CubeSTEM Digital Twin · Track 7
Track 7 — AI / ML Autonomy
A complete five-session AI/ML Autonomy mini-course — autonomy boundaries, features and labels, anomaly classification, false-alarm trade-offs, and human-in-the-loop decisions — building on Track 6 telemetry evidence habits.
Local-only teaching mini-course: no account, no submissions, no gradebook. Deterministic classroom models only — not certified AI, not flight software, not real satellite command authority.
Capability boundary
Teaching-grade AI / ML model — read before use
- ✓ Browser-local deterministic teaching classifiers — no external AI API, no ML library, no training pipeline.
- ✓ Local Assessment Engine v0 — self-check questions, no submission, no grade, no roster.
- ✓ Local Evidence Engine v0 — copy/export text or JSON, no backend, no cloud storage.
- ✗ Not certified AI, not flight software, not real onboard autonomy or anomaly diagnosis.
- ✗ Not a real satellite command interface — “Execute (Simulated)” issues no real commands.
- ✗ No student accounts, no teacher roster visibility, no LMS integration, no grades.
- ✗ No real-time data feed, no hardware connection, no remote lab in this track.
At a glance
Evidence and assessment summaries
Evidence checklist summary
Expect one copied artifact per session: autonomy permissions table, feature/label rationale, classifier outputs + disagreement note, threshold justification + confusion counts, and decision debrief with safety rule result.
Open full evidence checklist →Assessment map summary
15 local self-check questions across five sessions — autonomy framing, data quality, classifier interpretation, threshold trade-offs, and human-in-the-loop judgement.
Open full assessment map →Core activity sequence
Five implemented sessions in canonical order
- S1What Does Autonomy Mean?core20–25 min
- S2Features, Labels and Training Datacore30–35 min
- S3Anomaly Classifiercore25–30 min
- S4Confidence and False Alarmscore30–35 min
- S5Human-in-the-Loop Decisioncore35–40 min
Track 7 — AI / ML Autonomy mini-course
Five sessions, one autonomy story — from permissions to human-in-the-loop review
A complete five-session AI/ML Autonomy mini-course — autonomy boundaries, features and labels, anomaly classification, false-alarm trade-offs, and human-in-the-loop decisions — building on Track 6 telemetry evidence habits.
Follow Next step → inside each activity shell and on this page to stay in teaching order. Use Track 6 evidence habits (timestamped quotes, structured claims) before you interpret classifier outputs here.
- Session 1core20–25 minWhat Does Autonomy Mean?
What Does Autonomy Mean?
Student action: Pick an autonomy level + scenario, then write what the system may do and what humans must still approve.
Assessment focus: Autonomy levels, permission boundaries, and human oversight.
Misconception focus: Autonomy does not remove human accountability.
- Session 2core30–35 minFeatures, Labels and Training Data
Features, Labels & Training Data
Student action: Choose telemetry features (building on Track 6 evidence habits), assign labels, and justify each choice.
Assessment focus: Feature selection, label correctness, and data-quality impact.
Misconception focus: ML quality depends on telemetry quality and labels.
- Session 3core25–30 minAnomaly Classifier
Anomaly Classifier / Normal vs Abnormal Telemetry
Student action: Run one case through both classifier views, compare agreement/disagreement, and interpret confidence.
Assessment focus: Rule-based vs preset-model output interpretation.
Misconception focus: Confidence is not certainty.
- Session 4core30–35 minConfidence and False Alarms
Confidence and False Alarms / Fault Rules
Student action: Move sensitivity, record TP/FP/TN/FN changes, and defend one threshold choice with risk reasoning.
Assessment focus: Threshold tuning, FP/FN trade-offs, and alarm fatigue risk.
Misconception focus: Higher sensitivity is not automatically better.
- Session 5core35–40 minHuman-in-the-Loop Decision
Human-in-the-Loop Decision / Safe Mode Review
Student action: Review evidence cards, choose a simulated action, apply safety check, and write a short debrief.
Assessment focus: Evidence-based action choice with safety-rule review.
Misconception focus: Teaching model action is not flight command authority.
When you finish Session 5, the mini-course is complete. No account required — progress stays local to this browser unless you copy or screenshot to share.
