THE LIVING
DISSERTATION
A research environment for knowledge becoming.
This dissertation is not presented as a record of completed thought. It is a record of thought becoming.
Its propositions are exposed to interrogation, revision, contradiction and evidence.
Some ideas will survive. Some will change. Some will fail.
The record preserves all three.
Companion environments
Living research network
Questions → hypotheses → interaction → discovery → revision → test → emergence → current knowledge. Human, AI and emergent nodes are distinguishable but wired together.
Research state
77 records13
17
25
7
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Counted from the research data model, not written into the design.
Collaboration central
This research is developed through human–AI interaction. The AI does not author it.
Human
Question
AI
Intervention
Human
Response
Emergent
Insight
Evidence
Test
Revised
Understanding
Provenance trail
The relevant unit may be the human–AI cognitive system.
- Origin · HumanIs offloading the right model?
- Interaction · AIOffloading implies one-way transfer of load.
- Transformation · HumanReframed from offloading to collaboration.
- Evidence · Human + AIInteractions in which the AI response changes the human's next move.
- Current form · EmergentCognitive collaboration — emerging trajectory, not established terminology.
Provenance trail
A provenance gap is evidence, not an accusation.
- Origin · HumanUntraceable material appears in a submission.
- Interaction · AIListed innocent explanations for gaps.
- Transformation · HumanRejected the automatic misconduct inference.
- Evidence · Human + AIOffline and peer-developed work produces gaps by construction.
- Current form · HumanThe system provides evidence; it does not manufacture accusations.
01 / Section
The question
Primary research question
What are we actually trying to know when we assess a student?
If we want to know how students think, how they question, how they evaluate, how they make decisions, how they collaborate with increasingly capable systems and what they themselves contribute to the work, then the final artifact may no longer be sufficient.
Question map · branch to sub-question, hypothesis, evidence, challenge, discovery
02 / Section
The proposition
The future of assessment may not be about proving that a student worked without AI. It may be about determining what the student actually did when AI was available.
Not presented as established fact. Status: under investigation.
Evidence
Both students used AI. The final documents may look remarkably similar. Cognitively they may represent two entirely different processes.
Inference
If the artifact cannot separate those processes, the artifact alone is an insufficient basis for the inference assessment is trying to make.
Proposition
The future of assessment may not be about proving that a student worked without AI. It may be about determining what the student actually did when AI was available.
Open question
Can what the student actually did be established from the recorded interaction without distorting the behaviour being recorded?
03 / Section
The architecture
Click a node to open its definition, current understanding, evidence, open questions and last revision.
Conceptual architecture · Con-Met AI
Human contribution
Under interrogationDefinition
What the student originated, decided, questioned, evaluated, researched, revised, rejected, discovered and ultimately chose.
Current understanding
Treated as a dimension of attribution, not achievement. A value records the degree of contribution to a defined function, not its quality.
Evidence
- ·Interaction records contain questions asked, claims rejected and evidence introduced by the student.
Open questions
- ·Can minimal interaction conceal substantial intellectual contribution?
- ·Can sophisticated prompting masquerade as sophisticated thinking?
Related artefacts
- ·Contribution Metric specification
- ·Evidence Map
04 / Section
The investigation
Expandable research threads. Each carries a question, a working hypothesis, arguments, counterarguments and status.
05 / Section
Now we try to break it.
A beautiful framework can still be wrong.
A sophisticated metric can still measure the wrong thing.
An evidence trail can still be misinterpreted.
A provenance map can still create false confidence.
Assumption
Visible process is evidence of meaningful learning.
Attack
Can process be simulated or manipulated?
Counterexample
A student who has already written the work can stage an interrogation afterwards to produce a convincing record.
Result
Under investigation.
Assumption
Contribution values represent contribution, not quality.
Attack
Will teachers read a high number as a good number regardless of the label?
Counterexample
AI receives a high value for language refinement while the student supplied the argument.
Result
Mitigation in place: attribution and assessment are held in separate stages with separate records. Reading behaviour untested.
Assumption
More interaction indicates more cognition.
Attack
Can quantity masquerade as cognition?
Counterexample
A high-volume, low-depth exchange versus a single decisive intervention by an expert student.
Result
Under investigation. Volume sensitivity not yet characterised.
Assumption
Sophisticated prompting indicates sophisticated thinking.
