[Nov 15th 2023] Applications of Large Language Models in the field of education

KAMYAR ZEINALIPOUR

When: Nov 15th, 2023 – 11:30 – 12:00 AM
Where: Google meet link
Description

Applications of Large Language Models in the field of education

This seminar explores the transformative impact of Large Language Models (LLMs) on education, focusing on their application in the generation of multilingual educational crosswords and quizzes. The seminar delves into the utilization of LLMs to enhance educational content creation, demonstrating how these models can be harnessed to create engaging and diverse learning materials in various languages.

The first part of the seminar addresses the novel approach of employing LLMs for crafting educational crosswords. Attendees will gain insights into the methodology used to leverage the linguistic capabilities of these models, resulting in the generation of crosswords that cater to different languages and linguistic nuances. The presentation will showcase the adaptability and efficiency of LLMs in customizing educational content for a global audience, fostering inclusivity and accessibility.

The second segment of the seminar focuses on the utilization of LLMs in quiz generation, exploring both text and image inputs. The speaker will elucidate the intricate process of leveraging the language comprehension capabilities of LLMs to formulate challenging and contextually relevant quiz questions. Additionally, the integration of image inputs into quiz creation will be discussed, highlighting how LLMs can analyze and interpret visual information to generate meaningful questions that augment traditional text-based quizzes.

Throughout the seminar, I will share practical examples and case studies, demonstrating the real-world application of LLMs in education. Attendees will leave with a comprehensive understanding of the potential of LLMs to revolutionize content creation in the educational domain, making learning materials more dynamic, inclusive, and engaging.

 |  Category: Other, Seminars