Research Journal of Applied Sciences, Engineering and Technology 12(3): 258!263, 2016 DOI: 10.19026/rjaset.12.2332 ISSN: 2040!7459; e!ISSN: 2040!7467 © 2016 Maxwell Scientific Publication Corp. Submitted: July 2, 2015 Accepted: August 2, 2015 Published: February 05, 2016   Madhumitha Ramamurthy, Department of CSE, Sri Krishna College of Engineering and Technology, Coimbatore!641008, TamilNadu, India This work is licensed under a Creative Commons Attribution 4.0 International License (URL: http://creativecommons.org/licenses/by/4.0/). 258                Madhumitha Ramamurthy and Ilango Krishnamurthi Department of CSE, Sri Krishna College of Engineering and Technology, Coimbatore!641008, TamilNadu, India  Artificial Intelligence has many applications in which automating a human behavior by machines is one of very important research activities currently in progress. This paper proposes an automated assessment system which uses two novel similarity measures which evaluate students’ short and long answers and compares it with cosine similarity measure and n!gram similarity measure. The proposed system evaluates the information recall and comprehension type answers in Bloom’s taxonomy. The comparison shows that the proposed system which uses two novel similarity measures outperforms the n!gram similarity measure and cosine similarity measure for information recall questions and comprehension questions. The system generated scores are also compared with human scores and the system scores correlates with human scores using Pearson and Spearman’s correlation. ! Artificial intelligence, assessment, education, sentence similarity, similarity, WordNet "#$%#"$ Artificial intelligence creates machines with intelligence. Many e!learning applications are examples for machines with intelligence. Automation is also an important research area where automation of assessment of students’ answers is an important research in the educational sector. Computer Assisted Assessment (CAA) helps to automate the assessment of answers by using computers. Students’ answers can be divided into objective and subjective answers where objective answer assessment is the most common one when compared to subjective answers assessment which includes short answers and long answers. In subjective assessment, more focus is given on short answers and have many approaches for assessment when compared to assessment of long answers. Evaluation of answers is based on six types of questions according to bloom’s taxonomy. Those six categories of questions are information recall, comprehension, application, analysis, synthesis and evaluation questions. Information recall questions (Questions Skills, year) makes the students to recall the studied information. The students remember the studied information to answer an information recall question. Comprehension questions (Questions Skills, year) make the students to use the studied information and express the information in their own words. Application questions (Questions Skills, year) make the students use the studied information and to apply what they have learned to solve the problem. Analysis questions (Questions Skills, year) make the students to analyze the questions by the studied information and answer those questions and they also reason out their findings. Synthesis questions (Questions Skills, year) make the students to answer the questions by thinking innovatively by finding their own ways for solving the problems. Evaluation questions (Questions Skills, year) make the students to answer the questions by evaluating and judging their idea and coming to a conclusion why an idea is better than the another idea and they should also give based on what criteria they have given this evaluation. The humans can evaluate all these types of questions. But there is a challenge for computers to do this task. The proposed assessment system automates the evaluation of information recall type questions and comprehension questions. &"## ’"( PEG (Project Essay Grade), (Whittington and Hunt, 1999). This was one of the earliest implementations for automatic assessment. It did not consider NLP and lexical content to grade the essays and focused on only simple style analysis. The strength