AI Drives Shift in Global Language and Education

India's linguistic diversity is positioning the country to lead global efforts in developing multilingual AI systems, offering more inclusive solutions for sectors like healthcare and education. Despite this, the continued dominance of English in AI training data is resulting in significant performance gaps for many major world languages, causing issues such as mistranslations and unnatural outputs. Rapid advancements in AI-powered translation tools, now achieving over 95% accuracy at much lower costs than human translators, are disrupting traditional language education and career paths, as shown by Chinese universities scaling back foreign language majors. Stanford researchers have addressed technical challenges in evaluating AI language models by introducing a cost-effective method that considers question difficulty, leading to fairer comparisons and more accurate assessments. These developments highlight the need for more inclusive, well-evaluated, and locally relevant AI as societies confront the opportunities and disruptions brought by language technologies.