Automated Generation and Evaluation of MultipleChoice Quizzes using Langchain and Gemini LLM

The research study investigates the use of cutting-edge technologies like Langchain and Gemini AI for the automated creation and assessment of multiple-choice questions (MCQs). Although producing multiple-choice questions (MCQs) has traditionally been done by hand and required a lot of work, advances in artificial intelligence (AI) and natural language processing (NLP) have opened up new possibilities. The creation and implementation of an MCQ generator—which uses Gemini AI to generate MCQs and Langchain for rapid engineering are covered in this paper. Customizing prompts for prompt engineering, chaining prompts with the Gemini AI model, and utilizing OpenAI's GPT-3.5 and multiple Language Learning Models (LLMs) to assess the created MCQs are the steps in the process. The study intends to assess these models' efficiency in handling intricate queries, producing appropriate answers, and examining the caliber of the MCQs that are produced.

Authors

Institutions

Publication Details

Published
2024-08-29
DOI
https://doi.org/10.1109/iceect61758.2024.10739326
Citations
9
Primary Topic
Cryptographic Implementations and Security
Type
article
Field-Weighted Citation Impact
3.13
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Automated Generation and Evaluation of MultipleChoice Quizzes using Langchain and Gemini LLM

Ratna Patil, Amogh Joshi, P.P. Pawar, R. P. Dube et al.
9 citations
Cryptographic Implementations and Security
3.13
article

Automated Generation and Evaluation of MultipleChoice Quizzes using Langchain and Gemini LLM

Ratna Patil, Amogh Joshi, P.P. Pawar, R. P. Dube, Zinee Gulhane
article en
9 citations

Abstract

The research study investigates the use of cutting-edge technologies like Langchain and Gemini AI for the automated creation and assessment of multiple-choice questions (MCQs). Although producing multiple-choice questions (MCQs) has traditionally been done by hand and required a lot of work, advances in artificial intelligence (AI) and natural language processing (NLP) have opened up new possibilities. The creation and implementation of an MCQ generator—which uses Gemini AI to generate MCQs and Langchain for rapid engineering are covered in this paper. Customizing prompts for prompt engineering, chaining prompts with the Gemini AI model, and utilizing OpenAI's GPT-3.5 and multiple Language Learning Models (LLMs) to assess the created MCQs are the steps in the process. The study intends to assess these models' efficiency in handling intricate queries, producing appropriate answers, and examining the caliber of the MCQs that are produced.

International Institute of Information Technology (IN)
Openalex Percentile: Top 7%
Cryptographic Implementations and Security
3.13
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

Automated Generation and Evaluation of MultipleChoice Quizzes using Langchain and Gemini LLM — Ratna Patil, Amogh Joshi, et al. · (2024) | TGRS Research Map | TGRS