Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
5.3
2.5
Journals
Applied Sciences
Special Issues
Artificial Intelligence Technologies for Education: Advancements, Challenges, and Impacts
applsci-logo
Submit to Special Issue Submit Abstract to Special Issue Review for Applied Sciences Propose a Special Issue

Journal Menu

► Journal Menu

Journal Browser

► Journal Browser
share announcement

Artificial Intelligence Technologies for Education: Advancements, Challenges, and Impacts

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

Deadline for manuscript submissions: 30 June 2025 | Viewed by 36360

Special Issue Editors

Dr. Yu Liang

E-Mail Website
Guest Editor
Faculty of Information Technology, Beijing University of Technology, Beijing, China
Interests: artificial intelligence; smart education; educational data mining; human-computer interaction; cognitive modeling and intelligent tutoring
Prof. Dr. Wenjun Wu

E-Mail Website
Guest Editor
Institute of Artificial Intelligence, Beihang University, Beijing 100191, China
Interests: swarm intelligence and multi-agent systems; cognitive modeling and intelligent tutoring; intelligent cloud services; intelligent software engineering
Dr. Ying Li

E-Mail Website
Guest Editor
Associate Professor, School of Computer Science and Engineering, Beihang University, Beijing 100191, China
Interests: intelligent scheduling; embedded real-time system; distributed parallel computing; program analysis; smart education

Special Issue Information

Dear Colleagues,

We are accepting submissions to the Special Issue on “Artificial Intelligence Technologies for Education: Advancements, Challenges and Impacts”.

This Special Issue seeks to explore the application of artificial intelligence (AI) technologies within the field of education. The Special Issue will focus on various educational domains and aims to highlight the unique task areas and challenges that arise in applying AI in education, as well as the potential positive impacts it can have. Authors are encouraged to present advances in AI techniques and methodologies specifically tailored to educational tasks and domains. This Special Issue aims to showcase innovative approaches that leverage AI to enhance personalized learning experiences, facilitate adaptive assessment, leverage natural language processing for educational purposes, utilize machine learning and data mining techniques to analyze educational data, and explore the cognitive modeling of learners. Additionally, it will emphasize the importance of considering human–computer interactions in the context of AI in education.

In this Special Issue, we invite the submission of research papers that delve into different aspects of AI in education, including the development of novel student models, the design and implementation of intelligent learning environments, the use of automated assistants to support learning processes, and the role of AI in providing instructional support to both educators and learners. Both theoretical and experimental studies are welcome, as well as comprehensive review and survey papers.

We look forward to receiving your contributions.

Dr. Yu Liang
Prof. Dr. Wenjun Wu
Dr. Ying Li
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

  • artificial intelligence
  • educational data mining
  • educational technology, pedagogical strategies and instructional support
  • intelligent learning environments
  • student modeling and cognitive modeling
  • learning analytics and personalized learning
  • automated assistants and intelligent tutoring
  • adaptive assessment
  • human–computer interaction

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • e-Book format: Special Issues with more than 10 articles can be published as dedicated e-books, ensuring wide and rapid dissemination.

Further information on MDPI's Special Issue polices can be found here.

Related Special Issue

Published Papers (22 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

26 pages, 8717 KiB  
Article
A Study of Potential Applications of Student Emotion Recognition in Primary and Secondary Classrooms
by Yimei Huang, Wei Deng and Taojie Xu
Appl. Sci. 2024, 14(23), 10875; https://doi.org/10.3390/app142310875 - 24 Nov 2024
Cited by 1 | Viewed by 443
Abstract
Emotion recognition is critical to understanding students’ emotional states. However, problems such as crowded classroom environments, changing light, and occlusion often affect the accuracy of recognition. This study proposes an emotion recognition algorithm specifically for classroom environments. Firstly, the study adds the self-made [...] Read more.
Emotion recognition is critical to understanding students’ emotional states. However, problems such as crowded classroom environments, changing light, and occlusion often affect the accuracy of recognition. This study proposes an emotion recognition algorithm specifically for classroom environments. Firstly, the study adds the self-made MCC module and the Wise-IoU loss function to make object detection in the YOLOv8 model more accurate and efficient. Compared with the native YOL0v8x, it reduces the parameters by 16% and accelerates the inference speed by 20%. Secondly, in order to address the intricacies of the classroom setting and the specific requirements of the emotion recognition task, a multi-channel emotion recognition network (MultiEmoNet) has been developed. This network fuses skeletal, environmental, and facial information, and introduces a central loss function and an attention module AAM to enhance the feature extraction capability. The experimental results show that MultiEmoNet achieves a classification accuracy of 91.4% on a homemade classroom student emotion dataset, which is a 10% improvement over the single-channel classification algorithm. In addition, this study also demonstrates the dynamic changes in students’ emotions in the classroom through visual analysis, which helps teachers grasp students’ emotional states in real time. This paper validates the potential of multi-channel information-fusion deep learning techniques for classroom teaching analysis and provides new ideas and tools for future improvements to emotion recognition techniques. Full article
(This article belongs to the Special Issue Artificial Intelligence Technologies for Education: Advancements, Challenges, and Impacts)
►▼ Show Figures

