Generative AI Technology and Teacher Language Assessment Literacy – Can Language Teachers Keep up?

Investigating Pre-Service Language Teachers’ Experiences with Generative AI in Language Education

The rapid expansion of generative artificial intelligence (Gen AI) tools, such as ChatGPT, has significantly influenced practices in language teacher education, creating new opportunities for innovation and instructional support. This study explores how pre-service English teachers experience and perceive the use of Gen AI in language education contexts. 14 participants engaged in AI-supported activities, and data were collected through reflective journals, semi-structured focus group interviews, and analysis of instructional materials they developed. Thematic analysis revealed several affordances of Gen AI, including enhanced creativity, increased efficiency, adaptability, and support for professional development. However, participants also reported challenges, such as concerns about reliability, risks of over-reliance, cultural limitations, and ethical considerations. The findings emphasize the importance of maintaining a balance between AI use and pedagogical judgment, highlighting the need for teacher guidance and critical engagement. Overall, the study underscores the potential of Gen AI as a supportive tool in teacher education while reinforcing the necessity of human oversight and reflective practice.

Rethinking Generative AI Literacy: An Integrative, Developmental, and Dialectical Framework for K-12 Teacher Education

The Responsible AI Literacy in Education (RAIL-Ed) framework is introduced, developed through a systematic review and qualitative framework analysis of 67 studies (2023-2025), grounded in critical, pragmatist, sociocultural, and human-centered traditions.

Contextualizing Teacher AI Literacy in English Language Teaching: Developing an AI Literacy Framework for College English Teachers

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.

Artificial Intelligence in Language Education

Student-facing conversational AI in primary classrooms: Should it be allowed?

This paper examines the opportunities and risks associated with student-facing conversational artificial intelligence (AI) in primary education. It aims to evaluate how large language models (LLMs) can support personalised learning while identifying developmental, pedagogical and ethical challenges. Rather than treating benefits and risks as discrete factors, the study conceptualises AI as a socio-technical intervention that reshapes relationships between learners, teachers and knowledge. The paper adopts a conceptual and theory-driven approach, synthesising current literature on AI in education, pedagogical theories and emerging practices in primary classrooms. The analysis is structured through a tension-oriented synthesis, identifying points of alignment and misalignment between AI affordances and core learning processes in primary classrooms. Based on this synthesis, the study develops a set of guiding principles grounded in developmental and educational considerations. Conversational AI offers significant benefits, including personalised learning support, immediate feedback and reduced teacher workload. However, risks include cognitive offloading, overreliance on AI, misalignment with curriculum goals and ethical concerns such as bias and privacy. The analysis suggests that these are not independent challenges but reflect underlying tensions between technological capabilities and pedagogical requirements. The study is conceptual and lacks empirical validation. Future research should focus on longitudinal and classroom-based studies to assess the actual impact of AI on primary learners' cognitive and social development. The paper highlights the need for interdisciplinary research bridging education, AI and developmental psychology. The study proposes a set of guiding principles derived from the identified tensions, emphasising teacher-mediated interaction, developmental calibration of AI use, transparency, curriculum alignment, privacy protection and equity considerations. These principles provide a structured basis for integrating AI in ways that support learning processes while mitigating potential risks. The adoption of AI in primary education raises concerns about equity, access and digital divides. Without careful implementation, AI may reinforce existing inequalities. Promoting critical AI literacy and ethical awareness among young learners is essential to prepare them for responsible participation in an AI-driven society. This paper contributes a developmentally informed, tension-based conceptual framework for understanding student-facing AI in primary education. By reframing commonly identified opportunities and risks as interrelated tensions, it offers a more analytically grounded basis for guiding AI integration beyond descriptive or normative approaches.

Navigating the AI Turn: Teacher Agency, Role Transformation, and Pedagogical Strategies in Technology-Enhanced Foreign Language Education

Large language models and generative artificial intelligence, as representative language intelligence technologies, are driving foreign language teaching to evolve from the traditional "teacher–student" dyadic structure toward a "teacher–student–AI" triadic collaborative paradigm. Based on the rapid development of artificial intelligence at home and abroad in recent years, this paper systematically reviews the current applications of AI in foreign language teaching, the transformation of teacher roles and identity crises, as well as ethical and equity-related challenges. The study finds that AI applications in foreign language teaching are growing rapidly, yet exhibit marked imbalances across regions, educational stages, and domains—namely, an overrepresentation of higher education research relative to basic education, and a predominance of writing assistance studies at the expense of oral communication and critical reading research. Teachers' roles are shifting from knowledge transmitters to human–AI collaborative instructional designers, while simultaneously facing a dual crisis of technological anxiety and identity recognition. Issues of ethical governance and educational equity urgently demand systematic responses. This paper argues that the future development of foreign language teaching must move beyond mere technology adoption; instead, it should be teacher-centered, constructing systematic coping strategies through the enhancement of AI literacy, human–AI collaborative instructional design, curriculum and assessment reconstruction, ethical governance, and institutional support.