Memory decays. Not randomly, along a precise exponential curve understood for over a century. Almost no classroom has changed in response. Cognivia treats learning as something you can measure: an instrument that reads how each mind forms, holds, and loses knowledge, then adapts to what it finds. Proven first in India's hardest classrooms, and built to generalise.
Percentages tell you what was right on a given day. Percentiles tell you where you ranked against others on that same day. Neither tells you anything about how memory forms, how knowledge consolidates, or how a student thinks when no one is watching. The Cognitive Passport is a first attempt at answering those questions with evidence gathered continuously as a student works, not a single impression at term's end. It won't replace every form of assessment, but it may be a start at treating learning as the individual, biological process it is.
"What did you score?"
A number. A rank. An average of averages, designed to sort, not to understand. It compresses everything you are as a learner into a signal optimised for comparison, not growth.
"How does your mind actually work?"
A profile built from your own session data, that evolves with you and could one day tell a university, an employer, or a scholarship committee something no transcript ever could: the shape of your cognition.
Getting an answer right or wrong is the shallowest thing about learning. Every session, Cognivia reads how your specific mind is working, and adapts to it.
A wrong answer is never simply wrong. Cognivia tells apart forgetting, a half-formed grasp, and a confident misconception, and responds to each one differently.
Each idea gets its own curve. The system learns how fast you personally lose it, then reschedules that idea on its own timeline, instead of one fixed interval applied to everyone.
Answer with conviction and still get it wrong, and Cognivia flags it, surfacing the illusions of knowing that quietly fall apart under pressure.
By watching how quickly you recall and how stable that memory becomes, it separates knowledge that will last from an answer you happened to get right.
Everything above becomes a portable profile of how your mind learns, one that travels with the student, where a single score never could.
In any browser, on a shared classroom computer or a basic phone, with no install and little bandwidth. Adaptive learning without the infrastructure it usually demands.
Over a century ago, Hermann Ebbinghaus demonstrated what is now called the Forgetting Curve: without deliberate reinforcement, most newly learned information disappears within days. This is not new knowledge. What is surprising is how little it has changed classrooms.
In most learning environments, especially those with limited resources, the structure remains unchanged: information is delivered, briefly practiced, tested, and then forgotten. The system assumes learning has happened, but memory rarely agrees.
Cognivia began with a single question: what if we designed learning around how memory actually consolidates, not around how teaching is conveniently organised?
The world teaches roughly 1.5 billion students every year. A tiny fraction reach anything resembling adaptive instruction. The classroom is not going to be rebuilt around each individual learner, not in any country, not in any decade soon. What can be rebuilt is the small block of revision each learner does on their own. Cognivia takes one narrow, falsifiable question: does adapting the schedule to the type of error a learner makes improve retention? Because if the answer is yes, the answer is portable: it scales to a shared device, it does not need a school to approve it.
If the trial comes back null, the repository and the data stay public, so the next person with a better hypothesis can start from there.
Memory is not permanent by default. Newly learned information begins fading within hours unless it is revisited at precisely the right moments. A century of cognitive science has documented exactly how and when this happens. Almost none of it has reached the classrooms where it could make the most difference. That gap is what this work is about.
Distributing practice over increasing intervals exploits the spacing effect. Each successful retrieval strengthens the memory trace and extends the optimal review window. Cognivia implements the full adaptive review algorithm: a personalised ease factor and interval per card per student, updated after every response using a 1–5 quality rating. Struggling items return the next day; mastered items in weeks.
The review algorithm ease factor adjusts continuously per student per card, creating a schedule that is individualized, not uniform.
Effect size: high · d ≈ 0.6–0.8 across populations Lab → field test: rural classrooms, shared devices, no prior exposureForcing reconstruction of an answer from memory, rather than passive re-exposure, produces substantially deeper encoding. Roediger and Karpicke (2006) found retrieval practice outperformed re-study by ~50% on one-week delayed tests, even when re-study groups spent more total time. The generation effort is the encoding event.
Every Cognivia session is structured retrieval. The correct answer is revealed only after the student attempts a response. No passive exposure mode exists.
Effect size: high · 50% delayed recall advantage over re-study Lab → field test: cued retrieval practice in resource-constrained settingsThere is a well-documented gap between what feels like effective studying and what actually produces lasting memory. Re-reading notes feels productive because the material looks familiar. But familiarity is not the same as genuine recall. The methods that feel harder in the moment, trying to remember without looking, testing yourself before you feel ready, reviewing material that has already started to slip away, consistently produce better long-term retention because of that difficulty, not in spite of it.
