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- How AI can help design a course and speed up expert work;
- How neural networks are useful in group work and feedback;
- What to consider when working with AI and what services are worth trying.
In the future, corporate training may be integrated into wearable devices such as smartwatches, and artificial intelligence will significantly simplify the work of specialists in this field. Although full automation has not yet been achieved, neural networks are already capable of significantly simplifying processes in the field of employee training and development. The use of AI in corporate training allows you to optimize individual programs, analyze employee needs and offer suitable materials, which in turn increases the effectiveness of training and promotes professional growth.
The webinar "Artificial Intelligence for Specialists in Online Learning: Practical Application in 2023" from e-Learning Master provided participants with up-to-date knowledge on the implementation of artificial intelligence in online education. Experts shared practical examples of AI use, demonstrating how modern technologies can improve the quality of training and enhance the productivity of specialists. Find out how the integration of AI into educational processes is changing the approach to learning and what tools are already available.
How AI can help in corporate training
Neural networks cannot solve all existing problems, but they are already demonstrating interesting results in various fields. Experts propose several solutions that can significantly simplify life and improve work efficiency.
Vladimir Kazakov, Product Owner of the training platform development team at Raiffeisenbank, conducted an interesting experiment that demonstrated the capabilities of modern technology. He asked ChatGPT to generate the text for his webinar presentation. He then uploaded the prepared text to Slider, an AI-powered service for creating PowerPoint presentations. In just a few minutes, a fully functional presentation was generated. The neural network automatically distributed the text across the slides, adding headings, bulleted lists, and images found online. Some phrases were slightly reworded to improve comprehension. This experiment highlights the potential of artificial intelligence in educational and business processes, opening up new horizons for the preparation of materials and presentations. Vladimir noted that text-based presentations of AI capabilities aren't the only possible application. He explained, "If we add a test, we'll only need to further train the model and specify that when certain elements are detected, it should generate a test task in our iSpring with X questions, each containing Y answers." This approach allows for the effective integration of AI into educational platforms, improving learning and assessment.
Text chatbots significantly simplify interaction with experts, as Vladimir Kazakov claims. Neural networks can generate ideas for educational courses and develop their structure, which is often a challenging task for specialists without training experience. Using chatbots in the educational process opens up new opportunities to optimize workflows and improve the quality of training.
The expert noted that while establishing communication with professionals previously took several weeks, this process now takes just one day. A prepared structure, ready for editing and revisions, significantly simplifies the work. However, Vladimir advises avoiding unnecessary details and not emphasizing the fact that the advice was generated using artificial intelligence.
Vladimir Kazakov gave an example of using the artificial intelligence built into the Notion service to analyze feedback from Raiffeisenbank employees about the department's work. The neural network evaluates statements according to predefined parameters and can present the results in various formats, such as a scale from 1 to 5 or as characteristic symbols. While it can take several hours for a person to process reviews, AI completes this task in just a couple of minutes. This approach significantly simplifies and accelerates the analysis process, allowing companies to quickly gain valuable insights into the performance of their departments.
The advantage of using neural networks to analyze feedback lies not only in evaluating reviews but also in their clustering. The expert suggested the neural network analyze the reviews, highlighting key strengths and weaknesses in the department's performance. It is important to ignore short comments such as "Everything is great, thank you" in favor of more meaningful feedback. This approach allows for a deeper understanding of customer needs and expectations, as well as identifying areas for improvement, which ultimately contributes to improved service quality and departmental efficiency.
According to Vladimir, the neural network identified several key issues related to navigation and the content of mandatory courses. Although these issues were known, manually compiling the report would have taken a significant amount of time, making the use of a neural network a cost-effective solution. Vladimir noted, "Imagine if this applied to questions about courses, not just departments. We could receive weekly feedback for experts and methodologists, for example, saying, 'There are complaints about such-and-such topics.' It would be more specific and concise, not 33 pages long."
Artificial intelligence is becoming an important tool in the idea generation process, says Oleg Zamyshlyaev, a leading Russian expert in change management and founder of the Mozlab service. In his practice, he actively uses solutions like ChatGPT in strategic sessions with teams. As an example, he cited a Digital Learning community meeting, where participants explored the causes of potential community failure. In 40 minutes, the group proposed 25 ideas, while the neural network generated about 15 options in just one minute. Interestingly, approximately 60% of the ideas proposed by the neural network were similar to those put forward by humans. This highlights the potential of AI to support creativity and optimize the brainstorming process.
