Reframing teacher digital competence for generative AI: a task–capability analysis of multi-platform educational discourse

Generative Artificial Intelligence in Education: Bridging the Gap between Opportunity and Teacher Readiness via the Extended UTAUT Framework

The world of education is undergoing a transformative paradigm shift, as generative artificial intelligence is being integrated into the learning environment. The rapid development in Generative AI recently have revolutionized the classroom and teaching methodologies. In that regard, this study focuses on how teachers perceive generative AI under the formulation of an Extended UTAUT (Unified Theory of Acceptance and Use of Technology) model, the advantages and challenges related with its application. A descriptive method was adopted for this study and teachers' perceptions in regard to Performance Expectancy (pedagogical effectiveness) and Effort Expectancy (AI literacy) was explored. Data were obtained from 146 teachers through use of survey methodology. Descriptive and inferential statistics were used in the analysis of the collected data. The result has a crucial difference, because the perceived pedagogical utility and the technical expertise may amend the teacher's confidence for using the system. However, lack of indispensable facilitating conditions (deficient training, unclear governance etc) and ethical concerns may hinder implementation. These findings emphasize the urgent demand for professional development and clear institutional policy, announced by the UNESCO (2023) and Floridi et al. (2018) normative anchors. By isolating the crucial components of fairness, accountability, and agency which underpin the factors most predictive of teacher readiness, this study lays a framework for leaders and policy makers searching to translate teacher doubt into enthusiastic and responsible carrying out of AI tools. Clearly defining governing rules, strengthening facilitating conditions, will help address the fears that shortly impede the adoption of generative AI in schools.

Generative AI as a Learning Partner or Pedagogical Challenge? Insights from Pre-service Teachers

This study investigates how pre-service teachers experience generative artificial intelligence tools in their learning processes and how they plan to integrate these technologies into their future teaching practices. The study employed a qualitative phenomenological design and was conducted with 28 pre-service teachers enrolled in the English language teaching program of a public university in Türkiye. Semi-structured online interviews were carried out, and the data were analyzed through content analysis. Coding was conducted independently by two coders, achieving a high level of inter-rater agreement. The findings reveal that pre-service teachers perceive generative artificial intelligence as a supportive learning partner in areas such as time management, idea development, linguistic accuracy, and self-regulation. However, they also highlighted risks such as over-reliance, diminished creativity, ethical concerns, and the generation of inaccurate information. The participants emphasized that while generative artificial intelligence may serve as a complementary tool in learning processes, classroom integration requires transparency, critical verification, and active teacher guidance as essential conditions. The originality of the study lies in its holistic exploration of pre-service teachers’ experiences with generative artificial intelligence across pedagogical, ethical, and professional identity dimensions. These findings provide qualitative evidence for the relatively underexplored perspectives of pre-service teachers, thereby offering a new lens for ongoing debates on the role of generative artificial intelligence in teacher education.

Rethinking digital competence for pre-service teachers in the GenAI era: a conceptual framework for pedagogical GenAI judgment

Abstract Generative artificial intelligence (GenAI) tools are disrupting how pre-service teachers plan lessons, produce feedback, assess learning, and evaluate professional practice. Current digital competence frameworks offer valuable starting points but lack specificity around how pre-service teachers should exercise judgment over AI-supported decisions. Through comparison of selected digital competence, teacher technology, AI competence, and future-skills frameworks, this conceptual article presents a digital-GenAI competence framework grounded on pedagogical GenAI judgment for pre-service teachers (PST). Updating our initial analysis, pedagogical GenAI judgment is identified as the key construct—a competency that requires teachers to decide when and how to use GenAI, frame pedagogically aligned prompts, evaluate/revise AI-generated output, contextualize the output for students and learning objectives, disclose AI use transparently, and take responsibility for final decisions. Named DigiGen-PST 2030, the framework describes four layers of competence—namely foundational digital competence, pedagogical transformation, pedagogical GenAI judgment, and future-skills orientation—and maps related constructs across TPACK, DigCompEdu, the ISTE Standards for Educators, UNESCO’s AI Competency Framework for Teachers, and OECD education guidance. We offer propositions and suggestions for teacher education programs, teacher educators, and future research validation.

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

Research on the Construction and Enhancement Pathways of Primary School Teachers’ Artificial Intelligence Literacy from the Perspective of AI‑TPACK

The rapid development of artificial intelligence (AI) is transforming educational practice and reshaping the professional competencies required of teachers. Drawing on the AI-TPACK framework, this study aims to construct a context-sensitive AI literacy framework for primary school teachers and identify practical pathways for its development. A qualitative grounded theory approach was employed. Semi-structured interview data collected from primary school teachers were analysed with NVivo 12 through open, axial, and selective coding. The analysis generated 32 initial categories, which were subsequently integrated into four core dimensions: AI education awareness, AI education knowledge, AI-supported teaching competence, and AI education ethics. Three reserved interview transcripts were used to test theoretical saturation, and no new concepts, categories, or relationships emerged. The findings indicate that primary school teachers’ AI literacy is not limited to technical proficiency but constitutes an integrated professional competence characterised by an “awareness-driven, knowledge-supported, competence-oriented, and ethics-guided” mechanism. The study further identifies four major challenges: insufficient recognition of AI’s educational value, fragmented AI-related knowledge, superficial integration of AI into classroom practice, and inadequate awareness of ethical risks. To address these challenges, five development pathways are proposed: strengthening teachers’ AI education awareness, establishing an AI-TPACK-oriented training system, promoting practice through teaching-research communities, reinforcing ethical education and institutional safeguards, and developing multidimensional evaluation mechanisms. This study extends the application of AI-TPACK to primary education and provides a theoretical and practical reference for supporting teachers’ professional development in AI-enhanced educational environments.

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