The study proposes and verifies a framework intended to support curriculum alignment, instructional control, and academic integrity preservation within AI-enabled learning systems, and contributes a systems-oriented framework for embedding AI within educational systems while preserving pedagogical intent and governance requirements.
Abstract
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.
Artificial intelligence (AI) is increasingly embedded in English as a Foreign/Second Language (EFL/ESL) education, yet existing research remains fragmented across tools, skills, and short-term outcomes. This review examines how AI is integrated into EFL/ESL education across language skills, instructional domains, and educational contexts.
This PRISMA-guided systematic review synthesised 221 unique peer-reviewed publications. Moving beyond a tool-centred inventory, the review analysed AI through four interrelated dimensions: pedagogical roles, mediating processes, reported outcomes, and contextual constraints.
AI systems increasingly operated as multifunctional pedagogical actors rather than isolated instructional aids. The most frequently coded roles were teacher orchestration/support, content and materials generation, assessment or diagnosis, coaching or practice companionship, tutoring or scaffolding, and conversational partnership. AI-mediated learning was especially concentrated in writing and speaking/communication, where text-based, voice-based, multimodal, immersive, and adaptive systems supported feedback, revision, rehearsal, and learner–system interaction. Reported benefits included expanded practice opportunities, accelerated feedback cycles, redistributed instructional labour, skill development, learner autonomy, affective support, assessment and monitoring, collaboration, and multilingual engagement. Recurring challenges included technical reliability and feedback quality, teacher readiness, privacy and data security, learner over-reliance, infrastructural inequality, bias and cultural mismatch, authorship and academic-integrity concerns, and methodological weaknesses.
Interpreted through a three-layer framework of efficiency, pedagogy, and ideology, the synthesis conceptualises AI in EFL/ESL education as a pedagogical ecology in which tools, learners, teachers, feedback regimes, assessment practices, institutional infrastructures, and governance arrangements interact. The review provides a theory-informed framework for analysing, designing, and governing AI-mediated language education beyond simple claims of technological effectiveness.
Arash Javadinejad, M. Davari· 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.
: The pervasiveness of digital technologies and the breakthrough of AI-generated content in numerous fields have significantly impacted education and teaching practices. AI platforms are increasingly being integrated into all segments of instructional design, delivery and assessment. This paper investigates the characteristics and quality of lesson plan designs generated by four AI platforms – ChatGPT, Gemini, Claude, and DeepSeek – by contrasting and comparing the solutions each provides. The aim of this study is to identify the possibilities AI platforms offer in creating English-speaking classes, with particular attention to pedagogical aspects underlying lesson planning processes. Since the quality of automated plan designs depends heavily on the formulation of prompts, this paper used an identical prompt for all models. Based on clearly defined evaluation criteria, a comparative analysis of four AI-generated lesson plans is conducted, highlighting their key similarities and differences. Ultimately, the paper emphasizes the pedagogical implications of utilizing AI platforms in the creation of English-speaking lesson plans and supports an informative and reflective teaching approach.
Ana Tripković, Valentina Gavranović· SINTEZA· 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
The research outcomes demonstrate that AIAS functions effectively as a learning architecture, aligning academic integrity with instructional design, and offers a replicable model for fashion programs and other disciplines seeking responsible AI integration.
D. Shen· PUPIL International Journal...· 0 citations
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.