This section describes the study design, what it is testing, and what it is not. It was written before data collection began, and the analysis plan was fixed at that point. Results are reported as they are, not selected for the ones that look best.
Classrooms everywhere face a structural learning problem that predates technology, curriculum, and teacher quality: the way students review material actively works against how human memory consolidates. Standard revision, re-reading notes, oral repetition, massed practice before tests, is near-universal in classrooms of every kind. According to five decades of cognitive research, it is also among the least effective methods available.
Cognivia is a student-led research initiative that attempts to close this gap. It builds on two of the most robustly replicated findings in cognitive science, spaced repetition and active recall, and translates them into a lightweight tool that runs on a single shared classroom computer. The tool logs every student response, timestamps every session, and captures the data needed to measure whether the intervention actually changes how much students retain a week after instruction.
The core question is direct: does a structured, algorithmic review system produce measurably better retention one week after instruction than the traditional method it replaces? The study is ongoing. This page reports the methodology, the live data tool, and, as evidence becomes available, the results, including any findings that do not support the hypothesis.
Cognivia is not a classroom tool. It is an instrument for how memory works, made for anyone who needs to learn something and hold onto it. If you are thirteen or older, it was built for you.
Memory doesn't discriminate by age, curriculum, or institution. The same cognitive machinery, the same forgetting curves, the same retrieval benefits, operates in a schoolchild reviewing science and a postgraduate preparing for a high-stakes exam.
Cognivia was designed with resource constraints in mind, nothing to install, no setup, a single shared computer. But those constraints turned out to be advantages. A system that works for the hardest case works for every case.
School students, university undergraduates, self-directed learners, competitive exam aspirants, researchers, the underlying mechanism is the same. Cognivia adapts to the learner, not the other way around.
Enrol with a first name, age, subject, and an email. We send a one-time sign-in link, so there is no password to set. After that you return with your Cognivia ID. (The demo above needs none of this.)
Passwordless sign-inA unique identifier is generated for every student. It links your session data, genome profile, and review history across all study sessions.
Persistent across sessionsPick your class level and subject. Questions are drawn from Cognivia’s reviewed bank, every item tagged by topic and Bloom level, so what you are asked matches where you actually are.
Reviewed question bankQuestions are drawn in order of memory urgency, what you are most likely to forget is reviewed first. The schedule updates after every response using the SM-2 algorithm with personalised ease factors.
SM-2 spaced repetitionEach question presents four options. Selection and response time are both logged. Distractors are engineered as adjacent concepts, so a wrong pick reveals which misconception is active, not just that one exists. Every mechanism maps to a published cognitive science finding.
Testing effect · Spacing · Error diagnosticsEvery attempt is recorded: timestamp, response time, the confidence you rated, and the error type behind a wrong answer. Those feed your seven Learning Genome variables, and your Cognitive Passport is recomputed after every session.
Per-attempt loggingRuns on any shared classroom computer. Nothing to install, nothing to configure. Every feature maps directly to a mechanism in the cognitive science literature, nothing else.
Question → four options → selection + response time → feedback. Distractors are engineered as adjacent concepts, so a wrong pick reveals which misconception is active. Latency separates fast recall from cognitive load.
Cognitive principle: testing effect + error-type diagnosisQuality ratings 1–5, not binary. Personalised ease factor and review interval per question per student. Faithfully implements Wozniak (1990). No approximations.
Cognitive principle: spacing effectEvery response carries a 1–5 confidence rating alongside it. The gap between stated confidence and actual accuracy is what exposes a confident error, the kind that survives revision because the student never doubts it.
Cognitive principle: metacognitive calibrationEvery attempt logged: timestamp, response time, quality rating, ease factor, next review interval, similarity ratio. 16 columns. Streamed live to your Passport. No aggregation.
Empirical principle: trial-level longitudinal trackingAccuracy trends, retention curves, and quality distributions render from your live attempt log instantly, no upload, no waiting.
Cognitive principle: metacognitive monitoringBrowser-based. Nothing to install, nothing to configure. Designed to run on a shared classroom computer, a laptop, or a phone, anywhere.
Deployment principle: access is a prerequisite for any interventionA brief guide for anyone running Cognivia for the first time, student, teacher, or reviewer.
Most tools tell you your score.
Cognivia tells you how your mind is learning.
Behind every session is a cognitive engine tracking memory strength, decay rate, and thinking speed
not to grade students, but to understand them.