Attack
Can prompt craft be learned independently of understanding?
Counterexample
A memorised interrogation script applied to any task.
Result
Under investigation.
Assumption
Minimal interaction indicates minimal contribution.
Attack
Can minimal interaction conceal substantial intellectual contribution?
Counterexample
A student who thinks offline and uses AI only once, precisely, at the end.
Result
Considered a live failure mode of the metric. The system cannot currently see contribution it did not record.
Assumption
The metric can be optimised only by thinking better.
Attack
Can students learn to optimise the metric without becoming better thinkers?
Counterexample
Metric-aware behaviour taught as exam technique.
Result
Unknown. This is the distortion risk the research treats as disqualifying if confirmed.
Assumption
The metric behaves consistently across tasks, disciplines and students.
Attack
Does the construct transfer?
Counterexample
A mathematics derivation versus a literature essay.
Result
Unknown. Not yet tested.
Assumption
A provenance gap indicates misconduct.
Attack
Does a gap license that conclusion?
Counterexample
Work developed offline, on paper, or in conversation with a peer.
Result
Rejected. A gap is evidence requiring investigation, not proof of wrongdoing. The system provides evidence; it does not manufacture accusations.
Assumption
“Did the student use AI?” is a useful assessment question.
Attack
What does the answer establish?
Counterexample
Two students, both using AI, producing similar documents through entirely different processes.
Result
Rejected. It establishes that a tool was present and little else.
Assumption
A beautiful framework is a correct framework.
Attack
Is the architecture persuasive because it is true, or because it is elegant?
Counterexample
A sophisticated metric can still measure the wrong thing; an evidence trail can still be misinterpreted.
Result
Standing principle of the research. Abandonment remains a legitimate outcome.
06 / Section
The discoveries
Unexpected conceptual developments, preserved with the interaction that produced them.
Perhaps cognitive offloading is the wrong question.
Original question
Is cognitive offloading the correct model for describing human–AI cognition?
Intervention
Conceptual interrogation.
Human
Reframed the problem from offloading to collaboration.
AI
Identified implications for distributed cognition and emergent contribution.
Emergent insight
The relevant unit may be the human–AI cognitive system rather than the individual actor.
Consequences for the research
Opened the cognitive-collaboration research thread.
Perhaps the more important question is what happens when cognitive load becomes collaborative.
Original question
Who carries the cognitive load in an AI-assisted task?
Intervention
Reformulation of the load question.
Human
Rejected the single-carrier assumption.
AI
Supplied the redistribution framing as an intermediate step.
Emergent insight
A trajectory rather than a category: offloading → redistribution → collaboration → emergent cognition.
Consequences for the research
Marked as an emerging conceptual trajectory, not established terminology.
What happens when the machine talks back?
Original question
Is the AI a tool or a participant in the interaction?
Intervention
Adversarial framing of the researcher's own model.
Human
Refused to settle the agency question prematurely.
AI
Surfaced the ambiguity in the word “artificial”.
Emergent insight
Did AI create collaborative cognition, or did it make an existing phenomenon visible?
Consequences for the research
Became a featured research thread; question remains open.
AI may not have broken assessment. It may have exposed where assessment was already broken.
Original question
Did generative AI break assessment?
Intervention
Deliberately uncomfortable proposition.
Human
Redirected the problem from the tool to the measurement model.
AI
Held the counterposition that the artifact retains diagnostic value.
Emergent insight
The artifact still matters; it may simply no longer be the only thing that matters.
Consequences for the research
Set the framing for the whole investigation.
We built the metric. Which means we have no business trusting it.
Original question
How should the researchers relate to their own instrument?
Intervention
Adopted an explicitly adversarial relationship with the creation.
Human
Committed to attacking the metric rather than defending it.
AI
Enumerated attack surfaces: gaming, concealment, volume, prompt craft, transfer, distortion.
Emergent insight
The purpose is not to prove the metric works, but to determine whether it deserves to exist.
Consequences for the research
Created the pressure-test section as a first-class part of the research.
We did not build two systems. We built one instrument twice.
Original question
Is there any relationship between Con-Met AI and Neuro-Mirror?
Intervention
Cross-project comparison, unplanned.
Human
Noticed the resemblance without having designed it.
AI
Located the shared thesis: the interaction, not the artefact, is the evidence.
