AI has added a new scope and dimension to companies’ learning and development initiatives. The term technical training has acquired a new context with the development of AI. But chatbots are no longer adequate to answer the queries of the employees. Companies must develop and implement large language models (LLM) that can answer employees in real time.
How does the AI function?
A large language model functions with a transformer model, which reads and simplifies the text, and then the LLM produces the text in response to human questions. A transformer model was first invented in 2017 when this neural network structure-based model was used to convert one kind of input into another. This neural network was used to produce French from English.
The companies can now use large language models that can browse the complicated company manuals with a transformer model so that employees can use them to search for information. They can analyze the company’s compliance database compared to chatbots, which only provide employees with basic information.
Let’s talk about VR simulations:
Although the Metaverse is the coolest thing to have hit the industry now, it’s not always the best answer. The first reason for criticizing this much-celebrated simulations-based technology is that employees are taken from work for a long time, which is not feasible in every industry.
Companies must make critical decisions like where to use the simulations based technology to get the best return.
The companies have to create a plan and then follow it to ensure that such technologies are not overused, leading to an excess budget.
However, immersive learning is needed when it comes to learning technical skills for employees, like using heavy machinery. The employees can’t be trained on using such machines in person due to the cost of accidents. When AR/VR-based technologies are used, the solution happens in a simplified manner because the learners imbibe the exact manner in which a robot or machine has to be used during their work.
In certain industries like manufacturing and healthcare, it’s the demand of the competition to use such technologies.
- Industrial demand:
It’s the job of the L&D professionals to see that success equals prudence, too, and using technologies like augmented reality/virtual reality is well justified. It can only happen when they have done a thorough analysis to prove why such simulation-based technologies were the best solution possible in a certain situation.
They can also implement a small program to check whether the investment in AR/VR systems is necessary, and then scale up everything if necessary.
- Better results:
They can lead to lesser costs and lead to easier recall. In such a situation, making employees learn about surgeries through such technologies is the most effective way to create efficient surgeons of the future with the least possible training time.
- Need for data collection:
Implementing VR is another task, but collecting data after its execution is also equally important. This data helps the company analyze whether the learners are truly gaining anything from this implementation. If the learners find any problems with the simulation that they cannot fully engage in the experience, it is time to contact the vendor.
Who uses the VR experience to learn and cannot understand anything? All this data has to be collected by a company before implementing VR for training.
Why is data collection necessary?
The data needs to be collected according to the stakeholders who are responsible for making decisions about implementing this technology. xAPI is a brilliant way to capture all the data about a user’s movements during virtual reality technology, where his hands are positioned on the controllers. For example, when the learner is putting off fire in a simulation, how long does it take to do that?
Does he consider certain other factors before taking that decision, like how many people had to be evacuated before the fire was extinguished?
In this kind of simulation, did the learner consider the number of people in the room who had to be evacuated? If the fire could not be extinguished, the occupants had to be evacuated, but it was not the case when it was possible to douse the fire(Decision point). Also, it is important to set a milestone so that the time taken by the learner to extinguish the fire is measured(Milestone).
So, when the learner’s time exceeds that milestone, he can’t be considered relevant for the job. All this data assortment is necessary for VR/AR to generate successful results in a company and be considered a useful training resource. Whether VR simulations actually lead to intended training results causing a better performance in the company, or does the performance between a simulation and real-life events differ?
In every decision a learner takes, it’s important to evaluate the chain of events, like when a learner took a certain decision. He might have extinguished the fire when he saw people running, not when the smoke alarms started beeping. So, such a learner needs more training, depending on his slow reaction to the beeping smoke alarms.
Blended learning is a better approach than virtual reality training. It’s because a company needs to analyze data like learners who were trained before performed better in VR simulations. Was it necessary to distribute instructions to elicit a better response from learners?
At Creativ Technologies, we provide 2D simulations for our clients so that they can provide unrestricted learning experiences to their employees. With the right integration of authoring tools and craftsmanship, our niche simulations have been used by clients all over the world for focused experiential learning.