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Intelligent Systems and Tools for Education
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Intelligent Systems and Tools for Education

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: 20 August 2025 | Viewed by 9972

Special Issue Editors

Prof. Dr. Edwin Sha

E-Mail Website
Guest Editor
School of Computer Science, East China Normal University, Shanghai 200062, China
Interests: AI systems optimizations; data storage; quantum computing
Dr. Bo Jiang

E-Mail Website
Guest Editor
Shanghai Institute of AI for Education, East China Normal University, Shanghai 200062, China
Interests: intelligent tutoring system; educational data mining

Special Issue Information

Dear Colleagues,

In recent years, the integration of intelligent systems and tools in education has revolutionized the way we teach and learn. The advancements in artificial intelligence (AI) technologies has provided unprecedented opportunities to enhance personalized learning experiences, improve instruction efficiency, and support educators in various education contexts. These intelligent systems and tools are now playing a crucial role in addressing challenges such as scalability, accessibility, and individualized learning needs in education.

This Special Issue aims to provide a platform for researchers and practitioners to present their novel and unpublished works in the domain of intelligent educational systems and tools. Potential topics include, but are not limited to, the following:

  • AI-driven personalized learning systems;
  • Intelligent tutoring technologies and systems;
  • Educational resource generation system and tools;
  • Intelligent assessment and feedback systems for education;
  • Intelligent management systems and tools for education;
  • AI systems and tools for STEAM education;
  • AI agents for education;
  • Applications of large language models in education;
  • Educational data mining and learning analytics;
  • Gamification and AI in education;
  • Virtual and augmented reality in education;
  • AI for special education needs;
  • Ethical and privacy issues in AI-based educational tools

Prof. Dr. Edwin Sha
Dr. Bo Jiang
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • intelligent educational system
  • intelligent educational tools 
  • artificial intelligence

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Published Papers (7 papers)