Emergent insight
The two systems form a closed loop — each one's output is the other's missing input — along with the risk that the loop confirms itself.
Consequences for the research
Opened the convergence section and the self-confirming-loop pressure test.
07 / Section
The evolution
Versioned conceptual change. Compare any two states of an idea.
Did the student use AI?
The future of assessment may not be about proving that a student worked without AI. It may be about determining what the student actually did when AI was available.
What changed
Reframed from detection to determination of contribution.
Why it changed
The question the research can actually answer with evidence.
What caused the change
Convergence of the contribution and provenance layers.
What evidence informed it
Metric → Evidence → Conversation → Context.
08 / Section
The artefacts
A research library. Provenance is recorded as human, AI, human + AI or emergent.
09 / Section
The evidence
Observation, interpretation and inference are kept visually distinct so they cannot be confused with conclusion.
Evidence
Two students both used AI and produced similar documents through entirely different processes.
Interpretation
The artifact does not discriminate between those processes.
Inference
Artifact-only assessment is insufficient where AI is available.
Evidence
Interaction records contain questions, decisions, revisions, rejections and introduced evidence.
Interpretation
The process leaves a traceable surface.
Inference
Contribution may be attributable at dimension level.
Evidence
A student can think offline and interact with AI only once, briefly, at the end.
Interpretation
Substantial contribution can exist outside the record.
Inference
The metric cannot see contribution it did not record.
Evidence
A staged interrogation can be produced after the work is already written.
Interpretation
Visible process is forgeable.
Inference
Visible process is not, by itself, evidence of meaningful learning.
Evidence
AI may receive a high value for language refinement while the student supplied the argument.
Interpretation
Dimension-level values separate function from worth.
Inference
Whether readers preserve that separation in practice is not established.
Evidence
The metric has not been run across disciplines, tasks or cohorts.
Interpretation
Transfer is untested.
Inference
No inference is currently licensed.
Evidence
Work developed offline, on paper, or with a peer produces a provenance gap.
Interpretation
A gap has innocent explanations.
Inference
A gap is evidence requiring investigation, not proof of misconduct.
Evidence
Students who know the metric may adapt their behaviour toward it.
Interpretation
Measurement may distort the measured behaviour.
Inference
If confirmed at scale, this would be disqualifying for the metric as designed.
10 / Section
Current state of knowledge
Not a conclusion. The present position of the research.
What we currently believe
- ·The question “Did the student use AI?” establishes that a tool was present and little else.
- ·Human, AI and emergent contribution are usefully distinguishable channels rather than a division of ownership.
- ·A contribution value must be an attribution of function, never a judgment of quality.
- ·Con-Met measures contribution. The teacher evaluates learning.
Why we believe it
- ·Two students using the same tool can produce near-identical artifacts through entirely different cognitive processes.
- ·The interaction record contains questions, rejections, revisions and introduced evidence that the artifact does not.
- ·Separating attribution from assessment preserves professional judgment rather than automating it.
What evidence supports it
- ·Metric → Evidence → Conversation → Context: every value can be traced to interaction segments.
- ·The provenance gap is defined as evidence requiring investigation, not proof of wrongdoing.
What remains uncertain
- ·Whether visible process can be reliably distinguished from simulated process.
- ·Whether the metric transfers across tasks, disciplines, students and forms of AI use.
- ·Whether emergence is a property of the interaction or of the researcher's interpretation.
- ·Whether the metric introduces incentives that distort the behaviour it measures.
What could change our minds
- ·Evidence that students can optimise the metric without becoming better thinkers.
- ·Evidence that attribution profiles are unstable across repeated analysis of the same transcript.
- ·Evidence that the system produces distortions greater than the problem it addresses.
- ·If the foundational premise collapses, abandonment remains a legitimate outcome.
11 / Section
Research ledger
The heart of the living system. Searchable, filterable, chronological.
Is cognitive offloading the correct model for describing human–AI cognition?
Intervention
Conceptual interrogation.
Evidence
Interaction sequences in which the AI response changes the human's next move.
Human contribution
Reframed the problem from offloading to collaboration.
AI contribution
Identified implications for distributed cognition and emergent contribution.
Emergent insight
The relevant unit may be the human–AI cognitive system rather than the individual actor.
Decision
Open research thread.
Next question
What makes a computational system a cognitive partner?
Did generative AI break assessment?
Intervention
Deliberately uncomfortable proposition.
