Picture the following scenario…
Students walk in for 8th grade math class on Monday morning at 10am. They are scheduled to have a test on linear functions that day. There’s a camera in the front of the classroom that scans the room and notices Linda and Emma are not in class at the start time. It scans again 5 and 10 minutes later and still no sign of Linda and Emma.
This camera isn’t just any camera, it is powered by IBM’s Watson or another artificial intelligence system and it is connected to the teacher’s calendar, the grade-level calendar, and the school calendar. It immediately knows by cross-referencing all calendars that there was a test scheduled for that day and that Linda and Emma will need to schedule a make-up test. Since the AI is also connected to Linda and Emma’s class and extra-curricular activity calendars and cross references their availability with the teacher’s availability, it knows that Tuesday between 2-3pm and Wednesday between 3-4pm are the optimal times for a make up test. Taking it another step further, it also looks into the room schedules and knows which rooms are available during those two time slots. Next, the AI sends out emails to the teacher, Emma, and Linda to offer the two optimal make-up test time slots in a format where all the students have to do is click on a link or button to select one of the two time slots. This action automatically creates calendar entries in all parties involved, books the room, and sends out a reminder 1 day, 12 hours, 1 hour, and 15 minutes before the scheduled make-up test.
On a teacher dashboard, the AI would have important attendance-related issues highlighted, such as the fact that Linda and Emma missed Monday’s 10am original test time. It would not clear these issues until they were marked resolved either by the teacher or through some sort of confirmation that the two girls completed the test. This could be triggered by the teacher entering grades into the system or simply marking the issues as resolved.
Normally, all of these activities are performed, and not always optimally, by teachers while managing countless other class-related details and tasks. Optimal make-up time opportunities are missed or students reschedule on short notice without the opportunity to prepare for their make-up test adequately. This is just one opportunity for AI to partner with teachers to liberate them to focus on what they do best – teach.
Just for fun, take this a couple of steps before the scenario. A teacher could have simply asked the AI what days/times in a particular week it would recommend scheduling the test. The AI would cross-reference all relevant calendars, look for issues that might cause a conflict or inconvenience to any party involved, and suggest optimal test days and times.
Just for a little more fun, take this a few more steps back and imagine the AI reviewing the lesson plan agenda and crawling the internet for potential test questions, test samples, or study-guides that it can offer the teacher in advance of scheduling the test.
Or, what if SoftBank’s Watson-powered robot, Pepper, was your co-teacher?
Just for fun, I could do this all day, but I’ll leave it at that 🙂