Time AI for Schools

Editors Note: This is part one in a series.

With AI Appointments, time AI finds the nearest common date and hour for the requested amount of time between people and sets automatically. It can manage this for many people at once, setting for the highest percentage of respondents found or all of them within a specified period.

With AI Meetings, one created meeting splits into many and waits to set for subsets of the participants spread across time. The AI sets the nearest common date for each set of participants called a cohort as they enroll by accepting the meeting. Each meeting has instructions issued with the invitation for the participants to follow before they enroll. The difference in time of enrollment causes some participants to be in early meetings, others in later meetings with a different set of fellow participants. This allows learning to be pace-based. Knowstory refers to these as “Meets” because that fits closely with “Class Meetings,” and sometimes calls them “AI Cohorting Meets.”

With AI, schedules can be bi-level. This means that the allocation of time to duties can be preconfigured but have “overrides” using a separate AI mechanism.

Level 1: A level one schedule provides each user with general hour-by-hour activity guidance. In schools this means to study math in hour one, language in hour two, and so forth for students. For teachers it means what hours they have and which classes.

Level 2: Users can bracket time and label it with a “hold” to tell the AI when to drop in what kind of appointments or meetings. A hold disregards the level one schedule and pretends the bracketed time is open time. A hold, or several of them, means a teacher can take all their math units and make one extended hold of all math unit classes for those schedule hours to operate as one. A second hold could be science units also being taught. Using a hold, the class meetings governed by the rate of cohort enrollment for the next class, have more open time to set. No conflicts will occur because the AI takes care of that. Students would still use their level one schedule to get their study time in but attend class meetings whenever they are calendared in holds.

The traditional concept of a class meeting is typically the teacher with a whole group of students for forty five minutes to an hour or more on repeat several times a week for many weeks. The entire group moves together across time for each class.

Time AI proposes better efficiency by fractionalizing the time spent with a teacher to just the verbal instruction in active teaching. The remainder of the time students spend separately. That would typically be in a homeroom in a school with oversight, but the instructional minutes would remain the same – time spent with a teacher versus time spent studying separately in some other room are just added together to be equivalent to the same class time in old style class schedules. Using time AI, the same class moment will now calendar multiple times for smaller cohorts on demand, as they arrive at that point in the curriculum sequence, creating pace based learning. The whole moving together in the same time series of classes goes away. This also means every teaching moment intends to have teachers with students for briefer periods, which is workable because there are fewer, and they are all at the same point rather than mixed grade levels typical in today’s classrooms because some students may be behind or ahead of grade level but are placed by age into grades.

Note that once the class hour block is fractionalized, the number of students meeting with a teacher at once can also be fractionalized. Whole group classes become smaller cohorts who are, again, at the same point in the curriculum sequence, a major feature that unburdens teaching and learners themselves.

With two levels of fractionalization, the whole pattern of learning shifts to resemble something akin to students all studying in homerooms that appear more like airport waiting rooms before various flights appear on tracking boards and take students off to short or long classes. They fly back and disassemble to study some more before other flights to other destination classrooms, which are their other subjects with teachers in various aggregations of fellow students being cohorted together by time AI.

When schools also put each student at the right course level regardless of grade, the entire pattern can shift to adapt to the right level in every subject for every student.

Long time educators may try to compare the restructuring of schooling through time AI as “like” other pace based learning, competency based learning, online learning, flipped learning, etc. Yet none of those models attempt to fractionalize the whole group as well as the amount of time for purposeful intersection with live teaching and course levels. Some digital courseware programs do cohort students into levels of reading or math, but not necessarily as groups to work together, just points where each student works in the programs.

In addition, online learning nearly always removes much of the live teaching. Only time AI allows a complete break from grade by age and whole group class structure across entire master schedules. Time AI also does not force the issue of competency for students to move to the next lesson because the decision is entirely on the teacher whether to restrict a student to a course step to restudy or release them to the next. It is also content agnostic.

The distinction between being present physically or arriving via video conference for class meets by the teacher for any cohort is immaterial when the school provides paraprofessionals for oversight of the students physically present. This also means schools can develop a matrix of teachers who are not on campus while others are present. Districts can create fractional shared teaching resources across multiple schools.

Breaks whole groups into small groups called cohorts and lets them pace independently. A long held dream of educational institutions has been to have some help in the logistics of managing smaller groups or individuals for their unique pace of learning – and leave the mass manufacturing line structure behind.

Teachers are willing to pivot to using time AI once they understand what it does for them.

Yes, a move away from traditional block schedules where a teacher can know they are in the same place hour by hour and has all of one class together every day at the same time, to a system of meetings which are a more randomized pattern of points in the curriculum, is a major difference. It is normal for administrators and executives in most other professions, however.

The gains:

Time savings of up to fifty percent, depending on number of units a teacher carries. Saved time is now “open” for roaming, planning, managing and tracking individual students. Every meeting is a cohort of students who are at the same point in the curriculum. This alone is major for teachers since they are not facing a whole group with between ten and forty percent behind or ahead in grade level.

Accommodations for special needs and foreign language students is much simpler because cohorts are smaller and those students can get direct instructional help during the teacher’s roaming time.

Less class management and discipline because AI manages cohorts and discipline is mostly managed by homeroom leaders. Students are on an individualized set of courses and are socialized in homerooms. It will be expected that meetings are purposeful moments to have active engagement.

Teachers would need to check their calendars and manage tracking panels all day, every day they are on duty. They may not even have the same classroom for each meeting.

The teacher may be in one of several locations during any one day, primarily in classrooms, roaming to do direct instruction and check-ins, or in an office doing planning and tracking student progress to manage the time schedules of individual students. In this example, any part of these three courses could show up on any one day with cohorts landing on the calendar needing that class moment.

Every meeting would give the teacher a short reminder of what to teach with verbal instruction of some kind for that moment before they step in to meet that cohort. Time AI may put two or more of the same class moment on one day based on cohort pace. This is really not much different than teachers doing the same thing within a regular class hour for several small groups, or their regular class hour across several days. It is just managed now by AI.

This one day example is one subject across multiple courss and possibly multi grades. Each color represents a different course. Notice how some meetings are shorter and others longer. Some meetings say they are just “check-ins.” A check-in would normally be done in homerooms with the teacher roaming to individual students and checking understanding only, not a formal instructional moment.

LeiLani Cauthen is the most connected thought leader in K12 education. She is the CEO and Publisher of the Learning Counsel and produces leadership training events in twenty two or more U.S. cities annually and keynotes other conferences. Her on the ground approach for the last ten years connected her with districts of all sizes as well as charter schools, private schools, and government leaders. LeiLani has over 28 years of experience in News Media and both quantitative and qualitative research, four years in software, two years in legislative work in California – a particular achievement of which included language in SB1386 passed by Senator Steve Peace in California related to software security and encryption. LeiLani Cauthen is well versed in the digital content universe, software development, the school adoption process, school digital curriculum and systems coverage models, and helping define this century’s real change to teaching and learning. She is an author of The Consumerization of Learning, many articles and Special Reports, a Podcaster, and can be seen frequently on video recordings of live events.