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Artificial Intelligence for Learning and 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 November 2026 | Viewed by 3569

Special Issue Editors


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Guest Editor
Department of Educational Sciences, Faculty of Education and Psychology, University of Extremadura, 06006 Badajoz, Spain
Interests: reseach methods; educational innovation; artificial intelligence; teaching communication; ICT; vocational education; SDGs 2030

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Guest Editor
Departamento de Didáctica, Organización y Métodos de Investigación, Facultad de Educación, Universidad de Salamanca, Paseo de Canalejas 169, 37008 Salamanca, Spain
Interests: digital skills; E-learning; media literacy; digital leisure; educational Internet of Things; learning games; micro-learning; digital divide; inclusiveness; ICT teacher training; educational artificial intelligence

Special Issue Information

Dear Colleagues,

In the last decade, artificial intelligence (AI) has established itself as an important technology within education, not only due to its technical capabilities but also to its potential for integration into innovative pedagogical practices. Many studies have shown how intelligent tutoring systems, AI-based adaptive learning, and learning analytics facilitate the personalization of the educational process and the monitoring of student progress. More recently, the emergence of generative models and conversational agents has provided opportunities for pedagogical mediation, from automated feedback to content co-creation. However, researchers have also underscored the need to understand AI not only as a technical tool, but as a pedagogical mediator capable of transforming teaching, the role of the teacher, and the nature of educational interaction. Research suggests that true innovation lies in integrating these technologies into robust teaching approaches that promote motivation, deep learning, and educational equity.

We hope that this Special Issue will encourage researchers to explore how artificial intelligence can be integrated into pedagogy, fostering innovative methodologies, personalized learning, and new approaches to enhancing teaching and learning outcomes.

Proposed lines of research:

This Special Issue seeks contributions that explore, substantiate, and evaluate the role of AI in education from a pedagogical and critical perspective. The scope of this Special Issue includes, but is not limited to, the following topics:

  • Pedagogical Integration of AI: Studies on how AI-based systems can be incorporated into constructivist, sociocultural, or collaborative learning frameworks.
  • AI for Learning Personalization: Platforms that tailor instruction to individual needs, and analyses of their impact on motivation and equity.
  • AI-Augmented Teaching: Research on how teachers can rely on intelligent assistants to design, assess, and reflect on their pedagogical practice.
  • Human–AI Interaction: Student and teacher experiences with virtual tutors, educational chatbots, and adaptive feedback systems.
  • Ethics and Pedagogical Responsibility: Reflections on bias, transparency, and teacher training for the critical use of AI in educational contexts.
  • Curricular innovations: proposals that link AI with the development of critical, computational, and collaborative thinking skills at different educational levels.
  • Emerging technologies and pedagogy: integration of generative AI, augmented reality, and advanced analytics as mediators of interdisciplinary STEM and humanities learning.

Expected contribution:

This Special Issue aims to contribute to the scientific research on how AI can be meaningfully integrated into pedagogy, beyond its instrumental dimension. We expect contributions to present empirical evidence, theoretical developments, and innovative experiences that enable us to move toward more personalized, inclusive, and learning-centered education, while addressing the challenges associated with the implementation of AI.

Dr. Juan Luis Cabanillas-García
Dr. María Cruz Sánchez-Gómez
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 in education
  • pedagogical integration of AI
  • intelligent tutoring systems
  • adaptive learning
  • learning analytics
  • teacher education and AI

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Published Papers (3 papers)

