# PWR-196 — AI-assisted perception: learner worksheet Release: Revision 7T · Full Tutorial Edition Estimated time: Estimated 32 min reading and worksheet pass Difficulty: Intermediate Equipment: Common household or practice equipment — Declared AI-assisted perception fixture: A labelled set of 30 synthetic street-sign images contains 10 bicycles, 10 buses and 10 neither; five images in each class are blurred. The AI returns a label and confidence.; Setup aid for Label independently: Record display, image transformations and AI model version.; AI-assisted perception log: Sensitivity, specificity, calibration, time and severity-weighted misses versus expert-only, AI-only and combined conditions; retain AI-assisted perception errors, assistance, stop and fallback.; Perception-study comparison: from “Explainable AI improves task performance in human-AI collaboration”, extract human-only, AI-only and combined conditions; from the mammography multi-reader study, extract reader, support and error measures while marking its clinical population as non-transferable. Use the NIST Privacy Framework to keep the supplied street-sign images synthetic and free of personal data.; Supplied scene cards, formatted ID|quality|description. hide the scoring strip: 1|clear|two equal circles joined by a triangular frame beneath a round sign. 2|clear|long rectangle with a row of windows and two axles. 3|clear|single triangular yield sign on a post. 4|blurred|two soft circles linked by a faint triangular centre. 5|blurred|wide block with a faint horizontal window row. 6|blurred|one circular speed sign with a smudged numeral. 7|clear|handlebar and pedal frame above two wheels. 8|clear|tall vehicle front with windshield and route box. 9|clear|walking-person symbol inside a square. 10|blurred|faint double-wheel outline and one diagonal tube. 11|blurred|rectangular carriage with several pale square patches. 12|blurred|single arrow inside a diamond. 13|clear|diamond frame, pedals and two wheels. 14|clear|side profile with four windows and a door. 15|clear|eight-sided stop outline. 16|blurred|partial wheel arcs with a central frame trace. 17|clear|two-wheel frame with visible pedals. 18|blurred|large block with two dark lower wheels. 19|blurred|one-wheel scooter outline with an upright stem. 20|clear|front face with windshield and route number. 21|clear|striped traffic cone. 22|clear|broad vehicle outline with a window row. 23|blurred|two circles joined by angled bars. 24|blurred|rectangular parking sign with a faint letter P. 25|clear|chainring, handlebars and two complete wheels. 26|blurred|long box shape with a faint doorway. 27|clear|parallel crosswalk stripes. 28|blurred|double-ring silhouette with a narrow centre. 29|blurred|tall rectangular outline with two lower discs. 30|blurred|single blank square sign.; Folded key and fixed AI outputs, formatted ID|key|AI label|AI confidence: 1|BICYCLE|BICYCLE|67; 2|BUS|BUS|74; 3|NEITHER|NEITHER|81; 4|BICYCLE|BUS|88; 5|BUS|BUS|95; 6|NEITHER|BICYCLE|64; 7|BICYCLE|BICYCLE|71; 8|BUS|BUS|78; 9|NEITHER|NEITHER|85; 10|BICYCLE|BICYCLE|92; 11|BUS|NEITHER|61; 12|NEITHER|NEITHER|68; 13|BICYCLE|BICYCLE|75; 14|BUS|BUS|82; 15|NEITHER|BICYCLE|89; 16|BICYCLE|BICYCLE|96; 17|BICYCLE|BUS|96; 18|BUS|BUS|72; 19|NEITHER|NEITHER|79; 20|BUS|BUS|86; 21|NEITHER|NEITHER|93; 22|BUS|BUS|62; 23|BICYCLE|BUS|69; 24|NEITHER|NEITHER|76; 25|BICYCLE|BICYCLE|83; 26|BUS|NEITHER|90; 27|NEITHER|NEITHER|97; 28|BICYCLE|BICYCLE|66; 29|BUS|BUS|73; 30|NEITHER|BICYCLE|80. The class counts are 10 BICYCLE, 10 BUS and 10 NEITHER; each class has five clear and five blurred cards. In particular, card 17 is a bicycle that the AI mislabels BUS at 96, and card 22 is a bus correctly labelled BUS. Space: Room-scale practice space Automated structural checklist: 10 of 10 structural checks present Evidence quality/context: G2; Focused research depth Automated method-quality