Figure 1

11 pages, 820 KiB  
Article
Attitudes of EFL Learners to the Implementation of the Area9 Lyceum Online Platform Based on the UTAUT Model
by Iman Oraif
Appl. Sci. 2024, 14(21), 9769; https://doi.org/10.3390/app14219769 - 25 Oct 2024
Viewed by 638
Abstract
The advancement of technology has led to the creation of numerous platforms that could potentially be used for remote education. For example, the recent development of the English Diploma Programme at a top university in the Kingdom of Saudi Arabia (KSA) deploys a [...] Read more.
The advancement of technology has led to the creation of numerous platforms that could potentially be used for remote education. For example, the recent development of the English Diploma Programme at a top university in the Kingdom of Saudi Arabia (KSA) deploys a new platform known as Area9 Lyceum (Area9). Because the English Diploma Programme is a recent development, and especially given its use of a new platform, this proposed research will investigate learners’ attitudes to and acceptance of using the platform. Furthermore, it will look at how other universities could benefit from this experience to develop their own English as a Foreign Language (EFL) programmes along technological lines, specifically by deploying a survey tool based on the unified theory of acceptance and use of technology (UTAUT). The results reflect the positive attitude of the participants. Recommendations can be drawn from this study to help persuade stakeholders in higher education to adopt such platforms in the teaching of EFL or English as a Second Language (ESL). Full article
(This article belongs to the Special Issue Artificial Intelligence Technologies for Education: Advancements, Challenges, and Impacts)
►▼ Show Figures

Figure 1

27 pages, 7286 KiB  
Article
A Novel Predictive Modeling for Student Attrition Utilizing Machine Learning and Sustainable Big Data Analytics
by Chiang Liang Kok, Chee Kit Ho, Leixin Chen, Yit Yan Koh and Bowen Tian
Appl. Sci. 2024, 14(21), 9633; https://doi.org/10.3390/app14219633 - 22 Oct 2024
Viewed by 1385
Abstract
Student attrition poses significant societal and economic challenges, leading to unemployment, lower earnings, and other adverse outcomes for individuals and communities. To address this, predictive systems leveraging machine learning and big data aim to identify at-risk students early and intervene effectively. This study [...] Read more.
Student attrition poses significant societal and economic challenges, leading to unemployment, lower earnings, and other adverse outcomes for individuals and communities. To address this, predictive systems leveraging machine learning and big data aim to identify at-risk students early and intervene effectively. This study leverages big data and machine learning to identify key parameters influencing student dropout, develop a predictive model, and enable real-time monitoring and timely interventions by educational authorities. Two preliminary trials refined machine learning models, established evaluation standards, and optimized hyperparameters. These trials facilitated the systematic exploration of model performance and data quality assessment. Achieving close to 100% accuracy in dropout prediction, the study identifies academic performance as the primary influencer, with early-year subjects like Mechanics and Materials, Design of Machine Elements, and Instrumentation and Control having a significant impact. The longitudinal effect of these subjects on attrition underscores the importance of early intervention. Proposed solutions include early engagement and support or restructuring courses to better accommodate novice learners, aiming to reduce attrition rates. Full article
(This article belongs to the Special Issue Artificial Intelligence Technologies for Education: Advancements, Challenges, and Impacts)
►▼ Show Figures

Figure 1

22 pages, 2575 KiB  
Article
Evaluating the Conformity to Types of Unified Modeling Language Diagrams with Feature-Based Neural Networks
by Irina-Gabriela Nedelcu and Anca Daniela Ionita
Appl. Sci. 2024, 14(20), 9470; https://doi.org/10.3390/app14209470 - 17 Oct 2024
Viewed by 954
Abstract
This article investigates the application of a deep learning model for evaluating the conformity of model images to types of UML diagrams to be used in self-training and educational settings. Our approach leans on a feature-based dataset that captures a broad range of [...] Read more.
This article investigates the application of a deep learning model for evaluating the conformity of model images to types of UML diagrams to be used in self-training and educational settings. Our approach leans on a feature-based dataset that captures a broad range of modeling elements from class, state machine, and sequence diagrams, enhancing the ability to recognize a larger variety of categories selected for this research. The neural network trained with these features representing parts of the UML concrete syntax demonstrates 90% in classification accuracy on average, in respect to our previous research on UML diagrams classification without using a feature-based dataset. This study concludes that a feature-based approach, combined with advanced neural network architectures, can improve the classification of such images, especially in edge cases where diagrams contain similar graphical details but the whole does not represent a UML diagram. For the given research, we obtained a 0.87 F1 score. Full article
(This article belongs to the Special Issue Artificial Intelligence Technologies for Education: Advancements, Challenges, and Impacts)
►▼ Show Figures

Figure 1

15 pages, 368 KiB  
Article
Leveraging Large Language Models to Support Authoring Gamified Programming Exercises
by Raffaele Montella, Ciro Giuseppe De Vita, Gennaro Mellone, Tullio Ciricillo, Dario Caramiello, Diana Di Luccio, Sokol Kosta, Robertas Damaševičius, Rytis Maskeliūnas, Ricardo Queirós and Jakub Swacha
Appl. Sci. 2024, 14(18), 8344; https://doi.org/10.3390/app14188344 - 16 Sep 2024
Viewed by 909
Abstract
Skilled programmers are in high demand, and a critical obstacle to satisfying this demand is the difficulty of acquiring programming skills. This issue can be addressed with automated assessment, which gives fast feedback to students trying to code, and gamification, which motivates them [...] Read more.
Skilled programmers are in high demand, and a critical obstacle to satisfying this demand is the difficulty of acquiring programming skills. This issue can be addressed with automated assessment, which gives fast feedback to students trying to code, and gamification, which motivates them to intensify their learning efforts. Although some collections of gamified programming exercises are available, producing new ones is very demanding. This paper presents GAMAI, an AI-powered exercise gamifier, enriching the Framework for Gamified Programming Education (FGPE) ecosystem. Leveraging large language models, GAMAI enables teachers to effortlessly apply storytelling to describe a gamified scenario, as GAMAI decorates natural language text with the sentences needed by OpenAI APIs to contextualize the prompt. Once a gamified scenario has been generated, GAMAI automatically produces exercise files in a FGPE-compatible format. According to the presented evaluation results, most gamified exercises generated with AI support were ready to be used, with no or minimum human effort, and were positively assessed by students. The usability of the software was also assessed as high by the users. Our research paves the way for a more efficient and interactive approach to programming education, leveraging the capabilities of advanced language models in conjunction with gamification principles. Full article
(This article belongs to the Special Issue Artificial Intelligence Technologies for Education: Advancements, Challenges, and Impacts)
►▼ Show Figures