Every layer both consumes and produces structured data. The loop closes at session end and reopens smarter at the next session. SM-2 is the foundation, every layer above it modulates, never replaces it.
Every adaptive system applies one algorithm to all learners. Cognivia applies a personal coefficient, derived from 6 live behavioral signals, on top of SM-2. A SLOW-forgetter and FAST-forgetter receive structurally different schedules even when they score identically.
Final interval = SM-2 × f(λ, RS, CE, FSI, streak, memory_state)Duolingo, Khan Academy, BYJU's, all defer adaptation to the next session. Cognivia detects FALSE_CONFIDENCE during the session and acts: requeue, interval compression, quality override. The intervention window is minutes, not 24 hours.
CognitiveDiagnostic → within-session requeue → live memory state chip in UICognivia doesn't produce a teacher dashboard as a secondary feature. It produces a pre-publication research dataset as a first-class output. Every classroom running Cognivia is simultaneously generating data toward peer-reviewed hypotheses, without any manual data entry by anyone.
H1: d ≥ 0.3 adapted vs SM-2 · H2: λ clusters into 3 archetypes (ANOVA) · Target: Computers & Education IF≈11Most study tools tell you what you got wrong. Cognivia pays attention to something harder: why you got it wrong, and when you're about to forget it. As you answer, it tells apart plain forgetting, half-knowing, and being confidently wrong, and it works out how fast each idea fades for you in particular. Then it brings each thing back right before you'd lose it, instead of on a fixed schedule that ignores how your memory actually works. Everything below is how that gets measured, in detail. You never need any of it to use the app.
Most learning systems treat students as if they learn in the same way
at roughly the same pace, with the same patterns of forgetting.
In practice, this is rarely true.
Instead of measuring learning through grades or progress bars, Cognivia models each student through a set of evolving variables, updated after every session, every response, every hesitation.
Together, these variables form a dynamic profile: a Learning Genome, a continuously updated representation of how a student’s memory actually behaves. Not how fast they finish, or what they scored. How their memory consolidates, decays, and retrieves knowledge over time.
Most systems ask only one thing of a wrong answer, whether it was right. Everything follows from that binary: uniform review schedules, passive re-reading that still counts as revision, and no way to tell a student who simply forgot from one who never understood in the first place. Cognivia asks harder questions, how stable is this knowledge, how fast is it decaying, is the student retrieving it or guessing, and does their confidence line up with reality. Each review, each response, each hesitation becomes a signal, and the system adapts not to the content but to the structure of the learner’s memory itself.
Several things happen inside every Cognivia session, all running automatically in the background. Each one is grounded in research on how memory works. Together they form a complete loop: the system learns how each student remembers, schedules each review at the right moment, detects when a student’s understanding is wrong rather than just incomplete, and records everything for research. It runs in the browser on a single low-cost computer, and a full session takes under twenty minutes.
These seven formulas and their cutoffs are Cognivia's own operationalisations, not results borrowed from the papers cited across this site. The decay form draws on Ebbinghaus and on Wixted & Carpenter (2007); the storage-versus-retrieval distinction on Bjork & Bjork (1992); the confidence and error-type signals on the metacognition literature. But the exact expressions, the smoothing coefficients (α = 0.75, α = 0.30), and every threshold (λ ≤ 0.7, CE > 0.85, FSI > 0.20, PDS > 0.6) are engineering defaults we chose, seeded from the n = 5 pilot and the literature. They are how the system computes today, not validated constants, and not claims any cited author has made. Calibrating these thresholds against a real student population is one of the things the trial is for.
How fast a topic fades for you. A student at 0.5 remembers twice as long as one at 1.0, even if they score the same today, so the two need different review schedules. We estimate it from your own answers and sharpen it with every review.
How reliably you can pull a fact back right now. You can score well today but still be weak here if you crammed yesterday, so it separates real mastery from a recent glance.
How long your recall takes, next to others on the same difficulty. Fast and correct means it is becoming automatic; slow and wrong points to a genuine gap, not just hesitation.
The gap between how confident you are and how right you are, read from the 1–5 rating on each answer. Confident and repeatedly wrong is not ignorance, it is a misconception, and it needs correcting rather than more review.
How steadily your memory settles after the first few reviews. High means recall is consistent and your projections are reliable; low means you might score 90% one day and 50% the next on the same material.
How much your accuracy drops from the first third of a session to the last. A high value means the session should be shorter, not that you are learning less.
How much of your errors are the same type. A high value means you are failing in one specific way, not at random, so the fix is targeted correction, not more repetition.
