Artificial intelligence in contemporary Moocs for teacher training: didactic, ethical, and technical analysis of courses through an emergent category system

Revista de Educación Mediática y TIC

Artificial intelligence in education: Possibilities and challenges for pedagogical practice

Artificial Intelligence (AI) has brought about significant changes in the educational landscape, driven primarily by the advancement of generative models and the increasing availability of tools capable of supporting teaching and learning processes. This study aimed to analyze the main possibilities and challenges associated with AI in pedagogical practice through a structured narrative literature review. Searches were conducted in the Web of Science Core Collection, Scopus, ERIC, SciELO, and Google Scholar, prioritizing publications from 2019 to 2026, while earlier foundational sources were retained when methodologically or conceptually relevant. The final interpretive corpus comprised 31 core sources, including peer-reviewed empirical studies, systematic and scoping reviews, meta-analyses, and institutional guidance. Evidence was synthesized into four themes: evolution and educational applications of AI, pedagogical potential, ethical and institutional challenges, and the changing role of teachers. The literature identifies opportunities for personalized learning, pedagogical planning, accessibility, formative assessment, and teaching-material development, but also recurring concerns involving information reliability, academic integrity, privacy, algorithmic bias, digital inequality, and teacher preparedness. Recent evidence indicates that positive outcomes are heterogeneous and depend on pedagogical scaffolding, human verification, institutional governance, and AI literacy. AI should therefore be understood as a supportive educational technology rather than a substitute for teacher mediation. Its integration requires critical, ethical, and pedagogically grounded use that preserves student autonomy, assessment validity, and educational equity.

Theoretical Foundations for Higher Education Pedagogy with Generative Artificial Intelligence (AI): The DIDACT.IA Model

A conceptual framework is proposed for symbiotic learning, academic tasks as the instructional context of this interaction, and critical thinking as a condition for preserving students’ epistemic agency, and a didactic model, called DIDACT.IA, is presented.

AI-Powered Intelligent Transformation of Teaching: A Case Study

This article presents a case study of AI-powered intelligent transformation in teaching, drawing upon practical implementations in both science and humanities education, and illustrates how generative AI tools can be strategically deployed to enhance diagnostic assessment, personalize learning, create interactive content, and facilitate conver sational inquiry.

Exploring the Influence of Artificial Intelligence Adaptation on Pre-Service Science Teachers’ Pedagogical Content Knowledge

Background and Objectives: In the 21st century learning environment, the integration of emerging technologies has transformed the educational landscape. For pre-service science teachers, the challenge lies in developing strong pedagogical content knowledge (PCK) while also adapting to rapidly evolving technologies. This study aimed to explore how Artificial Intelligence (AI) tools influence the development of PCK among pre-service science teachers in the fields of physical and biological sciences. Specifically, it investigated their lived experiences of adapting to AI in lesson planning, instruction, and assessment, contributing to the broader discourse on AI integration in teacher education. Methodology: This qualitative study employed a descriptive phenomenological approach to capture the authentic experiences of six (n = 6) fourth-year pre-service science teachers specializing in physical and biological sciences at a state university in Bataan, Philippines. Participants were selected via purposive sampling based on their documented experience with AI tools in coursework and teaching demonstrations. Data were analyzed using descriptive phenomenological procedures, including bracketing, horizontalization, clustering, and textualization to identify the essence of participants’ experiences.  Results: The findings revealed that AI has a significant influence on all five domains of PCK. In teaching orientation, pre-service teachers embraced learner-centered strategies but raised concerns about AI’s pedagogical implications. For curricular knowledge, AI was useful in aligning instruction with standards, though participants emphasized its limitations in content depth. Instructional strategies were enhanced through AI-powered simulations and visuals, promoting interactivity. In the assessment, AI improved accuracy and efficiency, yet participants stressed the importance of teachers’ professional judgment. Lastly, regarding understanding learners, AI was seen as supportive, but concerns remained about its ability to address emotional and contextual learning factors. Discussions: AI is instrumental in supporting learner-centered approaches, promoting differentiated instruction, and aligning lesson plans with curricular standards. These themes indicate that AI can enhance teacher preparedness by offering practical solutions for instructional planning and responding to the diverse needs of learners. Pre-service teachers leverage AI to create meaningful learning experiences and more efficient assessments, yet they also express valid concerns about overreliance, equity, and the need for human judgment. The themes collectively underscore the dual role of AI as an enabler of innovation and as a potential source of pedagogical tension. These findings emphasize the importance of integrating training in AI use within teacher education to inform policy at the classroom and institutional levels, promoting ethical and pedagogically sound practices. Conclusions: Pre-service science teachers view AI as a helpful tool for lesson planning, assessment, and tailoring instruction to learner needs. AI supported teachers in generating materials, aligning lessons with curricular standards, and providing differentiated strategies. However, challenges such as overreliance on AI, risks to learners’ critical thinking, and the need for teacher oversight were also identified. While AI can enhance pedagogical content knowledge (PCK) and classroom practice, its effectiveness depends on thoughtful integration and continued professional development. These findings have implications, especially for educators and policymakers in comparable teacher education contexts, emphasizing the need to bridge the gap between technological advancement and pedagogical effectiveness. 

Creating AI Applications for EFL Teaching: Perceptions and Challenges in Initial Teacher Education

It is concluded that initial teacher education must shift the focus from the tool to the pedagogical problem, preparing teachers as learning sequence designers who create, adapt, and critically evaluate AI-powered resources.