Figure 1

15 pages, 2762 KiB  
Article
Research on Student Classroom Behavior Detection Based on the Real-Time Detection Transformer Algorithm
by Lihua Lin, Haodong Yang, Qingchuan Xu, Yanan Xue and Dan Li
Appl. Sci. 2024, 14(14), 6153; https://doi.org/10.3390/app14146153 - 15 Jul 2024
Cited by 1 | Viewed by 1582
Abstract
With the rapid development of artificial intelligence and big data technology, intelligent education systems have become a key research focus in the field of modern educational technology. This study aims to enhance the intelligence level of educational systems by accurately detecting student behavior [...] Read more.
With the rapid development of artificial intelligence and big data technology, intelligent education systems have become a key research focus in the field of modern educational technology. This study aims to enhance the intelligence level of educational systems by accurately detecting student behavior in the classroom using deep learning techniques. We propose a method for detecting student classroom behavior based on an improved RT DETR (Real-Time Detection Transformer) object detection algorithm. By combining actual classroom observation data with AI-generated data, we create a comprehensive and diverse student behavior dataset (FSCB-dataset). This dataset not only more realistically simulates the classroom environment but also effectively addresses the scarcity of datasets and reduces the cost of dataset construction. The study introduces MobileNetV3 as a lightweight backbone network, reducing the model parameters to one-tenth of the original while maintaining nearly the same accuracy. Additionally, by incorporating learnable position encoding and dynamic upsampling techniques, the model significantly improves its ability to recognize small objects and complex scenes. Test results on the FSCB-dataset show that the improved model achieves significant improvements in real-time performance and computational efficiency. The lightweight network is also easy to deploy on mobile devices, demonstrating its practicality in resource-constrained environments. Full article
(This article belongs to the Special Issue Artificial Intelligence Technologies for Education: Advancements, Challenges, and Impacts)
►▼ Show Figures

Figure 1

19 pages, 2880 KiB  
Article
Collaborative Analysis of Learners’ Emotional States Based on Cross-Modal Higher-Order Reasoning
by Wenyan Wu, Jingtao Zhao, Xingbo Shen and Guang Feng
Appl. Sci. 2024, 14(13), 5513; https://doi.org/10.3390/app14135513 - 25 Jun 2024
Viewed by 895
Abstract
Emotion is a significant factor influencing education and teaching, closely intertwined with learners’ cognitive processing. Conducting analysis of learners’ emotions based on cross-modal data is beneficial for achieving personalized guidance in intelligent educational environments. Currently, due to factors such as data scarcity and [...] Read more.
Emotion is a significant factor influencing education and teaching, closely intertwined with learners’ cognitive processing. Conducting analysis of learners’ emotions based on cross-modal data is beneficial for achieving personalized guidance in intelligent educational environments. Currently, due to factors such as data scarcity and environmental noise, data imbalances have led to incomplete or missing emotional information. Therefore, this study proposes a collaborative analysis model based on attention mechanisms. The model extracts features from various types of data using different tools and employs multi-head attention mechanisms for parallel processing of feature vectors. Subsequently, through a cross-modal attention collaborative interaction module, effective interaction among visual, auditory, and textual information is facilitated, significantly enhancing comprehensive understanding and the analytical capabilities of cross-modal data. Finally, empirical evidence demonstrates that the model can effectively improve the accuracy and robustness of emotion recognition in cross-modal data. Full article
(This article belongs to the Special Issue Artificial Intelligence Technologies for Education: Advancements, Challenges, and Impacts)
►▼ Show Figures

Figure 1

22 pages, 2873 KiB  
Article
Computational Thinking Measurement of CS University Students
by Raquel Hijón-Neira, Celeste Pizarro, John French, Daniel Palacios-Alonso and Emre Çoban
Appl. Sci. 2024, 14(12), 5261; https://doi.org/10.3390/app14125261 - 18 Jun 2024
Viewed by 1035
Abstract
The measurement of computational thinking ability among computer science (CS) university students is of paramount importance. This study introduces UniCTCheck, a novel method designed to assess the main components of computational thinking in CS students. Utilising two key instruments, namely, the web application [...] Read more.
The measurement of computational thinking ability among computer science (CS) university students is of paramount importance. This study introduces UniCTCheck, a novel method designed to assess the main components of computational thinking in CS students. Utilising two key instruments, namely, the web application CTScore and the psychometric scale CTProg, this research aims to precisely evaluate seven core components of computational thinking and six programming concepts skills essential for CS students. The study, conducted at Rey Juan Carlos University and Atlantic Technological University Galway, involved a diverse sample of students from different year levels and programme specialisations. Through a rigorous research design, including sampling strategies and data collection tools, this study seeks to address critical research questions related to the measurement of variations in students’ computational thinking and programming skills by gender, university level, and location. By shedding light on the significance of computational thinking and programming in the educational realm, this research contributes to the existing literature and underscores the essential role of computational skills in the modern era. Full article
(This article belongs to the Special Issue Artificial Intelligence Technologies for Education: Advancements, Challenges, and Impacts)
►▼ Show Figures

