Large Language Models Could Transform Education in Nepal—Evaluating AI Tutors in Low-Resource Environments

Could Large Language Models Shape the Future of Education?

The use of AI in education has entered a new phase. A recent study published on arXiv evaluates how well large language models (LLMs) can function as educational tools in low-resource environments, particularly in countries like Nepal. The research assessed the capabilities of four advanced models—GPT-4, Claude Sonnet 4, Qwen3-235B, and Kimi K2—against the K-10 curriculum in Nepal, which spans primary to secondary education.

The study suggests that AI tutors could personalize education and help bridge learning gaps. However, there has been little research on how these technologies can adapt to educational settings outside the Western world. This study aims to fill that gap, marking an important step in understanding the practical potential and challenges of AI in education.

Study Overview and Findings

The research team evaluated the performance of LLMs based on the following key criteria:

1. Curriculum Alignment: Accuracy in answering specific questions based on Nepal’s K-10 curriculum.
2. Cultural Context Understanding: The ability to utilize region-specific examples and background knowledge.
3. Educational Effectiveness: The extent to which the models can support student learning.

The results showed that GPT-4 demonstrated the highest adaptability, particularly excelling in math and science subjects. Meanwhile, Claude Sonnet 4 and Qwen3-235B lagged slightly in understanding cultural contexts. Kimi K2, on the other hand, performed well in resource-constrained environments but was rated lower in terms of response depth and accuracy compared to the other models.

Technical Challenges and Social Implications

The study highlighted several challenges in using LLMs as educational tools:

• Infrastructure Constraints: In low-resource environments, limited internet connectivity and device capabilities make it difficult to deploy advanced AI models.
• Bias Issues: There is a risk of the models exhibiting biases toward specific cultures or languages, underscoring the need to ensure fairness, particularly in non-English-speaking regions.
• Cost and Sustainability: The high costs associated with implementing and maintaining LLMs remain a significant barrier, especially in economically constrained regions.

At the same time, AI tutors offer immense potential. In regions facing severe teacher shortages, AI could serve as a means to reduce educational disparities. Additionally, personalized learning support tailored to individual students is something traditional education systems have struggled to achieve.

Future Prospects

To advance the use of LLMs in education, the research team is exploring next steps such as localizing models and optimizing their operation in low-cost environments. They also propose collaborating with governments and NGOs to integrate AI educational tools into existing systems.

If these efforts succeed in addressing technical challenges, AI has the potential to revolutionize education worldwide. In low-resource environments, in particular, it could not only improve the quality of education but also significantly expand access to learning opportunities.

Conclusion

Large language models hold the potential to be transformative in the field of education. However, overcoming technical and social challenges is key to realizing this potential. By focusing on Nepal, this study sheds light on both the opportunities and complexities of leveraging AI for education. The coming years will reveal how AI can shape the future of global education.