Introduction
Robotics is the buzzword these days and we cannot find anything better than introducing robots in our learning curriculum/activities at schools and universities. Manual tasks are set to be automated as much as possible, even though it is not possible to automate 100% and human intervention is needed to make sure the system is correct and in-tact.
In the future, schools will install robots as their teachers and tutors, with minimum human intervention and this could cause reduction of jobs and many other issues. There are many good prospects when it comes to installing robotic teachers, they can be trained to have patience and good amount of knowledge to pass to the students as these robots are trained using LLM transformers with ridiculous amount of terabytes of data. Hence, knowledge will be in abundance. At the same time, there are many disadvantages that we will discuss further in this article. We will also touch upon the ethical grounds.
We are very aware that the education system is flawed in many ways and these measures can definitely help to fix these problems.
Robot Learning
Combination of Machine Learning and Robotics
As discussed earlier, there will be an insane amount of training data prepared before building these educational robots. These robots will have be trained by building Machine Learning models, leveraging algorithms such as Random Forest, extracting the correct features and efficiently training these models.
Learning Algorithms
These educational robots can adapt to the environment using learning algorithms. Even algorithms such as Adaboost that offer weights to each robotic action, can determine whether the robot is performing its duties correctly or not. This will require months of training in order to make the robot adapt to the learning environment.
Ensemble Models
Training set
Training data will include various educational curriculum, behavioural videos of actual teachers performing, proper ethical constraints, etc. This will help the educational robots to replicate what actual teachers do and be on the same page. Later, test data can be used to accurately make sure that the experiment is successful or not.
The training data will also consist of the body language of the students and how teachers respond to the student's emotions. This will be a huge leap in the world of robotics as robots understanding human emotions is a sticky subject.
Classifiers
Ensemble learning can be used to classify the educational data into families and extract the various features. Once the Machine Learning model is built, the train data can be used to classify into the families and efficiently train the robot to make sure it behaves correctly based on the input from the environment.
Optimisation
In real world scenarios, optimisation becomes a huge problem when it comes to building Machine Learning models. These models consume a lot of time and memory. Also, if these LLM transforms are run on GPUs then it becomes a catastrophe unless it is handled by proper switches or network fabrics. Final optimisation helps in making the models more efficient and robust.
Overall Architecture
Feature Engineering
As explained in the above topic, the student behaviour and motion capture technology are used as inputs to feed into the classifiers, along with the remaining training data. Feature engineering packages such as "XGBOOST" are used to extract the key features based on the input.
Machine Learning Model
Further, the models are trained and they classify the data into different families/categories based on the features extracted from the initial training sets. These models are perfectly trained to make sure that robots do not cross any line or behave in a random manner when a student approaches them.
Ethical Issue Concerns
Attachment, Deception and Loss of Human Contact
Robots can be misused and we do not mean in ways where they show in movies, but also in unethical ways that may cause harm to students. These robots are emotionless machines and it can be manipulated in any way possible. They can used to show fear to students who do not perform well in academics. So, we need to establish a common ground where this behaviour is widely not accepted across the nations, when it comes to installing educational robots.
Students often tend to get attached to some of their teachers. This is not possible with robots as they do not have any human emotion, this could create an emotional gap for these students to learn effectively from them. This leads to loss of human contact and this is the key reason why human teachers cannot be completely replaced by these robots.
Control and Accountability
As we have seen in countless movies where robots go out of human control, those are extreme scenarios and it may not be practically possible, but we need to make sure that these educational robots are completely in our control. In schools, it is pivotal to make sure that the students do not get emotionally affected due to any task performed by these robots. Only way to make sure is by establishing a committee that looks after these robots technically and also, assessing behaviour related problems.
When we train these robots, we must establish the ethical boundaries as part of the training data fed into the classifiers. This will make sure the robots do not cross any line or perform unethical tasks.
Conclusion
Training these models efficiently will help make the educational robots behave as humanly as possible, just like normal teachers. However, we need to keep in mind the ethical points listed in the articles that may cause some severe problems with the students. These robots will make the right decisions based on the student behaviour as trained by the training set comprising of the various educational videos or behavioural curriculum.
This type of training will also help to make robots more responsible in the field of robotics. This may not be just limited to education, but can also be expanded to various domains, where similar practices can be installed to make sure robots are under human control and perform responsible actions.