How to make career guidance system intelligent

Question

Well, at last, I am working on my final year project which is Intelligent web-based career guidance system the core functionality of my system is

Recommendation System

Basically, our recommendation system will carefully examine user preferences by taking Interest tests and user’s academic record and on the basis of this examined information, it will give the user the best career options i.e the course like BS Computer Science, etc...

• Input of the recommendation system will be the student credentials and Interest test and in interest test the questions will be given according to user academic history and the answers that he is giving in the test, so basically test will not be asking the same questions from everyone it will decide on real time about what to ask from which user according to rules defined by the system.

• 
  Its output will be the option of fields which will be decided on the basis of Interest test.

Problem

When I was defending my scope in front of the committee they said "this is simple if-else" this system is not intelligent.

My question is which AI technique or Algorithm could be used to make this system intelligent. I have searched a lot but papers related to my system are much more superficial they are just emphasizing on the idea, not on methodology.

I want to do all my work in Java. It is great if the answer is technology specific.  

Edit

After getting some idea from answers I want to implement an expert system with rule-based and inference engine. Now I want to be more clear on the technology aspect to implement a rule-based engine. After searching I have found Drools to be best but Is it also compatible with web applications? And I also found Tohu to be the best dynamic form generator (as this is also needed of my project). can I use tohu with drools to make my web application? Is it easy to implement this type of system or not?

Answer 1

The traditional or you can say a basic approach to career guidance is a manual method that is ineffective and inefficient. The electronic approach provides effective and efficient career guidance. The intelligent system AI model uses the data which was student-driven parameters such as favorite science subjects combination, career interest inventory analysis result, and intelligent quotient test result for career recommendation. The web-based intelligent system was designed and implemented with the principle of a rule-based expert system using a forward chaining algorithm, the client-side/interface pages (front-end) were designed using Bootstrap 3 as a front-end framework that contains HTML5, CSS3, and JavaScript. For the back-end, XAMPP was used. The system was implemented and evaluated using 200 pre-tertiary science students; they took the career choice tests and provided their feedback for the evaluation of the system performance. 

If you have a large number of questions, each of them can represent a feature. Assuming you are going to have a LOT of features, finding the series of if-else statements that fulfills the criteria is hard (Recall that a full tree with n questions is going to have 2^n "leaves" - representing 2^n possible answers for these questions, assuming each question is yes/no question).

Regarding the "technology" aspect - there is a nice library in java - called Weka which implements many of the classification algorithms out there.