Background: Introductory computer science courses use “Explain in Plain English” (EiPE) activities to develop and assess students’ code comprehension skills, but creating effective autograders for these questions is challenging and limited to English. This is a particular challenge in linguistically diverse countries like India where students may have limited proficiency in English. Objective: We evaluate the efficacy of a recently introduced approach called Code Generation Based Grading (CGBG) in enabling language agnostic “Explain in Plain Language” (EiPL) activities. Here students’ EiPL responses generate code that is tested for functional equivalence to the original which was being described. Method: We initially evaluate the correctness of code generated from correct EiPL responses provided in 10 of India’s most commonly spoken languages. To evaluate the effectiveness of the approach in practice, we assess student success and perceptions of EiPL questions in a NPTEL (National Programme on Technology Enhanced Learning) course. Results: We find promising results for the correctness of code generated from translations of correct EiPL responses, with most languages achieving a correctness rate of 75% or higher. However, in practice, many students preferred to respond in English due to greater familiarity with English as a technical language, difficulties writing in their native language, and perceptions of the grader being less capable of generating code from prompts in their mother tongue.