AI-Driven Question Generation for Automated E-Learning Assessment and Personalized Feedback

The utilization of artificial intelligence (AI) in educational settings has become a topic of increasing significance in recent years, with particular emphasis on its role in evaluating online learning experiences. This survey explores the innovative use of AI- driven question generation and personalized feedback mechanisms, highlighting their potential to enhance learner engagement and improve educational outcomes. We begin by examining various AI technologies, including natural language processing (NLP) and machine learning algorithms, that facilitate the automatic generation of diverse assessment items tailored to individual learner profiles. The paper discusses the importance of real-time feedback in fostering a growth mindset, enabling learners to identify strengths and areas for improvement. We also review existing literature on adaptive learning systems and their effectiveness in creating personalized learning experiences. Additionally, this review examines the obstacles encountered when integrating AI into educational settings, including issues related to data security and the necessity for reliable validation techniques. Through the compilation of results from diverse studies, we seek to offer a thorough examination of AI's current role in e- learning assessment practices.

Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2024-12-12
DOI
https://doi.org/10.5281/zenodo.14408780
Primary Topic
Intelligent Tutoring Systems and Adaptive Learning
Type
article
Field-Weighted Citation Impact
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article

AI-Driven Question Generation for Automated E-Learning Assessment and Personalized Feedback

Zenodo (CERN European Organization for Nuclear Research)
Intelligent Tutoring Systems and Adaptive Learning
article

AI-Driven Question Generation for Automated E-Learning Assessment and Personalized Feedback

article en

Abstract

The utilization of artificial intelligence (AI) in educational settings has become a topic of increasing significance in recent years, with particular emphasis on its role in evaluating online learning experiences. This survey explores the innovative use of AI- driven question generation and personalized feedback mechanisms, highlighting their potential to enhance learner engagement and improve educational outcomes. We begin by examining various AI technologies, including natural language processing (NLP) and machine learning algorithms, that facilitate the automatic generation of diverse assessment items tailored to individual learner profiles. The paper discusses the importance of real-time feedback in fostering a growth mindset, enabling learners to identify strengths and areas for improvement. We also review existing literature on adaptive learning systems and their effectiveness in creating personalized learning experiences. Additionally, this review examines the obstacles encountered when integrating AI into educational settings, including issues related to data security and the necessity for reliable validation techniques. Through the compilation of results from diverse studies, we seek to offer a thorough examination of AI's current role in e- learning assessment practices.

Zenodo (CERN European Organization for Nuclear Research)
Quality Education
Openalex Percentile: Top 68%
Intelligent Tutoring Systems and Adaptive Learning
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AI-Driven Question Generation for Automated E-Learning Assessment and Personalized Feedback · Zenodo (CERN European Organization for Nuclear Research) (2024) | TGRS Research Map | TGRS