The genome feeds a six-state memory classifier. State determines how the SM-2 interval is overridden, not replaced. The base algorithm remains intact; the genome modulates it.
After each session the engine forecasts how likely you are to recall every concept on a future date. When that date arrives, it compares the forecast to what you actually remembered and records its own error. Most tools grade the student. This one is graded too.
The metric is the Brier score, the squared gap between a forecast and what actually happened, where 0 is a perfect prediction and 0.25 is a coin toss. If the engine says you have an 80% chance of recalling a concept and you do, that forecast scores 0.04; if it says 80% and you forget, it scores 0.64. A session's score is the average across all its forecasts. The pre-registered bar is simple: beat a naive baseline that just predicts the class's average recall rate. If a per-student model can't beat that, it isn't earning its complexity. The live number stays blank on purpose until enough forecasts have been verified, this is the one figure the page won't show before it's real.
After enough sessions, Cognivia knows things about your memory that you don't. Your optimal study time. Which subjects you forget fastest. Whether your confidence tracks your actual knowledge. The Cognitive Passport surfaces all of it, in one page, updated after every session.
A grade tells you what you got right.
The Cognitive Passport tells you who you are as a learner.
Every score you've ever received collapses an enormously complex cognitive process, weeks of encoding, forgetting, retrieval, consolidation, into a single number. The Passport doesn't. It captures the full geometry of how you learn, so that for the first time, a document can exist that reflects not your performance on one day, but the shape of your mind across all of them.
The Cognitive Passport is recomputed after every session, reflecting changes in your Genome variables as they happen. Unlike any transcript or marksheet, it captures how you learn, not just whether you passed.
A transcript records performance on a single day, in a single room, under artificial pressure, then averages it into a number that follows you for life, none of which reflects how you think. The Passport records what that number hides: how quickly you forget, how fast you retrieve, how closely your confidence tracks your accuracy, the patterns in how you consolidate, even the times of day you encode best, the shape of how your memory works.
Submitted to a university, a document like that could tell admissions something a GPA never can, that a student doesn't just know the material but understands how their own mind works. And where coaching and tutors are unequally distributed, it levels the field: it is evidence of learning rather than purchased preparation.
Cognivia is not making promises. It is being tested under the specific conditions it was built for, Class 6 to 8 students in a low-resource setting, studying on a shared classroom computer, with no prior exposure to structured review methods. Those conditions are very different from a university laboratory, and whether the science actually holds in a setting like this is exactly what we are trying to find out.
“Does error-taxonomy-aware adaptive learning, adapting each review to the type of error a student makes, produce greater 2-week delayed retention than correctness-only adaptive learning and standard classroom instruction, in Class 6–8 students in rural government schools?”
| Phase | Week | Standard instruction (C) | Cognivia arms (A & B) | Measurement |
|---|---|---|---|---|
| Baseline | Week 0 | 30-item NCERT pre-test | Same 30-item pre-test | Prior knowledge score |
| Instruction | Weeks 1–6 | Standard NCERT teaching | 18 Cognivia sessions (3×/week, 45 min) | Per-attempt logs streamed to Passport |
| Post-Test | Week 6 | Same instrument | Same instrument | Immediate learning gain |
| Delayed Test | Week 8 | Same test; no review since post-test | Same test; no review since post-test | Primary: 2-week delayed retention |
“Group A (error-taxonomy-aware) will show higher 2-week delayed retention than Group C (standard instruction) and Group B (correctness-only), controlling for pre-test (one-way ANCOVA; planned contrasts, Bonferroni α = .025; Cohen’s d).”
“The Confusion Fingerprint Index (CFI-CF), computed from early sessions, will predict 2-week retention above and beyond overall accuracy, validating it as an individual-difference construct (hierarchical regression; F-change).”
The trial randomly assigns students to three arms. Two use Cognivia, one adapting to the type of error a student makes, one adapting to correctness alone; the third is standard NCERT classroom instruction. All three cover the same content, in the same schools, over the same six weeks. The charts below show what the data records.
The trial has a closing date. If the scheduling result comes back null, that one claim is reported as null and set aside; the data stays public, and the measurement layer beneath it, the Genome and the Passport, carries on as a diagnostic in its own right.
Every claim here traces to a public, citable record, pre-registered before data, with preprints and code openly available.
