  Artificial Intelligence (AI) is a fast-growing area of study that stretching
its presence to many business and research domains. Machine learning, deep
learning, and natural language processing (NLP) are subsets of AI to tackle
different areas of data processing and modelling. This review article presents
an overview of AI impact on education outlining with current opportunities. In
the education domain, student feedback data is crucial to uncover the merits
and demerits of existing services provided to students. AI can assist in
identifying the areas of improvement in educational infrastructure, learning
management systems, teaching practices and study environment. NLP techniques
play a vital role in analyzing student feedback in textual format. This
research focuses on existing NLP methodologies and applications that could be
adapted to educational domain applications like sentiment annotations, entity
annotations, text summarization, and topic modelling. Trends and challenges in
adopting NLP in education were reviewed and explored. Contextbased challenges
in NLP like sarcasm, domain-specific language, ambiguity, and aspect-based
sentiment analysis are explained with existing methodologies to overcome them.
Research community approaches to extract the semantic meaning of emoticons and
special characters in feedback which conveys user opinion and challenges in
adopting NLP in education are explored.

Computation and Language