With Artificial Intelligence, teachers can improve their classes, provide help to those who need it most, and focus more on putting the knowledge of their students into practice. Beyond the master, they now becomes accompaniers.
Artificial intelligence (AI) facilitates student learning: by analyzing their data with a magnifying glass, the machine is able to choose the most appropriate courses and exercises so that everyone can progress at their own pace. But AI also promises to make life easier for teachers: by providing them with more detailed and relevant information about their learners, they can improve their teaching and better support their students.
Even for an experienced teacher, it is not always easy to know if his or her course has any shortcomings that hinder students in their progress, or if the teaching materials he offers them are all really useful for their learning. Data analytics could make it possible to point these elements, and thus to improve pedagogy.
Some educational technology companies (EdTech) are already putting this idea into practice: when a large number of students fail on the same questions in a test, an alert is sent to the teacher, who then all the elements in hand to adjust his course on the notions badly acquired by his class.
Export Learning Data
If AI is able to analyze a class or an amphitheater as a whole, it can also provide a diagnosis of each particular student. Of course, the teacher already has an idea of the level of the students, but when they are working on digital platforms, the AI can give the teacher a more accurate assessment by detailing the progress of each, the points on which block some or, on the contrary, on which they have facilities. This is essential information to help the teacher make the right decisions.
Today, teachers have access to this data via the dashboards offered by many digital learning tools, especially those used in primary schools, colleges and high schools. They can learn the strengths and weaknesses of each, and based on this information, decide to go see some student or other to provide additional help outside the educational software. Or group students by level groups to work together.
Ultimately, it is even the machine that could automatically induce the teacher to make such decisions. Already, the algorithms of machine learning can identify students in particular difficulty, and then recommend to the teacher to speak with them. With other algorithms, called data clustering, we can also segment the class into homogeneous groups and advise them to work on a particular subject they are stumbling over.
But Also the Physiological Data
In order to establish a diagnosis on the students, AI analyzes of course the traditional learning data: correct or erroneous answers, type of errors, response time. But more and more, to refine this diagnosis, Physiological data about the learners themselves can also be used to reveal their emotions or level of attention.
By following the movements of the eyes of a pupil reading with eye-tracking technologies. Teachers can thus determine if certain passages have been read very slowly, posing apparent difficulties of comprehension, or on the contrary overflowed too quickly, sign of a loss of attention. With voice recognition methods, one can both spot the pronunciation errors of a student reading a text and also estimate his/her level of attention when answering a question asked by the teacher. Using cameras to examine the facial expressions, teachers can know if the students are bored, are annoyed by an exercise, surprised by the reading of a statement or still anxious. With a bracelet that records the electrical activity on the surface of the skin, one can determine their level of stress.
Today, all these technologies are mainly used in research laboratories. The idea is not to equip classrooms with a lot of instruments – it would be expensive and certainly intrusive. It is rather to succeed in identifying certain emotions from the actions of a student on the digital system.
Keep Your Hand
Of course, the diagnosis and the recommendations given to the teachers on the students based on their learning data and these physiological data must always remain informative and not prescriptive. The role of the data is to enable the teachers to be better informed, and not to make decisions in the place.
AI is a powerful tool to reinforce the teacher’s lighting on his students, but the teacher must always keep his/her hand on the evaluation of the students, because the teacher alone knows them in all their complexity and in their environment. For example, a teacher may very well decide in some cases to have the students work in non-homogeneous groups, different from those offered by the machine, because the teacher believes that it will be more beneficial for them. Or change the exercise allowances of a student made by the software because the teacher knows that it is disturbed by family concerns.
In the same way, the teacher must always have a say in the learning strategy implemented by digital tools. In order for these tools to be used by teachers and integrated into their teaching, they must be able to adapt them to their teaching practices. If the software customizes learning paths for each student only from the data, then teachers may well not appropriate them.
Be that as it may, new AI tools have made their way into classrooms and lecture halls, and are preparing to profoundly change the role of the teacher. More than a master, this one will become from now on an accompanist and a facilitator of knowledge.
With a better knowledge of the level and attention of the students, a teacher can help at the right time those who need it most, and by relying on software to make students work on specific skills at their own pace, they will be able to focus more on other tasks, inaccessible to machines: to create interactions between students, to develop their creativity , put their knowledge into practice through experiences.