Jul 2026· Journal of Digital Learning in Teacher Education· Vol 42, pp. 168 - 181· 0 citations· 48 references
TL;DR
A digital-GenAI competence framework grounded on pedagogical GenAI judgment for pre-service teachers (PST) named DigiGen-PST 2030 is presented, which describes four layers of competence—namely foundational digital competence, pedagogical transformation, pedagogical GenAI judgment, and future-skills orientation.
Abstract
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.
Positive associations of all five task areas were found in pedagogical assistance; the greatest estimates were made with classroom enactment and professional learning; the competence model is confirmed, which is task sensitive and is based on assessment of capability, orchestration of instruction, the verification of evaluation, mediation of the learner and inquiry of the profession.
Na Zhao· Advances in Social Behavior...· 0 citations
Purpose. This study compares Türkiye's Digital Ecosystem for Teacher Training (DETT/ÖGEDEP) Digital Teacher Competencies Framework with three internationally established frameworks for teachers' digital competence, focusing on structural and conceptual alignment and differentiation.
Method. A qualitative document analysis was conducted to systematically compare the DETT framework with DigCompEdu (Redecker, 2017), the ISTE Standards for Educators (2024), and UNESCO's ICT Competency Framework for Teachers (ICT-CFT v3.0; 2018). Deductive thematic coding was combined with matrix-based cross-walking across eight analytical dimensions, using explicit coding units and full, partial, and indirect match criteria.
Findings. The DETT teacher digital competency framework aligns substantially with international frameworks in core competency domains and pedagogical orientation. Its Awareness–Functionality–Actionability structure conceptualizes teacher development as a context-sensitive, practice-oriented spiral, rather than a linear proficiency scale, aligning with UNESCO's phased model and ISTE's role-based conception of teacher development. The DETT framework's distinctive contribution lies in its explicit emphasis on sustainability, ethical responsibility, and the continuity of education during emergencies as competency indicators, which are addressed only indirectly or peripherally in the international frameworks reviewed.
Implications. This study provides a conceptual foundation for developing ecosystem-oriented and ethically grounded approaches to digital teacher education. It also presents a policy model for integrating competency frameworks with national education priorities. These implications arise from the DETT framework's distinctive focus on sustainability, ethical responsibility, and continuity of education during emergencies, as well as its alignment with international frameworks. Recommendations for instrument development and qualitative field research are also provided.
Davut Atış· Qualitative Inquiry in Educa...· 0 citations
This critical narrative review examines evidence published from 1 January 2000 to 19 June 2026 to analyse how teachers’ roles are being reconfigured across technology-enhanced, blended, online and artificial intelligence-mediated learning to argue that contemporary teacher professionalism is best understood as technologically mediated human judgement rather than technological replacement.
D. Bora, Kate Dandesh Kumar· Asian Journal of Education a...· 0 citations
This focused article argues that generative AI (GenAI) in TESOL teacher education (TTE) must be reconceptualized to address the risk of teacher de‐skilling and GenAI dependency. Otherwise, teachers may offload professional work onto GenAI before developing evidence‐informed pedagogies and safeguards. Drawing on the five dimensions of professional GenAI competence (P‐GenAI‐C), we outline how TTE programs can organize sustainable teacher learning for the GenAI age. We then offer practical design principles and activities for pre‐service and in‐service contexts, including low‐stakes tool engagement, scenario‐based microteaching, feedback routines, and structured reflection. Across these approaches, we emphasize teacher agency, transparency, and ethical responsibility, including attention to digital exclusion, labor conditions, and environmental impacts. We argue that effective GenAI integration is predicated on teacher competence and professional judgment.
Lucas Kohnke, Benjamin Luke Moorhouse· TESOL Quarterly (Print)· 0 citations
Generative artificial intelligence (GenAI) offers opportunities for innovative teaching but also raises ethical concerns. While pre-service teachers are expected to integrate GenAI responsibly, little is known about how they engage in reasoning processes that guide such integration. This study investigates pre-service teachers’ pedagogical and ethical reasoning through case-based scenarios involving GenAI. Pre-service teachers (N = 49) from two Finnish universities reflected on GenAI-related pedagogical ethical cases. Their reflections were analyzed using Shulman’s Pedagogical Reasoning and Action Model and Rest’s Four-Component Model. Findings show that participants’ pedagogical reasoning focused on aligning GenAI with curricula, adapting outputs, and critical evaluation while deeper reflections were less frequent. Ethical reasoning was strongest in fairness cases and motivation-related dimensions, but weaker in accountability cases and policy-related dimensions. This study contributes to understanding pre-service teachers’ pedagogical and ethical reasoning for GenAI integration and informs the development of teacher education programs.
Alanur Ahsen Dalyanci, Ismail Çelik, Sini Kontkanen et al.· Journal of educational compu...· 0 citations
The arrival of generative artificial intelligence (GenAI) in educational settings has sparked debate over how it will transform teaching and learning. Language education is already grappling with teacher shortages, workload intensification, and shifting program viability. While conceptual work has outlined theoretical use cases, little empirical research has examined how world language teachers actually use GenAI or the challenges they encounter. In this study, we used a mixed‐methods design to survey U.S. world language teachers, followed by interviews with eight teachers who were highly proficient in GenAI implementation. Findings revealed various factors shaping GenAI adoption and challenged claims that GenAI diminishes teacher expertise or undermines critical thinking. Access to GenAI and policies for its use were uneven across school districts and grade levels. Without institutional guidance, many teachers were self‐taught, underscoring the need for targeted professional development. We discuss how, with appropriate guidance, emerging AI‐integrated pedagogical models have the potential to revitalize and sustain language education programs through strategic, teacher‐led use of GenAI.
Jue Wang, K. Davin, Scott Kissau et al.· Foreign language annals· 1 citation