This study examines the practical applications and methodological implications of Machine Translation in Indian Languages, specifically Bangla, Malayalam, and Telugu, within emerging translation workflows and in relation to existing evaluation frameworks. The choice of languages prioritized in this study is motivated by a triangulation of linguistic diversity, which illustrates the significance of multilingual accommodation of educational technology under NEP 2020. This is further supported by the largest MOOC portal, i.e., NPTEL, which has served as a corpus to facilitate the arguments presented in this paper. The curation of a spontaneous speech corpora that accounts for lucid delivery of technical concepts, considering the retention of suitable register and lexical choices are crucial in a diverse country like India. The findings of this study highlight metric-specific sensitivity and the challenges of morphologically rich and semantically compact features when tested against surface overlapping metrics.

翻译：本研究探讨了机器翻译在印度语言（特别是孟加拉语、马拉雅拉姆语和泰卢固语）中的实际应用与方法论意义，重点关注新兴翻译工作流程及其与现有评估框架的关系。本研究选择这些优先语言是基于语言多样性的三角验证，这体现了《2020年国家教育政策》背景下教育技术多语言适配的重要性。作为印度最大的慕课门户，NPTEL平台为本文论点提供了语料支持。在印度这样多元化的国家，构建能够清晰传递技术概念的自发言语语料库，同时保持恰当的语域和词汇选择至关重要。本研究发现凸显了形态丰富且语义紧凑的语言特征在表层重叠度量测试中表现出的指标特异性敏感度及相关挑战。