Aug 2026· Frontiers in Education· 0 citations· 15 references
TL;DR
The Pedagogical Prompting Feedback Cycle is offered as a teacher-oriented way of translating existing instructional expertise into AI-supported practice and presented 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.
Abstract
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.
It is argued that students' needs matter on their own, the field should start from what students need when deciding how to use AI in design education, and good educational frameworks should be anchored in the learner, not driven by technology.
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.
A. Fan· International Journal of Sec...· 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.
This narrative aims to examine what writing feedback has come to mean in a multilingual provincial university classroom where students increasingly revise between teacher guidance and AI-generated suggestions.
Grounded in classroom experience across Advanced Grammar in English, History of the English Language and Language and Journalism, courses in a university in the Philippines with a number of Indigenous students, the paper reflects on how students negotiate feedback in contexts shaped by language diversity, institutional expectations and emerging AI use.
Students value teacher feedback for clarity, explanation, personalization and support for deeper revision, while AI feedback is appreciated for speed, accessibility and help with surface-level concerns.
As a teacher narrative rooted in one context, the paper offers situated insight rather than broad generalization. It points to the need for further classroom-based work on feedback, AI-assisted revision and multilingual writing.
English teachers need to help students distinguish between surface correction and deeper revision, and to use AI critically rather than unreflectively.
Feedback shapes not only writing quality but also students’ sense of legitimacy, voice and belonging, especially in multilingual and Indigenous-facing classrooms.
The paper shows that AI has not displaced teacher feedback but clarified its interpretive, relational and ethical value in English teaching.
J. R. Paloma· English Teaching: Practice &...· 0 citations
As generative AI evolves from an assistive tool to an increasingly autonomous system, higher education faces new challenges for teaching and learning. Emerging research describes the rise of the “ghost student,” who completes coursework without active participation, and the “cognitive debt” that accrues when productive struggle is outsourced to automated systems. These developments expose the limits of reactive, compliance-focused approaches. In response, this conceptual paper argues that purposeful, human-centered AI engagement offers a better path forward. Drawing on constructionist learning theory, we introduce the Instructional Model for Human-Centered Generative AI Engagement, a pedagogical framework designed to help faculty guide students in engaging with generative AI as a thinking partner rather than a shortcut. The model consists of five recursive phases: critical and ethical awareness, prompt literacy, AI-supported learning, reflection and revision, and independent application. Central to its implementation is the Prompt Literacy Cycle, nested within Phase Two, which guides students through iterative prompting, critical interpretation of LLM-generated outputs, and reflective revision. Together, these elements support a shift towards process-oriented assessment while fostering ethical awareness, metacognition, and intentional engagement with LLM-mediated knowledge construction.
A. Miles, Paige Haber-Curran, Khalid H. Arar· Open Praxis· 0 citations