On the rules given to lms programs using artificial intelligence with the help of natural language processing

Purpose of Study 
Humans have curios nature. We ask question to gain more knowledge and try 
to link them with other things that we know. Our daily lives include asking questions 
in conversations. For example, student questions play an important role in their 
learning process and help them to learn more from their teachers. In addition, teacher 
questions help students to assess their performance. In a nutshell, questions are one 
of the primary sources of learning from daily conversations to verbal tutoring and 
assessments. Most of learners are not good at asking questions. Dunlosky and 
Graesser (1998) states that learners have a problem with identifying their own 
knowledge deficits. Therefore, they ask very few questions. Automation of question 
generation systems can help learners to find out their own knowledge gaps by 
helping them to reach their valuable inquiries. Learners are not the only one who 
encounter limitations in their question generation skills. 
Other examples are: 
• Tutors have trouble to generate good hints to encourage students think and 
talk (Chi et al., 2001; Corbett & Mostow, 2008; DiPaolo et al., 2004). Tutors need 
to assess the students knowledge by asking good questions and address their 
knowledge deficits (Corbett & Mostow, 2008). 
• Users of Google and other search engine users tend to input only few words 
rather than full sentences and queries (Lin, 2008; Marciniak, 2008). Guided question 
reformulation is needed to quickly retrieve answers. 
• Frequently Asked Questions (FAQ) are usually developed by designers of 
the system, rather than real questions that are asked by clients. 
Automation of question generation systems help automated question 
answering systems such as IBM Watson to perform self training (IBM Watson 
Ecosystem, 2014). Instead of relying on human experts to manually define ground 
truth answers from the questions, automatic question generation systems automatize 
this process. Intelligent tutoring systems can also get benefit from that. Rather 
relying on human experts to manually extract questions from study materials, each 
end user can define its own tutoring system automatically from the study material. 

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Finally, question generation systems help the development of annotated datasets for 
question answering and reading comprehension.