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How Is AI Transforming Education?
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How Is AI Transforming Education?

A special issue of AI (ISSN 2673-2688). This special issue belongs to the section "AI Systems: Theory and Applications".

Deadline for manuscript submissions: 15 December 2026 | Viewed by 3303

Special Issue Editor

Dr. Andrej Flogie

E-Mail Website
Guest Editor
Faculty of Natural Sciences and Mathematics, University of Maribor, 2000 Maribor, Slovenia
Interests: AI in education; innovative pedagogy; cognitive science
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The development of 21st-century competences among learners, such as creativity, critical thinking, and digital literacy, has become a central goal of contemporary education. The rapid evolution of Artificial Intelligence (AI) presents both unprecedented opportunities and challenges to achieving this goal. As with previous technological revolutions, Artificial Intelligence is expected to transform the nature of work, knowledge production, and social interaction, thereby reshaping educational paradigms at all levels.

This Special Issue will explore how AI, and especially generative Artificial Intelligence (GEN-AI), is transforming education through pedagogically grounded applications, new models of learning design, and evidence-based research on its cognitive, social, and ethical implications. We welcome empirical, theoretical, and design-based studies that examine how AI (GEN-AI) can serve not merely as a digital tool, but as a co-creator of learning processes, enhancing personalisation, formative assessment, collaborative inquiry across formal and informal contexts, etc.

Our aim is to advance the current scholarly understanding of AI-enhanced pedagogy, going beyond technological enthusiasm and instead fostering critical reflection on the philosophical, pedagogical, and psychological foundations of AI integration in education. While the literature has extensively addressed AI integration in education from technological and policy perspectives, this Special Issue will fill a crucial gap by integrating pedagogical intentionality and human-centred design into the discourse. By linking educational theory with practice and governance, it will contribute to a holistic framework for understanding how AI can responsibly and effectively transform teaching, learning, and assessment.

Dr. Andrej Flogie
Guest Editor

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. AI 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

  • artificial intelligence in education (AIED)
  • transformative learning and teaching
  • generative artificial intelligence (Gen-AI)
  • AI-driven educational innovation
  • pedagogical transformation

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Published Papers (4 papers)