Evidence
Assessment has long inferred thinking from the final product.
Human contribution
Proposed that AI exposed an existing weakness rather than creating a new one.
AI contribution
Held the counterposition that the artifact retains diagnostic value.
Emergent insight
The artifact still matters, but may no longer be the only thing that matters.
Decision
Adopt as the framing proposition of the research.
Next question
What are we actually trying to know when we assess a student?
What does the question “Did you use AI?” establish?
Intervention
Analysis of the two-student case.
Evidence
Similar artifacts, divergent processes.
Human contribution
Constructed the paired counterexample.
AI contribution
Enumerated the dimensions the binary question fails to capture.
Emergent insight
Presence of a tool is not authorship of thought.
Decision
Reject detection framing.
Next question
How was AI used?
Should a contribution value carry academic meaning?
Intervention
Definitional separation.
Evidence
High AI value for refinement alongside a student-supplied argument.
Human contribution
Stripped achievement semantics from the metric.
AI contribution
Supplied the language-refinement case as a stress example.
Emergent insight
Contribution answers what function was performed, not how well.
Decision
Metric represents intensity of contribution to a defined dimension only.
Next question
Do readers preserve that separation in practice?
How should the researchers relate to their own instrument?
Intervention
Adversarial research design.
Evidence
Pressure-test log opened with ten assumptions.
Human contribution
Committed to attacking the metric: gaming, concealment, volume, prompt craft, transfer.
AI contribution
Enumerated attack surfaces and failure conditions.
Emergent insight
The objective is not to prove the metric works, but to determine whether it deserves to exist.
Decision
Publish breakage rather than hide it. Abandonment is a legitimate outcome.
Next question
Can the metric be manipulated?
What does a provenance gap license a teacher to conclude?
Intervention
Boundary definition.
Evidence
Offline and peer-developed work produces gaps by construction.
Human contribution
Refused the misconduct inference.
AI contribution
Listed innocent explanations for untraceable material.
Emergent insight
A gap is evidence requiring investigation.
Decision
System provides evidence; it does not manufacture accusations.
Next question
Will gaps be read as guilt regardless of framing?
What assumptions are introduced when we describe intelligence as artificial?
Intervention
Terminological interrogation.
Evidence
Insufficient.
Human contribution
Opened the question of agency and responsibility in attribution language.
AI contribution
Traced the term's implications for authorship and provenance.
Emergent insight
Attribution presupposes participants; the term both grants and withholds that status.
Decision
Open featured thread.
Next question
Does the terminology change how contribution is attributed?
12 / Section
The convergence
An unplanned feedback loop between Con-Met AI and Neuro-Mirror. Recorded here because it was discovered, not designed.
Two instruments, built separately, turned out to be one instrument built twice.
Noticed · 22 Aug 2026 · not a design decision
Closed cognitive loop
Hover a node. Each system's output is the other's missing input, so the sequence has no end point — it re-enters itself.
Shared thesis
EmergingThe artefact you produce is not the evidence. The interaction that produced it is. Con-Met refuses to read the essay and reads the transcript; Neuro-Mirror refuses the questionnaire and reads what you did under load. Both treat self-report and final output as the same category of unreliable witness.
How it was found
The connection was not designed. It was noticed mid-conversation while comparing Con-Met AI with Neuro-Mirror, a separately conceived system for profiling how a person actually thinks under task conditions.
Shared commitments
- ·Mirror, not judge — describe where thinking came from rather than rank how good it was.
- ·Behaviour over declaration — measure what was done, not what was claimed.
- ·The self is partly opaque to itself — a student cannot reliably name which idea was theirs.
- ·Provisional by design — every inference stays revisable, never hardened into a verdict.
What each system gives the other
Neuro-Mirror → Con-Met
A demonstrated-capability baseline.
Con-Met cannot currently distinguish “this student contributed little” from “this student contributed at their ceiling”.
Con-Met → Neuro-Mirror
An explicit evaluation rubric and a formal task context (Step 2).
Neuro-Mirror needs a structured evaluation frame to keep its inferences from floating free of any task.
Failure mode · Self-confirming loop
If each system treats the other's inference as ground truth, the pair can converge on a confident, self-generated fiction: a weak baseline predicts low contribution, the low contribution confirms the weak baseline.
