In recent years, there has been a growing trend in the use of Large Language Models such as ChatGPT or Bard in the academic domain, particularly regarding examinations. Large Language Models are a form of artificial intelligence that can understand and process natural language, making them useful for a wide range of applications. While there are some clear advantages to using Large Language Models, there are also some significant disadvantages that need to be considered.
- Improved Efficiency: One of the primary advantages of using Large Language Models such as ChatGPT and Bard in examinations is improved efficiency. These models can process natural language at a much faster rate than a human examiner, allowing for quicker grading and more efficient examination processes.
- Increased Objectivity: Another advantage of using Large Language Models is increased objectivity. These models can evaluate exam responses without bias or preconceptions, ensuring that all students are evaluated fairly.
- Improved Feedback: Large Language Models can provide more detailed feedback to students on their exam responses, highlighting specific areas where the student may need improvement. This can be especially helpful for students who are struggling in a particular subject area.
- Dependence on Technology: One of the primary disadvantages of using Large Language Models in examinations is dependence on technology. If the technology fails, it can disrupt the examination process and cause delays or inaccuracies in grading.
- Limited Understanding of Context: While Large Language Models are highly advanced, they still have limitations in their understanding of context. This can result in incorrect evaluations or misunderstandings of student responses.
- Limited Flexibility: Large Language Models may not be able to handle certain types of exam questions or responses, limiting their flexibility and applicability in certain situations.
Overall, while there are advantages to using Large Language Models such as ChatGPT and Bard in examinations, it is important to consider the potential disadvantages and limitations as well. As technology continues to advance, it will be interesting to see how Large Language Models are integrated into academic domains and how they can continue to improve the examination process.