• Save 50% of teachers’ time and reduce total academic staff needed
• Unburden teachers from lesson planning and mixed ability whole-group teaching
• Pivot to all small group learning, each class at same ability levels
• Offer full personalization
• Save costs while developing new value appeals to families
• Align to predicted AI convergence timeline
In 2025, the trend lines all indicate a national move away from traditionally structured schooling. By mathematical modeling to extend these trends across population figures and schooling, predicting the future is simple.
• Homeschooling growth is between 51-65% year-over-year in 2025, down slightly from 2020 and 2021 in rate of growth.
• School Choice with vouchers continuing to pass legislatively in more and more Stats could displace as much as $360B, 40% of all funding for traditional schools by 2026.
• Continued 3-20% year-over-year student attrition from traditional schools, especially public urban and many suburban districts.
• 10-40% absenteeism rates depending on geographic area.
• 8%+ year-over-year teacher losses, and additional loss of 6% nationally for the 2025-26 school year, now equivalent to a shortage of 30% of needed classroom teachers.
• School closures, public traditional as well as all charters, microschools and private schools displaced 984,020 students in 2023-24 in nearly 5,000 closures, a trend that accelerated in 2024-25.
• Massive culture shifts in these arenas:
o Pre-pandemic demand for individual educational plans (IEPs) due to advances in other industries providing customized experiences,
o Post-pandemic expectations of remote-on-demand learning and far more IEPs,
o Only 28% of Americans have confidence in public schools.
o Political ideological drivers away from all government institutions,
o Alpha Generation rejection of schooling formality with increasing disciplinary issues and decreasing achievement nationwide,
o Out-migration from cities with commercial real estate collapsing,
o Mass demographic shift with major burdens on schools to teach to as many as a hundred or more foreign languages and populations with low historic regard for learning or expected behaviors,
o Economic pressures on schools with the loss of pandemic relief funds,
o Expected AI convergence, past Generative AI to involve many types of AI in schooling, driving a restructure.
Despite consumers clamoring for results through customization of learning, teachers can’t really achieve personalization at scale despite all the new technologies: 36% of educators say the “one teacher, many students” model is not working anymore, 36% were neutral and only 29% said they could personalize learning for students. (Powerschool 2024)
Learning Counsel Research surveys in August 2024 also showed that:
• 87% of teachers are overwhelmed by the number of expectations on their practice.
• The #1 hurdle with teachers and administrators is traditional beliefs about what education “looks like” and unwillingness to consider divergent structures.
What administrators are now challenged with solving without changing structure encompass the combined trends already mentioned that are driving the collapse of traditional schooling, while ignoring these things:
• The factory form of by-age in grades with linear time schedules creates a rigid calendaring that pushes students along even when they haven’t mastered earlier material, resulting in lower achievement scores.
• Moving to a 4-day week, as many districts have done, has little effect on overall cost efficiency and puts parents in the position of finding childcare for their younger children.
• Accommodating certain disabilities, sports and acting career children, discipline cases and other anomalies
is difficult in traditional master schedules, reducing their school’s appeal.
• Offering online learning is often mostly teacherless and does not work for many students and additionally may not gain the school or district the needed seat time to be paid.
• Time-bound lessons once missed are not easy for the teacher to give again or for students to catch up, making absenteeism the major problem it is.
• Differentiation and individualized instruction by teachers in an age of mixed ability classes forces onerous burdens on teachers. Teachers must create lesson plans and activities that cater to a wide range of abilities (grade levels different than the one being taught), making it challenging to ensure all students are learning. This includes differentiating learning objectives, content, and tasks for groups and individuals.
• Teachers must now accommodate different learning preferences and consider a wide range of new cultural backgrounds, language proficiency, and individual learning styles when planning lessons.
• Teachers are challenged by increasingly disruptive behavior which then has a negative effect on all the other students.
• The factory model structure requires one teacher per classroom plus a lot of administrative support in every school. With mass attrition, districts consolidate to fewer schools, the opposite of the expectation of consumers which want more walkable neighborhood schools and more home-like smaller environments.
• Having to have a teacher for each and every subject on campus for live delivery handicaps many schools who just cannot get teachers to cover every subject, or cannot afford them, and thus have to reduce their subject offerings and therefore their experiential appeal to families.
• Substitutes are hard to find and enabling tutors is not only an added cost, but also difficult to enable within the already tight time constraints of teachers trying to manage mixed-ability classes on their moving train of grade-level lessons.
• Safety and security concerns by families are at an all-time high, adding cost to deliver campus and virtual learning.
