  In the era of generative artificial intelligence (AI), the fusion of large
language models (LLMs) offers unprecedented opportunities for innovation in the
field of modern education. We embark on an exploration of prompted LLMs within
the context of educational and assessment applications to uncover their
potential. Through a series of carefully crafted research questions, we
investigate the effectiveness of prompt-based techniques in generating
open-ended questions from school-level textbooks, assess their efficiency in
generating open-ended questions from undergraduate-level technical textbooks,
and explore the feasibility of employing a chain-of-thought inspired
multi-stage prompting approach for language-agnostic multiple-choice question
(MCQ) generation. Additionally, we evaluate the ability of prompted LLMs for
language learning, exemplified through a case study in the low-resource Indian
language Bengali, to explain Bengali grammatical errors. We also evaluate the
potential of prompted LLMs to assess human resource (HR) spoken interview
transcripts. By juxtaposing the capabilities of LLMs with those of human
experts across various educational tasks and domains, our aim is to shed light
on the potential and limitations of LLMs in reshaping educational practices.
