This new AI-based Internship Recommendation System can be a Game Changer
Technology has become synonymous to simplification. There is no denying that all the gadgets and tech have made our day to day life easy and simple. It keeps getting better, as technology is advancing and evolving in leaps and bounds. The world has very much entered the age of Artificial Intelligence with voice-controlled smart assistants like SIRI, Google Assistant, Alexa, Bixby, Cortana, and the likes. These allow us to enjoy the perks of a personal assistant without having to keep one on the payroll.
There is still a lot of work going on AI. Techies are trying to implement it in all the sectors and a new idea has emerged that speaks of an AI-based internship recommendation system. It has been developed by the researchers at Universitas Pendidikan Ganesha, Indonesia, and the tech world is already buzzing with excitement.
This AI based recommendation system can assign or get the best internship placements for the students matching their knowledge, skills, and goals.
The Internship Dilemma
Students spend years studying and acquiring degrees, all the while building their resume step by step for the ultimate goal of getting the dream job. Many take up different courses to forge their skills. All these add up to a strong resume to woo the recruiters. Now, we all know that the rite of passage from being a student to a full-time employee is getting an internship.
An internship is where you learn the tricks of the trade from your predecessors in the field. It is a great way to get into the groove of a working individual leaving the student life behind. That’s why every student aims to land a good internship in the final stages of their education. The experience not only helps them to build a career but it also gives a boost to the resume.
Besides that, it also helps in understanding whether a certain line of work will suit one well or not. It often happens to people who get a job without first doing an internship. After a few days, they might feel that the sector or work is not suitable for them. It is easy to leave an internship but not when it comes to resigning from a job. Hence, the end result is a bad start to the career, which can either slow a person’s growth or derail them from their initial goal eventually. So, the career chart should have three stages i.e. student then intern and finally a professional or employee.
Though an internship is very important, getting one becomes a hassling process. Not all universities guide their students or provide them with internship placements. For average students, it takes a lot of net searching, going to job fairs, etc to finally land a good internship. Even then one might find the place not suitable for him/her. In such a scenario the AI tech developed by Universitas Pendidikan Ganesha can be of a blessing to students to land the perfect internship that matches their profile.
Let AI get You an Internship Placement
The researchers from this University in Indonesia came up with a brilliant idea to solve the internship dilemma of students. They started to work on a recommendation system that will help the students in their graduation year to find the best-suited internship placement for them. It is totally AI based that makes it all the more interesting.
The system developed by them utilizes a recurring artificial neural network (ANN). It is called the Elman neural network. Their system’s job is to assess the student test results and use it to choose the internship placement which is the best match for their skills.
The students are required to give two tests. In the first one, they have to give sufficient information about their grades, knowledge, skills, goals, and likes. The second test is known as the Inventory Personal Survey. Here they have to answer a few questions that will reveal their behavior and overall attitude.
The researchers explained that the students need only to take the test and answer the questions. After that, the results of the test gets processed and analyzed by the artificial neural network or ANN.
The Development Phase
In the development phase, the university researchers ran many tests on the system. Sample information was collected from graduate students who wanted to apply for an internship after studies were over. The system was trained and tested in various ways with the help of the data collected from the students.
After running the assessments they were able to come up with great results. The system developed was able to achieve a 95 percent accuracy level. In all those cases the students have suggested internship placements that were ultimately given to the particular students by the University using the usual manual methods.
According to the researchers, the AI based recommendation system can identify both training and the testing data. This observation was based on the results of all the tests run by them in the development phase. They further clarified that the system can recommend internships like administration jobs, networking, software, etc to students who already in the lookout for internships based on their education and skills.
It is Just the Beginning
This AI based recommendation system can have great use to the management and staff of Universitas Pendidikan Ganesha. It can help them provide internships to students who are seeking it in a faster and more efficient way. It is also highly beneficial to the students as they will get the best places to learn the job from.
To see whether the system works with the same efficiency for a larger population of diverse students, the researchers will have to run more tests and study it further. For that, they will need a huge training dataset.
As of now, they have only used the system to get placements for students who are in the field of informatics. However, they believe that the technology can be further extended to other streams as well.
Nonetheless, this new AI-based internship recommendation system can be a game changer in the field of education and placements. That will be beneficial to both the institutions as well as the students.
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