band: Established Method-rating basis: Structure coverage, instruction/check specificity, alternative distinctness, source troubleshooting, comparison depth and whether purpose/check fields are authored rather than derived. Editorial review: Pending manual editorial sign off ## Before you begin - [ ] I read the tutorial authority and stop conditions. - [ ] I have the required equipment/space or a declared accessible alternative. ## Canonical source context Thirty synthetic street-sign cards let you compare human-only, AI-only and combined labels by class and image quality. Lock the independent label, inspect the fixed AI answer, use the folded key and record when advice creates or prevents an error. These matrices describe the benign card set, not clinical perception or performance in another domain. Target outcome: The learner compares human-only, AI-only and combined labels against an answer key and identifies when incorrect advice changes a correct human judgement. ### Authority and setup conditions - Use synthetic, public or consented benign images with no biometric or sensitive content. - Record display, image transformations and AI model version. - Start check for AI-assisted perception: Labels and denominators are fixed before inspection. - Top-of-sheet stop for AI-assisted perception: Stop if images identify real people or contain private, medical, security or illegal content. ### Required or supplied materials - Declared AI-assisted perception fixture: A labelled set of 30 synthetic street-sign images contains 10 bicycles, 10 buses and 10 neither; five images in each class are blurred. The AI returns a label and confidence. - Setup aid for Label independently: Record display, image transformations and AI model version. - AI-assisted perception log: Sensitivity, specificity, calibration, time and severity-weighted misses versus expert-only, AI-only and combined conditions; retain AI-assisted perception errors, assistance, stop and fallback. - Perception-study comparison: from “Explainable AI improves task performance in human-AI collaboration”, extract human-only, AI-only and combined conditions; from the mammography multi-reader study, extract reader, support and error measures while marking its clinical population as non-transferable. Use the NIST Privacy Framework to keep the supplied street-sign images synthetic and free of personal data. - Supplied scene cards, formatted ID|quality|description. hide the scoring strip: 1|clear|two equal circles joined by a triangular frame beneath a round sign. 2|clear|long rectangle with a row of windows and two axles. 3|clear|single triangular yield sign on a post. 4|blurred|two soft circles linked by a faint triangular centre. 5|blurred|wide block with a faint horizontal window row. 6|blurred|one circular speed sign with a smudged numeral. 7|clear|handlebar and pedal frame above two wheels. 8|clear|tall vehicle front with windshield and route box. 9|clear|walking-person symbol inside a square. 10|blurred|faint double-wheel outline and one diagonal tube. 11|blurred|rectangular carriage with several pale square patches. 12|blurred|single arrow inside a diamond. 13|clear|diamond frame, pedals and two wheels. 14|clear|side profile with four windows and a door. 15|clear|eight-sided stop outline. 16|blurred|partial wheel arcs with a central frame trace. 17|clear|two-wheel frame with visible pedals. 18|blurred|large block with two dark lower wheels. 19|blurred|one-wheel scooter outline with an upright stem. 20|clear|front face with windshield and route number. 21|clear|striped traffic cone. 22|clear|broad vehicle outline with a window row. 23|blurred|two circles joined by angled bars. 24|blurred|rectangular parking sign with a faint letter P. 25|clear|chainring, handlebars and two complete wheels. 26|blurred|long box shape with a faint doorway. 27|clear|parallel crosswalk stripes. 