Mitigation
No hypothesis produced by one pass may be promoted to ground truth on the next. `unknown` stays a live epistemic state the loop is forbidden to overwrite; each system records the provenance of every input it received from the other.
Open questions
- ·Is the loop genuinely self-correcting, or only self-reinforcing under favourable conditions?
- ·Can a demonstrated-capability baseline be built without becoming a fixed label attached to a learner?
- ·Does convergence indicate a real underlying framework, or the researcher's own priors appearing twice?
13 / Section
Tool-choice provenance
The student does not work inside one tool. Selection, rejection, switching and return are themselves contribution evidence.
The student's selection, rejection, switching and evaluation of AI tools constitute potentially meaningful evidence within the contribution process.
Con-Met cannot treat the use of another AI system as an event outside the provenance framework. A student who leaves Con-Met to seek divergent ideas, verify a fact or take a second opinion is making an intellectual decision, and that decision is data.
What the record captures
- · Tool used and tool category
- · Purpose of the tool and stage of work
- · Student's stated reason for choosing it
- · Expected contribution versus actual contribution received
- · Whether the output was evaluated
- · Whether it was accepted, rejected, modified or challenged
- · Whether the student returned to Con-Met afterwards
- · Relationship between the external interaction and the final contribution
Reasons kept distinct, never collapsed
- · It performs a particular function better
- · They want a second opinion
- · They want divergent ideas
- · They want factual verification
- · They do not understand Con-Met's purpose
- · Con-Met does not support the task
- · They prefer another interface
- · They misunderstand the distinction between tools
- · They are attempting to avoid provenance documentation
What this is not
- · Not surveillance.
- · Not an integrity flag — external-tool use is never treated as suspicious by default.
- · Not a claim that Con-Met is the only legitimate tool.
AI tool ecology — observed, not prescribed
Procedural compliance
The student uses Con-Met because they were instructed to.
Conceptual understanding
The student understands why Con-Met exists and what contribution provenance is intended to reveal.
Con-Met usage does not equal Con-Met understanding. The research model keeps them apart.
New research dimension
AI Decision Literacy
Observation first. Deliberately not reduced to a score at this stage.
Open questions
- · Does recorded tool choice change tool choice? Reflective checkpoints may alter the behaviour they observe.
- · Can “avoiding documentation” be offered as a selectable reason without punishing honesty?
- · Is non-use reliably distinguishable from non-disclosure?
14 / Section
Familiarity through adaptive processing
As the AI accumulates history with a student, less needs to be said. Less said is not less thought.
Accumulated human-AI interaction can reduce the amount of explicit information required to communicate complex intent. Visible input therefore cannot be treated as a complete representation of human contribution.
Early interaction · low familiarity
“Explain this concept and help me develop three possible approaches.”
The AI holds almost no relevant history. Everything must be stated.
Later interaction · high familiarity
“Take the mechanism we developed before and apply it here.”
Dramatically shorter, yet it invokes a whole shared framework: prior definitions, rejected alternatives, established terminology and previous decisions.
Shorter input does not necessarily mean lower contribution. The contribution may be compressed into shared contextual knowledge.
Familiarity state · adaptive processing trajectory
Stage 1
Initial
Student explains → AI responds
Stage 2
Emerging
Student directs → AI develops
Stage 3
Established
Student cues → AI anticipates
Stage 4
High
Student evaluates → AI refines
Stage 5
Highly contextual
Student provides minimal contextual signal → AI reconstructs likely intended direction
Research descriptors, not measurements of cognition. The underlying evidence for every classification is retained.
Explicit contribution
What the student directly communicates in the current interaction.
Contextual contribution
Relevant information about the student's thinking the AI can draw on because of accumulated interaction history.
Con-Met must not attribute intellectual expansion to the AI merely because the current prompt is short.
The provenance record must answer
- · How long has the student interacted with this AI?
- · How much relevant prior interaction exists?
- · Is the current prompt dependent on previous interaction?
- · Does the current input contain an explicit reference to prior work?
- · How much of the AI's response appears to rely on accumulated context?
- · Did the student confirm, reject or modify the AI's reconstruction?
- · Did the student introduce new information? Did the AI?
- · Did the student recognise when the AI misunderstood their intended meaning?
Heuristics Con-Met refuses
- · More words = more contribution.
- · More prompts = more contribution.
- · Longer interaction = more contribution.