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Research

15 pages, 425 KB  
Article
Fine-Tuned Prompt Literacy for GenAI-Mediated L2 Writing: An Interaction-First Learning-and-Accountability Framework
by Joohoon Kang and Jongsung Won
Appl. Sci. 2026, 16(9), 4198; https://doi.org/10.3390/app16094198 - 24 Apr 2026
Viewed by 246
Abstract
Generative AI (GenAI) is reshaping second language (L2) writing not only by altering how learners generate, revise, and refine text but also by changing how writers justify, disclose, and remain accountable for AI-mediated decisions. Yet much prompt-literacy work still treats prompting as output [...] Read more.
Generative AI (GenAI) is reshaping second language (L2) writing not only by altering how learners generate, revise, and refine text but also by changing how writers justify, disclose, and remain accountable for AI-mediated decisions. Yet much prompt-literacy work still treats prompting as output optimization or leaves it under-theorized as a general ability to “use AI well.” This conceptual article addresses that gap by reconceptualizing Fine-Tuned Prompt Literacy (FTPL) as an interaction-first learning-and-accountability framework for GenAI-mediated L2 writing. We argue that prompt literacy should be understood not simply as better prompting, but as the trained ability to set communicative and genre constraints, interrogate provisional AI outputs, corroborate claims, revise prompts and texts iteratively, and document accountable uptake decisions. To clarify FTPL’s theoretical distinctiveness, we position it in relation to AI literacy, critical GenAI literacy, and prompt literacy research, and define four interlocking dimensions—learner empowerment, prompt optimization, critical evaluation, and ethical responsibility. We further operationalize the framework through observable interactional indicators, process evidence, and assessment/accountability implications relevant to instructional and institutional contexts. By reframing prompt literacy as a genre-sensitive and ethically accountable interactional competence, this article offers a conceptual model for studying and designing GenAI-mediated writing beyond product improvement alone. Full article
(This article belongs to the Special Issue Artificial Intelligence for Learning and Education)
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23 pages, 306 KB  
Article
Higher Mathematics Education and AI Prompt Patterns: Examples from Selected University Classes
by Oana Brandibur, Marzena Filipowicz-Chomko, Ewa Girejko, Eva Kaslik, Dorota Mozyrska, Raluca Mureșan, Nikos Pappas, Adriana Loredana Tănasie and Claudia Zaharia
Appl. Sci. 2026, 16(1), 339; https://doi.org/10.3390/app16010339 - 29 Dec 2025
Cited by 1 | Viewed by 1503
Abstract
The rapid integration of large language models into higher education creates opportunities for mathematics instruction, but also raises the need for structured interaction strategies that support reflective learning rather than passive answer consumption. This study, conducted within the Erasmus+ MAESTRO-AI project, examines how [...] Read more.
The rapid integration of large language models into higher education creates opportunities for mathematics instruction, but also raises the need for structured interaction strategies that support reflective learning rather than passive answer consumption. This study, conducted within the Erasmus+ MAESTRO-AI project, examines how selected AI prompt patterns can be implemented in concrete university mathematics activities and how students evaluate these AI-supported experiences. Two experimental modules were compared: complex numbers for first-semester Applied Mathematics students in Poland (n=100) and conditional probability for second-year Computer Science students in Romania (n=213). After completing AI-assisted learning activities with ChatGPT and/or Gemini, students completed a common evaluation questionnaire assessing engagement, perceived usefulness, and reflections on AI as a tutor. Group comparisons and experience-based analyses were performed using the Mann–Whitney test. Results indicate that students who reported regular prior use of AI tools evaluated AI-supported learning significantly more positively than those with occasional or no prior experience. They gave higher ratings across most questionnaire items as well as for the overall score. The findings suggest that prompt-pattern-based designs can support engaging AI-assisted mathematics activities. They also indicate that such designs can provide a structured learning experience, while introductory guidance may be important to ensure comparable benefits for less experienced students. Full article
(This article belongs to the Special Issue Artificial Intelligence for Learning and Education)
17 pages, 12120 KB  
Article
Control Applications with FPGA: Case of Approaching FPGAs for Students in an Intelligent Control Class
by Dušan Fister, Alen Jakopič and Mitja Truntič
Appl. Sci. 2025, 15(24), 12884; https://doi.org/10.3390/app152412884 - 5 Dec 2025
Viewed by 947
Abstract
Experience shows that knowledge transfer and understanding of fundamental FPGA principles are greatly improved by exercising laboratory practices and manual hands-on operations. Hence, a case study was performed on two didactic platforms for students of intelligent control techniques that were upgraded with FPGAs [...] Read more.
Experience shows that knowledge transfer and understanding of fundamental FPGA principles are greatly improved by exercising laboratory practices and manual hands-on operations. Hence, a case study was performed on two didactic platforms for students of intelligent control techniques that were upgraded with FPGAs to be involved in laboratory practices. Among others, platforms allow implementation of traditional linear control algorithms, such as PID, or modern non-linear control algorithms, such as fuzzy logic or artificial neural networks. Initially, the underlying physics can be carefully studied, and the mathematical model can be derived. Then, such a model can be discretized into its digital form, an appropriate controller can be designed, and its performance can be compared to the known benchmark. Controllers and control parameters can be practiced by students themselves, offering underlying potential for improving students’ understanding of the fundamentals of FPGA. Full article
(This article belongs to the Special Issue Artificial Intelligence for Learning and Education)
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