Four hypotheses were registered on OSF before the trial began. H1–H3 are confirmatory, evaluated with Bonferroni-corrected planned contrasts (α = .025). H4 is explicitly exploratory, reported with effect sizes but never used to confirm or disconfirm the primary conclusions. Keeping the confirmatory set small is deliberate: piling on corrected tests at this sample size would be a formality, not an analysis.
Power, stated plainly. This is a pilot trial powered for effect-size estimation, not confirmatory certainty. With n = 30 per arm, the primary contrast (A vs C) has about 76% power to detect the conservatively assumed effect (d = 0.40) under a Bonferroni-corrected ANCOVA. A null result here narrows the plausible effect; it does not rule out a smaller one, and this page will not claim otherwise. The adequately powered confirmatory trial is a later, larger study.
These are the real results from the pilot study, 548 questions answered, 36 sessions, 5 students across 7 subjects, from February to April 2026 in India. They were collected on the earlier desktop prototype, not on this web platform, and are reported here as a feasibility check rather than as trial evidence. Every number was computed directly from that pilot log. If something did not work, it is reported here the same way as something that did.
This section compares the same students against themselves, their performance in the first half of the study against their performance in the second half. Both groups are the same people using the same tool. The only thing that changed is time, and how many times they had reviewed each piece of material. If the numbers improve, it is because practice and spaced repetition actually worked.
Every study has limits. Stating them clearly is not weakness; it is the only way the results carry weight. The limitations below are not buried in footnotes. They are the conditions under which anything in this study can be trusted.
Decades of research have shown that spaced practice and retrieval testing improve memory. Almost all of that research was done with university students sitting at individual computers in quiet rooms. Whether the same effects appear in a resource-limited school, with many students sharing one device, irregular attendance, and teachers under pressure to cover a fixed syllabus, is an open question. We do not assume the answer.
There may be subjects, age groups, or classroom conditions where these methods simply do not work as well. A result that shows no difference between the two groups would be reported here with the same weight as a positive finding.
This pilot involved five students from one school. That is too small a group to make any general claim about how students learn everywhere, or even in all classrooms like it. What it can do is establish whether the method is worth testing more rigorously, with a larger, properly randomized group spanning different countries and contexts.
The students who completed fewer sessions have less data behind their individual profiles. Those profiles are less reliable and should not be used to make strong predictions about how those specific students will perform.
Cognivia tracks seven variables about each student’s learning behaviour and builds a profile from them. Whether that profile can reliably predict who will struggle or identify the best time to intervene has not yet been proven. This study begins to generate the kind of data needed to test that.
Response speed is recorded and used as a rough measure of how fluently a student can recall information. But on a shared device, speed is also affected by how fast the computer responds on a given day. This is a real confound that cannot be fully eliminated in this setting, and it is factored into how we interpret those numbers.
Both groups share the same teacher, subject, and week, which controls for a lot. What it does not control for is enthusiasm: a teacher who knows one class is using new technology may, without meaning to, teach it with more energy or attention. That expectancy effect would inflate the result independently of the tool. Full blinding is impossible here, so we treat it as a live threat to validity, plan a teacher-behaviour check, and read the two-arm technology comparison (A versus B, where both classes use Cognivia) as the cleaner test precisely because it holds that enthusiasm roughly constant.
In the classrooms we work in, irregular attendance is a fact, not an edge case, so the analysis is pre-specified for it rather than surprised by it. The primary analysis is intention-to-treat: a student is analysed in the arm they were assigned to, whether or not they completed every session. The number of sessions attended is recorded as a dosage covariate. Missing post-test or retention scores are handled by a pre-registered rule (multiple imputation under a missing-at-random assumption, with a complete-case sensitivity analysis reported alongside), and attrition is reported per arm in a CONSORT flow diagram. If dropout is heavy or unbalanced between arms, that is reported as a limitation on the result, not smoothed over.
None of what follows is certain. Each possibility depends on the results being real, the methodology holding up under review, and the work being replicated at a larger scale. Each depends on the evidence holding up at a larger scale.
The study design, the data format, and the analysis approach are all documented and publicly available. A teacher in a different city, working with different students and a different subject, could run the same experiment and compare the results. The value of this kind of work multiplies with each independent replication.
There are very few long-term, question-level datasets tracking how real students in low-resource schools actually forget and retain material over time. If this study scales, even modestly, it begins to fill a gap in the research that no single experiment can address alone.
The methodology, results, and limitations are being prepared for submission to a peer-reviewed journal. A negative result will be submitted alongside a positive one. The credibility of the work depends on being willing to publish both.
