Paper Title
SHADOWS IN THE CLASSROOM: THE UNSEEN CONSEQUENCES OF LARGE LANGUAGE MODELS IN EDUCATIONAL SETTINGS

Abstract
This study rigorously analyses the incorporation of Large Language Models (LLMs) within educational frameworks, interrogating their transformative potential while critically evaluating the challenges they present. LLMs, exemplified by systems such as OpenAI's GPT-4, have showcased remarkable advancements in areas including personalised instruction, automated grading, and adaptive learning solutions. These technologies promise to revolutionise educational practices by offering scalable, efficient, and customisable support to both educators and learners. However, their uncritical deployment raises significant concerns, particularly regarding their impact on educational equity, the preservation of academic integrity, and the cultivation of critical thinking skills among learners. Through a comprehensive multidimensional approach, this paper elucidates the risks associated with an overreliance on algorithmic outputs, including reduced cognitive engagement, the perpetuation of systemic biases, and the exacerbation of the digital divide. It further examines how these risks interact with broader socio-economic inequities, compounding existing disparities in access to quality education. The findings underscore the necessity of robust ethical frameworks and informed policy interventions to guide the integration of LLMs. Such frameworks must address the inherent biases of AI systems, ensuring their outputs do not disproportionately disadvantage marginalised communities. Additionally, the study advocates for pedagogical strategies that prioritise critical evaluation, intellectual independence, and reflective learning practices. These strategies are essential for counteracting the passive consumption of AI-generated content, which risks undermining the development of higher-order cognitive skills essential for lifelong learning. The paper further emphasises the importance of interdisciplinary collaboration among educators, policymakers, and AI developers to ensure that LLMs are harnessed as tools for empowerment and innovation rather than as drivers of inequity. By fostering transparency, inclusivity, and balanced integration, stakeholders can collectively mitigate potential harms while maximising the educational benefits of these technologies. This collaborative approach necessitates continuous dialogue and research to refine the ethical deployment of LLMs, incorporating feedback from diverse communities and adapting to evolving educational needs.By fostering transparency, inclusivity, and balanced integration, this work envisions an educational ecosystem that leverages the potential of LLMs while steadfastly upholding the principles of academic integrity, equity, and intellectual rigour. The ultimate goal is to create a sustainable model of AI integration that enriches the educational experience, promotes equitable learning opportunities, and prepares students to navigate an increasingly AI-driven world with confidence and critical discernment.