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Innovative Applications of Artificial Intelligence in Education

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

Deadline for manuscript submissions: 20 July 2026 | Viewed by 782

Special Issue Editors


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Guest Editor
Graduate Studies and Research Department, Instituto Tecnológico de Culiacán, Sinaloa, Mexico
Interests: artificial intelligence; affective learning; intelligent learning environments; blended learning; programming; E-learning; extended reality

E-Mail Website
Guest Editor
Graduate Studies and Research Department, Instituto Tecnológico de Culiacán, Sinaloa, Mexico
Interests: artificial intelligence in education; affective computing; learning technologies; extended reality

Special Issue Information

Dear Colleagues,

Artificial intelligence (AI) has become an integral force in modern life, significantly optimizing efficiency across commercial and industrial sectors. However, its impact is perhaps most profound in the field of education, where it promises to revolutionize the student experience. By embedding intelligent modules into learning environments, AI facilitates a shift toward personalized instruction, adaptive assessment, and emotional responsiveness. This integration allows for a comprehensive educational approach that not only tracks academic progress but also addresses the affective needs of learners, creating a more supportive environment.

Recent advancements in hardware and software have further accelerated this progress, yielding superior learning outcomes. Notably, Generative AI—exemplified by the rapid rise in ChatGPT—has emerged as a pivotal innovation. By enabling the creation of dynamic text and media through user prompts, these tools are actively reshaping teaching methodologies within educational institutions.

This Special Issue calls on the academic community and research groups specializing in Artificial Intelligence in Education (AIEd) to submit their latest research on innovative AI-driven educational technologies. The central objective is to demonstrate the transformative power of these innovations in adapting and improving learning methods to meet contemporary demands through the effective application of AI tools.

The scope of the Special Issue includes, but is not limited to, the following topics:

  • Generative artificial intelligence in education.
  • LLM in education.
  • Intelligent Learning/Tutoring Environments/Systems.
  • Intelligent chatbots for education.
  • Metaverso.
  • Affective tutoring systems.
  • Authoring tools for intelligent tutoring systems.
  • Sentiment analysis on educational applications.
  • Gamification and game-based learning.
  • Learning analytics.
  • Data Science in Education.

Prof. Dr. María Lucia Barrón-Estrada
Prof. Dr. Ramón Zatarain-Cabada
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

  • artificial intelligence
  • advanced learning technologies
  • generative AI
  • LLM in education
  • pedagogical agents
  • personalized learning
  • affective computing
  • intelligent learning environments
  • intelligent tutoring systems

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Published Papers (1 paper)

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Research

24 pages, 304 KB  
Article
Engineering Predictive Applications for Academic Track Selection and Student Performance for Future Study Planning in High School Education
by Ka Ian Chan, Jingchi Huang, Huiwen Zou and Patrick Pang
Appl. Sci. 2026, 16(7), 3286; https://doi.org/10.3390/app16073286 - 28 Mar 2026
Viewed by 328
Abstract
With the rapid development in data mining and learning analytics, integrating predictive analytics into educational data has become increasingly critical for supporting students’ learning trajectories. In many schooling systems, the academic tracks (such as Liberal Arts or Science) and the performance of junior [...] Read more.
With the rapid development in data mining and learning analytics, integrating predictive analytics into educational data has become increasingly critical for supporting students’ learning trajectories. In many schooling systems, the academic tracks (such as Liberal Arts or Science) and the performance of junior high school students can substantially shape their subsequent university pathways and career planning. Despite the long-term impact of these decisions, academic track selections and the evaluation of students’ potential are often made without systematic and evidence-based guidance. Predictive computer applications can assist, but the training of accurate models and the selection of adequate features remain key challenges. This paper details our process of engineering such an application comprising two tasks based on 1357 real-world junior high school academic performance records. The first task applies a classification approach to predict students’ academic track orientation, while the second task employs a multi-output regression model to forecast students’ future academic performance in senior high school. Our approach shows that the stacking ensemble model achieved a classification accuracy of 85.76%, whereas the Bi-LSTM model with multi-head attention attained an overall R2 exceeding 82% in performance forecasting; both models demonstrated strong and reliable predictive capability. Moreover, the proposed approach provides inherent interpretability by decomposing predictions at the subject level. Feature importance analysis reveals how different academic subjects contribute variably to both academic track decisions and future academic performance, offering actionable insights for academic counselling and future study planning. By bridging predictive modelling with students’ educational and career planning needs, this study advances the practical application of educational data mining and provides support for evidence-based academic guidance and future career choices in real-world contexts. Full article
(This article belongs to the Special Issue Innovative Applications of Artificial Intelligence in Education)
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