Implementing AI in learning and education comes with its own challenges. In our earlier blog, we have already touched upon the latest trends in the application of artificial intelligence ( AI) in learning and education. While advancement in technology is a boon, its challenges need to be counteracted, to harness its potential.
Let’s first define what artificial intelligence is.
According to Wikipedia, Artificial intelligence (AI) is intelligence demonstrated by machines, as opposed to the natural intelligence displayed by animals and humans. AI research has been defined as the field of study of intelligent agents, which refers to any system that perceives its environment and takes actions that maximize its chance of achieving its goals.
In this blog, you will come across some of the common challenges that any project involving AI faces in the learning and education sector.
- Lack of data– Without data, it is challenging to train AI models. No two children are the same, so their learning curves are also different. In the absence of data, it is very difficult to create an AI model that can be customized to meet the exact requirement. This includes developing algorithms that can identify the most important information for the students to learn, and providing customized feedback. Additionally, the applications must be able to adapt to the changing needs of the students, as their understanding of the material grows. Also, AI systems need to work together with humans in order to create truly effective learning experiences.
- Lack of understanding of how AI in learning works- This can lead to issues such as biases in data. AI bias or algorithmic bias or machine learning bias occurs when an algorithm produces results that are systemically prejudiced due to erroneous assumptions in the machine learning process. For example, training of machine learning software has been done on a dataset that underrepresents a particular factor like age group, educational need, etc. This includes ensuring that the data used to train the AI applications is appropriate and that the applications do not discriminate against any groups of people.
- Challenges in deploying systems for AI in learning- The deployment of AI systems can be difficult due to the need for infrastructure, resources, and access to the right technology. For ensuring better efficacy of AI systems, updated and upgraded technological advancements are required. This not only requires huge capital investment in creating the AI system but also in training students to access the latest technology. This also includes ensuring that they have access to computers and the internet, as well as having the upgraded software and applications installed. Besides the installation of AI software, schools would also need to consider the cost of maintenance of the software.
- Challenges related to funds– AI projects are highly capital intensive. From investment in technology to human resources, AI projects require deep pockets. Often the best-case estimates fail as most of the time, the budget is difficult to forecast. Eg: If all government schools think of deploying robot assistants, then the cost of deployment would be huge and the country would have to finance a high budget for recovering expenses. Along with this, the requirement for power would also make a deep dent in the pocket.
At Xaigi, we have helped several project managers with AI in learning and development to come to life.
In our quest, we have also come up with our own model of cash plus equity to help with project management and funding for start-ups in AI in leraning and education.
If you are facing any of the challenges in project management while implementing AI in learning and education, then get in touch with us by booking a free consult here