The integration of Generative Artificial Intelligence into education has the potential to revolutionize the way vernacular medium students engage with and learn complex subjects such as programming where most available material is in English. This exploratory study is the first step of the Sanganmitra project, where we investigate the effectiveness of a Generative AI-powered programming chatbot for Vernacular Medium with English Materials (VMEM) students. The chatbot was designed to assist students with programming challenges without providing direct solutions. Students were presented with 3 programming problems and could interact with the tool in either English or Marathi. We conducted a study with 42 VMEM students to understand how they interacted with the tool. We also explored if there are differences in the usage between students who use it in English or Marathi. Our findings show that 21% of students conversed with the chatbot in Marathi, a significant number considering the predominance of English in STEM. A slightly higher percentage of Marathi users asked conceptual or relevant questions. However, in general students merely asked for the full solution by repeating the question. We discuss some implications of these patterns for future studies.

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Exploring the Effectiveness of a Multilingual Generative AI Programming Chatbot for Vernacular Medium CS Students

  • Avval Halani,
  • Prajish Prasad,
  • Aamod Sane

摘要

The integration of Generative Artificial Intelligence into education has the potential to revolutionize the way vernacular medium students engage with and learn complex subjects such as programming where most available material is in English. This exploratory study is the first step of the Sanganmitra project, where we investigate the effectiveness of a Generative AI-powered programming chatbot for Vernacular Medium with English Materials (VMEM) students. The chatbot was designed to assist students with programming challenges without providing direct solutions. Students were presented with 3 programming problems and could interact with the tool in either English or Marathi. We conducted a study with 42 VMEM students to understand how they interacted with the tool. We also explored if there are differences in the usage between students who use it in English or Marathi. Our findings show that 21% of students conversed with the chatbot in Marathi, a significant number considering the predominance of English in STEM. A slightly higher percentage of Marathi users asked conceptual or relevant questions. However, in general students merely asked for the full solution by repeating the question. We discuss some implications of these patterns for future studies.