to help organizations grow.
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Hajra Shannon

Hajra Shannon

Paula M. Graham

Paula M. Graham

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