Contribution is modelled as current input + prior context + student decisions + AI processing + evaluation + revision + reflection.
Research questions added
- · How does increasing AI familiarity with a student alter the observable distribution and interpretation of human and AI contribution over time?
- · Does increased familiarity reduce the amount of explicit prompting required from the student?
- · Does reduced prompting represent reduced contribution, or compressed contribution?
- · How does accumulated interaction history affect provenance attribution?
- · Does increased familiarity improve the AI's ability to reconstruct student intent?
- · When the AI reconstructs incorrectly, how does the student correct it?
- · Does familiarity create new forms of ambiguity in contribution attribution?
Combined provenance model
01 · Human contribution
What the student originates, decides, evaluates, rejects, modifies and contributes.
02 · AI contribution
What the AI generates, transforms, extends, reconstructs or suggests.
03 · Tool ecology
Which tools the student chooses, rejects, switches between and returns to.
04 · Familiarity
How accumulated interaction history changes what the AI can infer from limited explicit input.
05 · Evaluation
How the student judges and responds to machine contribution.
06 · Integration
How human and AI contributions become incorporated, transformed or rejected in the final work.
07 · Provenance
A traceable record of the contribution trajectory.
Con-Met is not an AI detector. It investigates how human and machine contributions interact, evolve and become distinguishable within an educational process. Contribution must therefore be understood longitudinally, not merely transactionally: human-AI contribution is dynamic, relational, contextual and longitudinal.
Open the tool ecology & familiarity analytics15 / Section
Institutional integration
Provenance capture only becomes research data if it lives inside the work teachers already set.
A contribution-provenance layer that requires teachers to re-enter tasks, cohorts and deadlines already held in Blackboard, Canvas or Moodle will not survive contact with a real school week. Redundant administration is the most common cause of death for otherwise sound educational instrumentation.
Con-Met should not be a second gradebook. It should be a provenance layer that borrows identity, roster and task context from the institution's existing system and returns evidence, not marks.
Primary route
LTI 1.3 / LTI Advantage
Con-Met is registered as an external tool inside Blackboard. A teacher places a Con-Met activity in a course; the student clicks it and lands directly in the workspace, already identified.
- · Single sign-on via the LMS — no separate Con-Met account
- · Names and Role Provisioning gives the roster and teacher/student role automatically
- · Deep Linking lets the teacher attach an existing Blackboard assignment to a Con-Met task
- · Assignment and Grade Services can return a quality mark once the teacher has finalised it
Cost: Requires tool registration by the institution's LMS administrator and a formal privacy review.
Secondary route
REST API sync
A scheduled job reads course, roster and assignment data from the Blackboard REST API and mirrors it as Con-Met tasks; submissions and provenance summaries are written back.
- · Works without embedding Con-Met in the LMS interface
- · Useful for research deployments that need bulk historical data
Cost: Needs institutional API credentials, and identity mapping must be maintained separately.
Available now
Manual bridge
Teachers export a task list or roster from the LMS and import it into Con-Met; Con-Met exports a contribution and origin record that can be attached to the LMS submission.
- · No institutional approval required
- · Enough to run a pilot cohort this term
Cost: Still involves a copy step; acceptable for pilots, not for scale.
What crosses the boundary
- · Con-Met sends the LMS an evidence record and, where the teacher has finalised one, a quality mark the teacher authored.
- · Con-Met never sends the contribution intensity values as a grade. They are not a grade.
- · The LMS remains the system of record for enrolment and assessment. Con-Met remains the system of record for provenance.
Why this is a research question
Institutional integration is not only a convenience question. Attaching provenance capture to the assignment flow teachers already use is what converts ordinary coursework into a longitudinal dataset — which is the precondition for studying how human-AI contribution changes over a year rather than over a single task.
Open questions
- · Does embedding Con-Met inside the LMS change student behaviour, given it now appears as institutional surveillance rather than a workspace?
- · Where does consent sit when provenance capture is delivered through a compulsory course activity?
- · Does returning only a teacher-authored mark, and never a contribution score, survive institutional pressure to rank students by the contribution index?
- · What is the minimum viable integration that removes teacher redundancy without requiring a full LTI registration?
Closed research–development loop
Con-Met AI is the artefact being developed. The Living Dissertation is the research environment documenting its intellectual development. Concepts here link to the components that operationalise them; the system links back to the propositions that generated it.