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Applied Sciences
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Artificial Intelligence in Education: Latest Advances and Prospects
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Artificial Intelligence in Education: Latest Advances and Prospects

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 November 2026 | Viewed by 3971

Special Issue Editors

Prof. Dr. Yasmin Hernandez

E-Mail Website
Guest Editor
Computer Science Department, Tecnológico Nacional de México/Cenidet, Cuernavaca 62490, Mexico
Interests: artificial intelligence in education; intelligent tutoring systems; educational data mining; technology enhanced learning; affective computing; data science
Dr. Karina Figueroa

E-Mail Website
Guest Editor
Facultad de Ciencias Físico-Matemáticas, Universidad Michoacana, Morelia 61850, Mexico
Interests: inteligencia artificial; sistemas inteligentes; algoritmos
Dr. Maria Elena Chavez-Echeagaray

E-Mail Website
Guest Editor
Ira A. Fulton Schools of Engineering, Arizona State University, Tempe, AZ 85281, USA
Interests: human computing interaction; affective computing; educational technology; engineering education

Special Issue Information

Dear Colleagues,

Historically, education has been one of the favorite fields to prove computational theories, and it has been demonstrated that computers are very helpful in supporting human learning. Particularly, artificial intelligence techniques provide knowledge to tailor every aspect of the educational process to the particular needs of each actor, and provide timely, useful suggestions and recommendations. Applications of artificial intelligence, such as Intelligent tutoring systems, have been successful in promoting learning by providing personalized advice at a pedagogically correct time.

However, the traditional use of artificial intelligence in education is evolving, and we have witnessed the birth of tools that provide content on demand. The generative artificial intelligence generates texts, images, and solutions to enhance human learning and capabilities. Instead of trying to reproduce human behavior or recognize human traits using traditional artificial intelligence techniques, generative artificial intelligence provides a means to increase human potential. These rising technologies are reshaping human learning, and they pose big challenges, such as how to provide an ethical and inclusive learning experience, while also promoting deeper insights into prompting an engineering era. There is an excellent opportunity for exploring new ways of applying traditional and innovative artificial techniques to education.

This Special Issue aims to gather innovative and high-quality research contributions on artificial intelligence in education. Additionally, it focuses on providing insights into the recent advances in these topics by calling original scientific contributions in the form of theoretical foundations, models, experimental research, and case studies for developing or applying artificial intelligence techniques to address problems in the education field.

Prof. Dr. Yasmin Hernandez
Dr. Karina Figueroa
Dr. Maria Elena Chavez-Echeagaray
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 250 words) can be sent to the Editorial Office for assessment.

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

  • educational data mining
  • intelligent tutoring systems
  • pedagogical agents
  • generative artificial intelligence
  • student modeling
  • learning analytics
  • AI literacy
  • tutor modeling
  • ethics
  • large language models

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Published Papers (3 papers)