Learning Counsel Research’s 2023 Digital Transition Survey asked schools and districts to respond as to which schooling strategy innovations they had tried already. Respondents could check every category. The survey had 4,912 Responders, 28 percent urban, 39 percent suburban, 17 percent rural and 16 percent other, including online schools and combo districts.
Note upfront that none of these arenas of innovation have worked to turn the tide very significantly for traditional schools. They are still failing, seeing turnover, seeing their market slip away to homeschooling, online schools, and many new alternatives.
The things tried by schools included changing curriculum, which might mean they’ve tried a different curriculum resource (courseware, textbook, any sort of program like a focus on literacy or STEM). The frenzy of focus in this area continued through 2024 and is expected with 2025 surveys.
Curriculum Culture
Implementation Supports
Space and Time Use
Refining Teaching Roles
Overall Structure (public, private, online, etc.)
Grades/Age Segmentation vs. Competency or Interest
Refining Student Roles
Course vs. Class Orientation
Culture innovation is normally a new administration-led theme like:
Belonging & Inclusion; Safe, Seen, Supported; Thrive Together; Kindness Counts; Leading with Integrity; etc.
Other programs address cultural issues such as high absenteeism, school violence prevention, managing conflict and tension, accommodating different world views.
Adding staff has helped with implementation supports like Individual Education Plans (IEPs), which in many places nearly every student has, making teachers’ jobs untenable. Dropping to four-day school weeks and dedicating open flex time for students who need to use those hours for catching up has also been a trend.
Schools have attempted to redefine both teaching and student roles, but only within the context of the existing traditional schooling structure of whole-group teaching and learning by grade batches.
Some schools have done interesting things with how they are segmenting by grades or age, sometimes offering placement by competency or interest to students in various ways, yet still within the traditional factory model of schooling. Many times these ideas have been touted as new “models,” but are largely shifts in method, emphasis or theme.
Less than 20 percent have offered a course versus class orientation, and those were all predominantly online programs or schools with mostly teacher-less structure for courses.
Six percent of schools and districts in the past several years have hired public relations and marketing staff due to steep drops in enrollment.
It’s time to pivot how schooling is organized and executed, flipping the administrative pattern from learning distribution by teachers in a factory model to direct learning distribution to each student with teachers. With Time AI and a school’s existing systems, buildings and teachers, this pivot is possible.
The pivot means schools will change their form and model. This has been called “Flex Learning Logistics,” and “Hybrid Learning Logistics,” among other labels, but its central change agent is a form of AI called Time AI, which offers a type of group calendaring which manages cohorts one at a time.
Structure could remain public, private, online or hybrid.
Teaching methods will remain the same but be deployed in a different framing of time, with each teaching moment being a class cohort that is at the same point in a course — dynamically on-demand. No more mixed ability classes and most of the work of class management, discipline and personalization done differently by “fractionalizing” a class into component parts of live teaching and independent study of any kind. Each part is done either in homerooms or in classroomsas-meeting spaces.
The pivot also means the following definitions will shift as schooling flips how leadership is done to become a technology enterprise with direct delivery of extreme learning personalization first, and secondly distributes on-demand human teaching. Principally this change will be created through Time AI, but convergence of all the types of AI will take this to a deeper level over time. This pivot should not be confused with just Generative AI in the hands of teachers or learners.
Time AI allows a pivot to pace-based learning that still intersects with live teaching but is not whole group oriented and may be non-grade-banded. Time AI is materials-centric and administratively centralized while also being more human. Currently, top-level administrators think of their human organizational chart, the campus buildings, the flow of students in linear schedules with full control over subjects and progression by grades with metrics that are fed back to States and the Federal level on long-ago established patterns. Each school and district is an island, manufacturing a pattern of normalization by whole groups across subjects, when the present expectation is total personalization within an intentionally social environment. Time AI creates a form and model shift that allows schools to leverage their human teaching assets and the fact that they are physical campuses where new methods of intentional social dynamics can help students both learn academics and social skills.
These are the four ways schooling has been defined in the past, with the old thinking and definitions versus the new. These definitions take into account how Time AI calendaring changes master schedules and the fact that AI will converge the various “types” into a new holistic reality.
Schooling STRUCTURE — ownership as public or private institution with sole dominion in which subjects offered, which teachers, what schedule.
School FORM — the configuration of space, time-use and resources, organized for whole group instruction or online whole group via virtual meetings, synchronous instruction with some asynchronous study. Form is constrained by resources (teacher-student ratios, technologies, physical spaces, linear time schedules.)
