  Large Language Models (LLMs) have significantly transformed the educational
landscape, offering new tools for students, instructors, and teaching
assistants. This paper investigates the application of LLMs in assisting
teaching assistants (TAs) with viva and code assessments in an advanced
computing class on distributed systems in an Indian University. We develop
TAMIGO, an LLM-based system for TAs to evaluate programming assignments.
  For viva assessment, the TAs generated questions using TAMIGO and circulated
these questions to the students for answering. The TAs then used TAMIGO to
generate feedback on student answers. For code assessment, the TAs selected
specific code blocks from student code submissions and fed it to TAMIGO to
generate feedback for these code blocks. The TAMIGO-generated feedback for
student answers and code blocks was used by the TAs for further evaluation.
  We evaluate the quality of LLM-generated viva questions, model answers,
feedback on viva answers, and feedback on student code submissions. Our results
indicate that LLMs are highly effective at generating viva questions when
provided with sufficient context and background information. However, the
results for LLM-generated feedback on viva answers were mixed; instances of
hallucination occasionally reduced the accuracy of feedback. Despite this, the
feedback was consistent, constructive, comprehensive, balanced, and did not
overwhelm the TAs. Similarly, for code submissions, the LLM-generated feedback
was constructive, comprehensive and balanced, though there was room for
improvement in aligning the feedback with the instructor-provided rubric for
code evaluation. Our findings contribute to understanding the benefits and
limitations of integrating LLMs into educational settings.
