The Impact of AI on Curriculum and Education Innovation

A special issue of Education Sciences (ISSN 2227-7102).

Deadline for manuscript submissions: 31 August 2026 | Viewed by 1899

Special Issue Editors


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Guest Editor
Faculty of Engineering, Università Telematica Internazionale Uninettuno, 00186 Rome, Italy
Interests: electrical engineering; digital transformation; engineering education
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Guest Editor
EADTU (European Association of Distance Teaching Universities), Maastricht, The Netherlands
Interests: technology enhanced learning; active learning and methodologies (i.e., gamification); expanding educational access and fostering learner-centered approaches that effectively respond to the evolving demands of digital transformation

Special Issue Information

Dear Colleagues,

The emergence of generative AI—particularly large language models (LLMs)—has transformed higher education at an unprecedented pace. These technologies offer great promise for enhancing teaching, learning, and assessment. At the same time, they raise urgent questions about ethics, academic integrity, inclusion, and institutional readiness. Studies and experiments are still underway to understand how to integrate AI in education, updating teaching methodologies and curricula even in non-technical fields.

This Special Issue invites contributions that address the integration of generative AI in education through ethical, evidence-based, and pedagogically grounded approaches. We seek papers that move beyond general debates to present practical solutions, policy-informed frameworks, and tested interventions that support the responsible adoption of GenAI tools across all levels of education.

We welcome original research, case studies, conceptual models, institutional strategies, and policy insights that explore the following:

  • Ethical and inclusive GenAI adoption in education;
  • Institutional frameworks and readiness for AI-enhanced teaching;
  • Pedagogical design of GenAI-integrated learning activities;
  • Certified CPD and micro-credential pathways for AI in education;
  • AI literacy and competence development aligned with DigComp and DigCompEdu;
  • Policy-making and quality assurance for AI use in higher education;
  • Institutional self-assessment tools and maturity models for GenAI integration;
  • Co-designed AI learning labs and participatory methods in curriculum innovation;
  • GenAI for personalisation, inclusion and lifelong learning;
  • Risks and safeguards: transparency, bias, fairness, and academic integrity;
  • Comparative analyses of governance models across Europe;
  • Interplay between the AI Act, UNESCO/OECD frameworks, and institutional practice.

This Special Issue will particularly favour contributions grounded in empirical evidence and connected to current European policy frameworks, including the Digital Education Action Plan, DigComp 3.0, and the forthcoming AI literacy guidelines from UNESCO and the OECD.

Submission themes may include (but are not limited to) the following:

  • GenAI-enhanced teaching in real-world university settings;
  • Competence frameworks for ethical AI use in teaching and learning;
  • Digital toolkits and AI-readiness assessment in HEIs;
  • Staff and student engagement with GenAI: practices, perceptions, and impacts;
  • Governance and policy responses to the challenges of GenAI in education;
  • The role of distance, open and online universities in pioneering ethical AI practices.

We aim to assemble a high-quality, interdisciplinary collection of studies that will support educators, institutional leaders, policymakers and researchers in navigating the complex intersection of AI, pedagogy, and educational values.

Prof. Dr. Dario Assante
Dr. Alessandra Antonaci
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 double-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Education Sciences 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 1800 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 (GenAI)
  • large language models (LLMs)
  • ethical AI in education
  • AI Literacy
  • digital competence (DigComp/DigCompEdu)
  • academic integrity
  • AI governance and policy
  • AI assessment tools
  • competence frameworks
  • AI toolkit for educators

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Published Papers (2 papers)

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Research

31 pages, 402 KB  
Article
Analysis of AI-Readiness of University Students Using AI-Competency Measurement Framework
by Roman Chinoracky, Natalia Stalmasekova, Margita Majercakova and Rebecca Neumannova
Educ. Sci. 2026, 16(5), 692; https://doi.org/10.3390/educsci16050692 - 27 Apr 2026
Viewed by 300
Abstract
Historically, technological progress has driven shifts in the labour market, leading to the disappearance of certain jobs while simultaneously creating new roles fueled by the need to work with emerging technologies. The technological advancements of the early 2020s are inherently linked to Artificial [...] Read more.
Historically, technological progress has driven shifts in the labour market, leading to the disappearance of certain jobs while simultaneously creating new roles fueled by the need to work with emerging technologies. The technological advancements of the early 2020s are inherently linked to Artificial Intelligence (AI) and the rise in chatbots, whose accessibility and ease of use have become paramount for business development. Given this context, the aim of this study is to analyse frameworks describing the AI competencies of students who will constitute the future workforce. Based on an analysis of existing frameworks, a new framework is formulated through synthesis and operationalized into survey items representing AI-related competencies. These survey items are measured by primary research focused on a sample of undergraduate students at a selected faculty and university. The research provides valuable insights for curriculum development policy by highlighting competencies that students perceive as significant versus those they find less important. Building on these findings, the study offers policy recommendations for curriculum designers. The proposed recommendations enable the creation of educational programmes with relevance to the practical needs of the business sector, increasingly impacted by the emergence of AI. Full article
(This article belongs to the Special Issue The Impact of AI on Curriculum and Education Innovation)
17 pages, 720 KB  
Article
Leveraging Generative AI Through Vibe Coding: A Case of Simulation-Based Curriculum Redesign in Management Education
by Albert Munoz and Laura Rook
Educ. Sci. 2026, 16(4), 558; https://doi.org/10.3390/educsci16040558 - 2 Apr 2026
Viewed by 850
Abstract
Generative Artificial Intelligence (GenAI) tools such as Large Language Models (LLMs) have uncovered new possibilities for educators to develop interactive learning resources, yet practical guidance on harnessing these capabilities remains limited. This paper examines how GenAI and LLMs can support curriculum redesign through [...] Read more.
Generative Artificial Intelligence (GenAI) tools such as Large Language Models (LLMs) have uncovered new possibilities for educators to develop interactive learning resources, yet practical guidance on harnessing these capabilities remains limited. This paper examines how GenAI and LLMs can support curriculum redesign through their capabilities in generating and testing code. We present a case study of a postgraduate operations management course redesigned to include simulation-based learning, to strengthen analytical and decision-making skills. The case demonstrates how a replicable prompt-driven workflow can enable educators to co-create HTML/JavaScript simulations with GenAI without programming expertise. Simulation-based learning has an established evidence base for improving student learning outcomes; the challenge has been accessibility. This paper’s contribution is not to re-validate that evidence, but to demonstrate that pedagogies once beyond the reach of resource-constrained educators are now meaningfully accessible through GenAI-enabled vibe coding. We document the design and implementation of this approach, and the opportunities and constraints encountered, to provide a practical roadmap for educators seeking to close the gap between what simulation-based education can offer and what has historically been possible to deliver. Full article
(This article belongs to the Special Issue The Impact of AI on Curriculum and Education Innovation)
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