AI-Based Adaptive Quiz Generation and Personalized Learning System

International Research Journal of Engineering and Technology (IRJET)

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Volume: 13 Issue: 04 | Apr 2026

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AI-Based Adaptive Quiz Generation and Personalized Learning System Dr. Mercy S¹, Saurav Suman², Om Tayal², Shivanjay Tripathi², Syed Sabeel² ¹ Associate Professor, Department of Information Science and Engineering, Bangalore Institute of Technology, Bengaluru, Karnataka, India ² Final Year Student, Department of Information Science and Engineering, Bangalore Institute of Technology, Bengaluru, Karnataka, India ---------------------------------------------------------------------***--------------------------------------------------------------------QUIZ-AI is proposed as an intelligent assessment platform Abstract - Traditional quiz and assessment systems are largely static, offering limited personalization, adaptability, and learner engagement. These systems often fail to address individual learning needs, resulting in reduced motivation and ineffective knowledge assessment. To overcome these limitations, this paper presents QUIZ-AI, an Artificial Intelligence–based adaptive quiz generation and personalized learning platform. The proposed system integrates Natural Language Processing (NLP), Machine Learning (ML), and Optical Character Recognition (OCR) to automatically generate quizzes from textual documents and PDFs. QUIZ-AI dynamically adjusts quiz difficulty based on learner performance and provides real-time feedback, analytics, and progress tracking. The system supports multiple user roles, including students and educators, enabling automated quiz creation, intelligent evaluation, and data-driven insights. Experimental evaluation demonstrates improved learner engagement, enhanced assessment accuracy, and efficient content generation compared to traditional quiz systems. The proposed approach highlights the potential of AI-driven educational platforms to transform assessment into a personalized, scalable, and adaptive learning experience.

that generates quizzes automatically from uploaded documents, personalizes question difficulty, and offers meaningful feedback to both students and educators. The system aims to transform traditional assessments into an engaging, data-driven, and learner-centric process.

Key Words: Artificial Intelligence, Quiz Generation, Personalized Learning, Adaptive Assessment, Natural Language Processing, Machine Learning, Educational Technology.

In this paper, we address the limitations of traditional quiz and assessment systems used in educational and training environments. These systems are largely static, rely on manually created question banks, and fail to adapt to individual learner abilities, often resulting in reduced engagement and ineffective evaluation of knowledge.

Manual quiz creation and grading also increase the workload for educators and limit scalability in large or diverse learning settings.

This paper explores the integration of Artificial Intelligence (AI) techniques, particularly Natural Language Processing (NLP) and Machine Learning (ML) models, into an intelligent framework for automated quiz generation and personalized learning.

By learning meaningful representations from unstructured educational content such as text documents and PDFs, the proposed system reduces dependence on manual question creation, enhances adaptability to diverse learning levels, and improves robustness across multiple subjects and domains.

The AI-driven approach enables dynamic quiz generation, difficulty adjustment based on learner performance, and real-time feedback, thereby transforming traditional static assessment methods into adaptive learning experiences.

1.1 Problem Statement

1. INTRODUCTION The rapid evolution of digital education has increased the demand for intelligent and adaptive learning systems that can cater to diverse learner needs. Traditional quiz platforms rely on fixed question banks and manual content creation, which limits personalization and adaptability.

Such systems often present the same difficulty level to all learners, regardless of their knowledge state, resulting in disengagement and poor learning outcomes.

Although Artificial Intelligence (AI) techniques have shown potential for automating question generation and personalizing learning experiences, their practical adoption is hindered by challenges such as inconsistent question quality, limited contextual understanding, data sparsity across subjects, and difficulty in accurately modeling learner performance. Additionally, concerns related to interpretability of AI decisions and adaptability across different educational domains remain unresolved in realworld academic and large-scale educational deployment environments.

Recent advancements in Artificial Intelligence (AI) and Natural Language Processing (NLP) have enabled automated content generation, learner profiling, and realtime analytics. These technologies provide an opportunity to design quiz systems that adapt dynamically to learner performance.

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1.2 Objectives

Natural Language Processing (NLP) models based on Deep Learning are commonly used to classify questions generated from textual content and documents. Models such as transformer-based architectures and neural embeddings automatically capture semantic patterns, contextual relationships, and linguistic structures, which relate to question types such as factual, conceptual, or analytical questions. Recurrent Neural Networks (RNNs), particularly Long Short-Term Memory (LSTM) models, are effective when processing sequential text data, as they capture contextual dependencies across sentences and phrases. Hybrid models that combine contextual text understanding with learner performance analysis further enhance classification accuracy under diverse educational conditions. Autoencoders (AEs) are also employed for unsupervised feature learning, where they compress textual representations into low-dimensional embeddings before classification.

