Addressing Bias in AI-driven EdTech: Challenges and Solutions

Addressing Bias in AI-driven EdTech: Challenges and Solutions

Every new thing makes people wary. The same is the case with Artificial Intelligence, and when AI gets combined with the education sector, people become both curious and doubtful. In the EdTech sector, bias in regard to AI refers to the ways in which AI-powered educational tools can reflect or amplify existing biases in the data they are trained on or in the way they are designed and implemented. This can lead to unfair or discriminatory outcomes for students, such as perpetuating stereotypes or denying access to opportunities.

Along with this, there are biases regarding the sudden change in educational techniques and how it would impact students and teachers. Let’s check out what these challenges are and how to deal with them.

Challenges of Addressing AI bias in EdTech

There are a number of challenges to addressing AI bias in EdTech, including:

  • Difficult to Use: AI systems are often complex and opaque, making it difficult to understand how they work. This particular bias is true to some extent especially for individuals from non-tech backgrounds. Training on handling AI tools and features would be the best way to remove this bias.
  • Not Accurate Datasets: Since AI systems are trained on data sets handled by both humans, and AI models themselves, there is the bias of the data being inaccurate. Plus, the group of humans training the AI could have certain biases of their own in terms of religion, culture, gender roles, and many others. These biases can be reflected in the AI datasets creating the same bias in the way the AI responds.
  • Sources of AI Models: In the Edtech industry, AI models often rely on historical data sources, which predominantly consist of outdated information. However, this practice could potentially impact both present and future data accuracy. Given that information undergoes significant changes over the years, failing to train AI models on current datasets could lead to compromised outcomes.

Solutions to AI bias in EdTech

Although there are many challenges in front of the AI-supported EdTech industry, they are not inevitable. There are a number of solutions that can be used to address AI bias in EdTech. Some of them are:

  • Using more diverse data sets: The problem of inaccuracy in AI datasets can be solved by bringing more diversity while training the AI model. Using more diverse data sets can help to mitigate the effects of bias in AI systems. People from different genres and mindsets should be chosen to train the AI, and the accuracy of the data should be prioritized. This can be done by collecting data from a wider range of sources or by using techniques such as data augmentation to artificially increase the diversity of the data set.
  • Constant Training: Along with being transparent about the sources AI models use to forge decisions and solutions, they should also be in constant training mode. Data is ever-expanding and can’t be submerged in a single location. By constantly learning the changing parameters of data in the world, AI models would be able to provide more accurate solutions to all.
  • Cross Checking Certain Words or Phrases: There are a number of words and phrases that signify biases. Those words could be added to the AI model’s mainframe as being a red light zone. So, whenever those words or phrases arrive in AI datasets, they would be automatically removed from the systems or dealt with unbiasedly.

According to Manish Mohta, founder of Learning Spiral says:-

Addressing AI bias in EdTech is a complex challenge, but it is one that is essential to ensuring that AI is beneficial to all, especially students. By using the solutions outlined above, one can create AI-powered educational tools that are fair, unbiased, and inclusive. Also, being involved in every procedure of the AI data set creation and advancement is also crucial. Universities should pay heed to what the students are learning by keeping a check on their sources and providing them with the right resources.

Along with this, handling the responsibility of an AI dataset creation should be given to reliable platforms. One such platform is Learning Spiral. The experience they have generated in the educational sector and the advances they have made in terms of keeping in pace with the constantly evolving EdTech industry is commendable. So, help your students out by utilizing the features provided by this wonderful platform right now.

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