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

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

Deadline for manuscript submissions: 31 October 2024 | Viewed by 9761

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Special Issue Editors

Dr. Yu Liang

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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

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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

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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

Related Special Issue

Technologies and Environments of Intelligent Education in Applied Sciences (13 articles)

Published Papers (12 papers)

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Research

26 pages, 5860 KiB

Open AccessArticle

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

Viewed by 270

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 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)

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28 pages, 13856 KiB

Open AccessArticle

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

Viewed by 787

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 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)

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17 pages, 1448 KiB

Open AccessArticle

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

Viewed by 737

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 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)

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18 pages, 6573 KiB

Open AccessArticle

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 - 05 Mar 2024

Viewed by 693

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 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)

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19 pages, 1161 KiB

Open AccessArticle

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

Viewed by 591

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 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)

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22 pages, 3928 KiB

Open AccessArticle

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

Viewed by 749

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 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)

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22 pages, 903 KiB

Open AccessArticle

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 642

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 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)

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14 pages, 880 KiB

Open AccessArticle

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

Viewed by 751

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 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)

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16 pages, 9648 KiB

Open AccessArticle

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 609

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 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)

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18 pages, 2982 KiB

Open AccessArticle

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

Viewed by 854

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 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)

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23 pages, 2639 KiB

Open AccessArticle

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 637

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 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)

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14 pages, 2523 KiB

Open AccessArticle

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 1236

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 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)

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