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Research

14 pages, 1204 KiB  
Article
TwinStar: A Novel Design for Enhanced Test Question Generation Using Dual-LLM Engine
by Qingfeng Zhuge, Han Wang and Xuyang Chen
Appl. Sci. 2025, 15(6), 3055; https://doi.org/10.3390/app15063055 - 12 Mar 2025
Viewed by 722
Abstract
In light of the remarkable success of large language models (LLMs) in natural language understanding and generation, a trend of applying LLMs to professional domains with specialized requirements stimulates interest across various fields. It is desirable to further understand the level of intelligence [...] Read more.
In light of the remarkable success of large language models (LLMs) in natural language understanding and generation, a trend of applying LLMs to professional domains with specialized requirements stimulates interest across various fields. It is desirable to further understand the level of intelligence that can be achieved by LLMs in solving domain-specific problems, as well as the resources that need to be invested accordingly. This paper studies the problem of generating high-quality test questions with specified knowledge points and target cognitive levels in AI-assisted teaching and learning. Our study shows that LLMs, even those as immense as GPT-4 or Bard, can hardly fulfill the design objectives, lacking clear focus on cognitive levels pertaining to specific knowledge points. In this paper, we explore the opportunity of enhancing the capability of LLMs through system design, instead of training models with substantial domain-specific data, consuming mass computing and memory resources. We propose a novel design scheme that orchestrates a dual-LLM engine, consisting of a question generation model and a cognitive-level evaluation model, built with fine-tuned, lightweight baseline models and prompting technology to generate high-quality test questions. The experimental results show that the proposed design framework, TwinStar, outperforms the state-of-the-art LLMs for effective test question generation in terms of cognitive-level adherence and knowledge relevance. TwinStar implemented with ChatGLM2-6B improves the cognitive-level adherence by almost 50% compared to Bard and 21% compared to GPT-4.0. The overall improvement in the quality of test questions generated by TwinStar reaches 12.0% compared to Bard and 2% compared with GPT-4.0 while our TwinStar implementation consumes only negligible memory space compared with that of GPT-4.0. An implementation of TwinStar using LLaMA2-13B shows a similar trend of improvement. Full article
(This article belongs to the Special Issue Intelligent Systems and Tools for Education)
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20 pages, 32359 KiB  
Article
High-Quality Classroom Dialogue Automatic Analysis System
by Linzhao Jia, Han Sun, Jialong Jiang and Xiaozhe Yang
Appl. Sci. 2025, 15(3), 1613; https://doi.org/10.3390/app15031613 - 5 Feb 2025
Viewed by 769
Abstract
Classroom dialogue analysis is crucial as it significantly impacts both knowledge transmission and teacher–student interactions. Since the inception of classroom analysis research, traditional methods such as manual transcription and coding have served as foundational tools for understanding these interactions. While precise and insightful, [...] Read more.
Classroom dialogue analysis is crucial as it significantly impacts both knowledge transmission and teacher–student interactions. Since the inception of classroom analysis research, traditional methods such as manual transcription and coding have served as foundational tools for understanding these interactions. While precise and insightful, these methods are inherently time-consuming, labor-intensive, and susceptible to human bias. Moreover, they struggle to handle the scale and complexity of modern classroom data effectively. In contrast, many contemporary deep learning approaches focus primarily on dialogue classification, but often lack the capability to provide deeper interpretative insights. To address these challenges, this study introduces an automated dialogue analysis system that combines scalability, efficiency, and objectivity in evaluating teaching quality. We first collected a large dataset of classroom recordings from primary and secondary schools in China and manually annotated the dialogues using multiple encoding frameworks. Based on these data, we developed an automated analysis system featuring a novel dialogue classification algorithm that incorporates speaker role information for more accurate insights. Additionally, we implemented innovative visualization techniques to automatically generate comprehensive classroom analysis reports, effectively bridging the gap between traditional manual methods and modern automated approaches. Experimental results demonstrated the system’s high accuracy in distinguishing various types of classroom dialogue. Large-scale analysis revealed key patterns in classroom dynamics, showcasing the strong potential of our system to enhance teaching evaluation and provide valuable insights for improving education practices. Full article
(This article belongs to the Special Issue Intelligent Systems and Tools for Education)
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16 pages, 2857 KiB  
Article
Combining Multi-Scale Fusion and Attentional Mechanisms for Assessing Writing Accuracy
by Renyuan Liu, Yunyu Shi, Xian Tang and Xiang Liu
Appl. Sci. 2025, 15(3), 1204; https://doi.org/10.3390/app15031204 - 24 Jan 2025
Cited by 1 | Viewed by 801
Abstract
Traditional methods of assessing handwritten characters are often too subjective, inefficient, and lagging in feedback, which makes it difficult for educators to achieve fully objective writing assessments and for writers to receive timely suggestions for improvement. In this paper, we propose a convolutional [...] Read more.
Traditional methods of assessing handwritten characters are often too subjective, inefficient, and lagging in feedback, which makes it difficult for educators to achieve fully objective writing assessments and for writers to receive timely suggestions for improvement. In this paper, we propose a convolutional neural network (CNN) architecture that combines the attention mechanism with multi-scale feature fusion; specifically, the features are weighted by designing a bottleneck layer that combines the Squeeze-and-Excitation (SE) attention mechanism to highlight the important information and by applying a multi-scale feature fusion method to enable the network to capture both the global structure and the local details of Chinese characters. Finally, a high-quality dataset containing 26,800 images of handwritten Chinese characters is constructed based on the application scenario of the writing grade test, covering the common Chinese characters in the writing grade exam; The experimental results show that the proposed method achieves 98.6% accuracy on the writing grade exam dataset and 97.05% on the ICDAR-2013 public dataset, significantly improving recognition accuracy. The constructed dataset and improved model are suitable for application scenarios such as writing grade exams, which helps to improve marking efficiency and accuracy. Full article
(This article belongs to the Special Issue Intelligent Systems and Tools for Education)
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36 pages, 2054 KiB  
Article
Large Language Models as Evaluators in Education: Verification of Feedback Consistency and Accuracy
by Hyein Seo, Taewook Hwang, Jeesu Jung, Hyeonseok Kang, Hyuk Namgoong, Yohan Lee and Sangkeun Jung
Appl. Sci. 2025, 15(2), 671; https://doi.org/10.3390/app15020671 - 11 Jan 2025
Viewed by 1806
Abstract
The recent advancements in large language models (LLMs) have brought significant changes to the field of education, particularly in the generation and evaluation of feedback. LLMs are transforming education by streamlining tasks like content creation, feedback generation, and assessment, reducing teachers’ workload and [...] Read more.
