Abstract:
The rise of artificial intelligence (AI) in education has primarily been dominated by Large Language Models (LLMs), which can handle complex tasks and generate detailed responses. However, these models require significant resources, making them difficult to use in resource-constrained educational settings. This proposal explores how Small Language Models (SLMs) — simpler, open-source and more affordable AI systems — can transform education by offering personalized learning and scalable solutions, even in low-resource environments.

Introduction:
SLMs are smaller, simpler versions of AI models that are easier to use and cost less. Unlike Large Language Models (LLMs), which need powerful computers, SLMs can work on basic devices, making them ideal for schools with limited budgets. They maintain efficiency and accessibility while offering targeted solutions tailored to specific educational needs. By focusing on domain-specific knowledge, local languages, and contextual customization, SLMs address the equity and inclusivity gaps often exacerbated by larger models.

Methodology:
This approach leverages insights from recent implementations of Small Language Models (SLMs) in education. Key methodologies include:
1. Fine-tuning on Localized Data: Customizing SLMs to address specific learner needs and proficiency levels.
2. Using Lightweight AI Frameworks: Deploying open-source, resource-efficient tools for underserved communities and low-resource settings.
3. Integrating SLMs into Systems: Adding SLMs to existing learning platforms to create personalized plans and adjust lessons based on students' progress.
4. Real-World Success Stories: Demonstrating how SLMs enhance teaching through automation, support student progress, and operate efficiently without high-end infrastructure.

Demo Proposal:
We will showcase a live demonstration to highlight how Small Language Models (SLMs) can be effectively deployed in real-world educational settings. The demonstration will feature:
Personalized Learning Assistant:
A fine-tuned open-source Small Language Model(SLM) will be used to create personalized learning plans tailored to students' progress and feedback. The key benefits include:
1. Adaptability to Individual Needs: The system adjusts the complexity of learning materials based on each student’s progress. For slow learners, subjects are presented gradually, with difficulty levels, without overwhelming them. This step-by-step progression builds confidence, enhances engagement, and promotes a deeper understanding of concepts, contributing to their long-term success and mental well-being.
2. Affordability in Resource-Constrained Settings: SLMs are optimized for low-resource hardware, making them a cost-effective solution for schools and communities with limited budgets.
This demo will illustrate how SLMs can empower educators and improve learning outcomes, making quality education accessible to all.

Strengths of SLMs:
Small Language Models offer several key advantages for education:
1. Low Resource Requirements: They run on basic devices like low-cost laptops or mobile phones.
2. Cost-Effective: Affordable for schools with limited budgets or in developing regions.
3. Ease of Updates: Easy to adapt to new curricula or local content.
4. Privacy-Friendly: On-device processing ensures student data security.
5. Personalization: Tailors learning to individual student needs and paces.
6. Support for Local Languages: Adaptable to regional languages and cultural contexts.
7. Energy Efficient: Uses less energy, supporting sustainable AI use.
8. Scalable: Easy to implement across schools and districts.

Limitations of SLMs:
1. Less Powerful: SLMs may struggle with complex tasks like handling multi-disciplinary contexts or generating nuanced responses.
2. Performance Risks: Aggressively scaling down models can lead to degraded accuracy, especially for tasks requiring deep understanding.
3. Requires Careful Design: Achieving balance between simplicity and performance demands thoughtful optimization and regular evaluation.

Final thoughts:
Small Language Models hold transformative potential for democratizing AI in education, offering a pathway to equitable, scalable, and sustainable educational innovation. By strategically scaling down, we can scale up educational impact, addressing the diverse needs of learners worldwide. This approach reinforces the importance of context-aware AI design in fostering an inclusive digital future for education.

References
1.https://www.researchgate.net/publication/386358963_Applications_of_Artificial_Intelligence_for_Modern_Businesses_and_Entrepreneurial_Decision_Making_A_Systematic_Review
2. https://hammer.purdue.edu/articles/thesis/ANALYSIS_AND_MODELING_OF_STATE-LEVEL_POLICY_AND_LEGISLATIVE_TEXT_WITH_NLP_AND_ML_TECHNIQUES/27956481?file=50976462
3. https://link.springer.com/chapter/10.1007/978-3-031-46677-9_40
4. https://www.sciencedirect.com/science/article/abs/pii/S1041608023000195