Figure 1

26 pages, 3572 KiB  
Article
Prediction of Students’ Adaptability Using Explainable AI in Educational Machine Learning Models
by Leonard Chukwualuka Nnadi, Yutaka Watanobe, Md. Mostafizer Rahman and Adetokunbo Macgregor John-Otumu
Appl. Sci. 2024, 14(12), 5141; https://doi.org/10.3390/app14125141 - 13 Jun 2024
Cited by 1 | Viewed by 2863
Abstract
As the educational landscape evolves, understanding and fostering student adaptability has become increasingly critical. This study presents a comparative analysis of XAI techniques to interpret machine learning models aimed at classifying student adaptability levels. Leveraging a robust dataset of 1205 instances, we employed [...] Read more.
As the educational landscape evolves, understanding and fostering student adaptability has become increasingly critical. This study presents a comparative analysis of XAI techniques to interpret machine learning models aimed at classifying student adaptability levels. Leveraging a robust dataset of 1205 instances, we employed several machine learning algorithms with a particular focus on Random Forest, which demonstrated highest accuracy at 91%. The models’ precision, recall and F1-score were also evaluated, with Random Forest achieving a precision of 0.93, a recall of 0.94, and an F1-score of 0.94. Our study utilizes SHAP, LIME, Anchors, ALE, and Counterfactual explanations to reveal the specific contributions of various features impacting adaptability predictions. SHAP values highlighted ‘Class Duration’ significance (mean SHAP value: 0.175); LIME explained socio-economic and institutional factors’ intricate influence. Anchors provided high-confidence rule-based explanations (confidence: 97.32%), emphasizing demographic characteristics. ALE analysis underscored the importance of ‘Financial Condition’ with a positive slope, while Counterfactual scenarios highlighted the impact of slight feature variations of 0.5 change in ‘Class Duration’. Consistently, ‘Class Duration’ and ‘Financial Condition’ emerge as key factors, while the study also underscores the subtle effects of ‘Institution Type’ and ‘Load-shedding’. This multi-faceted interpretability approach bridges the gap between machine learning performance and educational relevance, presenting a model that not only predicts but also explains the dynamic factors influencing student adaptability. The synthesized insights advocate for educational policies accommodating socioeconomic factors, instructional time, and infrastructure stability to enhance student adaptability. The implications extend to informed and personalized educational interventions, fostering an adaptable learning environment. This methodical research contributes to responsible AI application in education, promoting predictive and interpretable models for equitable and effective educational strategies. Full article
(This article belongs to the Special Issue Artificial Intelligence Technologies for Education: Advancements, Challenges, and Impacts)
►▼ Show Figures

Figure 1

21 pages, 3463 KiB  
Article
Educational Data Clustering in Secondary School Sensor-Based Engineering Courses Using Active Learning Approaches
by Taras Panskyi, Ewa Korzeniewska and Anna Firych-Nowacka
Appl. Sci. 2024, 14(12), 5071; https://doi.org/10.3390/app14125071 - 11 Jun 2024
Cited by 1 | Viewed by 858
Abstract
The authors investigated the impact of active learning STEM and STEAM approaches on secondary school students’ general engineering knowledge, intrinsic relevance, and creativity. Three out-of-school sensor-based courses were held successively. Every sensor-based course involved the final project development. A structured questionnaire was administered [...] Read more.
The authors investigated the impact of active learning STEM and STEAM approaches on secondary school students’ general engineering knowledge, intrinsic relevance, and creativity. Three out-of-school sensor-based courses were held successively. Every sensor-based course involved the final project development. A structured questionnaire was administered to 379 students and consisted of two critical factors: creativity and intrinsic relevance. The third factor was dedicated to the students’ engineering learning outcomes. Two factors were addressed to secondary school students, while the third factor was addressed to the tutors’ observations of the students’ general sensor-based knowledge. Clustering validation analysis quantified the obtained results and justified the significant differences in all estimated factors for different educational modes. Moreover, the study showcases the value of the arts in sensor-based learning-by-doing courses when tackling complex issues like engineering topics. The authors suggest that broader research be undertaken, involving a larger sample, a greater scale, and a diversity of factors. Full article
(This article belongs to the Special Issue Artificial Intelligence Technologies for Education: Advancements, Challenges, and Impacts)
►▼ Show Figures

Figure 1

26 pages, 5860 KiB  
Article
Identification of Students with Similar Performances in Micro-Learning Programming Courses with Automatically Evaluated Student Assignments
by Valerii Popovych and Martin Drlik
Appl. Sci. 2024, 14(9), 3615; https://doi.org/10.3390/app14093615 - 24 Apr 2024
Cited by 1 | Viewed by 1326
Abstract
The identification of heterogeneous and homogeneous groups of students using clustering analysis in learning analytics is still rare. The paper describes a study in which the students’ performance data stored in the micro-learning platform Priscilla are analyzed using learning analytics methods. This study [...] Read more.
The identification of heterogeneous and homogeneous groups of students using clustering analysis in learning analytics is still rare. The paper describes a study in which the students’ performance data stored in the micro-learning platform Priscilla are analyzed using learning analytics methods. This study aims to identify the groups of students with similar performances in micro-learning courses focused on learning programming and uncover possible changes in the number and composition of the identified groups of students. The CRISP-DM methodology was used to navigate through the complexity of the knowledge discovery process. Six different datasets representing different types of graded activities or term periods were prepared and analyzed for that purpose. The clustering analysis using the K-Means method found two clusters in all cases. Subsequently, performance metrics, the internal composition, and transfers of the students between clusters identified in different datasets were analyzed. As a result, this study confirms that analyzing student performance data from a micro-learning platform using learning analytics methods can reveal distinct groups of students with different academic performances, and these groups change over time. These findings align with teachers’ assumptions that the micro-learning platform with automated evaluation of programming assignments highlights how the students perceive the role of learning tools during learning programming in different term periods. Simultaneously, this study acknowledges that clustering, as an exploratory method, provides a solid basis for further research and can identify distinct groups of students with similar characteristics. Full article
(This article belongs to the Special Issue Artificial Intelligence Technologies for Education: Advancements, Challenges, and Impacts)
►▼ Show Figures