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27 pages, 315 KB  
Article
A Phenomenological Investigation of Teacher Candidates’ Metaphorical Views on AI in Language Learning
by Ahmet Güneyli, Selma Korkmaz, Havva Esra Karabacak and Fatma Aslantürk Altıntuğ
AI 2026, 7(3), 100; https://doi.org/10.3390/ai7030100 - 9 Mar 2026
Viewed by 440
Abstract
The implementation of artificial intelligence (AI) in education is gaining more attention, and as a result, more research is being conducted on the views and conceptualisations of AI by educators. The understanding of teacher candidates is vital for the AI integration in education, [...] Read more.
The implementation of artificial intelligence (AI) in education is gaining more attention, and as a result, more research is being conducted on the views and conceptualisations of AI by educators. The understanding of teacher candidates is vital for the AI integration in education, which should be human-centred, and still, there is a lack of studies focusing mainly on teacher candidates in the field of the native language. This qualitative phenomenological research aimed to explore metaphors of 46 Turkish language teacher candidates (third- and fourth-year undergraduates in Northern Cyprus) representing their answer to the prompt “AI is like because…”. The data were collected through open-ended questions and analysed using content analysis along with expert validation. Participants produced 46 valid metaphors, which were divided into five thematic categories: (1) AI as Teacher or Learner (21.7%), (2) AI as Method/Strategy (21.7%), (3) AI as Evolving Living Organism (13%), (4) AI as Guide/Helper (21.7%), and (5) AI as Danger/Threat (21.7%). Four groups expressed positive or neutral attitudes towards AI, such as considering it a clever teacher, a useful tool, a growing entity, or a guide. One category revealed negative views, perceiving AI as a destructive force. Overall, 78.3% of participants expressed optimistic views about AI, while 21.7% of them pointed to concerns. Turkish language teacher candidates generally perceive AI as a supportive, human-like assistant in the classroom, but a few of them express concerns about its existence. These results emphasise the importance of incorporating AI literacy and ethics into teacher education. Equipping future language teachers with the skills to use AI in the classroom might be a way of implementing AI in schools that is confident, critical, and human-centred. Full article
(This article belongs to the Special Issue How Is AI Transforming Education?)
22 pages, 2872 KB  
Article
A Multisite Study of an Animated Cinematic Clinical Narrative for Anticoagulant Pharmacology Education
by Amanda Lee, Kyle DeWitt, Meize Guo and Tyler Bland
AI 2026, 7(2), 59; https://doi.org/10.3390/ai7020059 - 5 Feb 2026
Viewed by 656
Abstract
Anticoagulant pharmacology is a cognitively demanding domain in undergraduate medical education, with persistent challenges in learner engagement, retention, and safe clinical application. Cinematic Clinical Narratives (CCNs) offer a theory-informed multimedia approach designed to support learning through narrative structure, visual mnemonics, and affective engagement. [...] Read more.
Anticoagulant pharmacology is a cognitively demanding domain in undergraduate medical education, with persistent challenges in learner engagement, retention, and safe clinical application. Cinematic Clinical Narratives (CCNs) offer a theory-informed multimedia approach designed to support learning through narrative structure, visual mnemonics, and affective engagement. We conducted a multi-site quasi-experimental study within a six-week Cancer, Hormones, and Blood course across a distributed medical education program. First-year medical students received either a traditional case-based lecture or an animated CCN (Twilight: Breaking Clots) during a one-hour anticoagulant pharmacology session. Learning outcomes were assessed using pre- and posttests, learner engagement was measured with the Situational Interest Survey for Multimedia (SIS-M), and exploratory eye tracking with second-year medical students was used to assess visual attention to embedded mnemonics. Both instructional groups demonstrated significant learning gains, with fold-change analyses indicating greater relative improvement among students exposed to the CCN. The animated CCN elicited significantly higher triggered situational interest compared with non-animated cases (p = 0.019), while also being preferred by the majority of participants. Qualitative analysis revealed that learners perceived CCNs as particularly effective for initial encoding and memorization, while non-animated cases supported subsequent clinical application. Eye-tracking data demonstrated high visual uptake and sustained attention to key mnemonic elements. Together, these findings support expert-designed, genAI-assisted CCNs as a validated and complementary instructional approach in medical education. Full article
(This article belongs to the Special Issue How Is AI Transforming Education?)
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26 pages, 911 KB  
Article
Pedagogical Transformation Using Large Language Models in a Cybersecurity Course
by Rodolfo Ostos, Vanessa G. Félix, Luis J. Mena, Homero Toral-Cruz, Alberto Ochoa-Brust, Apolinar González-Potes, Ramón A. Félix, Julio C. Ramírez Pacheco, Víctor Flores and Rafael Martínez-Peláez
AI 2026, 7(1), 25; https://doi.org/10.3390/ai7010025 - 13 Jan 2026
Viewed by 873
Abstract
Large Language Models (LLMs) are increasingly used in higher education, but their pedagogical role in fields like cybersecurity remains under-investigated. This research explores integrating LLMs into a university cybersecurity course using a designed pedagogical approach based on active learning, problem-based learning (PBL), and [...] Read more.
Large Language Models (LLMs) are increasingly used in higher education, but their pedagogical role in fields like cybersecurity remains under-investigated. This research explores integrating LLMs into a university cybersecurity course using a designed pedagogical approach based on active learning, problem-based learning (PBL), and computational thinking (CT). Instead of viewing LLMs as definitive sources of knowledge, the framework sees them as cognitive tools that support reasoning, clarify ideas, and assist technical problem-solving while maintaining human judgment and verification. The study uses a qualitative, practice-based case study over three semesters. It features four activities focusing on understanding concepts, installing and configuring tools, automating procedures, and clarifying terminology, all incorporating LLM use in individual and group work. Data collection involved classroom observations, team reflections, and iterative improvements guided by action research. Results show that LLMs can provide valuable, customized support when students actively engage in refining, validating, and solving problems through iteration. LLMs are especially helpful for clarifying concepts and explaining procedures during moments of doubt or failure. Still, common issues like incomplete instructions, mismatched context, and occasional errors highlight the importance of verifying LLM outputs with trusted sources. Interestingly, these limitations often act as teaching opportunities, encouraging critical thinking crucial in cybersecurity. Ultimately, this study offers empirical evidence of human–AI collaboration in education, demonstrating how LLMs can enrich active learning. Full article
(This article belongs to the Special Issue How Is AI Transforming Education?)
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42 pages, 16990 KB  
Perspective
Epistemic Agency in the Age of Large Language Models: Design Principles for Knowledge-Building AI
by Earl Woodruff and Jim Hewitt
AI 2026, 7(3), 99; https://doi.org/10.3390/ai7030099 - 9 Mar 2026
Viewed by 747
Abstract
Introduction: Large language models (LLMs) are increasingly employed as cognitive aids in research and professional inquiry, yet their fluent outputs are frequently regarded as authoritative knowledge. We contend that this practice signifies a fundamental epistemic misalignment. Methods/Approach: Building on Peirce’s theory of inquiry, [...] Read more.
Introduction: Large language models (LLMs) are increasingly employed as cognitive aids in research and professional inquiry, yet their fluent outputs are frequently regarded as authoritative knowledge. We contend that this practice signifies a fundamental epistemic misalignment. Methods/Approach: Building on Peirce’s theory of inquiry, Sellars’ concept of the space of reasons, Stanovich’s tripartite model of cognition, and knowledge-building theory, we develop a conceptual framework for analyzing epistemic agency in human–LLM collaboration. Results/Argument: We demonstrate that LLM outputs fail to satisfy the conditions for knowledge because they lack reflective regulation, resistance to revision, and normative commitment. While LLMs display strong autonomous and algorithmic abilities (e.g., pattern recognition and hypothesis development), reflective control remains a distinctly human function. This asymmetry supports a principled division of epistemic labour and motivates the concept of the Knowledge-Building Partner (KBP): an AI system designed to support inquiry without claiming epistemic authority. Discussion/Implications: We identify prompt-, system-, and model-level design requirements and introduce a triangulated framework for operationalizing epistemic agency through explainable AI, discourse analysis, and rational-thinking measures. These contributions collectively reposition LLM limitations as epistemic design challenges rather than technical issues. Full article
(This article belongs to the Special Issue How Is AI Transforming Education?)
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