Artificial Intelligence for Education

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closed (31 October 2024)

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closed (31 December 2024)

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Topic Information

Dear Colleagues,

Artificial intelligence (AI) has shown great potential in tackling numerous educational challenges in the classroom and school management.

At the classroom level, AI applications have been designed to support instruction by customizing learning materials, sequencing learning activities, and providing individualized feedback and scaffolding based on individual learners’ profiles. In this regard, AI is used to identify resources and pedagogical approaches that are considered appropriate for learners’ needs, predict potential outcomes, and recommend the next steps of the learning process for them. At the school level, AI applications are designed to support both school management and the system. Some examples include reducing dropout through predictive analysis and offering timely assessment of new skills like higher cognitive skills.

Despite its benefits, AI applications in education have faced criticism for various reasons, such as the lack of control over their behavior, the exclusion of practitioner expertise in their design, and the lack of interpretability. Despite these concerns, AI methods are being integrated into public sector education systems through machine learning, natural language processing, image processing, and expert systems.

Improving these systems to retain public sector values involves addressing major issues, including the above-mentioned challenges.  Failing to do so is considered a huge disadvantage as, in practice, learners’ performance, grade, risk of failure, etc., predicted through such AI methods should be accurate, unbiased, and transparent, accompanied with reasons on why a specific feedback, intervention, or pedagogical tool is appropriate for a learner.

Given the growing importance of AI in society and supporting education and the existing challenges in their applications, this topical collection focuses on AI for education. This collection expects original research and review articles that combine computer science and informatics ideas with the social sciences. Articles can be within (but are not limited to) the following areas:

Topics of Interest

• Artificial intelligence for education (AIEd);
• Natural language processing for education;
• Education data mining and learning analytics;
• Educational recommender systems;
• Affective computing for education;
• Neural-symbolic AI for education;
• Artificial neural networks, machine learning, and statistical and optimization methods for education;
• Evaluation of artificial intelligence, adaptive, or personalized educational systems;
• AI-based adaptivity and personalization for education;
• Intelligent tutoring systems, serious games, simulations, and dialog systems for education;
• Multimodal multichannel trace data for AI systems;
• AI for Education and ethics.

Dr. Danial Hooshyar
Prof. Dr. Roger Azevedo
Prof. Dr. Raija Hämäläinen
Topic Editors

Keywords

Participating Journals

Journal Name	Impact Factor	CiteScore	Launched Year	First Decision (median)	APC
Applied Sciences applsci	2.5	5.3	2011	18.4 Days	CHF 2400
Education Sciences education	2.5	4.8	2011	29.8 Days	CHF 1800
Machine Learning and Knowledge Extraction make	4.0	6.3	2019	20.8 Days	CHF 1800

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Published Papers (8 papers)

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23 pages, 1398 KiB  

Open AccessArticle

An Experiment with LLMs as Database Design Tutors: Persistent Equity and Fairness Challenges in Online Learning

by Hasan M. Jamil

Educ. Sci. 2025, 15(3), 386; https://doi.org/10.3390/educsci15030386 - 19 Mar 2025

Viewed by 233

Abstract

As large language models (LLMs) continue to evolve, their capacity to replace humans as their surrogates is also improving. As increasing numbers of intelligent tutoring systems (ITSs) are embracing the integration of LLMs for digital tutoring, questions are arising as to how effective [...] Read more.

As large language models (LLMs) continue to evolve, their capacity to replace humans as their surrogates is also improving. As increasing numbers of intelligent tutoring systems (ITSs) are embracing the integration of LLMs for digital tutoring, questions are arising as to how effective they are and if their hallucinatory behaviors diminish their perceived advantages. One critical question that is seldom asked if the availability, plurality, and relative weaknesses in the reasoning process of LLMs are contributing to the much discussed digital divide and equity and fairness in online learning. In this paper, we present an experiment with database design theory assignments and demonstrate that while their capacity to reason logically is improving, LLMs are still prone to serious errors. We demonstrate that in online learning and in the absence of a human instructor, LLMs could introduce inequity in the form of “wrongful” tutoring that could be devastatingly harmful for learners, which we call ignorant bias, in increasingly popular digital learning. We also show that significant challenges remain for STEM subjects, especially for subjects for which sound and free online tutoring systems exist. Based on the set of use cases, we formulate a possible direction for an effective ITS for online database learning classes of the future. Full article