Figure 1

28 pages, 13856 KiB  
Article
Tayseer: A Novel AI-Powered Arabic Chatbot Framework for Technical and Vocational Student Helpdesk Services and Enhancing Student Interactions
by Abeer Alabbas and Khalid Alomar
Appl. Sci. 2024, 14(6), 2547; https://doi.org/10.3390/app14062547 - 18 Mar 2024
Cited by 3 | Viewed by 2588
Abstract
The rise of conversational agents (CAs) like chatbots in education has increased the demand for advisory services. However, student–college admission interactions remain manual and burdensome for staff. Leveraging CAs could streamline the admission process, providing efficient advisory support. Moreover, limited research has explored [...] Read more.
The rise of conversational agents (CAs) like chatbots in education has increased the demand for advisory services. However, student–college admission interactions remain manual and burdensome for staff. Leveraging CAs could streamline the admission process, providing efficient advisory support. Moreover, limited research has explored the role of Arabic chatbots in education. This study introduces Tayseer, an Arabic AI-powered web chatbot that enables instant access to college information and communication between students and colleges. This study aims to improve the abilities of chatbots by integrating features into one model, including responding with audiovisuals, various interaction modes (menu, text, or both), and collecting survey responses. Tayseer uses deep learning models within the RASA framework, incorporating a customized Arabic natural language processing pipeline for intent classification, entity extraction, and response retrieval. Tayseer was deployed at the Technical College for Girls in Najran (TCGN). Over 200 students used Tayseer during the first semester, demonstrating its efficiency in streamlining the advisory process. It identified over 50 question types from inputs with a 90% precision in intent and entity predictions. A comprehensive evaluation illuminated Tayseer’s proficiency as well as areas requiring improvement. This study developed an advanced CA to enhance student experiences and satisfaction while establishing best practices for education chatbot interfaces by outlining steps to build an AI-powered chatbot from scratch using techniques adaptable to any language. Full article
(This article belongs to the Special Issue Artificial Intelligence Technologies for Education: Advancements, Challenges, and Impacts)
►▼ Show Figures

Figure 1

16 pages, 1448 KiB  
Article
Predicting Student Performance in Online Learning: A Multidimensional Time-Series Data Analysis Approach
by Zhaoyu Shou, Mingquan Xie, Jianwen Mo and Huibing Zhang
Appl. Sci. 2024, 14(6), 2522; https://doi.org/10.3390/app14062522 - 16 Mar 2024
Cited by 5 | Viewed by 4053
Abstract
As an emerging teaching method, online learning is becoming increasingly popular among learners. However, one of the major drawbacks of this learning style is the lack of effective communication and feedback, which can lead to a higher risk of students failing or dropping [...] Read more.
As an emerging teaching method, online learning is becoming increasingly popular among learners. However, one of the major drawbacks of this learning style is the lack of effective communication and feedback, which can lead to a higher risk of students failing or dropping out. In response to this challenge, this paper proposes a student performance prediction model based on multidimensional time-series data analysis by considering multidimensional data such as students’ learning behaviors, assessment scores, and demographic information, which is able to extract the characteristics of students’ learning behaviors and capture the connection between multiple characteristics to better explore the impact of multiple factors on students’ performance. The model proposed in this paper helps teachers to individualize education for students at different levels of proficiency and identifies at-risk students as early as possible to help teachers intervene in a timely manner. In experiments on the Open University Learning Analytics Dataset (OULAD), the model achieved 74% accuracy and 73% F1 scores in a four-category prediction task and was able to achieve 99.08% accuracy and 99.08% F1 scores in an early risk prediction task. Compared with the benchmark model, both the multi-classification prediction ability and the early prediction ability, the model in this paper has a better performance. Full article
(This article belongs to the Special Issue Artificial Intelligence Technologies for Education: Advancements, Challenges, and Impacts)
►▼ Show Figures

Figure 1

18 pages, 6573 KiB  
Article
Development and Evaluation of an Image Processing-Based Kinesthetic Learning System
by Deniz Yıldız, Uğur Fidan, Mehmet Yıldız, Büşra Er, Gürbüz Ocak, Fatih Güngör, İjlal Ocak and Zeki Akyildiz
Appl. Sci. 2024, 14(5), 2186; https://doi.org/10.3390/app14052186 - 5 Mar 2024
Viewed by 1745
Abstract
This study aims to develop an interactive language learning game and explore its efficacy for English language learners. A computer-generated playground was projected onto a large classroom floor (4 × 3 m) with a wide-angle projection device. A Kinect depth camera determined the [...] Read more.
This study aims to develop an interactive language learning game and explore its efficacy for English language learners. A computer-generated playground was projected onto a large classroom floor (4 × 3 m) with a wide-angle projection device. A Kinect depth camera determined the spatial positions of the playground and the positions of the students’ heads, feet, and bodies. Then, we evaluated the system’s effect on English education through pre- and post-tests. While there was no significant difference between the groups in terms of achievement in the pre-tests, the experimental group exhibited significantly greater improvement in the post-tests (F: 14.815, p < 0.001, η2p: 0.086). Also, both groups demonstrated significant learning gains in post-tests compared to pre-tests (F: 98.214, p < 0.001, η2p: 0.383), and the group x time interaction of the experimental group increased more in percentage (32.32% vs. 17.54%) compared to the control group (F: 9.166, p < 0.003, η2p: 0.055). Qualitative data from student views indicated enhanced learning pace, vocabulary acquisition, enjoyment of the learning process, and increased focus. These findings suggest that a kinesthetic learning environment can significantly benefit English language learning in children. Full article
(This article belongs to the Special Issue Artificial Intelligence Technologies for Education: Advancements, Challenges, and Impacts)
►▼ Show Figures