In this paper, the goals are to examine the limitations of traditional quiz and assessment systems and identify shortcomings in existing evaluation methods. We aim to design and implement a practical content acquisition strategy that captures educational data from text documents and PDF files in real-world learning environments. We will develop and compare AI-based models for automated question generation, difficulty classification, and personalized quiz delivery. Additionally, we will create data preprocessing and optimization strategies to address content imbalance and enhance the system’s ability to handle diverse subjects and learner profiles. We will also integrate AI-based assessment with real-time performance analytics for accurate learner evaluation and targeted feedback. We plan to evaluate system performance using standard learning and assessment metrics and test how well it works across different educational domains and user groups.

Comparative studies indicate that deep learning–based NLP models outperform traditional methods in question classification and difficulty estimation, while sequencebased models excel in handling varied and noisy textual inputs. Hybrid approaches consistently achieve higher accuracy across multiple educational domains.

2. METHODOLOGY In this paper, the methodology starts with gathering data from educational resources such as text documents and PDF files using Natural Language Processing (NLP) techniques and Optical Character Recognition (OCR) to extract relevant content. The collected textual data goes through preprocessing steps, including tokenization, stopword removal, normalization, sentence segmentation, and semantic filtering to prepare it for question generation.

Data augmentation techniques are also applied to enrich content diversity and balance subject coverage.For question generation and assessment, Artificial Intelligence models are employed. NLP-based models generate multiple-choice and short-answer questions from processed text. Machine Learning algorithms analyze learner responses to classify question difficulty and adapt quiz content dynamically. Hybrid models combine content understanding and learner performance analysis to support personalized quiz delivery. Training and optimization utilize techniques such as adaptive learning rates, regularization, and early stopping to ensure robustness and prevent overfitting.

3.1 Quiz Question Generation and User Response Pattern The figure represents a typical interaction pattern between a learner and the Quiz-AI system during a quiz session. The initial response peak indicates the user’s first interaction with a generated question, reflecting the cognitive effort required to understand and answer the question. This is followed by variations in response time and accuracy, which stabilize as the quiz progresses and the system adapts the difficulty level based on learner performance.

Such response patterns highlight the dynamic and adaptive nature of the Quiz-AI framework. Key indicators such as response time, correctness, and progression trend are important for identifying learner proficiency, knowledge gaps, and engagement levels.

3. CLASSIFICATION OF QUIZ QUESTIONS AND

LEARNER PERFORMANCE

Automated quiz and question classification is vital for identifying the difficulty level, relevance, and learning intent of assessment content in intelligent educational systems. Traditional quiz platforms rely on manually tagged question banks or basic rule-based logic for classification. These approaches typically use simple statistical methods or conventional machine learning classifiers such as Support Vector Machines (SVMs) or basic Artificial Neural Networks (ANNs).

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The figure illustrates a typical user response pattern during an adaptive quiz session in the Quiz-AI system. Key characteristics such as initial response time, rate of improvement, fluctuation range, and stabilization trend are derived from this graph to evaluate learner understanding, cognitive load, and engagement level. The high response time observed at the beginning reflects the learner’s initial effort to comprehend newly generated questions, while subsequent variations indicate the adjustment phase as the system adapts question difficulty based on user performance.

parameters: classification accuracy, adaptability, and response time cost. The Convolutional Neural Network (CNN) method shows the best performance, with a classification accuracy of 91.30%, adaptability of 88.90%, and a reduced response time of about 2 seconds. This makes it a strong choice for real-time assessment applications. The Artificial Neural Network (ANN) method has a similar classification accuracy of 84.50% but lower adaptability at 79.20%, and it takes around 3 seconds, which makes it less practical for highly adaptive quiz systems. The traditional rule-based method has moderate adaptability at 68.40% but the lowest classification accuracy at 72.80%. This shows its limited effectiveness for quiz question classification, even though it has a faster response time of less than 1 second. The hybrid CNN–LSTM method achieves a classification accuracy of 95.40% and adaptability of 93.80%, with a response time of around 3 seconds, but it still outperforms the other methods. In summary, this comparison shows that CNN-based and hybrid models provide higher accuracy and more reliable classification.

3.2 Feature Extraction and Difficulty Analysis in QuizAI

Table -1: Results comparing quiz classification methods.[2] Fig -2: Difficulty level variation based on extracted learner

Methods

interaction features.[2] Fig. 3.2 shows the variation of question difficulty level across a quiz session derived from learner interaction data.

Key features such as response time, answer accuracy, progression rate, and consistency are taken from these interactions to evaluate learner proficiency and question difficulty. The gradual increase in difficulty reflects improved learner performance, while stabilization indicates that the system has identified an appropriate challenge level. These characteristics provide valuable insight into learner understanding and engagement during the quiz.

Classification Accuracy (%)

Adaptability (%) Response Time

Traditional Method 72.80

68.40

< 1 sec

ANN

84.50

79.20

~3 sec

CNN

91.30

88.90

~2 sec

CNN-LSTM (Hybrid)

95.40

93.80

~3 sec

4. PROS AND CONS

4.1 Pros 

However, differences in learner background knowledge, subject familiarity, attention span, and external distractions often make manual difficulty assessment difficult in traditional quiz systems. In AI-based learning frameworks, raw user interaction data is fed directly into adaptive models that automatically learn performance patterns and temporal dependencies. This reduces the need for extensive manual feature engineering and allows for more accurate difficulty classification and personalized quiz adaptation in real-world educational environments.