The dashboard already shows which students are struggling with which subjects, and how their performance is changing week by week. Developing that into something a teacher can consult at the start of each lesson, without any technical knowledge, is a natural next step.
If the results hold up and replication supports them, Cognivia can be deployed in additional classrooms without any additional infrastructure. It runs on a single computer, with nothing to install or configure. The conditions that make it practical in a resource-limited school are the same ones that make it scalable, globally.
Cognivia is one tool. The more durable outcome is the evidence it collects about how students in underfunded schools actually learn and forget, evidence that does not currently exist in the research literature. That is what has the potential to influence how teaching is approached at a systemic level, in ways no single application ever could.
Across classrooms around the world, the same pattern recurs: students work hard, revise consistently, and still forget most of what they reviewed a week later. The problem was never effort or ability, it was the method.
Nothing about the way these students were being asked to revise was aligned with how memory actually works. The science of spacing, retrieval, and forgetting curves has existed for over a hundred years. It had simply never reached the classrooms where the need was most acute.
So I built Cognivia. It is not a quiz app or a learning platform; it is an instrument that models how each brain forgets and steps in at precisely the right moment, whether that person is in a rural school in India, a library in Lagos, or a bedroom in São Paulo. It runs on a single shared device, with nothing to install, because the classrooms that need it most cannot depend on heavy infrastructure.
A detour worth admitting: the first version was wrong. I spent months building teacher dashboards before I had anything worth putting on them, I had confused a product that looked serious with one that measured something. The measurement had to come first. Most of what Cognivia is now came out of scrapping that early build and starting from the memory science instead of the interface.
I locked the study design before collecting any data, and I report the analysis as it comes, including the results that contradict what I predicted. A null finding gets published with the same weight as a positive one. I would rather know the truth than protect the idea.
I did not start this to launch a product. I started it because a question would not leave me alone, and I built it where that question is hardest to answer. If it works, there is plenty to build on top of it, but the science has to come first, or none of the rest is worth trusting.
The shortest version of what Cognivia is for: The world teaches roughly 1.5 billion students in school every year. A tiny fraction of them are reached by anything resembling adaptive instruction. The classroom is not going to be rebuilt around each individual learner, not in any country, not in any decade I will be alive for. What can be rebuilt is the few minutes of revision each of those learners does on their own.
So this study asks a narrow question, does adapting the schedule to the type of error a learner makes improve retention? because if the answer is yes, the answer is portable. It runs on a phone or a shared classroom computer, and needs no teacher to install it and no school to approve it. It is, in the most literal sense, the only intervention I know how to build alone, from Kanpur, that could matter to a learner I will never meet.
This trial tests one falsifiable claim, that adapting to the type of error beats plain spacing. I report the result either way, with the data and protocol public. A null would mean that specific claim was wrong, not that the work ends, because the measurement layer beneath the scheduling, the Genome and the Passport, is useful as a diagnostic on its own, whatever the scheduling result.
The protocol, the analysis, and this trial's data are public, that is how the science earns trust and how the mission stays honest. What is not open is the part that compounds: the trained models. Every learner sharpens the per-topic forgetting priors, the distractor banks tuned to real confusion patterns, and the cold-start models that let a new student be understood in a handful of questions. Anyone can read the methodology; the calibrated dataset and the models built on it improve the more the system is used. Open science and something worth building are not in tension here, they are separated on purpose.
To settle it plainly: the code, the trained models, and the calibrated dataset are independently owned. They are not the output of any institution, lab, or employer, and no one else holds a claim on them. The research is run independently and kept separate from anything built on top of it, so there is no tangled ownership to unwind later. Clean from the start.
Each of these is stronger than Cognivia somewhere, in distribution, polish, or content. What none of them do is diagnose the type of error and schedule against a per-student forgetting model on low-end hardware, which is the position Cognivia is built to own.
The engine is subject-agnostic; the bottleneck is the question bank. Every subject needs items authored, and every wrong option tagged to the specific misconception it stands for, because that tagging is what lets the system read error type instead of just right-or-wrong. It is slow, expert work. Today the bank is CBSE maths and science for Classes 6–8, written against the NCERT sequence and checked by practising teachers before anything goes live. Scaling is a pipeline, not a prompt: draft from the curriculum, tag the misconceptions, teacher review, pilot, release. And it has to be rebuilt per board, since CBSE, ICSE, state boards, and international curricula order topics differently. I would rather own one board deeply than claim “any subject, anywhere” I cannot yet stand behind. As the catalogue widens past what I can author alone, each domain gets its own subject-matter reviewer, a chemistry item and a history item need different expertise, and nothing reaches students without that domain sign-off. Content quality cannot rest on one generalist once the subject list is long.