Figure 1

19 pages, 1161 KiB  
Article
Enhanced Student Admission Procedures at Universities Using Data Mining and Machine Learning Techniques
by Basem Assiri, Mohammed Bashraheel and Ala Alsuri
Appl. Sci. 2024, 14(3), 1109; https://doi.org/10.3390/app14031109 - 29 Jan 2024
Cited by 2 | Viewed by 1503
Abstract
The progress of technology has played a crucial role in enhancing various fields such as education. Universities in Saudi Arabia offer free education to students and follow specific admission policies. These policies usually focus on features and scores such as the high school [...] Read more.
The progress of technology has played a crucial role in enhancing various fields such as education. Universities in Saudi Arabia offer free education to students and follow specific admission policies. These policies usually focus on features and scores such as the high school grade point average, general aptitude test, and achievement test. The main issue with current admission policies is that they do not fit with all majors, which results in high rates of failure, dropouts, and transfer. Another issue is that all mentioned features and scores are cumulatively calculated, which obscures some details. Therefore, this study aims to explore admission criteria used in Saudi Arabian universities and the factors that influence students’ choice of major. First, using data mining techniques, the research analyzes the relationships and similarities between the university’s grade point average and the other student admission features. The study proposes a new Jaccard model that includes modified Jaccard and approximated modified Jaccard techniques to match the specifications of students’ data records. It also uses data distribution analysis and correlation coefficient analysis to understand the relationships between admission features and student performance. The investigation shows that relationships vary from one major to another. Such variations emphasize the weakness of the generalization of the current procedures since they are not applicable to all majors. Additionally, the analysis highlights the importance of hidden details such as high school course grades. Second, this study employs machine learning models to incorporate additional features, such as high school course grades, to find suitable majors for students. The K-nearest neighbor, decision tree, and support vector machine algorithms were used to classify students into appropriate majors. This process significantly improves the enrolment of students in majors that align with their skills and interests. The results of the experimental simulation indicate that the K-nearest neighbor algorithm achieves the highest accuracy rate of 100%, while the decision tree algorithm’s accuracy rate is 81% and the support vector machine algorithm’s accuracy rate is 75%. This encourages the idea of using machine learning models to find a suitable major for applicants. Full article
(This article belongs to the Special Issue Artificial Intelligence Technologies for Education: Advancements, Challenges, and Impacts)
►▼ Show Figures

Figure 1

22 pages, 3928 KiB  
Article
Enhanced Chinese Domain Named Entity Recognition: An Approach with Lexicon Boundary and Frequency Weight Features
by Yan Guo, Shixiang Feng, Fujiang Liu, Weihua Lin, Hongchen Liu, Xianbin Wang, Junshun Su and Qiankai Gao
Appl. Sci. 2024, 14(1), 354; https://doi.org/10.3390/app14010354 - 30 Dec 2023
Cited by 2 | Viewed by 1414
Abstract
Named entity recognition (NER) plays a crucial role in information extraction but faces challenges in the Chinese context. Especially in Chinese paleontology popular science, NER encounters difficulties, such as low recognition performance for long and nested entities, as well as the complexity of [...] Read more.
Named entity recognition (NER) plays a crucial role in information extraction but faces challenges in the Chinese context. Especially in Chinese paleontology popular science, NER encounters difficulties, such as low recognition performance for long and nested entities, as well as the complexity of handling mixed Chinese–English texts. This study aims to enhance the performance of NER in this domain. We propose an approach based on the multi-head self-attention mechanism for integrating Chinese lexicon-level features; by integrating Chinese lexicon boundary and domain term frequency weight features, this method enhances the model’s perception of entity boundaries, relative positions, and types. To address training prediction inconsistency, we introduce a novel data augmentation method, generating enhanced data based on the difference set between all and sample entity types. Experiments on four Chinese datasets, namely Resume, Youku, SubDuIE, and our PPOST, show that our approach outperforms baselines, achieving F1-score improvements of 0.03%, 0.16%, 1.27%, and 2.28%, respectively. This research confirms the effectiveness of integrating Chinese lexicon boundary and domain term frequency weight features in NER. Our work provides valuable insights for improving the applicability and performance of NER in other Chinese domain scenarios. Full article
(This article belongs to the Special Issue Artificial Intelligence Technologies for Education: Advancements, Challenges, and Impacts)
►▼ Show Figures

Figure 1

22 pages, 903 KiB  
Article
Using LSTM to Identify Help Needs in Primary School Scratch Students
by Luis Eduardo Imbernón Cuadrado, Ángeles Manjarrés Riesco and Félix de la Paz López
Appl. Sci. 2023, 13(23), 12869; https://doi.org/10.3390/app132312869 - 30 Nov 2023
Viewed by 1295
Abstract
In the last few years, there has been increasing interest in the use of block-based programming languages as well as in the ethical aspects of Artificial Intelligence (AI) in primary school education. In this article, we present our research on the automatic identification [...] Read more.
In the last few years, there has been increasing interest in the use of block-based programming languages as well as in the ethical aspects of Artificial Intelligence (AI) in primary school education. In this article, we present our research on the automatic identification of the need for assistance among primary school children performing Scratch exercises. For data collection, user experiences have been designed to take into account ethical aspects, including gender bias. Finally, a first-in-class distance calculation method for block-based programming languages has been used in a Long Short-Term Memory (LSTM) model, with the aim of identifying when a primary school student needs help while he/she carries out Scratch exercises. This model has been trained twice: the first time taking into account the gender of the students, and the second time excluding it. The accuracy of the model that includes gender is 99.2%, while that of the model that excludes gender is 91.1%. We conclude that taking into account gender in training this model can lead to overfitting, due to the under-representation of girls among the students participating in the experiences, making the model less able to identify when a student needs help. We also conclude that avoiding gender bias is a major challenge in research on educational systems for learning computational thinking skills, and that it necessarily involves effective and motivating gender-sensitive instructional design. Full article
(This article belongs to the Special Issue Artificial Intelligence Technologies for Education: Advancements, Challenges, and Impacts)
►▼ Show Figures

