Aug 2026· Language Teacher Education Research· Vol 4, pp. 51-61· 0 citations
TL;DR
A co-constructed model of AI literacy that values dialogue, shared inquiry, scaffolded experimentation, and process-oriented assessment that enables students to develop reflective, context-sensitive, and professionally relevant AI practices is proposed.
Abstract
This article argues for a reframing of AI literacy in MA TESOL education from a compliance-driven competency to a co-constructed process of inquiry, reflection, and critical engagement. The discussion is prompted by a classroom activity in which an AI-generated annotated bibliography, which contained entirely fabricated references, was initially judged by postgraduate students to be credible and academically appropriate. Whilst the exercise exposed challenges associated with verifying AI-generated academic content, it also highlighted broader questions about how AI literacy is conceptualised and developed within TESOL. Drawing on thematic analysis of interviews with two international MA TESOL students, the study explores how students understand, use, and evaluate AI following institutional guidance and targeted classroom input. Findings reveal emerging but cautious forms of AI literacy characterised by scepticism, risk awareness, verification practices, and anxiety regarding plagiarism and academic integrity. Despite explicit instruction, participants positioned AI primarily as a source of potential error rather than as a pedagogical resource. This article therefore proposes a co-constructed model of AI literacy that values dialogue, shared inquiry, scaffolded experimentation, and process-oriented assessment that enables students to develop reflective, context-sensitive, and professionally relevant AI practices.
This study examined students’ AI literacy, perceived learning benefits, and ethical awareness following participation in a structured, AI-integrated classroom activity. The study also investigated the relationships among these constructs and examined whether perceived learning benefits mediate the relationship betwee...
Dalia Alsaiid Abdelbaki· Frontiers in Education· 0 citations
AI is influencing how language is learned and taught and, thus, how language learning is assessed. This study explores the triadic relationship among AI literacy, mindset transformation (MiT), and assessment as learning (AaL) to redress AI-revamped language assessment forms in AI-driven language education. It elucidate...
Abdu Al-kadi, S. Alenezi· Education sciences· 0 citations
The widespread adoption of generative artificial intelligence (AI) has prompted renewed attention to how academic writing should be assessed in higher education. While AI supports multilingual writers throughout the writing process, it also challenges traditional product-based assessment by obscuring students' learning...
L. Noor· Language Testing in Focus: A...· 0 citations
This study explored how a higher education educator experienced and made sense of generative artificial intelligence within teaching, peer collaboration, and assessment practices, and identified three superordinate themes, including divergent peer adoption of GenAI, pedagogical adaptation and uncertainty, and assessmen...
Karen K. Fujii, N. Perez· IAFOR Journal of Education· 0 citations
It is argued that it is the pedagogical framing of digital tools that determines their capacity to support critical engagement and learner agency, preserving human criticality within AI-mediated educational systems.
This paper presents a qualitative experience report on a 60-hour online teacher education course at a Brazilian Federal Institute designed to reframe digital competencies and generative Artificial Intelligence (AI) literacy. Amidst the rapid expansion of Distance Education and generative AI, the study explores how teac...