A neural network is more effective at identifying the causes of potential community failure, particularly in terms of incorrect and insufficient monetization. It can provide two or three high-quality answers, but these results require verification. The speaker emphasizes that the use of neural networks can significantly improve problem analysis and aid in finding solutions, which is especially relevant for modern communities.
Oleg Zamyshlyaev noted that there are many ways to generate ideas without using artificial intelligence. However, neural networks can significantly accelerate this process and more accurately adapt the results to specific requirements. Using AI to generate ideas not only saves time but also produces more targeted and relevant solutions.
Working with a group of executives requires a careful approach to The creation of lists that include business ideas, risks, and threats. Neural networks can significantly simplify this process, adapting lists to specific industries, stages of company development, and the current market situation. They allow for the addition of several key parameters, making the results more relevant. The quality of lists refined by humans is highly valued by those also actively involved in this topic. Using neural networks in this context not only improves the quality of analysis but also accelerates the decision-making process, which is essential for successful business development. There are certain limitations when working with artificial intelligence. These limitations can relate to both technical aspects and ethical issues. For example, AI may have difficulty understanding the context or nuances of human communication, which can lead to errors in data interpretation. Furthermore, there are risks associated with the privacy and security of the data used to train models. Ethical aspects, such as algorithmic bias and the impact on the labor market, also require serious consideration. Understanding these limitations is key to the effective and responsible use of artificial intelligence technologies.
- Information generated by a neural network must be double-checked.
The expert shared an interesting case: while working on a study titled "Typical Mistakes of a New Manager," which was planned for use in a training course, his team used traditional data collection methods. However, to obtain additional information, they turned to a neural network. A chatbot presented intriguing statistics, supposedly taken from a study by a well-known foreign school, which was welcomed by the authors. When the researchers delved into the details and asked additional questions, the bot not only pointed to a specific source but also provided a link to it. It ultimately turned out that neither the study itself nor the proposed link existed. This case highlights the importance of critical thinking and verifying information, especially in the context of rapid technological advances and artificial intelligence.
- Managers experience "colossal mistrust and even disdain" for the bot as a source.
This barrier must be recognized and overcome. New technologies essentially perform the same function as old methods, and this is worth focusing on. In other words, it is just a new tool that opens up new possibilities.
- The neural network is not responsible for its proposals.
Oleg Zamyshlyaev emphasizes that within the framework of group work, it is necessary to have a person who will take responsibility for a specific idea, instead of using artificial intelligence. Such a person must not only agree with the idea, but also be ready to further develop it. This is important for the successful completion of tasks and achieving the set goals in the team.
How to work with AI in human training
Experts have provided a number of recommendations for those who intend to begin experimenting with artificial intelligence. Before working with AI, it's important to clearly define the project's goals and objectives. You should also explore available tools and technologies to select the most appropriate ones for your needs. It's recommended to start with small projects, which will allow you to master the basic principles of working with AI. Don't forget the importance of collecting and analyzing data, as high-quality data is the foundation for the successful application of machine learning algorithms. It's also important to consider the ethical aspects of using AI to avoid negative consequences. Effective collaboration with other specialists in this field can significantly speed up the development process and improve results.
- Practice formulating queries (prompts). "You need to learn to communicate with AI—it's not a person, not a machine, it's a third skill, and communicating with them [needs to be developed] separately," Vladimir Kazakov emphasized. The more precisely your queries are formulated, the better.
- Don't limit yourself to one query. Oleg Zamyshlyaev emphasized that each prompt should be followed by a new one: for example, "Come up with 15 ideas for how a community could fail," "Come up with 15 more non-trivial ideas for how a community could fail," "Cluster ideas into subgroups and name the clusters," "Uncover a specific cluster."
- The easiest way to try out AI is not through ChatGPT, but with the Notion service.
Finally, we present a list of services recommended by the speakers and discussion participants. These tools can be useful for your work and help improve the efficiency of your processes.
- ChatGPT is an AI-powered chatbot that works in a conversational format and can find, generate, and process text (and code).
- Notion AI is AI built into the Notion productivity service.
- Slider is an AI-powered service for creating slides and presentations.
- You.com is a search engine with built-in AI. Its advantage is that it provides not only information, but also links to sources.
- Midjourney is an AI-powered visual content generator.
- Stable Diffusion is an AI-powered visual content generator.
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