Figure 1

14 pages, 880 KiB  
Article
A Fusion Framework for Confusion Analysis in Learning Based on EEG Signals
by Chenlong Zhang, Jian He, Yu Liang, Zaitian Wang and Xiaoyang Xie
Appl. Sci. 2023, 13(23), 12832; https://doi.org/10.3390/app132312832 - 29 Nov 2023
Cited by 1 | Viewed by 1668
Abstract
Human–computer interaction (HCI) plays a significant role in modern education, and emotion recognition is essential in the field of HCI. The potential of emotion recognition in education remains to be explored. Confusion is the primary cognitive emotion during learning and significantly affects student [...] Read more.
Human–computer interaction (HCI) plays a significant role in modern education, and emotion recognition is essential in the field of HCI. The potential of emotion recognition in education remains to be explored. Confusion is the primary cognitive emotion during learning and significantly affects student engagement. Recent studies show that electroencephalogram (EEG) signals, obtained through electrodes placed on the scalp, are valuable for studying brain activity and identifying emotions. In this paper, we propose a fusion framework for confusion analysis in learning based on EEG signals, combining feature extraction and temporal self-attention. This framework capitalizes on the strengths of traditional feature extraction and deep-learning techniques, integrating local time-frequency features and global representation capabilities. We acquire localized time-frequency features by partitioning EEG samples into time slices and extracting Power Spectral Density (PSD) features. We introduce the Transformer architecture to capture the comprehensive EEG characteristics and utilize a multi-head self-attention mechanism to extract the global dependencies among the time slices. Subsequently, we employ a classification module based on a fully connected layer to classify confusion emotions accurately. To assess the effectiveness of our method in the educational cognitive domain, we conduct thorough experiments on a public dataset CAL, designed for confusion analysis during the learning process. In both subject-dependent and subject-independent experiments, our method attained an accuracy/F1 score of 90.94%/0.94 and 66.08%/0.65 for the binary classification task and an accuracy/F1 score of 87.59%/0.87 and 41.28%/0.41 for the four-class classification task. It demonstrated superior performance and stronger generalization capabilities than traditional machine learning classifiers and end-to-end methods. The evidence demonstrates that our proposed framework is effective and feasible in recognizing cognitive emotions. Full article
(This article belongs to the Special Issue Artificial Intelligence Technologies for Education: Advancements, Challenges, and Impacts)
►▼ Show Figures

Figure 1

16 pages, 9648 KiB  
Article
A Parallel Open-World Object Detection Framework with Uncertainty Mitigation for Campus Monitoring
by Jian Dong, Zhange Zhang, Siqi He, Yu Liang, Yuqing Ma, Jiaqi Yu, Ruiyan Zhang and Binbin Li
Appl. Sci. 2023, 13(23), 12806; https://doi.org/10.3390/app132312806 - 29 Nov 2023
Viewed by 1325
Abstract
The recent advancements in artificial intelligence have brought about significant changes in education. In the context of intelligent campus development, target detection technology plays a pivotal role in applications such as campus environment monitoring and the facilitation of classroom behavior surveillance. However, traditional [...] Read more.
The recent advancements in artificial intelligence have brought about significant changes in education. In the context of intelligent campus development, target detection technology plays a pivotal role in applications such as campus environment monitoring and the facilitation of classroom behavior surveillance. However, traditional object detection methods face challenges in open and dynamic campus scenarios where unexpected objects and behaviors arise. Open-World Object Detection (OWOD) addresses this issue by enabling detectors to gradually learn and recognize unknown objects. Nevertheless, existing OWOD methods introduce two major uncertainties that limit the detection performance: the unknown discovery uncertainty from the manual generation of pseudo-labels for unknown objects and the known discrimination uncertainty from perturbations that unknown training introduces to the known class features. In this paper, we introduce a Parallel OWOD Framework with Uncertainty Mitigation to alleviate the unknown discovery uncertainty and the known discrimination uncertainty within the OWOD task. To address the unknown discovery uncertainty, we propose an objectness-driven discovery module to focus on capturing the generalized objectness shared among various known classes, driving the framework to discover more potential objects that are distinct from the background, including unknown objects. To mitigate the discrimination uncertainty, we decouple the learning processes for known and unknown classes through a parallel structure to reduce the mutual influence at the feature level and design a collaborative open-world classifier to achieve high-performance collaborative detection of both known and unknown classes. Our framework provides educators with a powerful tool for effective campus monitoring and classroom management. Experimental results on standard benchmarks demonstrate the framework’s superior performance compared to state-of-the-art methods, showcasing its transformative potential in intelligent educational environments. Full article
(This article belongs to the Special Issue Artificial Intelligence Technologies for Education: Advancements, Challenges, and Impacts)
►▼ Show Figures

Figure 1

18 pages, 2982 KiB  
Article
Developing an Intelligent Recommendation System for Non-Information and Communications Technology Major University Students
by TaeYoung Kim and JongBeom Lim
Appl. Sci. 2023, 13(23), 12774; https://doi.org/10.3390/app132312774 - 28 Nov 2023
Cited by 1 | Viewed by 1722
Abstract
Various services and applications based on information and communications technology (ICT) are converging with cultural aspects of historical implementations. At the same time, developing a convergence course for non-ICT majors is becoming increasingly popular in universities. In this paper, we develop an AI [...] Read more.
Various services and applications based on information and communications technology (ICT) are converging with cultural aspects of historical implementations. At the same time, developing a convergence course for non-ICT majors is becoming increasingly popular in universities. In this paper, we develop an AI application course for non-ICT major university students toward convergence with recommendation systems and Silk Road studies. Based on our five-year research on the martial arts, dance, and play of seven Silk Road countries, we have created and categorized an accessible database for 177 items in those countries. For our convergence course, we measure the similarity between the items for summary and perform collaborative filtering based on alternating least squares (ALS) matrix factorization so that our prototyped intelligent recommendation engine can predict the items in which a user might be interested. The course is designed to teach non-ICT major university students not only historical aspects of the Silk Road but also implementation aspects of recommendation systems with web services. Full article
(This article belongs to the Special Issue Artificial Intelligence Technologies for Education: Advancements, Challenges, and Impacts)
►▼ Show Figures

