Improvement in the AI-Enabled Software for Identifying the Students who Need Help

In this digital and highly technical world, people are more dependent on automation, software technology, app, and mechanics than on manual activities. Therefore with the advancements in the research sector, the researchers have brought a vital connection between mechanical science and psychological science. Specific software and app have already been introduced in this technical world following the footprints of the Artificial Intelligence concept that can sense the presence of a human being within a specific range of areas. Thus accordingly, there has also been an improvement in the field of AI that enables a machine to perform all human-related activities like driving, cleaning, cooking, reading, etc.

The advent of the AI-enabled Software for Identifying Students’ Need 

According to the reports of the recent researches, it has been found that an artificial intelligence model can predict the quality of learning of the students in the educational games. The improved model of the application software has used an AI training concept called as multi-task learning. Thus this is used to improve both the instructions as well as the learning outcomes. In this approach of multi-task learning, one model is asked by the user to perform multiple tasks. In the case of researching the educational game, all the researchers wanted the model to predict whether a student can answer each question correctly on a conducted test. This action is said to be predicted based on the behavior of the student while playing an educational game called Crystal Island. Thus viewing the test as one task, the standard approach for solving the problem is looked for only at the overall test score.

Multi-Tasking Learning Model for Students

In the context of the researcher, the model of the multi-tasking learning framework has 17 tasks, as the test has 17 questions. The team of researchers had the gameplay along with the testing data from 181 students. The concept of AI applied looked at the gameplay of each student and even the way each student answered Question 1 on the test. With the identification of the common behaviors of the students that answered say Question 1 correctly and the identification of the students who got Question 1 wrong, the AI could determine that how a new student would answer Question 1. Therefore, at the same time, this function is performed for every question, and the gameplay that is being reviewed for a given student is the same. But accordingly, AI keeps on looking at the behavioral pattern in the context of Question 2, Question 3, and so on.

Hence, this AI-enabled multi-task approach made a difference. In relevance, the researchers found that the multi-task model was about 10 percent more accurate than the other types of models that relied on the conventional methods of AI training. This model is designed so to be used in a couple of ways that can benefit the students. This kind of model can be used to notify the teachers when the gameplay of a student suggests that the student might need additional instruction.