In recent years, Generative AI (GenAI) technology has emerged as a transformative force to generate content. With the advent of widely available Language Learning models (LLM), the implementation of Generative AI has been an constantly developing use case. In Assessment and Learning sector, one of the major use case of GenAI is the capability of question creation based on reference text making it easier for institutes and organization to better engage with their study materials.
Generative AI refers to algorithms that can create new content based on supplied prompts. While traditional AI models focus on prediction and analytics, Gen AI models focus on creating text, image, music etc. These models leverage deep learning techniques such as neural networks, to understand patterns and generate output that mimics human like creativity.
Mercer Mettl allows its user to add question to the content library. We have integrated our in-house LLM built on Open AI to generate questions like Multiple Choice, Multiple Correct, and Long Answer questions. User can provide background about the topic on which they want to create questions and within few seconds they can review the generated questions.
Generative AI Question Creation form
Generated Questions by the model
GenAI can significantly reduce the effort put in creating those questions. But it is also important to note the models are not human. There is a scope of error and the generated questions might be irrelevant or incorrect. A human review process before adding such questions improves the efficiency of such questions and ensure that a fair assessment happen.
In a world overflowing with information, generative AI stands out as a beacon of clarity, helping institutes and organizations navigate the complexities of knowledge with ease. With enhancing learning Gen AI is improving research efficiency and fostering engagement and this is just starting.
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Support for Meta and Sub Competencies in Evaluation Panel
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