School MODEL — The delivery framework a school, encompassing the organization of grades, schedules, specificity of instructional methods, and student support services. Examples include traditional brick-and-mortar, hybrid, project-based, competency-based, and virtual learning models. Model may also be inaccurately labeled with themes such as STEM, Gifted, Fine Arts, Outdoor/Expeditionary or Magnet, as well as methods such as Socratic questioning, Montesssori, Waldorf, Steiner, Special Education, Immersion, etc.
MODEL assumes one-to-many teacher-led timebound instruction, grade progression by age, fixed schedules for class periods.
Schooling STRUCTURE — ownership by the individual learner with ad hoc association to institutions whether public or private as well as online resources, gig teachers/tutors and personal schedule. Schooling structure becomes disaggregated learning.
School FORM — configuration of space, timeuse and resources is traditional synchronous whole group versus asynchronous personal pace-based with AI auto-cohorting in any configuration of physical campus or virtual, or all online, or a blend.
Teaching METHOD — techniques or strategies employed in instruction with teachers controlling the pacing of information in standardized content formats with learning outcomes measured by tests.
School MODEL — becomes nearly synonymous with BRAND as well as the delivery framework mix of physical campus spaces and virtual, whether teacher-led synchronous and timebound or asynchronously auto-cohorted (or single student on-demand) AI calendaring. Not all instruction in varied models is gradebound but could be course-centric or lesser modularization for extreme personalization with liquid every-changing curriculum. Disaggregated service offerings of pathway planning, courses with or without teaching, AI chatbots, tutoring, intentional socialization, weekend and summer schedules, networked offerings in partnership with other providers in a unlimited matrix are mixed into models, blurring the word “school” into more of a “hub.” As BRAND, the MODEL may use any theme and teaching methods.
Teaching METHOD — technique of conveying significance of parts of knowledge, guiding leaners to cognition. The teacher may or may not control pace or information but may cause increased personalization and use formative assessments to deepen learning.
Unlike typical scheduling tools, Time AI takes decision-making usually reserved for humans and automates it through dynamic, adaptive algorithms. The platform is more than just an intelligent calendar; it is a comprehensive system that manages complex time coordination, fractionalizes traditional classroom structures, and supports pacebased learning at scale.
You may be familiar with apps already in Microsoft and Google for sharing your booking link, or Calendly. You may also be familiar with how Uber or Lyft work to coordinate against time values to schedule you a ride.
Time AI is an artificial intelligence that goes beyond regular intelligent calendaring to make decisions for you to manage appointments and meeting times without so much human coordination labor.
For schooling, it may be the most revolutionary AI yet. Time AI on the Knowstory site will allow you to make meetings that “float” until a number of participants enroll equaling your set cohort value. These AI cohorting meetings are set for each smaller cohort set automatically while accruing the next set for a later date and time until all participants are through. A cohort can be set to each one participant with the meeting creator or each 2-100. In these types of meetings, a class group, or set of teachers doing some professional development, will all get the invitation with prior instructions, attachments, link and a target date that itself keeps shifting forward by preset increments. The meeting invite tells participants to complete the prior work before accepting. The act of accepting the meeting is an enrollment that “floats” until the cohort value is met. This creates a random pattern of cohort participants based on their individual pace through time.
Once you grasp what AI can do to cohort and set itself to calendars, you can imagine a sequence of these types of meetings replacing the regular class daily class meeting for whole groups that now disaggregate across time based on their individual pace with the AI watching to see which individuals can come together and when. This does not mean the learning has to be mastery-based.
The invention of Time AI allows for class time to be disaggregated, or fractionalized, into three arenas of instruction:
1. Live teacher moments of verbalization together with a cohort, be it lecture, discussion, project, or any method.
2. Student study time doing independent reading, courseware modules, watching videos, writing, or researching, etc.
3. Teachers roaming to do informal instruction when not in a formal instructional meeting.
By separating the togetherness that is normal in traditional schooling, meetings by teachers are always typically shorter and with smaller groups who are always at the same point in any course. That pulls a massive burden off of teachers while also randomizing the pattern of their day as to which part of any one course is going to fill up their day. Mathematical modeling shows they will save roughly 50% of their time by distinguishing live teaching time from study time and having students do their study time with separate oversight. Now the teacher has a lot of time to go to
shorter meetings and also to roam to help individuals at any time while they are in homerooms or remote.
Time is one of our resources and is easily wasted. When human minds think of adding or subtracting hours to consider meetings to set across multiple time zones, things can get tricky. Appointments may end up scheduled at the wrong hour and have to be reset. Humans tend to round time up to half or full hours, rarely setting a meeting for, say 10:13 am. Instead they will choose 10:00 am or 10:30 am. They will bracket time in easily managed chunks, rarely realizing that they might have broken up their day much differently. This has been the case for most school master schedules for a quarter of a century.
