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Artificial Intelligence for Education
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Dr. Danial Hooshyar
Learning Analytics and Educational Data Mining, School of Digital Technologies, Tallinn University, Narva Rd. 25, 10120 Tallinn, Estonia
Prof. Dr. Roger Azevedo
Director, SMART Lab, School of Modeling Simulation and Training, University of Central Florida, Orlando, FL, USA
Prof. Dr. Raija Hämäläinen
Faculty of Education and Psychology, Department of Education, University of Jyväskylä, 40014 Jyväskylän, Finland
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Artificial Intelligence for Education

Abstract submission deadline
31 October 2024
Manuscript submission deadline
31 December 2024
Viewed by
2313

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

  • artificial intelligence for education
  • education data mining and learning analytics
  • NLP and image processing for education
  • ethics of AI in education
  • affective computing for education

Participating Journals

Journal Name Impact Factor CiteScore Launched Year First Decision (median) APC
Applied Sciences
applsci 		2.7 4.5 2011 16.9 Days CHF 2400 Submit
Education Sciences
education 		3.0 4.0 2011 24.9 Days CHF 1800 Submit
Machine Learning and Knowledge Extraction
make 		3.9 8.5 2019 19.9 Days CHF 1800 Submit

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Published Papers (2 papers)

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20 pages, 3428 KiB  
Article
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
Viewed by 329
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  
Article
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
Viewed by 1322
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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