Track 7 completion bridge
After Session 5, continue to the curriculum map or demo pack for final learning-system walkthrough surfaces.
Teacher plan
Track 7 AI / ML Autonomy mini-course pack
Same five-session sequence as the hub — scale intensity with time. Evidence and assessment stay local-only; plan a manual share routine (copy/export or screenshot). No roster visibility.
Optional bridge — Mission Realism Lab
Use telemetry divergence and realism evidence before autonomy decisions — explore packet and link teaching models in Mission Realism Lab alongside Track 6 habits.
Delivery options
45 min
45-minute autonomy demo
Conference breakout or executive workshop slice.
- Boundaries + vocabulary (5 min): autonomy vs automation, confidence vs certainty, local-only evidence.
- Session 1 (12 min): autonomy levels + scenario — what may execute vs what stays human-approved.
- Session 3 (15 min): dual classifier views — agree/disagree + confidence interpretation.
- Session 5 (10 min): evidence cards + safety rule check — one simulated decision + debrief.
- Misconception debrief (3 min): AI is pattern matching; ML needs telemetry + labels.
90 min
90-minute AI/ML classroom lab
Single class period — Sessions 1–5 with threshold debate.
- Opening (5 min): tie Track 6 telemetry quotes to features/labels in Session 2.
- Sessions 1–2 (25 min): autonomy framing → feature/label lab + data-quality discussion.
- Session 3 (15 min): rule-based vs preset classifier — think-aloud on top features.
- Session 4 (20 min): sensitivity slider + confusion counts — class vote on threshold.
- Session 5 (15 min): human-in-the-loop decision + structured debate.
- Exit (10 min): copy one evidence line per session; preview Curriculum Map / Demo Pack.
3 hr
Half-day telemetry→autonomy workshop
Pilot classroom or PD — full depth plus debrief.
- Telemetry bridge (15 min): replay one Track 6 evidence habit (quote + timestamp) before ML.
- Session 1 (25 min): three autonomy levels — permissions table + oversight reflection.
- Session 2 (35 min): features/labels — dataset gap hunt (missing fault examples).
- Session 3 (30 min): classifier comparison — disagree cases drive discussion.
- Session 4 (35 min): TP/FP/TN/FN + alarm fatigue storytelling.
- Session 5 (40 min): evidence cards + safety rules — two teams defend different actions.
- Closing circle (20 min): boundary recap — teaching-grade only; no flight AI claims.
Facilitation prompts
- Which decisions must always stay human-approved in this scenario?
- Which missing feature would most likely break this classifier result?
- When outputs disagree, which evidence should dominate the decision?
- Which threshold trade-off is safest for this mission context and why?
- What evidence justifies override or approval for this simulated action?
Common misconceptions to preempt
- AI is not magic — it is pattern matching on selected data.
- ML depends on telemetry quality and label quality.
- Confidence is not certainty.
- False alarms and missed detections both matter.
- Safe autonomy requires explicit rules and human oversight.
- This is a teaching-grade model, not flight software and not certified diagnosis.
Teacher boundary notes
- Evidence and assessment are local-only in the browser.
- No roster, no submissions, no gradebook, no LMS pipeline.
- No external AI API calls, no heavy ML training stack, no backend ML jobs.
- Never describe Track 7 as real onboard autonomy or real command authority.
Hub entry: /twin/learn/tracks/ai_ml_autonomy
Student path
What to do in each Track 7 session
Follow sessions in order, copy evidence each session, and use Track 6 telemetry evidence habits as your baseline.
Optional — Mission Realism Lab
Use telemetry divergence and realism evidence before autonomy decisions — open Mission Realism Lab for checksum, stale data, and link-margin context.
- 1What Does Autonomy Mean?
Pick an autonomy level + scenario, then write what the system may do and what humans must still approve.
Copy evidence: Chosen autonomy level and scenario recorded
- 2Features, Labels and Training Data
Choose telemetry features (building on Track 6 evidence habits), assign labels, and justify each choice.
Copy evidence: Selected features and justification for each choice
- 3Anomaly Classifier
Run one case through both classifier views, compare agreement/disagreement, and interpret confidence.