A difficulty label is a hypothesis until the data backs it up. Right now item difficulty is assigned from Bloom level and expert judgement, and each distractor is designed to catch a specific misconception, but neither claim has been validated against real response patterns yet, and I won't pretend the difficulty engine has proven itself. The honest test is empirical: does a question tagged “hard” actually get missed more often, and does each wrong option get chosen by the students who hold the misconception it was built for, rather than at random? That is item-difficulty calibration (item-response theory) and distractor-functioning analysis, and it needs real answers at scale to run. The trial is what produces them. Until then the difficulty engine is a well-motivated design, not a validated instrument, and a page this quantified everywhere else should say so here.
A twelve-year-old on a shared classroom computer should not be self-registering. The flow is meant to be teacher-first: the teacher creates the class, adds the roster in one action, assigns a subject to everyone at once, and gets one class view of who is struggling and on which error type, not thirty separate sign-ups. I will be straight about the gap: today’s live path is still per-student registration, and the teacher-held roster with a class dashboard is the next thing I am building, not something already shipped. The school case only works when the teacher holds the roster, so that is where the product goes next.
If a Passport is ever going to sit next to a GPA, the hard part is not the model, it is trust: stopping a friend from doing someone's sessions, or gaming the confidence ratings. That means identity verification, anomaly detection on response patterns, and an audit trail, the standard infrastructure any credential needs. It is not built yet, and I will not pretend it is. It is the gate between a useful diagnostic and an accepted credential, and it sits on the roadmap deliberately.
And even solved, trust is only the technical half. Credentialing is a gatekept, institution-controlled space: boards, universities, and accreditation bodies do not adopt a new format because it is good, they adopt it slowly, through policy, pilots, and politics. Treating “replace the transcript” as a product-design problem alone is the naive version. The honest path is narrow and long, start as a supplement a student volunteers alongside a transcript, earn predictive-validity evidence a few institutions will actually look at, and let adoption follow proof rather than ambition. I would rather say that plainly than pretend an incumbent credential falls to a better UI.
A document meant to span years has to outlive whoever built it. So the rule is simple: the learner and their family own their Passport, not us. You can export the full record at any time in an open, machine-readable format, not a screenshot, the underlying data. And I have said elsewhere on this page that if the trial comes back null the scheduling claim is set aside; the same honesty applies to the data. If Cognivia ever changes hands, pivots, or winds down, personal records are returned to families and then deleted, the anonymised research set stays open, and a family's years of data are never sold off. A credential you cannot take with you is not a credential; it is a hostage.
A project that leans this hard on scientific integrity should be just as disciplined about how it holds attention. So, plainly: no guilt-trip streaks, no loss-aversion notifications engineered to make a child anxious about a broken chain, no manufactured urgency, no dark patterns tuned to maximise time-on-app. Those tactics lift engagement metrics and corrode the thing we claim to serve. Cognivia should be able to be put down. What replaces the tricks is the actual mechanism: a reminder arrives when the memory model says a review is due, because that is when it helps, not because a growth loop needs a daily open. The measure of success is what a student remembers weeks later, not how many minutes they spent in the app today.
Here is the real retention problem, named honestly: the deep signal, a stable forgetting curve, a confident read on how a mind learns, only emerges after dozens of sessions. So a twelve-year-old on day three has earned almost nothing from the part that makes Cognivia special, and “come back in a month and it gets good” is how you lose them. The answer is not a streak or a badge; it is to make each early session pay off now, without faking it. Every session ends with something true and immediate: the concepts you locked in today, the ones scheduled to come back and when, and one thing the system noticed about how you answered. From the very first session the Passport isn't blank, it fills in live, one variable at a time, so the student watches their own portrait being drawn rather than staring at empty gauges. And the only reason to return tomorrow is an honest one: these specific cards are due, because that is when recalling them actually works. Early wins that are real, not manufactured, that is the bridge across the first two weeks.
I have built this alone so far. The first hires are the gaps I can already name: machine-learning and data engineering to run the calibration at scale, someone who has sold into schools and coaching institutes, and curriculum design to extend past the current subjects. Advisors in learning science and edtech distribution come before any of that.
The largest version of Cognivia is probably not one more learning app. It is the measurement layer other products call: an API any quiz app, tutor, or LMS can use to get error-type classification and forgetting-curve calibration for its own users, the way Stripe became infrastructure for payments rather than another store. That position owns the cross-platform data and is the hardest thing to copy. It is years out, and it is a direction, not a promise.
