Aug 2026· International Journal of Second and Foreign Language Education· Vol 5, pp. 48-85· 0 citations
TL;DR
It is argued that AI adoption should not be viewed as a pedagogical rupture, but as a continuation of a long-standing shift toward learner-centred, meaning-focused, and interaction-driven language education.
Abstract
English language teaching (ELT) in higher education has undergone successive pedagogical transformations, evolving from rote memorisation to task-based and communicative approaches, integrating computer-assisted language learning (CALL), and now entering an era of artificial intelligence (AI)–mediated learning. This paper argues that AI adoption should not be viewed as a pedagogical rupture, but as a continuation of a long-standing shift toward learner-centred, meaning-focused, and interaction-driven language education. Situating AI within this trajectory, the paper contends that successful integration relies not on technological novelty, but on research-informed pedagogy, ethical awareness, and evidence-based practice. Echoing a longstanding caution against allowing education to be driven by the measurement of outcomes at the expense of purpose, it asks not what is technologically possible, but what is pedagogically meaningful. To illustrate this argument, the paper presents an action research project documenting the ongoing construction of a new elective course on vocabulary development at a research-focused liberal arts university in Hong Kong. Moving beyond rote memorisation, the course enhances vocabulary acquisition through active learning, textual analysis, and extensive reading, employing techniques such as morphological analysis and contextual inference. Drawing on literary and non-literary materials, it adopts a scaffolded design transitioning from guided instruction to independent learning, fostering critical thinking alongside lexical growth. Through discussions, presentations, and written analyzes understood as a recursive, problem-solving process, students apply new vocabulary in academic and real-world contexts—offering a practical model for pedagogically grounded, AI-mediated vocabulary learning.
The rapid growth of generative Artificial Intelligence (AI) is reshaping how higher education conceptualizes learning, assessment, and pedagogy. Many institutions respond by relying on restrictive policies. Unfortunately, this approach often fails to support meaningful and sustainable education. The objective of this study is to reinterpret the Artificial Intelligence Assessment Scale (AIAS) (Perkins et al., 2024) as a developmental pedagogical framework that enables transparent, ethical, and reflective integration of AI into teaching and learning. Methodologically, the study applies the five-level AIAS model, ranging from AI prohibition to full AI collaboration, in FASH 137: Clothing, Society, and Culture, a General Education course examining the sociocultural meanings of dress. AI integration is scaffolded across multiple assignments, each explicitly aligned with a designated AIAS level. Data are drawn from assignment design, faculty observations, and structured student reflections documenting AI use and learning outcomes. Findings reveal three interrelated pedagogical themes. First, transparency as pedagogical integrity emerges through required “AI Use Notes,” which normalize disclosure and foster academic trust. Second, critical evaluation as human distinction is strengthened as students compare AI-generated insights with their own analyses, reinforcing judgment, creativity, and cultural interpretation. Third, AI as a structured learning partner supports exploration, critique, writing development, and identity reflection without replacing human authorship. The research outcomes demonstrate that AIAS functions effectively as a learning architecture, aligning academic integrity with instructional design. The framework offers a replicable model for fashion programs and other disciplines seeking responsible AI integration. Future research will expand empirical assessment across courses, disciplines, and institutions, examine longitudinal learning impacts, and refine discipline-specific AIAS applications to guide higher education in the AI-driven future.
D. Shen· PUPIL International Journal...· 0 citations
It is suggested that AI can enhance drafting, revision, and feedback processes, improving coherence, metacognition, and writing confidence, however, these benefits are accompanied by persistent concerns regarding ethical ambiguity, inconsistent policy guidance, and insufficient faculty training.
Samira Dichari, Fadi Jaber· Journal of Education and Tra...· 0 citations
This perspective article is grounded in professional experience and classroom observation, and its aim is to raise an issue and open it for discussion rather than to settle it. AI is often presented to teachers as a way to make educational work easier, faster, and more flexible, yet they are simultaneously confronted with a growing array of AI platforms, agents, automation systems, and technical courses. This creates a practical tension: if AI is meant to reduce teachers’ workload, it is not obvious why using it well should require them to keep learning new technical systems. One source of this tension, we argue, is the weak distinction between AI literacy and prompt engineering. We take AI literacy in language education to be a broad competence concerned with how teachers, students, and institutions learn to live, work, study, and teach responsibly in a world shaped by AI, whereas prompt engineering concerns more specifically how users design, refine, evaluate, and revise prompts to guide AI tools toward useful and responsible outputs. For English and English medium instruction (EMI) teachers, we propose that this difference matters because these teachers do not need to master AI technically; they need to learn how to make it serve teaching, learning, language support, content understanding, and student participation. We refer to this teacher-facing capacity pedagogical prompting and distinguish it from both broad AI literacy and technical prompt engineering. To make the idea concrete, we offer the Pedagogical Prompting Feedback Cycle as a teacher-oriented way of translating existing instructional expertise into AI-supported practice. We present it as a tentative conceptual model rather than a validated framework: it is meant to provoke inquiry and design, and it requires empirical validation across diverse languages, disciplines, and educational settings.
Ali Khodi, Samantha M. Curle, Víctor Parra-Guinaldo· Frontiers in Education· 0 citations
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.
Artificial intelligence (AI) is rapidly transforming higher education. Notably, institutions are increasingly deploying AI-enabled chatbots across administrative, teaching and learning, and research functions. Despite the proliferation of these tools, most conversational systems remain generic and disconnected from subject-level context, curriculum design, and academic integrity requirements. A design-oriented framework is proposed in this paper to improve the effectiveness of AI-enabled chatbots used in teaching and learning, embedded within curriculum. The framework is grounded in systems thinking for context-aware AI-enabled learning systems that operate within bounded pedagogical and disciplinary environments. Adopting a design science approach, the study synthesises expert-informed insights from pedagogical, programmatic, and subject-level perspectives to develop a framework that integrates context alignment, instructional control, and integrity-preserving guardrails. The resulting artefact was verified through artefact-focused testing against the proposed design objectives using a structured non-human evaluation protocol, including requirement-based testing, baseline comparison with generic AI systems, academic integrity stress testing, and robustness analysis. The study proposes and verifies a framework intended to support curriculum alignment, instructional control, and academic integrity preservation within AI-enabled learning systems. The paper contributes a systems-oriented framework for embedding AI within educational systems while preserving pedagogical intent and governance requirements. Implications for scalable deployment of AI in higher education and future human-centred evaluation are discussed.
Ali Ahsan, Hayden McDonald, R. Saha et al.· Systems· 0 citations
In higher education, multimedia authoring, feedback, analytics and adaptive learning systems are increasingly embedded with artificial intelligence (AI). The impact of these changes on instructional design for personalised learning is examined. Instead of viewing personalisation as automatic optimisation, the review examines four interrelated design challenges: the pedagogical quality of AI-generated multimedia; the validity and fairness of automatic feedback; the interpretability of learning analytics; and the impact on established instructional design processes. Research suggests that AI can broaden the diversity and responsiveness of resources for learning, but evidence of lasting learning gains is more patchy and very much dependent on human oversight, curricula and student agency. The systems may then use non-transparent models or data which are unrepresentative, thus reproducing equity concerns. The review suggests a series of principles for responsible design: pedagogical purpose; cognitive coherence; human verification; contestable personalisation; and proportionate data use. AI should be viewed as a limited design resource instead of an independent system of instruction.
F. Shahbodin, C. K. Mohd, Z. Maksom et al.· International journal of res...· 0 citations