(This article belongs to the Topic Artificial Intelligence for Education)

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16 pages, 1645 KiB  

Open AccessEditor’s ChoiceArticle

Writing with AI: What College Students Learned from Utilizing ChatGPT for a Writing Assignment

by Changzhao Wang, Stephen J. Aguilar, Jennifer S. Bankard, Eric Bui and Benjamin Nye

Educ. Sci. 2024, 14(9), 976; https://doi.org/10.3390/educsci14090976 - 4 Sep 2024

Cited by 4 | Viewed by 8919

Abstract

To support the integration of AI in education, this empirical study investigated what lessons college students learned from using Generative AI for writing. We recruited 47 students in the United States from a university writing course. Students completed an assignment in which they [...] Read more.

To support the integration of AI in education, this empirical study investigated what lessons college students learned from using Generative AI for writing. We recruited 47 students in the United States from a university writing course. Students completed an assignment in which they used Generative AI tools (e.g., ChatGPT) to draft an application letter or personal statement. Data were collected using a survey of five open-ended questions about their writing process, what worked, what did not work, how to better write with AI, and general lessons learned. We applied thematic analysis and sentiment analysis methods to analyze students’ responses. Results show that (1) students went through multiple rounds of prompting; (2) students identified strengths of AI, such as connection to topic, template generation, and sentence quality; (3) the weaknesses of AI included general language, robotic tone and lacking emotion, lacking personal voice, and lacking critical thinking; (4) students wished to improve AI-generated writing by adding personal stories, connections to posting, feelings and thoughts, and deleting repetitive language; and (5) their overall attitudes toward AI tool were positive. We believe our findings can help relieve some concerns about cheating with AI. We also suggested strategies to regulate the use of AI. Full article

(This article belongs to the Topic Artificial Intelligence for Education)

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22 pages, 6300 KiB  

Open AccessArticle

Memory-Based Dynamic Bayesian Networks for Learner Modeling: Towards Early Prediction of Learners’ Performance in Computational Thinking

by Danial Hooshyar and Marek J. Druzdzel

Educ. Sci. 2024, 14(8), 917; https://doi.org/10.3390/educsci14080917 - 21 Aug 2024

Viewed by 1643

Abstract

Artificial intelligence (AI) has demonstrated significant potential in addressing educational challenges in digital learning. Despite this potential, there are still concerns about the interpretability and trustworthiness of AI methods. Dynamic Bayesian networks (DBNs) not only provide interpretability and the ability to integrate data-driven [...] Read more.

Artificial intelligence (AI) has demonstrated significant potential in addressing educational challenges in digital learning. Despite this potential, there are still concerns about the interpretability and trustworthiness of AI methods. Dynamic Bayesian networks (DBNs) not only provide interpretability and the ability to integrate data-driven insights with expert judgment for enhanced trustworthiness but also effectively process temporal dynamics and relationships in data, crucial for early predictive modeling tasks. This research introduces an approach for the temporal modeling of learners’ computational thinking abilities that incorporates higher-order influences of latent variables (hereafter referred to as memory of the model) and accordingly predicts learners’ performance early. Our findings on educational data from the AutoThinking game indicate that when using only first-order influences, our proposed model can predict learners’ performance early, with an 86% overall accuracy (i.e., time stamps 0, 5, and 9) and a 94% AUC (at the last time stamp) during cross-validation and 91% accuracy and 98% AUC (at the last time stamp) in a holdout test. The introduction of higher-order influences improves model accuracy in both cross-validation and holdout tests by roughly 4% and improves the AUC at timestamp 0 by roughly 2%. This suggests that integrating higher-order influences into a DBN not only potentially improves the model’s predictive accuracy during the cross-validation phase but also enhances its overall and time stamp-specific generalizability. DBNs with higher-order influences offer a trustworthy and interpretable tool for educators to foresee and support learning progression. Full article

(This article belongs to the Topic Artificial Intelligence for Education)

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21 pages, 1947 KiB  

Open AccessArticle

Learning Experiences and Didactic Needs of German Healthcare Professions: A Focus Group Study for the Design of Personalized Interprofessional Further Education in Dementia Healthcare

by Marie Stelter, Manuela Malek, Margareta Halek, Jan Ehlers and Julia Nitsche

Mach. Learn. Knowl. Extr. 2024, 6(3), 1510-1530; https://doi.org/10.3390/make6030072 - 3 Jul 2024

Cited by 1 | Viewed by 1791

Abstract

Considering the multifaceted nature of neurodegenerative diseases like dementia and the necessity for interprofessional knowledge, this research extends its scope to encompass professionals with diverse levels of training and experience in dementia care. A need analysis for the project “My INdividual Digital EDucation.RUHR” [...] Read more.