  

3.3 Comparison of Quiz Classification Methods

Automated Feature Extraction: Deep learning removes the need for manual question tagging and rule-based difficulty assignment, which reduces human effort and improves system efficiency.

High Accuracy: Models like CNN, LSTM, and hybrid architectures achieve high classification accuracy in quiz difficulty and learner performance analysis.

Scalability: These models can handle large numbers of learners and diverse question datasets, making them suitable for online learning platforms.

Personalized Learning: They enable adaptive quiz delivery by analyzing learner behavior in real time, which improves engagement.

The table compares the performance of different quiz question classification methods based on three main

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Volume: 13 Issue: 04 | Apr 2026

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role of AI in transforming traditional evaluation methods into efficient, adaptive, and learner-focused assessment solutions.

4.2 Cons  

   

High Data Requirement: Effective training requires large volumes of labeled educational data, which can be time-consuming.

Computational Cost: Training deep learning models requires powerful hardware such as GPUs and significant memory resources, increasing deployment cost.

Black Box Nature: Limited interpretability makes it difficult for educators to understand model decisions and explain assessment outcomes.

Generalization Limits: Models may not perform well across different subjects or learner groups unless they are retrained with representative data.

Model Bias: Learning models may reflect biases present in training data, which can affect fairness in question difficulty and learner evaluation.

Maintenance Overhead: Regular model updates and performance monitoring are required to maintain accuracy.

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[2] P. Laban, T. C. Miller, and J. Hearst, “Quiz Design Task: Helping Teachers Create Quizzes with Automated Question Generation,” Proceedings of the NAACL, pp.

354–368, 2022.

[3] A. K. Bhowmick, S. Das, and P. Mitra, “Automatic Question Generation from Educational Text Using Natural Language Processing,” Springer Lecture Notes in Computer Science, vol. 13738, pp. 210–221, 2023.

[4] Y. Wang, Z. Chen, and J. Zhang, “AI-Based Adaptive Quiz System for Personalized Learning,” Proceedings of the Educational Data Mining Conference, pp. 145–152, 2023.

5. CONCLUSION

[5] M. Delianidi, S. Papadopoulos, and I. Kompatsiaris, “DKPRACTICE: A Personalized Learning Platform Using Artificial Intelligence,” arXiv preprint arXiv:2402.01845, 2024.

The study demonstrates that applying Artificial Intelligence (AI) and Deep Learning (DL) techniques to quiz generation and assessment provides an effective solution for personalized and adaptive learning. Traditional quiz systems rely heavily on manual question design and static evaluation, which limits scalability and learner engagement. In contrast, AI-based models such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Long Short-Term Memory (LSTM) networks significantly enhance automated feature extraction, question classification accuracy, and learner performance analysis.

[6] K. Kurdi, J. Leo, and A. Parsia, “A Systematic Review of Automatic Question Generation for Educational Purposes,” International Journal of Artificial Intelligence in Education, vol. 30, no. 1, pp. 121– 204,2020.

[7] E. Kurdi, H. Leo, J. Parsia, M. Sattler, and A. Al-Emari, “A Systematic Review of Automatic Question Generation for Educational Purposes,” International Journal of Artificial Intelligence in Education, vol. 30, no. 1, pp.

121–204, 2020.

Experimental results show that deep learning-based approaches achieve higher classification accuracy and improved adaptability compared to conventional methods, making them suitable for real-time educational applications. Despite challenges related to data requirements, computational complexity, and interpretability, these limitations can be addressed through optimized training strategies and explainable AI techniques. Overall, the integration of AI-driven quiz systems represents a shift toward intelligent, scalable, and learner-centric assessment, offering significant potential for modern digital education platforms. Furthermore, the proposed Quiz-AI framework demonstrates the potential of intelligent assessment systems to support continuous learning improvement through data-driven decisionmaking. By adapting question difficulty and feedback based on learner performance, the system enhances engagement and knowledge retention. These capabilities highlight the

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[8] S. Afzal and R. Mitkov, “Automatic Generation of Multiple Choice Questions from Text,” Proceedings of the Workshop on Natural Language Processing for Education, pp. 12–19, 2014.

[9] Z. Liu, X. Wang, and Y. Liu, “Automatic Question Generation for Reading Comprehension Using Transformer-Based Models,” IEEE Access, vol. 9, pp.

112345–112356, 2021.

[10] T. Yang, K. Xiong, and J. Zhao, “An Intelligent Online Quiz System Based on Machine Learning and Student Behavior Analysis,” International Journal of Emerging Technologies in Learning, vol. 16, no. 8, pp. 45–58, 2021.

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[11] A. Heilman and N. A. Smith, “Good Question! Statistical Ranking for Question Generation,” Proceedings of the North American Chapter of the Association for Computational Linguistics (NAACL), pp. 609–617, 2010.

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