Jul 2026· International Educational Research· Vol 9, pp. p21· 0 citations· 10 references
TL;DR
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.
Abstract
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.
As generative artificial intelligence continues to be integrated into higher education, teacher AI literacy has become a critical foundation for promoting AI-enhanced English language teaching. However, existing frameworks have been developed primarily within general educational contexts, with insufficient attention to the disciplinary characteristics of college English teaching. Employing a qualitative framework development design, this study established an initial analytical framework based on TPACK, AI-TPACK, the AI Literacy Framework, DigCompEdu, and the UNESCO AI Competency Framework for Teachers, and then refined it through semi-structured interviews with eight university English teachers and thematic analysis. The findings reveal that college English teachers' AI literacy consists of five dimensions, namely cognitive understanding, AI application, pedagogical integration, reflective development, and ethical responsibility, which are further elaborated into fifteen contextualized domains. The findings further demonstrate that teacher AI literacy is not a simple transfer of general AI competencies but a contextualized competency system shaped by language teaching objectives, principles of language learning, and teachers' professional knowledge, as college English teachers emphasized competencies closely related to English teaching practices, including professional evaluation of AI-generated language content, AI-supported activity design, instructional feedback analysis, and guidance for students' responsible AI use. This study extends the contextualized perspective of teacher AI literacy research and provides theoretical insights and practical implications for college English teacher professional development, teacher training, and AI-enhanced language education.
Miao Wang, Tianying Yun· English Language Teaching· 0 citations
Artificial intelligence (AI) literacy has become an important component of teacher preparation, yet limited evidence is available on pre-service teachers’ self-reported AI literacy profiles and the challenges they perceive in connecting AI-related awareness with pedagogical practice. This study examined self-reported AI literacy and perceived pedagogical challenges among pre-service primary teachers using an explanatory sequential mixed-methods design. Survey data were collected from 161 third-year students in a university-based primary teacher education program in Central China, followed by semi-structured interviews with nine purposively selected participants. The study used a theoretically informed four-dimensional framework comprising AI Perception, AI Knowledge and Skills, AI Application and Innovation, and AI Ethics. Quantitative results showed a moderately high overall level of self-reported AI literacy, with AI Perception and AI Ethics slightly higher than AI Knowledge and Skills and AI Application and Innovation. Mathematics students reported higher self-rated scores than Chinese and English students across all four dimensions in this sample. The qualitative findings provided possible contextual interpretations of these survey patterns by highlighting participants’ perceptions of disciplinary alignment, unequal AI-related learning opportunities, limited subject-specific pedagogical support, and insufficient practicum-based experience. Participants recognized general ethical concerns but reported uncertainty about applying ethical principles to specific classroom situations.
Yu Hu, Lan Wang· Frontiers in Education· 0 citations
This case study examines how students were positioned as experts in shaping artificial intelligence (AI) literacy curricula at a United Kingdom university. Academic staff, students and professional services collaborated to co-create an AI literacy framework with a focus on generative AI, addressing institutional and learner needs. Guided by partnership principles of reciprocity, respect and shared responsibility, the project responded to the opportunities and challenges of AI in higher education (HE).
Following a cross-disciplinary survey which captured student perceptions of generative AI use, two student partners were appointed; their roles evolved from research interns to co-designers and co-evaluators during this funded project. They have co-led focus groups with students, conducted data analysis and synthesis and undertaken peer consultation and evaluation to co-develop guidance resources on ethics, integrity, bias and responsible AI use.
Two key outputs resulted from this collaboration: a four-step AI literacy framework and an online tutorial which are now embedded in central pre-enrolment and academic skills programmes. Framing students as co-designers helped both 1) to enrich staff understanding of student perceptions of generative AI use for learning and 2) to enable the students to enhance their research, collaboration, communication and leadership skills. The study contributes to the current discourse on fostering critical, responsible AI literacies in HE through the lens of students as partners.
Nurun Nahar, David Howard, Kater Akeren et al.· Compass: Journal of Learning...· 0 citations
The development of digital technology and Artificial Intelligence (AI) has significantly transformed education, including elementary school learning. However, a digital competency gap remains among elementary school teachers in their use of educational technology to support learning processes. This gap is not only related to technical skills in operating digital devices but also involves teachers' pedagogical abilities in integrating technology and their readiness in developing AI literacy to create innovative and high-quality learning. This study aims to analyze the digital competence conditions of elementary school teachers, identify factors contributing to the digital competency gap, and formulate educational technology development and AI literacy strategies to improve learning quality. This study employs a qualitative approach using a descriptive qualitative method. Data were collected through observation, in-depth interviews, and documentation involving elementary school teachers and relevant stakeholders in educational technology implementation. Data analysis was conducted through data collection, data reduction, data presentation, and conclusion drawing to gain an in-depth understanding of teachers’ experiences, challenges, and needs in developing digital competence and AI literacy. The findings indicate that strengthening elementary school teachers’ digital competence requires sustainable development strategies through professional capacity building, AI literacy training, technology utilization assistance, and the establishment of a supportive digital learning ecosystem. These strategies are expected to bridge the digital competency gap among elementary school teachers and improve learning quality to become more adaptive, creative, effective, and aligned with 21st-century education demands.
Unknown authors· International Conference on...· 0 citations
This study presents an abductive analysis of interview data from 13 Finnish teachers involved in national AI education development projects and conceptualises a potential synergy between the domains where students' self‐determined, informed engagement with AI is placed at the centre of educational efforts in AI education.
Janne Fagerlund, Pekka Mertala, Jukka Lehtoranta et al.· Journal of Computer Assisted...· 0 citations
This study examines teachers’ readiness, usage patterns, perceived benefits, barriers, and professional development needs related to integrating Generative Artificial Intelligence (Gen AI) into educational practice. A quantitative descriptive exploratory survey design was employed. Data were collected via online questionnaires from 80 teachers across multiple educational levels (early childhood to vocational secondary) in Bengkulu. Descriptive statistics and thematic analysis were used to analyse the data. Teachers reported high functional readiness and positive perceptions of Gen AI, particularly for lesson planning and content development, while its application for learning analytics and reflective pedagogical inquiry remained limited. The most significant barriers were lack of training and unclear school policies, whereas teachers did not view Gen AI as a threat to their professional role. Key professional development needs included practical training, ethical guidance, curriculum integration, and peer communities. The study provides empirical evidence on teachers actual pedagogical workflows in a developing‑country context, distinguishing functional readiness from conceptual literacy. Findings offer a clear agenda for policy development and teacher professional learning programmes. The research advances understanding of Gen AI integration in resource‑constrained settings and provides an evidence base for pedagogically grounded, ethically responsible AI adoption in education.
Novia Ayu Lestari, A. Susanta, Nurul Astuty Yensy· Journal of Social Work and S...· 0 citations