Considering the multifaceted nature of neurodegenerative diseases like dementia and the necessity for interprofessional knowledge, this research extends its scope to encompass professionals with diverse levels of training and experience in dementia care. A need analysis for the project “My INdividual Digital EDucation.RUHR” (MINDED.RUHR) is conducted to develop an automatized recommender system for individual learning content using AI. In this sub-study, the aim was to reveal didactic specialties, knowledge gaps, and structural challenges of further education in dementia care of different health professions and to derive learning preference personae. Eight focus group interviews among nine health professions and up to six participants (N = 34) each took place to survey distinct didactic experiences and learning needs. The results reflect various learning preferences, with a propensity to multimedia, practical, and interactive tasks. Health professions are used to digital education but show aversions against synchronous e-learning formats. The derived learning preference personae constitute profound blueprints for a user-centered digital education design process, aiming to establish personalized and representative further education in dementia care applicable to various individual preferences and structural workplace challenges of healthcare professions. Full article

(This article belongs to the Topic Artificial Intelligence for Education)

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19 pages, 1710 KiB  

Open AccessArticle

ChatGPT-Generated and Student-Written Historical Narratives: A Comparative Analysis

by Björn Kindenberg

Educ. Sci. 2024, 14(5), 530; https://doi.org/10.3390/educsci14050530 - 13 May 2024

Cited by 1 | Viewed by 2912

Abstract

This study investigates alternative approaches for demonstrating historical understanding in elementary school history education, motivated by challenges to educational institutions posed by increased ChatGPT-related plagiarism. Focused on secondary education, an area with scant research, this study, through sociocultural and linguistic methods of analysis, [...] Read more.

This study investigates alternative approaches for demonstrating historical understanding in elementary school history education, motivated by challenges to educational institutions posed by increased ChatGPT-related plagiarism. Focused on secondary education, an area with scant research, this study, through sociocultural and linguistic methods of analysis, contrasted human-generated historical narratives with those produced by ChatGPT. It was found that ChatGPT’s narratives, while stylistically superior, lacked emotional depth, highlighting a key differentiation from human storytelling. However, despite this differentiation, ChatGPT otherwise effectively mimicked typical discourse patterns of historical storytelling, suggesting that narrative-based writing assignments do not significantly reduce the likelihood of ChatGPT-assisted plagiarism. The study concludes by suggesting that rather than focusing on mitigating plagiarism, educational approaches to ChatGPT should seek to channel its potential for historical narratives into assistance with task design, delivery of content, and coaching student writing. Full article

(This article belongs to the Topic Artificial Intelligence for Education)

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17 pages, 2540 KiB  

Open AccessArticle

Beyond CheatBots: Examining Tensions in Teachers’ and Students’ Perceptions of Cheating and Learning with ChatGPT

by Christopher Mah, Hillary Walker, Lena Phalen, Sarah Levine, Sarah W. Beck and Jaylen Pittman

Educ. Sci. 2024, 14(5), 500; https://doi.org/10.3390/educsci14050500 - 7 May 2024

Cited by 5 | Viewed by 5697

Abstract

As artificial intelligence (AI) is increasingly integrated into educational technologies, teachers and students must acquire new forms of AI literacy, including an understanding of responsible use of AI. In this study, we explored tensions in teachers’ and students’ opinions about what constitutes learning [...] Read more.

As artificial intelligence (AI) is increasingly integrated into educational technologies, teachers and students must acquire new forms of AI literacy, including an understanding of responsible use of AI. In this study, we explored tensions in teachers’ and students’ opinions about what constitutes learning and cheating with AI. Using qualitative methods, we asked Pre-K through postsecondary writing teachers (n = 16) and a linguistically diverse group of students (n = 12) to consider examples of how students might use ChatGPT, rank them in order of how much they thought each student learned and cheated, and explain their rankings. Our study yielded three findings. First, teachers and students used similar criteria to determine their rankings. Second, teachers and students arrived at similar conclusions about learning with ChatGPT but different conclusions about cheating. Finally, disagreements centered on four main tensions between (1) using ChatGPT as a shortcut versus as a scaffold; (2) using ChatGPT to generate ideas versus language; (3) getting support from ChatGPT versus analogous support from other sources; and (4) learning from ChatGPT versus learning without. These findings underscore the importance of student voice in co-constructing norms around responsible AI use. Full article