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Research

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22 pages, 3197 KB  
Article
Dynamic Cognition Graph for Adaptive Learning: Integrating Reasoning Evidence and Reinforcement Learning
by Ying Li, Yiming Gai, Xingyu Wang, Leilei Sun and Xuefei Huang
Appl. Sci. 2026, 16(7), 3580; https://doi.org/10.3390/app16073580 - 6 Apr 2026
Viewed by 610
Abstract
Accurate modeling of learners’ evolving cognitive states is essential for intelligent educational systems, yet many existing knowledge tracing and graph-based approaches rely on static structures or purely sequential representations that inadequately capture dynamic structural changes in learning processes. This study proposes a Learner [...] Read more.
Accurate modeling of learners’ evolving cognitive states is essential for intelligent educational systems, yet many existing knowledge tracing and graph-based approaches rely on static structures or purely sequential representations that inadequately capture dynamic structural changes in learning processes. This study proposes a Learner Cognitive Graph (LCG) framework that integrates dynamic heterogeneous graph modeling, structured behavioral data acquisition, and reinforcement learning-based intervention optimization. A Dynamic Cognition Graph (DCG) is formally defined as a sequence of temporally evolving graph snapshots representing interactions among learners, knowledge concepts, and exercises. A reverse Turing test-based agent with structured prompting is introduced to collect reasoning-oriented behavioral evidence, improving data reliability for cognitive modeling. Temporal message passing, multi-scale memory updating, and self-supervised learning objectives are employed to construct dynamic cognitive representations. Personalized intervention is formulated as a Markov decision process to optimize long-term learning outcomes. Experiments conducted on real-world and simulated educational datasets demonstrate improved knowledge mastery prediction accuracy, cognitive state transition modeling, and intervention efficiency compared with representative baselines. The proposed framework provides a systematic and scalable approach for dynamic cognitive modeling and adaptive educational support. Full article
(This article belongs to the Special Issue Artificial Intelligence in Education: Latest Advances and Prospects)
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23 pages, 458 KB  
Article
Automated Generation and Evaluation of Interactive-Fiction Serious Games with Open-Weight LLMs
by Finn Rogosch and Andreas Schrader
Appl. Sci. 2026, 16(6), 2932; https://doi.org/10.3390/app16062932 - 18 Mar 2026
Viewed by 618
Abstract
This work investigates whether open-weight large language models can automatically generate runnable and educationally faithful serious games in a constrained, text-only interactive-fiction (IF) setting. The target games are station-based single-player serious games for knowledge assessment, implemented as IF in a structured, machine-readable text [...] Read more.
This work investigates whether open-weight large language models can automatically generate runnable and educationally faithful serious games in a constrained, text-only interactive-fiction (IF) setting. The target games are station-based single-player serious games for knowledge assessment, implemented as IF in a structured, machine-readable text format, and used here as a first step towards later ambient scenarios. A fully automated pipeline called SINE (Serious Interactive Narrative Engine) is evaluated with four prompting strategies, grammar-guided decoding, deterministic validation, and a repair agent. Across a staged evaluation with 240 seeds and increasing complexity, finalist configurations reach success rates between roughly 68% and 86% on the joint criterion of compilation, playability, and learning-goal fidelity. Repair iterations proved central to robustness, whereas grammar masking on top of reasoning prompts did not consistently improve outcomes. The study provides a reproducible benchmark setup, open artifacts, and a constrained generation pipeline as a basis for later extensions toward broader serious game scenarios. Full article
(This article belongs to the Special Issue Artificial Intelligence in Education: Latest Advances and Prospects)
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Review

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22 pages, 338 KB  
Review
Can ChatGPT Replace the Teacher in Assessment? A Review of Research on the Use of Large Language Models in Grading and Providing Feedback
by Marcin Jukiewicz and Michał Wyrwa
Appl. Sci. 2026, 16(2), 680; https://doi.org/10.3390/app16020680 - 8 Jan 2026
Cited by 2 | Viewed by 1764
Abstract
This article presents a systematic review of empirical research on the use of large language models (LLMs) for automated grading of student work and providing feedback. The study aimed to determine the extent to which generative artificial intelligence models, such as ChatGPT, can [...] Read more.
This article presents a systematic review of empirical research on the use of large language models (LLMs) for automated grading of student work and providing feedback. The study aimed to determine the extent to which generative artificial intelligence models, such as ChatGPT, can replace teachers in the assessment process. The review was conducted in accordance with PRISMA guidelines and predefined inclusion criteria; ultimately, 42 empirical studies were included in the analysis. The results of the review indicate that the effectiveness of LLMs in grading is varied. These models perform well on closed-ended tasks and short-answer questions, often achieving accuracy comparable to human evaluators. However, they struggle with assessing complex, open-ended, or subjective assignments that require in-depth analysis or creativity. The quality of the prompts provided to the model and the use of detailed scoring rubrics significantly influence the accuracy and consistency of the grades generated by LLMs. The findings suggest that LLMs can support teachers by accelerating the grading process and delivering rapid feedback at scale, but they cannot fully replace human judgment. The highest effectiveness is achieved in hybrid assessment systems that combine AI-driven automatic grading with teacher oversight and verification. Full article
(This article belongs to the Special Issue Artificial Intelligence in Education: Latest Advances and Prospects)
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