Machines add and subtract time very easily but ask someone to add up eleven minutes plus seventy-three minutes plus an hour and forty-one minutes and state the total hours and minutes, and it will take most people a bit of thinking. It’s a total of three hours and five minutes, by the way. Hours are sixty minutes, and adding and subtracting time is not something humans do routinely.
Excellent time management is a rare skill, and people rely on calendars. Yet, calendar coordination is itself rather arduous even when automated for someone to pick a time on someone else’s calendar, there is still manual comparison.
Time AI must count and weight minutes to resolve available common times between disparate calendars. Between two calendars might not be so much to calculate, but between thirty calendars is considerable. Time AI has to also know humans setting
appointments will want some parameters such as meetings not setting three years in the future, or setting so close to present time no one can scramble to make the appointment. Time AI needs some input but can treat time in layers so that even if a person has a schedule apparently booked for an hour’s class, AI could treat the same apparently blocked time as open time for a student in that class so that a few minutes appear on the teacher’s calendar to have a side conversation during that class.
Time AI will reshape how school spaces and master schedules work. It can be applied within existing master schedules by teachers, to just one program like an Alternative school-within-a-school or district, to online and remote learning, or it can replace the entire master schedule structure. It’s first adoptions are for schools offering courses which may have only one student or a handful and need to cause live teaching intersection rather than all independent study with no real teaching.
Time AI fundamentally changes the concept of classroom instruction by fractionalizing traditional time blocks. Instead of wholegroup classes moving in lockstep, smaller cohorts interact with teachers for focused instruction while spending independent study time in campus home rooms or elsewhere. This innovative approach provides:
• Efficient Use of Teacher Time: Teachers can allocate up to 50% more time to direct instruction by reducing total time in whole-group sessions.
• Personalized Learning: Cohorts are tailored to students at the same curriculum level, eliminating the challenges of mixed-grade abilities within a single class.
• Flexible Teaching Modalities: Teachers can instruct students both in-person and virtually, seamlessly integrating online participants with physical classroom cohorts.
• Enabled Gig and Fractional Teaching: Teachers can set up courses and offer windows of their time to teach independently from institutions or as fractional employees.
Time AI has these primary capabilities:
1. Common-Time Decisions
The platform finds the nearest common availability among participants and automatically schedules meetings or appointments.
2. Cohort-Based Meeting Setting
Time AI introduces “AI Cohorting Meets,” where a single meeting splits into multiples at different times for subsets of participants based on when they enrolled by accepting. One meeting with thirty participants and a cohort value of ten will be set at least three times (3 x 10 = 30 participants). This AI function is perfect for self-paced learning that still intersects with live teaching, training in any organization, and other business purposes. Each meeting also generates a link that can be used in outside Learning Management Systems for participants to click to enroll.
Cohort-based meetings can be tethered together into a sequence for use inside an LMS or into a course frame in the same Time AI system. The AI will provide tracking of each step, so schools are meeting their instructional minutes
requirements. With complex tracking of both study steps and class meetings, participants can be seen to be ahead, behind or right on target to get through the sequence of instruction steps and the bisecting AI cohorting meets.
4. Rolling Enrollment Courses
Courses with Time AI class meetings are able to add new participants at any time for the AI to manage intersection with live teaching and fellow students.
5. Push-Forward Recalculation
For meetings and appointments, users can opt to use a push-forward function
to have the AI recalculate and reset their date of participation.
6. Bi-Level Scheduling for Flexibility
Time AI’s layered scheduling allows users to balance structured schedules with on-demand adaptability.
7. Labeled Open Time
Users can label and assign blocks of open time for specific subjects or activities so that the AI will set requested appointments only in those available times.
Time AI addresses many challenges faced by modern educators and schools, including:
• Resource Sharing and Simplification: Schools can collapse online programs into live campus programs and vice versa because the live-teaching intersection, whether small group or just an individual student check-in, is managed by the Time AI and any member can be remote or in-person, including the teacher. This also allows teachers to be resourceshared across multiple institutions.
• Better Equity and Outcomes: Time AI helps remove the inequity of nonhomogeneous learning levels through assignment to grades and courses by age, even adjusting underlying time-ontask in one subject over others to catch individual students up. Every student can be placed at the right point on any course in any grade. Absenteeism becomes a non-issue as every student is self-paced. Algorithms quantify task and teaching time to meet any instructional minutes requirements.
• Improved Social Dynamics: Schools using Time AI as a backdrop to reorganizing the physical learning environments put students in age-andgrade-level appropriate homerooms while allowing any aged student to be on any grade-level in all subjects.