The India story is the strongest, most real thing here, and it stays front and centre, Kanpur, government schools, NEET and JEE and CBSE, a founder from exactly the context the product serves. But a visitor from Nairobi or São Paulo should read this and know the truth: it is proven in India and built to generalize, not already global. The universal part is the cognitive science, forgetting curves and retrieval practice work in every language. Almost everything else is real, hard, multi-year work I would rather name than gloss:
So the honest positioning is not “global today.” It is: prove the mechanism where I have the deepest context and the most authentic right to build, then generalise deliberately, one region’s content, calibration, compliance, and proof at a time. And the second market is a deliberate choice, not “everywhere”, the working plan names Nigeria (WAEC secondary), precisely because a different curriculum, a data-cost-sensitive network, and a different regulator (NDPA) force the platform to genuinely generalise rather than paper over the gap, while English-medium holds one variable steady. The full sequence is written down, not left to a hand-wave.
For students, parents, and teachers. If your question isn't here, write to us, the address is at the bottom.
Yes. Cognivia is free for students, and deliberately so for low-income learners, the whole point is to reach classrooms that can't pay. The research protocol, analysis, and trial data are open too. If a paid tier ever exists, it will be for institutions, never for a child trying to study.
Only what the learning needs: which answers were right, how long they took, and the confidence rating on each one. Records are pseudonymised (a code, no names), stored on access-controlled servers, never sold, and never used for advertising. For anyone under 18, nothing is processed until a parent or guardian gives verifiable consent, as India's DPDP Act requires. Full detail is in the Privacy Policy.
You can withdraw at any time, no reason needed. Your identifiable record is deleted within 30 days, and you can ask to see or remove your data whenever you like. There is no penalty and nothing follows the student afterward.
That's the design target. Cognivia runs in the browser with nothing to install, the pages are light, and a session is a handful of small requests rather than video or large downloads, so it holds up on a basic phone and a weak connection. A full offline mode that caches a session and syncs later is on the roadmap; I'd rather build it properly than claim it before it's real.
Nothing is lost. Every answer is saved the instant it's submitted, not at the end, so if the connection drops on question seven, the first six are already stored. The session stays open, and when the student comes back it picks up where they left off instead of restarting. For the research, a partial session is still a valid, resumable session, so a flaky rural connection never silently costs a data point.
Yes. Cognivia is an installable web app: on Android or a laptop you'll see an “Add” prompt, and on an iPhone or iPad you add it from Share → “Add to Home Screen.” It then opens from an icon in full screen, no browser bar, no URL to remember, which matters a lot when the device is shared and the user is twelve. Nothing to download from a store, and it stays light.
Not a wall of empty gauges. A brand-new account doesn't show a broken-looking dashboard of blank variables; it shows a clear first step, “Your passport opens after your first session”, and then fills in live as you answer, one variable at a time. The system is honest that it needs a handful of answers before it can say anything real about your memory, and it says so plainly instead of showing zeros pretending to be data.
This is the real problem in the classrooms we target, so it's designed around a teacher's roster: a student picks their name or a short code to begin their own short turn, and the previous session closes first so no one's answers ever log under someone else. The roster-based switch is part of the teacher flow I'm building next; today's single-student sign-in works, but the fast in-class handoff is not shipped yet, and I won't pretend it is.
Fair catch, it looks inconsistent with a system that adapts to each student. During the trial the session length is held constant on purpose: to measure whether the method works, every student has to get the same "dose," or you can't compare them. Outside the trial, letting session length flex to each student's fatigue signal is exactly where it should go, and that's the plan once the fixed-dose study is done.
The interface is in English and Hindi today. Being honest about the harder part: the question content itself has to be authored per language, and many government schools teach in a regional medium, Tamil, Bengali, Telugu, Marathi, and more. Extending the actual item banks to those languages is a content-operations goal, not something already done. Translating the buttons is easy; translating a misconception-tagged question bank is the real work.
The short version: set up the class once, assign a subject, and let students take short turns while you watch a single class view of who is struggling and on which kind of error. A proper teacher guide, setup, a period-by-period routine, and change-management notes, is being written, because classroom tools usually fail on adoption long before they fail on technology. If you're a teacher and want to bring this to your class, write to me and I'll walk you through it directly.
Email manikmaurya.in@gmail.com. It reaches me directly, and I reply within a few days. Teachers and parents are welcome to write before signing anyone up.