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Systems
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Generative AI Transformation in Education: Current Issues and Challenges
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Generative AI Transformation in Education: Current Issues and Challenges

A special issue of Systems (ISSN 2079-8954). This special issue belongs to the section "Systems Practice in Social Science".

Deadline for manuscript submissions: 30 September 2025 | Viewed by 1933

Special Issue Editors

Prof. Dr. Gila Kurtz

E-Mail Website
Guest Editor
Faculty of Instructional Technologies, Holon Institute of Technology (HIT), Holon 5810201, Israel
Interests: interaction human_computer; AI learning technologies; instructional design; digital readiness
Prof. Dr. Antonella Poce

E-Mail Website
Guest Editor
Department of History, Heritage, Education and Society, University of Rome Tor Vergata, 00133 Rome, Italy
Interests: higher education; E-learning; digital learning

Special Issue Information

Dear Colleagues,

The emergence of Generative AI (GenAI) technologies, particularly tools like ChatGPT and other large language models, has catalyzed unprecedented transformations in educational landscapes worldwide. This technological shift fundamentally reshapes how knowledge is created, shared, and assessed across all educational levels. This Special Issue explores the multifaceted impact of GenAI on teaching, learning, and educational systems, from K-12 through higher education and professional development.

This Special Issue aims to examine the promising opportunities these technologies present, such as personalized learning experiences and automated feedback systems, alongside the critical challenges educators, students, and institutions face. These challenges include ensuring integrity, developing GenAI literacy, and addressing equity and ethical concerns in GenAI implementation.

We seek research contributions that focus on K-12, higher education, and professional development. We would like to include perspectives from multiple stakeholders, such as students, faculty members, administrators, policymakers, and national and international educational agencies and institutions. We encourage contributions that are theoretical or empirical in nature, encompassing qualitative and quantitative research. Interdisciplinarity is highly encouraged.

Prof. Dr. Gila Kurtz
Prof. Dr. Antonella Poce
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 100 words) can be sent to the Editorial Office for announcement on this website.

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. Systems is an international peer-reviewed open access monthly 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

  • generative artificial intelligence
  • teaching and learning with artificial intelligence
  • assessment-based artificial intelligence
  • ethical artificial intelligence
  • artificial intelligence literacy
  • artificial intelligence in education
  • learning analytics
  • educational innovation
  • educational policy
  • educational equity

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Published Papers (2 papers)

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Research

57 pages, 2043 KiB  
Article
From Transformative Agency to AI Literacy: Profiling Slovenian Technical High School Students Through the Five Big Ideas Lens
by Stanislav Avsec and Denis Rupnik
Systems 2025, 13(7), 562; https://doi.org/10.3390/systems13070562 - 9 Jul 2025
Viewed by 379
Abstract
The rapid spread of artificial intelligence (AI) in education means that students need to master both AI literacy and personal agency. This study situates a sample of 425 Slovenian secondary technical students within a three-tier framework that maps psychological empowerment onto AI literacy [...] Read more.
The rapid spread of artificial intelligence (AI) in education means that students need to master both AI literacy and personal agency. This study situates a sample of 425 Slovenian secondary technical students within a three-tier framework that maps psychological empowerment onto AI literacy outcomes within a cultural–historical activity system. The agency competence assessments yielded four profiles of student agency, ranging from fully empowered to largely disempowered. The cluster membership explained significant additional variance in AI literacy scores, supporting the additive empowerment model in an AI-rich vocational education and training context. The predictive modeling revealed that while self-efficacy, mastery-oriented motivations, and metacognitive self-regulation contributed uniquely—though small—to improving AI literacy, an unexpectedly negative relationship was identified for internal locus of control and for behavioral self-regulation focused narrowly on routines, with no significant impact observed for grit-like perseverance. These findings underscore the importance of fostering reflective, mastery-based, and self-evaluative learning dispositions over inflexible or solely routine-driven strategies in the development of AI literacy. Addressing these nuanced determinants may also be vital in narrowing AI literacy gaps observed between diverse disciplinary cohorts, as supported by recent multi-dimensional literacy frameworks and disciplinary pathway analyses. Embedding autonomy-supportive, mastery-oriented, student-centered projects and explicit metacognitive training into AI curricula could shift control inward and benefit students with low skills, helping to forge an agency-driven pathway to higher levels of AI literacy among high school students. The most striking and unexpected finding of this study is that students with a strong sense of competence—manifested as high self-efficacy—can achieve foundational AI literacy levels equivalent to those possessing broader, more holistic agentic profiles, suggesting that competence alone may be sufficient for acquiring essential AI knowledge. This challenges prevailing models that emphasize a multidimensional approach to agency and has significant implications for designing targeted interventions and curricula to rapidly build AI literacy in diverse learner populations. Full article
(This article belongs to the Special Issue Generative AI Transformation in Education: Current Issues and Challenges)
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23 pages, 364 KiB  
Article
Framing and Evaluating Task-Centered Generative Artificial Intelligence Literacy for Higher Education Students
by Arnon Hershkovitz, Michal Tabach, Yoram Reich, Lilach Lurie and Tamar Cholcman
Systems 2025, 13(7), 518; https://doi.org/10.3390/systems13070518 - 27 Jun 2025
Viewed by 527
Abstract
The rise in generative artificial intelligence (GenAI) demands new forms of literacy among higher education students. This paper introduces a novel task-centered generative artificial intelligence literacy framework, which was developed collaboratively with academic and administrative staff at a large research university in Israel. [...] Read more.
The rise in generative artificial intelligence (GenAI) demands new forms of literacy among higher education students. This paper introduces a novel task-centered generative artificial intelligence literacy framework, which was developed collaboratively with academic and administrative staff at a large research university in Israel. The framework identifies eight skills which are informed by the six cognitive domains of Bloom’s Taxonomy. Based on this framework, we developed a measuring tool for students’ GenAI literacy and surveyed 1667 students. Findings from the empirical phase show moderate GenAI use and medium–high literacy levels, with significant variations by gender, discipline, and age. Notably, 82% of students support formal GenAI instruction, favoring integration within curricula to prepare for broader digital society participation. The study offers actionable insights for educators and policymakers aiming to integrate GenAI into higher education responsibly and effectively. Full article
(This article belongs to the Special Issue Generative AI Transformation in Education: Current Issues and Challenges)
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