Time AI may be piloted very simply with existing teaching and learning programs by creating a single AI cohorting meet and giving a class a set of instructions to complete before enrolling. This might be for some project-based learning or other assignment flanking core teaching.
With Time AI, teachers handle cognition, connection, and care only. Time AI elevates teachers from task-driven roles to purposedriven ones—freeing them to be experts in student growth, not curriculum mechanics. The roles for teachers shift to:
1. Formal teaching — Just the live method of formal teaching called for at the specific step for that cohort whether it is a lecture, discussion, lab or other method. Each pre-cohorted class meeting will usually be a fraction of a full class period.
2. Learning Coach & Progress Monitor
— Ensures students stay on track, understand content, and persist through challenges.
3. Diagnostic Strategist — Makes human decisions about when to intervene, reteach, or extend.
4. Personalized Support Guide — Adapts the pre-set curriculum based on learner needs. Maintains human connection as a key driver of engagement and growth. Offers care and cultural awareness that technology cannot replace.
5. Mentor of Learning Behaviors —
Coaches executive function, selfregulation, motivation, and self-efficacy. Anchors the emotional and social experience of the learner.
Schooling that uses Time AI to establish pace-based learning needs prior lesson sequencing. Time AI may be used with simple chaining of AI cohorted meetings or use course frames.
In either case, the goal is to create true personalization of pace first and then allow for personalization of materials beyond the original resource plotting for each lesson by teachers.
With every lesson and class meeting defined in a sequence, whole groups can disaggregate across time with pace set by ability. Students can be issued courses for their correct ability level. Study time is averaged; class meet time is predetermined for the AI to set as each cohort fills.
Adopting Time AI typically means figuring out the right program and right level of pilot first. Next the school needs to organize lessons so that they can be independently paced through by learners. Time AI is content agnostic so any Learning Management System, courseware, discrete digital learning object and even paper textbooks can be used with AI meeting links pasted into outside systems or placed into the platform’s own course frames as references, links or attachments.
create increasingly robust personalization. Curriculum will become “liquid,” as in changing to suit the individual student.
Transforming the form and model of schooling using Time AI poses these shifts that require preparation.
• Students will be in self-competition in most subjects, cohorted with peers. Cohorts may be of ages two years or
A key to adopting is becoming aware of using teachers just for teaching, not oversight during independent study. Roaming time for teachers should be spent on remediation, reteaching, diagnostics or adaptations of materials. Over time, teachers adjusting materials and build separate side-paths off main courses will
more younger or older. Exceptions could be in physical education and electives.
• Student agency to follow their personal schedule will require strong leadership from homeroom leaders, who should provide good oversight and ensure daily every student is on the right subject and working per their individual schedule.
• Homeroom leadership must watch tracking panels to release students at random points for classes in singles or small groups.
• Teachers must now follow more randomized daily schedules and be prepared to teach at any lesson point at any designated open time for that subject on any day.
• Families and administrations must be aware students would be assigned courses for their ability level and not just grade-banded by their age. This could be lower grades or higher grades, and some subjects lower while others higher.
• Schools and teachers must expect that some students will progress very quickly. This is why lesson sequences must be done in advance. For single students moving rapidly, teachers can adjust to gate that student forward with oneon-one meetings rather than have the student wait for a full cohort.
• Other students may progress very slowly, requiring teachers to determine what could be done to help them whether it’s reduce the course grade level or diagnose what help would unblock the student’s learning.
• Students needing to concentrate on core subjects can have their personal schedule adjusted for more time on those with less or no time on other subjects.
• Schools may elect to do small pilots to test their population’s receptivity or use Time AI in programs that already need more teacher oversight that are otherwise mostly independent learning or all online.
• Whole school pilots should begin in lower grades where students are more likely easy to pivot to self-competition.
• Student progress monitoring has shown cohorts will try to stay together, a social dynamic. Falling behind a valuable set of relationships or working to catch up to a set the student desires, can add a new spiritedness to schooling.
• Curriculum leaders will find that a lot of pre-work needs to be done to build learning pathways — a reason to get started early and select a correct level of pilot.
• Schools will find that they need to do any required high-stakes testing based on level, not age/grade alone in every subject.
• Public relations programs to involve families, the entire administration, the teachers, and the community will lead to better success.
• Schools will need to come up with ideas for homeroom leaders to intentionally establish socialization, classroom manners, study discipline, and strong identification with the greater homeroom group.
Most educators and indeed, consumers, are aware so far only of Generative AI. That’s because it is consumer-facing and we can directly interact with it. Generative AI is only the beginning, the shallow end of the pool when compared with where the convergence of all the types of AI will take us.
