Is AI’s Bias Algorithm Affecting the Education System

As we move forward with the current technological revolution, reading artificial intelligence’s name being mentioned in every sector of life might not be surprising anymore. Whatever human beings have their hands on, artificial intelligence (AI) will be right behind them, why wouldn’t it? Artificial Intelligence learns from human beings to make their life easier. One of the critical areas that AI is set to impact is the education sector. However, it faces a set of challenges like public privacy, bias algorithm in education; training teachers to get accustomed to AI, and training AI to understand the education system and to provide a sound dataset to avoid problems.

With other essential aspects, one of the enormous problems artificial intelligence is facing is the problem of the bad data that has been given which has resulted in an even more significant problem, which we know as algorithm bias. And, this problem of algorithm bias has now crept into the education system too.

Why is the algorithm biased?

AI learns from the data provided to it by the human being who designed it. So, the kind of data that is produced depends upon the developers; thus, the developer inherently will be held responsible for the algorithms bias. The more bad data is fed to the algorithm, the more bias it will be.

While people and data experts argue about the level of efficiency AI can have in education systems, it still faces a lot of problems when it comes to algorithm bias. The industry requires more diligence and extensive exploration of the field when it comes to deploying technology in that field. It is unfortunate to say that in times like this, racism and discriminations are embedded in our education system, so developers need to make efforts on developing algorithm and datasets which are not racial. There are many examples around the world where AI has been shown levels of bias.

How is the algorithm biased?

A study by an education software solution provider saw a predictive algorithm bias against Guamanian students. The snapshot of the data is given below:

The above graph gives the likelihood of a student passing the test. 

The grey bar represents the actual proportion, and the teal ones are the ones which are provided by their algorithm. By looking at the data, it is clear that the algorithm showed the amount of bias towards the Guamanian students. The original data used to train the system had very few Guamanian students, which was later fixed after resampling the data and retraining of the algorithm. But, what if this algorithm bias was never have been questioned? The Guamanian students would have been falsely penalised by the algorithm.

Questions will definitely arise when such cases come to light in an actual scenario. The developer has to be able to identify these biases and should be able to show evidence about those algorithm’s bias.

Another problem with datasets is the number of people and the types of student information that is used in the algorithm’s training. If developers run training on the algorithm with fewer people belonging to specific skin colour, then the algorithm might take these group as less favoured. This problem will amplify when the algorithm is used in practical purposes in the education system, like the evaluation of the SAT or for some recruitment programs etc.

Apart from racial bias, AI can also create problems when it comes to its applications where it assesses student’s prior and ongoing learning, placing students in appropriate subject levels, individualising instructions and scheduling. The algorithm doesn’t consider students’ experiences, low-income ability and students in minority groups who are at a relatively small achievement record. The algorithm might also assign a bad instruction set and reduced expertise when it comes to a student with a low seeding record, which in turn can hinder student’s growth.


Well, on the other hand, artificial intelligence is not all that bad when it comes to making a positive impact on the education system. AI provides aids in a more efficient and time-saving manner for the teachers to pay more attention to the students and also helps in providing global access to education for students. However, unfortunately, algorithm bias has become such a common problem which ends up forcing people around the world to question AI’s promise to make human lives more comfortable.

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Sameer Balaganur
Sameer is an aspiring Content Writer. Occasionally writes poems, loves food and is head over heels with Basketball.

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