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Why it is important to know how chatbots based on neural networks work
Experts are confident that the experience and capabilities of specialists cannot be completely replaced by robots. This is due to the peculiarities of modern technologies. Chatbots, which have generated significant interest in educational practice, are based on large language models. They are trained on vast arrays of text, and the quality of their responses directly depends on the quality of the educational material. The better a neural network is trained on a particular topic, the more accurate and informative responses it can provide to users.
The technology in question does not possess logic or true knowledge, nor does it have any understanding of the world it describes. It functions by analyzing language structures and rules. A chatbot "understands" language features and text structures, allowing it to make educated guesses about how the text should appear. As a result, it generates the most suitable options based on its algorithms.
Artificial intelligence, like a child who eavesdrops on adult conversations and remembers them, is able to "retell" ideas and concepts. While AI responses may create the illusion of understanding, neither the child nor the AI actually comprehends the content of the discussion. Responses can be both amusing and embarrassing to adults, highlighting the limitations of AI in information processing.
AI-powered chatbots can generate responses that offer links to non-existent studies and articles. This occurs when the AI infers information from the text but is unable to find the relevant data. Such situations highlight the importance of properly training models and monitoring their output to avoid the dissemination of false information. Proper use of chatbots requires careful attention to sources and facts to ensure the reliability and accuracy of the responses provided.
Clark Quinn and Markus Bernhardt provide an example demonstrating the abilities of ChatGPT in the context of bar exams. This exam focuses on the use of legal language, court cases, and precedents. A comprehensive database containing such texts allows the neural network to effectively identify their structure, essence, and features, ensuring highly accurate responses. Thus, ChatGPT successfully handles tasks that require a deep understanding of legal terminology and context.
Language models perform less effectively in exams and competitions in physics, chemistry, and mathematics. This is because such situations require not only the retelling or formulation of concepts but also the ability to think logically, reason, and find answers based on knowledge. It is important to be able to analyze information and draw conclusions, which is beyond the capabilities of modern chatbots. They are not capable of the deep thinking required to successfully solve complex problems in these subjects.
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Neural networks significantly simplify work in the field of training and development (L&D). They are capable of analyzing large volumes of data on employee needs and the effectiveness of educational programs. This enables the creation of personalized learning paths that take into account individual preferences and learning styles.
The use of neural networks in L&D also helps automate routine processes, such as learning assessment and management of training materials. This not only saves time but also improves the quality of educational content.
Furthermore, neural networks can predict training needs, identifying gaps in employees' knowledge and skills. This allows organizations to proactively adapt their programs and activities, ensuring more effective staff development.
Thus, neural networks are becoming an important tool in the field of L&D, contributing to more effective and targeted employee training.
Why chatbots will not yet become full-fledged assistants in L&D
The structure of large language models imposes certain limitations on their use. In the field of corporate training, the authors of the article identify four main obstacles that can hinder the effective use of these technologies.
- Inability to work with images.
For image generation, there are specialized platforms based on generative neural networks, such as Midjourney. These tools function autonomously and are not integrated with text neural networks. If you need to create an infographic or visual image, a text-based chatbot won't be able to complete the task on its own. Clark Quinn and Markus Bernhardt emphasize that this is a significant limitation, as the visual component plays just as important a role in learning as the textual one. The right combination of text and visual content can significantly improve the effectiveness of learning and information comprehension.
- Text rewriting.
Chatbots most often base their responses on paraphrased texts rather than direct quotes from original sources. While their answers may be correct in some cases, relying on randomness in learning is impractical. The authors of the article warn that this practice can have serious consequences, especially in areas related to safety, health, or regulatory compliance.
- "Hallucinations."
Neural networks may have difficulty processing information if their database is insufficient for a particular topic. In such cases, they begin to generate answers that are inconsistent with reality, but appear correct in form. Experts point out that this phenomenon is called "hallucination"—when AI produces fictitious facts or links to non-existent sources. This underscores the importance of high-quality training of neural networks and the need to use reliable information to improve their performance.
- The inability to truly teach.
For effective teaching, it's important for a teacher or trainer not only to possess deep knowledge but also to be able to convey it in an accessible manner. The problem with large language models is that they only answer the questions asked, and you may not learn about what you didn't ask. This is reminiscent of the joke in which a student thinks they've mastered the material but then encounters difficulties in practice. Eventually, they realize they haven't received an explanation of a key point. When they turn to the teacher and ask, "Why didn't you tell me about this?", they receive the answer, "You didn't ask." This underscores the importance of active engagement in the learning process, where asking questions and receiving feedback are key to learning.
A neural network is not a human being or a training professional. It cannot account for all the aspects that a beginner unfamiliar with the topic should be aware of. Therefore, it is important to understand that neural networks can provide information, but they cannot always replace human experience and knowledge. When using neural networks, caution and critical thinking are essential, especially when dealing with complex or specialized topics.
Experts unanimously agree that training without human intervention is impossible. However, understanding the principles of generative neural networks and recognizing their limitations are key to developing high-quality training products. This understanding allows one to choose the right tools and avoid blind reliance on technology. Thus, a competent approach to the use of neural networks in the educational process can significantly improve the effectiveness of training.
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