Below is the answer to asking ChatGPT to “Provide a hypothesis of when generative AI converges with Time AI (auto-cohorting
calendaring that creates asynchronous pace-based learning that is still social intersected and has live teaching), recommendations AI, transformer AI, diffusion AI, autoregressive AI, generative adversarial for realistic output, flow-based simulation AI, variational auto-encoders to manipulate pathways and outcomes, neural radiance fields AI, agentic AI, theory of mind AI and computer vision AI for the form and model of schooling.”
“Here is a hypothesis projecting the convergence of generative AI with Time AI, Recommendations AI, and the full AI technology stack you listed, for the form and model of future schooling:
By the early 2030s, a full convergence of advanced AI modalities will give rise to an adaptive, sentient schooling matrix — a personalized, self-evolving educational ecosystem. This system will dynamically construct and reconfigure individualized learning pathways based on pace, preference, mastery level, social context, and emergent aptitude, orchestrated through a real-time, intelligent calendaring and cohorting engine.
2025–2027: Modular Integration Phase
• Time AI (Auto-Cohorting + Calendaring) integrates with LMS platforms for asynchronous pace-based
learning, intersecting learners based on shared milestones, performance, or schedule availability.
• Transformer AI + Recommendations AI personalize content delivery based on engagement patterns, learning styles, and semantic needs.
• Computer Vision AI starts to analyze visual learning behaviors (e.g., attention, confusion, excitement) in hybrid and virtual learning.
• Diffusion and GANs begin generating high-fidelity learning simulations and realistic virtual instructors or role-playing peers.
Outcome: Dynamic cohorting and content personalization become possible. Learners engage socially while remaining in unique timelines.
• Flow-Based Simulation AI + VAE (Variational Autoencoders) model multiple potential learning outcomes and let learners test alternate histories, scientific hypotheses, or philosophical positions.
• Neural Radiance Fields (NeRFs) are used for spatially accurate immersive education (e.g., exploring reconstructed historical events or planetary systems).
• Agentic AI enables autonomous learning assistants and instructor co-pilots to monitor progress, tutor, test, and intervene at key cognitive thresholds.
Outcome: Education becomes experiential and exploratory, with dynamically shifting forms based on
the learner’s actions, not just prewritten curriculum.
• Theory of Mind AI allows agents and systems to reason about students’ beliefs, intentions, and misunderstandings — enabling true empathy at scale.
• AI begins to simulate intrapersonal dialogue, coaching students through emotional blocks, motivation issues, or meta-cognitive hurdles.
• Full AI stack convergence leads to:
o Live human teaching seamlessly blending with AI-generated content.
o Learning objects being restructured on the fly based on pacing, comprehension, and mood.
o Dynamic social intersection through cohort-bubbles that form and dissolve across time zones and mastery bands.
Outcome: The Sentient Schooling Matrix — a holistic, intelligent, and emotionally aware educational reality — becomes viable, blending all intelligences and technologies.
Model of Schooling: Core Characteristics in 2032
1. Temporal Flexibility with Social Cohesion:
o Students navigate asynchronous paths but are never alone — they cross paths through interest-based, ability-based, and time-based nodes.
2. Continuous Realignment of Curriculum:
o Curriculum becomes liquid — reorganized daily by AI based on learner input, environmental factors, global trends, and collaborative needs.
3. AI Co-Agents for Learners and Teachers:
o Every user is partnered with an agentic AI with Theory of Mind, co-piloting progress, planning, reflection, and real-time adaptation.
4. Realism and Immersion:
o GANs, NeRFs, diffusion models, and VAE-powered simulations create deeply engaging, emotionally resonant environments that are as influential as real-world experience.
5. Equity through Personalization:
o Learning becomes not just personalized, but custom-sculpted per moment, removing the latency between comprehension gaps and assistance.
When Time AI, generative AI, and cognitionemulating AI (ToM, Agentic) converge, schooling will cease to be “delivered” and will instead become a living, sentient framework — always watching, adapting, and optimizing human development in real time, respecting both individual rhythm and collective evolution.”
Time AI is a competitive move, one that schools can make to deliver on personal pace-based learning without having courses be teacher-less and all online. It helps save teachers from overwork, especially because of mixed ability levels in classes going away, as well as student discipline issues and other burdens lessoning. Transitioning in whole or in part also gives efficiencies for the numbers of teachers and numbers of units carried, while allowing paraprofessionals to carry some of the new duties.
Time AI is the ultimate in providing for diversity, equity and inclusion because it enables total personalization rather than normalizing into same-age, same-grade and the factory pattern.