(This article belongs to the Topic Artificial Intelligence for Education)

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20 pages, 3428 KiB  

Open AccessArticle

Benefits and Challenges of Collaboration between Students and Conversational Generative Artificial Intelligence in Programming Learning: An Empirical Case Study

by Wanxin Yan, Taira Nakajima and Ryo Sawada

Educ. Sci. 2024, 14(4), 433; https://doi.org/10.3390/educsci14040433 - 20 Apr 2024

Cited by 7 | Viewed by 7090

Abstract

The utilization of conversational generative artificial intelligence (Gen AI) in learning is often seen as a double-edged sword that may lead to superficial learning. We designed and implemented a programming course focusing on collaboration between students and Gen AI. This study explores the [...] Read more.

The utilization of conversational generative artificial intelligence (Gen AI) in learning is often seen as a double-edged sword that may lead to superficial learning. We designed and implemented a programming course focusing on collaboration between students and Gen AI. This study explores the dynamics of such collaboration, focusing on students’ communication strategies with Gen AI, perceived benefits, and challenges encountered. Data were collected from class observations, surveys, final reports, dialogues between students and Gen AI, and semi-structured in-depth interviews. The results showed that effective collaboration between students and Gen AI could enhance students’ meta-cognitive and self-regulated learning skills and positively impact human-to-human communication. This study further revealed the difficulties and individual differences in collaborating with Gen AI on complex learning tasks. Overall, collaborating with Gen AI as a learning partner, rather than just a tool, enables sustainable and independent learning, beyond specific learning tasks at a given time. Full article

(This article belongs to the Topic Artificial Intelligence for Education)

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26 pages, 6113 KiB  

Open AccessArticle

Augmenting Deep Neural Networks with Symbolic Educational Knowledge: Towards Trustworthy and Interpretable AI for Education

by Danial Hooshyar, Roger Azevedo and Yeongwook Yang

Mach. Learn. Knowl. Extr. 2024, 6(1), 593-618; https://doi.org/10.3390/make6010028 - 10 Mar 2024

Cited by 9 | Viewed by 4595

Abstract

Artificial neural networks (ANNs) have proven to be among the most important artificial intelligence (AI) techniques in educational applications, providing adaptive educational services. However, their educational potential is limited in practice due to challenges such as the following: (i) the difficulties in incorporating [...] Read more.

Artificial neural networks (ANNs) have proven to be among the most important artificial intelligence (AI) techniques in educational applications, providing adaptive educational services. However, their educational potential is limited in practice due to challenges such as the following: (i) the difficulties in incorporating symbolic educational knowledge (e.g., causal relationships and practitioners’ knowledge) in their development, (ii) a propensity to learn and reflect biases, and (iii) a lack of interpretability. As education is classified as a ‘high-risk’ domain under recent regulatory frameworks like the EU AI Act—highlighting its influence on individual futures and discrimination risks—integrating educational insights into ANNs is essential. This ensures that AI applications adhere to essential educational restrictions and provide interpretable predictions. This research introduces NSAI, a neural-symbolic AI approach that integrates neural networks with knowledge representation and symbolic reasoning. It injects and extracts educational knowledge into and from deep neural networks to model learners’ computational thinking, aiming to enhance personalized learning and develop computational thinking skills. Our findings revealed that the NSAI approach demonstrates better generalizability compared to deep neural networks trained on both original training data and data enriched by SMOTE and autoencoder methods. More importantly, we found that, unlike traditional deep neural networks, which mainly relied on spurious correlations in their predictions, the NSAI approach prioritizes the development of robust representations that accurately capture causal relationships between inputs and outputs. This focus significantly reduces the reinforcement of biases and prevents misleading correlations in the models. Furthermore, our research showed that the NSAI approach enables the extraction of rules from the trained network, facilitating interpretation and reasoning during the path to predictions, as well as refining the initial educational knowledge. These findings imply that neural-symbolic AI not only overcomes the limitations of ANNs in education but also holds broader potential for transforming educational practices and outcomes through trustworthy and interpretable applications. Full article

(This article belongs to the Topic Artificial Intelligence for Education)

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