Figure 1

23 pages, 2639 KiB  
Article
Composing Multiple Online Exams: The Bees Algorithm Solution
by Manar Hosny, Rafa Hayel and Najwa Altwaijry
Appl. Sci. 2023, 13(23), 12710; https://doi.org/10.3390/app132312710 - 27 Nov 2023
Viewed by 1152
Abstract
Online education has gained increasing importance in recent years due to its flexibility and ability to cater to a diverse range of learners. The COVID-19 pandemic has further emphasized the significance of online education as a means to ensure continuous learning during crisis [...] Read more.
Online education has gained increasing importance in recent years due to its flexibility and ability to cater to a diverse range of learners. The COVID-19 pandemic has further emphasized the significance of online education as a means to ensure continuous learning during crisis situations. With the disruption of traditional in-person exams, online examinations have become the new norm for universities worldwide. Among the popular formats for online tests are multiple-choice questions, which are drawn from a large question bank. However, creating online tests often involves meeting specific requirements, such as minimizing the overlap between exams, grouping related questions, and determining the desired difficulty level. The manual selection of questions from a sizable question bank while adhering to numerous constraints can be a laborious task. Additionally, traditional search methods that evaluate all possible solutions are impractical and time-consuming for such a complex problem. Consequently, approximate methods like metaheuristics are commonly employed to achieve satisfactory solutions within a reasonable timeframe. This research proposes the application of the Bees Algorithm (BA), a popular metaheuristic algorithm, to address the problem of generating online exams. The proposed solution entails creating multiple exam forms that align with the desired difficulty level specified by the educator, while considering other identified constraints. Through extensive testing and comparison with four rival methods, the BA demonstrates superior performance in achieving the primary objective of matching the desired difficulty level in most test cases, as required by the educator. Furthermore, the algorithm exhibits robustness, indicated by minimal standard deviation across all experiments, which suggests its ability to generalize, adapt, and be practically applicable in real-world scenarios. However, the algorithm does have limitations related to the number of successful solutions and the achieved overlap percentage. These limitations have also been thoroughly discussed and highlighted in this research. Full article
(This article belongs to the Special Issue Artificial Intelligence Technologies for Education: Advancements, Challenges, and Impacts)
►▼ Show Figures

Figure 1

14 pages, 2523 KiB  
Article
AI Enhancements for Linguistic E-Learning System
by Jueting Liu, Sicheng Li, Chang Ren, Yibo Lyu, Tingting Xu, Zehua Wang and Wei Chen
Appl. Sci. 2023, 13(19), 10758; https://doi.org/10.3390/app131910758 - 27 Sep 2023
Cited by 1 | Viewed by 2415
Abstract
E-learning systems have been considerably developed after the COVID-19 pandemic. In our previous work, we developed a linguistic interactive E-learning system for phonetic transcription learning. In this paper, we propose three artificial-intelligence-based enhancements to this system from different aspects. Compared with the original [...] Read more.
E-learning systems have been considerably developed after the COVID-19 pandemic. In our previous work, we developed a linguistic interactive E-learning system for phonetic transcription learning. In this paper, we propose three artificial-intelligence-based enhancements to this system from different aspects. Compared with the original system, the first enhancement is a disordered speech classification module; this module is driven by the MFCC-CNN model, which aims to distinguish disordered speech and nondisordered speech. The accuracy of the classification is about 83%. The second enhancement is a grapheme-to-phoneme converter. This converter is based on the transformer model and designed for teachers to better generate IPA words from the regular written text. Compared with other G2P models, our transformer-based G2P model provides outstanding PER and WER performance. The last part of this paper focuses on a Tacotron2-based IPA-to-speech synthesis system, this deep learning-based TTS system can help teacher generate high-quality speech sounds from IPA characters which significantly improve the functionality of our original system. All of these three enhancements are related to the phonetic transcription process. and this work not only provides a better experience for the users of this system but also explores the utilization of artificial intelligence technologies in the E-learning field and linguistic field. Full article
(This article belongs to the Special Issue Artificial Intelligence Technologies for Education: Advancements, Challenges, and Impacts)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-22

Planned Papers

The below list represents only planned manuscripts. Some of these manuscripts have not been received by the Editorial Office yet. Papers submitted to MDPI journals are subject to peer-review.

Title: A Sensitive Analysis for Teacher Bunout Identification using Machine Learning Method: Jordan Case Study
Author: Surakhi
Highlights: 1. the creation of a new dataset tailored for estimating burnout levels. 2. We conducted an in-depth data analysis on the dataset using three main approaches. F 3. A sensitivity analysis is conducted to determine the optimal combination of variables for accurately estimating the level of burnout

Title: Adoption and Impact of ChatGPT in Computer Science Education: A Case Study on a Database Administration Course
Author: López-Fernández
Highlights: An empirical study with CS students using ChatGPT to learn Database Administration. The usage of ChatGPT was moderate, less than that of other traditional resources. Outstanding students used ChatGPT the most and grade-ChatGPT correlations were found. The combination of ChatGPT with traditional learning resources is very effective.

Back to TopTop