For the public: Every student on a personalized learning path with intentional live teaching, intentional social camaraderie with grade peers, and more flexible time.
For teachers: You’ll always be teaching only to small groups of students who are at the same level how you have always taught them. Lecture, discussion, any and all methods as well as use of tech modules remain the same. You’ll be teaching across the entire curriculum map of your assigned subjects/courses on-demand, but you will have so much time returned to you for individual roaming instruction to make every day highly rewarding. Student absences will also no longer matter when courses are selfpaced because a student who misses a few days is rescheduled automatically.
First, schools or districts need to work with a consultant to determine their pilot and implementation steps. In the first phase, there is a lot of just learning to understand Time AI — no differently than other AI has been.
Once a pilot gets going, the next step is purchase of licenses from a Time AI provider like Knowstory, which to date is the only provider. Contact Learning Counsel’s Innovation Services for consultants to help with implementation.
0. Purpose, Goal, Demoing Calls Paid Innovation Readiness Services
1. Choosing pilot scope
2. Edtech inventory (products, utilization)
3. Curriculum course inventory (course organized/paced)
4. Space and time use (current layout, enrolled numbers and staffing, existing schedule)
5. Cost-benefit analysis > Report
Small program cost: $5,600 | Large: $13,000
Typically it will be complete within 2-3 weeks.
0. Training Trainers on Knowstory Paid Innovation Services
1. Edtech inventory mapping (licenses, modernizations needed)
• Classroom, hallway, homeroom, other spaces for signage, calendar access
• Integrations development as needed
2. Curriculum course inventory (remodeling for time tagging, ensuring sufficient pre-creation)
3. Schedule building and prep for target deployment date
4. Space reorganization charts
5. Training roll-out based on implemented schedule and operational change
6. Implementation timeline > Completed Work Report
Scaled cost based on initial 8-week course framing and schedule $30,000$100,000 one-time cost plus new annual cost of number of Knowstory users. Ready within 1-3 months, continuing guidance for one year.
0. Annual Training Trainers on Knowstory Paid Innovation Services
1. Edtech inventory expansion
• New digital curriculum or tools
• New integrations development as needed
2. Curriculum course inventory build-out to stratify subjects, offer more electives
3. Defining new schedule, building each version
4. Space reorganization charts
5. Training roll-out based on implemented schedule and operational change
6. Implementation timeline > Completed Work Report
Scaled cost based on full annual course framing and schedule $60,000$250,000 one-time cost plus new annual cost of number of Knowstory users. Ready within 4-7 months, continuing 1-year guidance.
Eliminate back-and-forth by giving out your calendar link for bookings.
One Free Hold (open time bracketed for the AI to calendar meetings into)
Cannot create Course Frames or School Groups.
Essentials
All Member Types
$2.50 monthly or $25.00 yearly
For anyone who wants to make Holds — labeled open time which can also have minus-time breaks within it in order to control when the AI will place meetings, and to make Interest Groups.
Additional to Free level
Use Holds to tell the AI and outside requesters when to drop-in appointments and meetings.
A Hold has your label on bracketed open time which can also have minus-time breaks within it. Educators might use a label like “Parent Conference Time” from 3-4:00 pm Monday through Friday.
Cannot create Course Frames or School Groups.
Educator Accounts Only
$5.00 monthly or $50.00 yearly
For school or gig teachers creating a one-teacherschool group and inviting learners.
Educator accounts require 1-time background check fee of $20.
Additional to Free level
Create one-teacher-school group and interest groups.
Create Holds to bracket and label open time.
Create interest and event groups.
Frame your courses to be self-paced but liveintersecting on-demand by building descriptions per step and attaching auto-cohorting class meetings with any step.
Display courses in the featured products section.
Tracking panels.
Create ‘Awarded’ Cred from your Group (Grades/Marks, Certificates.)
Create a master calendar for your schedule.
Assign study schedules to members.
Use academic standards for all U.S. States and Canada in Course Frames.
Use bookmarking for planning.
School Premium
Educator Accounts Only
$2.25/seat monthly (Minimum 50)
Scale declines with number of seats.
For schools using complex master schedules. School or district, college or university must be authenticated to invite member learners.
Additional to Teacher level
Claim or create school and districts groups.
Associate schools under districts for collective analytics.
Authentication to invite learners.
Frame your courses to be self-paced but liveintersecting on-demand by building descriptions per step and attaching auto-cohorting class meetings.
Add academic standards, attachments, links and quizzes to steps.
Display courses in the featured products section.
Tracking panels.