Copy evidence: Chosen scenario and classifier type recorded
- 4Confidence and False Alarms
Move sensitivity, record TP/FP/TN/FN changes, and defend one threshold choice with risk reasoning.
Copy evidence: TP/FP/TN/FN counts at chosen sensitivity level
- 5Human-in-the-Loop Decision
Review evidence cards, choose a simulated action, apply safety check, and write a short debrief.
Copy evidence: Chosen predicted anomaly and confidence level
After Track 7 completion
When you finish Session 5 (Human-in-the-Loop Decision), the Track 7 mini-course is done.
You have completed the full CubeSat learning arc from mission concept through telemetry evidence to AI-assisted autonomy decisions. Continue to the Curriculum Map or open the Demo Pack for final review pathways.
Evidence checklist
Track-level evidence across all five sessions
Copy one evidence artifact per session (five sessions total). Evidence remains browser-local unless you manually share (copy/export or screenshot).
Session 1 — What Does Autonomy Mean?
- →Chosen autonomy level and scenario recorded
- →List of allowed vs disallowed actions at the selected level
- →One-sentence reflection on why human oversight matters
- →Self-check summary and copied evidence text
Session 2 — Features, Labels and Training Data
- →Selected features and justification for each choice
- →Assigned label and explanation for a given telemetry example
- →Note on one way a missing or biased feature would harm the model
- →Self-check summary and copied evidence text
Session 3 — Anomaly Classifier
- →Chosen scenario and classifier type recorded
- →Predicted class and confidence score
- →Top contributing features with brief explanation
- →One observation where rule-based and ML classifiers differ
- →Self-check summary and copied evidence text
Session 4 — Confidence and False Alarms
- →TP/FP/TN/FN counts at chosen sensitivity level
- →Chosen sensitivity setting with operational justification
- →One-sentence explanation of alarm fatigue risk
- →Self-check summary and copied evidence text
Session 5 — Human-in-the-Loop Decision
- →Chosen predicted anomaly and confidence level
- →Evidence card review — supporting / neutral / contradicting classification
- →Chosen action and safety rule check result
- →One-paragraph decision debrief
- →Self-check summary and copied evidence text
Evidence handling boundary
Local-only evidence workflow: no backend submissions, no roster visibility, no grading pipeline. Use copy/export text or screenshot capture for classroom sharing.
Assessment map
Formative assessment focus across Track 7
15 self-check questions across five sessions (Assessment Engine v0). No submissions and no official grading — discussion starters for class review.
Session 1 — What Does Autonomy Mean?
Assessment focus: Autonomy levels, permission boundaries, and human oversight.
Key prompt: Describe one situation where 'recommend action' autonomy is safer than 'execute' autonomy, and explain why.
Session 2 — Features, Labels and Training Data
Assessment focus: Feature selection, label correctness, and data-quality impact.
Key prompt: Name two ways a biased or incomplete training dataset could cause a fault classifier to make dangerous mistakes.
Session 3 — Anomaly Classifier
Assessment focus: Rule-based vs preset-model output interpretation.
Key prompt: Give one example of a telemetry pattern that looks abnormal but is actually expected during a planned mode change — and explain how a classifier might incorrectly flag it.
Session 4 — Confidence and False Alarms
Assessment focus: Threshold tuning, FP/FN trade-offs, and alarm fatigue risk.
Key prompt: A detector has TP=14, FP=7, TN=13, FN=1. Calculate precision and recall, and state which setting would cause alarm fatigue and why.
Session 5 — Human-in-the-Loop Decision
Assessment focus: Evidence-based action choice with safety-rule review.
Key prompt: Give one scenario where entering safe mode too early wastes science, and one where waiting too long risks the spacecraft bus — explain how evidence cards would help you decide.
Assessment boundary note
Local self-check only. No roster visibility, no grading backend, no LMS integration. Use teacher prompts for classroom discussion and evidence review.
Final bridge
Continue to final learning-system review surfaces
After Session 5, use Curriculum Map for the full eight-track view, Demo Pack for reviewer pacing, Student Mode to rehearse the learner arc, and Learn hub to jump back into any track overview—always browser-local with no submissions.