Transforming Education with AI: An Automated System for Question Generation and Evaluation

In modern education, teachers and educational institutions often face challenges in consistently generating high-quality questions and accurately evaluating student responses. This process is time-consuming and may lead to inconsistencies in assessment, particularly when handling large volumes of students with diverse learning abilities. To address these issues, we propose an intelligent framework to simplify and enhance the assessment process by automatically generating questions from provided content and evaluating answers with precise scoring. Additionally, the system offers translation capabilities, allowing both questions and answers to be converted into the user’s preferred language for convenience. The project utilizes advanced models like Large Language Models and Retrieval-Augmented Generation to generate curriculum-aligned, varied questions and enhance automated answer evaluation with personalized feedback. With language translation features, it ensures accessibility for diverse linguistic backgrounds, improving efficiency, promoting personalized learning, and maintaining fairness in education.

Authors

Publication Details

Published
2025-06-20
DOI
https://doi.org/10.1109/conit65521.2025.11166686
Primary Topic
Intelligent Tutoring Systems and Adaptive Learning
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Transforming Education with AI: An Automated System for Question Generation and Evaluation

Konda Srikar Goud, Akshara Akkaladevi, Poojitha Palle, Geetha Bangari et al.
Intelligent Tutoring Systems and Adaptive Learning
article

Transforming Education with AI: An Automated System for Question Generation and Evaluation

Konda Srikar Goud, Akshara Akkaladevi, Poojitha Palle, Geetha Bangari, Vyshnavi Kunta
article en

Abstract

In modern education, teachers and educational institutions often face challenges in consistently generating high-quality questions and accurately evaluating student responses. This process is time-consuming and may lead to inconsistencies in assessment, particularly when handling large volumes of students with diverse learning abilities. To address these issues, we propose an intelligent framework to simplify and enhance the assessment process by automatically generating questions from provided content and evaluating answers with precise scoring. Additionally, the system offers translation capabilities, allowing both questions and answers to be converted into the user’s preferred language for convenience. The project utilizes advanced models like Large Language Models and Retrieval-Augmented Generation to generate curriculum-aligned, varied questions and enhance automated answer evaluation with personalized feedback. With language translation features, it ensures accessibility for diverse linguistic backgrounds, improving efficiency, promoting personalized learning, and maintaining fairness in education.

Openalex Percentile: Top 86%
Intelligent Tutoring Systems and Adaptive Learning
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.

Transforming Education with AI: An Automated System for Question Generation and Evaluation — Konda Srikar Goud, Akshara Akkaladevi, et al. · (2025) | TGRS Research Map | TGRS