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Artificial Intelligence for Educational Technology

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 September 2026 | Viewed by 1823

Editors


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Guest Editor
Centre of Mathematics and Physics, Lodz University of Technology, 90-924 Lodz, Poland
Interests: E-learning; mathematics; higher education
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
Faculty of Educational Studies, Adam Mickiewicz University, Poznań, Poland
Interests: remote education; digital competencies; electronic aggression

Special Issue Information

Dear Colleagues,

This Special Issue focuses on how artificial intelligence is rapidly transforming the way we teach and learn.

In classrooms around the world, AI-powered tools support both students and teachers by providing personalized learning experiences, instant feedback, and access to a wide range of digital resources. Intelligent tutoring systems can identify a student’s strengths and weaknesses, allowing them to progress at their own pace. This helps create more inclusive learning environments where no one is left behind.

For teachers, AI can automate routine tasks such as grading or generating lesson materials, giving them more time to focus on creative and interactive activities. Additionally, advanced data analysis allows educators to better understand student performance and adjust their strategies accordingly.

Despite its many advantages, the use of AI also raises important questions about privacy, ethics, and the role of human interaction in education. As technology continues to evolve, it is essential to find the right balance between innovation and responsibility.

Dr. Jacek Stańdo
Prof. Dr. Jacek Pyżalski
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-anonymized 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
  • personalized learning
  • adaptive learning
  • intelligent tutoring systems
  • educational technology
  • learning analytics
  • digital classrooms
  • inclusive education

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Published Papers (1 paper)

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Research

21 pages, 1506 KB  
Article
Dual-Mode Adaptive AI Persona Recommendation for Blockchain Education: A Mixed-Method Evaluation of the PITL System Based on Dreyfus Competency Levels
by Buğra Ayan and Mutlu Tahsin Üstündağ
Appl. Sci. 2026, 16(6), 2998; https://doi.org/10.3390/app16062998 - 20 Mar 2026
Viewed by 1264
Abstract
The rapid proliferation of large language models has created significant opportunities for personalized education, yet existing systems rarely account for user competency as a determinant of interaction quality. This study introduces Persona in The Loop (PITL), a dual-mode adaptive framework that recommends AI [...] Read more.
The rapid proliferation of large language models has created significant opportunities for personalized education, yet existing systems rarely account for user competency as a determinant of interaction quality. This study introduces Persona in The Loop (PITL), a dual-mode adaptive framework that recommends AI personas for blockchain and smart contract education applications. PITL employs 100 AI personas organized across two domains, ten sub-specialties, and five Dreyfus competency levels, recommending personas via either similarity-based mode grounded in Cognitive Load Theory or complementary mode grounded in the Zone of Proximal Development, with an adaptive switching mechanism driven by NASA-TLX cognitive load feedback. A mixed-method study with 150 participants using a 2 × 5 factorial design showed that the complementary mode produced higher learning gains, while the similarity-based mode yielded lower cognitive load and higher code quality. The adaptive mechanism outperformed both fixed-mode conditions on learning gain and code quality. The Mode × Dreyfus interaction was significant for cognitive load and task duration but not for learning gains, suggesting mode effects on learning outcomes are consistent across competency levels. Qualitative interviews with 20 participants corroborated quantitative findings. PITL offers a theoretically grounded and empirically validated approach to competency-based AI persona recommendation in educational contexts. Full article
(This article belongs to the Special Issue Artificial Intelligence for Educational Technology)
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