Create Validated Cred badges from your Group – Social, Extra-Curricular, Grades/Marks, Certificates, Degrees and Diplomas.
Create a master calendar for your schedule.
Assign study schedules to members.
Use academic standards for all U.S. States and Canada in Course Frames.
Use bookmarking for planning.
Instant transfer of courses and associated class meetings to substitutes, tutors and new teachers.
Knowstory is education’s AI calendar social ecosystem platform. It offers free and premium calendaring services to make social intersection and live teaching more efficient. It has supporting closed group creation for school, district, company, interest and event groups. It also offers a featured marketplace function presenting ad tiles for courses or edtech, as well as bookmarking, credentialing/badging, academic standards library, dynamic inventory management, and meeting tracking panels. Knowstory, P.O. Box 652, Benton, AR 72018-0652 | 888-611-7709 | https://knowstory.com/contact-us
Sources:
(Additional to Learning Counsel News Media & Research original survey sources.)
Google Search – “Time AI Schooling”
ChatGPT – Time AI Schooling vs. Traditional Schooling https://ensemblelearning.org/ability-grouping/ https://www.proquest.com/docview/1797962307?pqorigsite=gscholar&fromopenview=true&sourcetype=Dissertations%20 &%20Theses
Time AI for Schools https://issuu.com/learningcounsel/docs/time_ai_for_schools-flipbook-2
What’s really the Future of Education? https://thelearningcounsel.com/articles/whats-really-the-future-of-education-school-aicalendaring/
K12 Horizon Room mic session featuring Karla Burkholder, Director of Technology, Schertz Cibolo Universal City ISD https:// thelearningcounsel.com/articles/video-articles/what-are-hybrid-logistics-and-hyflex-models-for-schools/
Video Predictions Panel with Senator Howard Stephenson, UT, Anthony Padrnos, Exec. Dir. Of Technology, Osseao Area Schools, MN, and Andrew Kim, Dir. Of research Programs and Outreach, Texas Behavioral Science and Policy Institute https:// thelearningcounsel.com/past-events/predictions-panel-open-mic/
Podcast with Drew Hinds, Exec. Dir. Of Technology Services, Huntington beach City School District, CA https:// thelearningcounsel.com/podcast/conversation-about-hybrid-logistics/
Video with Felicia Rattray, Founder and President, Permission to Succeed Education Center https://thelearningcounsel.com/ articles/video-articles/what-you-should-know-about-the-hybrid-logistics-project/
Video of Chris Knutsen, Superintendent, Florence Unified School District, AZ https://thelearningcounsel.com/past-events/ phoenix-az-educator-guest-keynote-becoming-human-alongside-tech/
Video Panel Discussion with Matt Yeager, Asst. Superintendent of Technology, Garland ISD, TX; Dr. Robin Stout, Sr. Director, Digital Learning Department, Carrollton-Farmers Branch ISD, TX; Dr. Mike Mattingly, Associate Superintendent of Curriculum & Instruction, Denton ISD, TX https://thelearningcounsel.com/past-events/learning-leadership-symposium-dallas-txadministrator-panel/
Video Panel Discussion with Shana Loring, Executive Director of Teaching & Learning and Secondary Education, Little Rock School District, AR; Dr. Jeremy S. Owoh, Superintendent, Jacksonville North Pulaski School District, AR; Dr. Eric Saunders, Asst. Superintendent for District Support, Arkansas Department of Education; Dr. Torrye Hooper, Deputy Superintendent, North Little Rock School District, AR https://thelearningcounsel.com/past-events/learning-leadership-symposium-little-rockadministrator-panel/
Video Panel Discussion with Dr. Royd Darrington, Assistant Superintendent, Juab SD, UT; Dr. Ryan Hansen, Director of Digital Learning, Davis SD, UT: Dr. Jill Gildea, Superintendent, Park City SD, UT; https://thelearningcounsel.com/articles/video-articles/ salt-lake-city-educator-panel-leading-the-learning-matrix/ School Logistics for New Models https://thelearningcounsel.com/articles/school-logistics-for-new-models/
The Case for Hybrid Logistics to Solve Equity – Part 1 https://thelearningcounsel.com/articles/for-vendors/case-hybrid-logisticssolve-inequity/
The Case for Hybrid Logistics to Solve Equity – Part 2 https://thelearningcounsel.com/articles/for-vendors/case-hybridlogistics-solve-inequity-part-2-letting-humans-be-human-0/ Responses to School Pressures https://thelearningcounsel.com/articles/responses-to-school-pressures/ A Schooling Metamorphosis that’s not What You Think https://thelearningcounsel.com/articles/a-schooling-metamorphosisthats-not-what-you-think/