The recent advancements in large language models (LLMs) have brought significant changes to the field of education, particularly in the generation and evaluation of feedback. LLMs are transforming education by streamlining tasks like content creation, feedback generation, and assessment, reducing teachers’ workload and improving online education efficiency. This study aimed to verify the consistency and reliability of LLMs as evaluators by conducting automated evaluations using various LLMs based on five educational evaluation criteria. The analysis revealed that while LLMs were capable of performing consistent evaluations under certain conditions, a lack of consistency was observed both among evaluators and across models for other criteria. Notably, low agreement among human evaluators correlated with reduced reliability in LLM evaluations. Furthermore, variations in evaluation results were influenced by factors such as prompt strategies and model architecture, highlighting the complexity of achieving reliable assessments using LLMs. These findings suggest that while LLMs have the potential to transform educational systems, careful selection and combination of models are essential to improve their consistency and align their performance with human evaluators in educational settings. Full article
(This article belongs to the Special Issue Intelligent Systems and Tools for Education)
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16 pages, 360 KiB  
Article
EduDCM: A Novel Framework for Automatic Educational Dialogue Classification Dataset Construction via Distant Supervision and Large Language Models
by Changyong Qi, Longwei Zheng, Yuang Wei, Haoxin Xu, Peiji Chen and Xiaoqing Gu
Appl. Sci. 2025, 15(1), 154; https://doi.org/10.3390/app15010154 - 27 Dec 2024
Viewed by 825
Abstract
Educational dialogue classification is a critical task for analyzing classroom interactions and fostering effective teaching strategies. However, the scarcity of annotated data and the high cost of manual labeling pose significant challenges, especially in low-resource educational contexts. This article presents the EduDCM framework [...] Read more.
Educational dialogue classification is a critical task for analyzing classroom interactions and fostering effective teaching strategies. However, the scarcity of annotated data and the high cost of manual labeling pose significant challenges, especially in low-resource educational contexts. This article presents the EduDCM framework for the first time, offering an original approach to addressing these challenges. EduDCM innovatively integrates distant supervision with the capabilities of Large Language Models (LLMs) to automate the construction of high-quality educational dialogue classification datasets. EduDCM reduces the noise typically associated with distant supervision by leveraging LLMs for context-aware label generation and incorporating heuristic alignment techniques. To validate the framework, we constructed the EduTalk dataset, encompassing diverse classroom dialogues labeled with pedagogical categories. Extensive experiments on EduTalk and publicly available datasets, combined with expert evaluations, confirm the superior quality of EduDCM-generated datasets. Models trained on EduDCM data achieved a performance comparable to that of manually annotated datasets. Expert evaluations using a 5-point Likert scale show that EduDCM outperforms Template-Based Generation and Few-Shot GPT in terms of annotation accuracy, category coverage, and consistency. These findings emphasize EduDCM’s novelty and its effectiveness in generating high-quality, scalable datasets for low-resource educational NLP tasks, thus reducing manual annotation efforts. Full article
(This article belongs to the Special Issue Intelligent Systems and Tools for Education)
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21 pages, 1731 KiB  
Article
Using Educational Chatbots with Metacognitive Feedback to Improve Science Learning
by Jiaqi Yin, Yi Zhu, Tiong-Thye Goh, Wen Wu and Yi Hu
Appl. Sci. 2024, 14(20), 9345; https://doi.org/10.3390/app14209345 - 14 Oct 2024
Cited by 1 | Viewed by 2272
Abstract
Educational chatbots (ECs) can offer instructional feedback to enhance learning. However, the effect of metacognitive feedback on science education has not been fully explored. This study focuses on the effect of the EC with metacognitive feedback on students’ knowledge retention, transfer, and intrinsic [...] Read more.
Educational chatbots (ECs) can offer instructional feedback to enhance learning. However, the effect of metacognitive feedback on science education has not been fully explored. This study focuses on the effect of the EC with metacognitive feedback on students’ knowledge retention, transfer, and intrinsic motivation in the field of biology science. A between-group experimental design with 62 college students was conducted. Students in the experiment group received metacognitive feedback, whereas students in the control group received no feedback. The results of the ANCOVA test showed that students in the experiment group demonstrated better knowledge retention and transfer than those in the control group (F = 13.11, p = 0.001; F = 14.39, p < 0.001). Further, students in the experiment group reported more learning interest and higher perceived competence and value than those in the control group (F = 3.72, p = 0.001; F = 1.91, p = 0.009; F = 2.70, p = 0.004). In addition, correlation analysis revealed that perceived competence in the metacognitive feedback group was positively related to knowledge transfer (r = 0.39, p = 0.032). However, there was no significant difference in perceived pressure between both groups (p = 0.203). This study highlights the potential of the EC with metacognitive feedback for science learning. Full article
(This article belongs to the Special Issue Intelligent Systems and Tools for Education)
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17 pages, 602 KiB  
Article
Bridging the Vocabulary Gap: Using Side Information for Deep Knowledge Tracing
by Haoxin Xu, Jiaqi Yin, Changyong Qi, Xiaoqing Gu, Bo Jiang and Longwei Zheng
Appl. Sci. 2024, 14(19), 8927; https://doi.org/10.3390/app14198927 - 3 Oct 2024
Viewed by 1300
Abstract
Knowledge tracing is a crucial task in personalized learning that models student mastery based on historical data to predict future performance. Currently, deep learning models in knowledge tracing predominantly use one-hot encodings of question, knowledge, and student IDs, showing promising results. However, they [...] Read more.
Knowledge tracing is a crucial task in personalized learning that models student mastery based on historical data to predict future performance. Currently, deep learning models in knowledge tracing predominantly use one-hot encodings of question, knowledge, and student IDs, showing promising results. However, they face a significant limitation: a vocabulary gap that impedes the processing of new IDs not seen during training. To address this, our paper introduces a novel method that incorporates aggregated features, termed ‘side information’, that captures essential attributes such as student ability, knowledge mastery, and question difficulty. Our approach utilizes side information to bridge the vocabulary gap caused by ID-based one-hot encoding in traditional models. This enables the model, once trained on one dataset, to generalize and make predictions on new datasets with unfamiliar students, knowledge, or questions without the need for retraining. This innovation effectively bridges the vocabulary gap, reduces the dependency on specific data representations, and improves the overall performance of the model. Experimental evaluations on five distinct datasets show that our proposed model consistently outperforms baseline models, using fewer parameters and demonstrating seamless adaptability to new contexts. Additionally, ablation studies highlight that including side information, especially regarding students and questions, significantly improves knowledge tracing effectiveness. In summary, our approach not only resolves the vocabulary gap challenge but also offers a more robust and superior solution across varied datasets. Full article
(This article belongs to the Special Issue Intelligent Systems and Tools for Education)
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