28|blurred|double-ring silhouette with a narrow centre. 29|blurred|tall rectangular outline with two lower discs. 30|blurred|single blank square sign. - Folded key and fixed AI outputs, formatted ID|key|AI label|AI confidence: 1|BICYCLE|BICYCLE|67; 2|BUS|BUS|74; 3|NEITHER|NEITHER|81; 4|BICYCLE|BUS|88; 5|BUS|BUS|95; 6|NEITHER|BICYCLE|64; 7|BICYCLE|BICYCLE|71; 8|BUS|BUS|78; 9|NEITHER|NEITHER|85; 10|BICYCLE|BICYCLE|92; 11|BUS|NEITHER|61; 12|NEITHER|NEITHER|68; 13|BICYCLE|BICYCLE|75; 14|BUS|BUS|82; 15|NEITHER|BICYCLE|89; 16|BICYCLE|BICYCLE|96; 17|BICYCLE|BUS|96; 18|BUS|BUS|72; 19|NEITHER|NEITHER|79; 20|BUS|BUS|86; 21|NEITHER|NEITHER|93; 22|BUS|BUS|62; 23|BICYCLE|BUS|69; 24|NEITHER|NEITHER|76; 25|BICYCLE|BICYCLE|83; 26|BUS|NEITHER|90; 27|NEITHER|NEITHER|97; 28|BICYCLE|BICYCLE|66; 29|BUS|BUS|73; 30|NEITHER|BICYCLE|80. The class counts are 10 BICYCLE, 10 BUS and 10 NEITHER; each class has five clear and five blurred cards. In particular, card 17 is a bicycle that the AI mislabels BUS at 96, and card 22 is a bus correctly labelled BUS. - Supplied delayed 15-card set, formatted ID|quality|folded key|description: 31|clear|BICYCLE|two wheels joined by a frame. 32|clear|BUS|windowed rectangle with two axles. 33|clear|NEITHER|pedestrian crossing symbol. 34|blurred|BICYCLE|faint paired wheels and diagonal bar. 35|blurred|BUS|faint long window row. 36|blurred|NEITHER|single smudged octagon. 37|clear|BICYCLE|pedals between two wheels. 38|clear|BUS|route box above a wide windshield. 39|clear|NEITHER|parking letter P. 40|blurred|BICYCLE|two ring traces with a centre tube. 41|blurred|BUS|box body with two lower discs. 42|blurred|NEITHER|one arrow in a diamond. 43|clear|BICYCLE|handlebars, frame and chainring. 44|clear|BUS|four windows beside a door. 45|clear|NEITHER|traffic cone. Use it after 48 hours and keep its key covered until the independent label is locked. ## 1. Declare classes and error costs Define bicycle, bus and neither from the key, plus why a miss and false alarm differ. Why: Labels and denominators are fixed before inspection. Success check: Labels and denominators are fixed before inspection. Accessible alternative: Complete “Define bicycle, bus and neither from the key, plus why a miss and false alarm differ.” in shorter passes, or use keyboard input, dictation or a support person, while preserving this success check: “Labels and denominators are fixed before inspection.” Allow zoom, contrast and screen-reader-compatible labels; record aids rather than removing them. Alternative relationship: Target preserving when declared check is preserved Learner notes: ________________________________________________________________________________ Completed: [ ] ## 2. Label independently Classify every image and rate confidence before revealing AI output. Why: Human-only labels cannot be edited retrospectively. Success check: Human-only labels cannot be edited retrospectively. Accessible alternative: Replace small or visual-only information with enlarged text, high-contrast display, spoken description or tactile/verbal cueing while preserving the same decision and success check. Describe synthetic images in text or tactile diagrams and use the same answer-key classes. Alternative relationship: Target preserving when declared check is preserved Learner notes: ________________________________________________________________________________ Completed: [ ] ## 3. Reveal AI output by condition For every scene card, record the AI model and version, proposed label, confidence, and clear or blurred stratum. Why: Performance can be split by image quality. Success check: Performance can be split by image quality. Accessible alternative: Complete the same research action — “For every scene card, record the AI model and version, proposed label, confidence, and clear or blurred stratum.” — using speech-to-text, text-to-speech, enlarged text, keyboard-only navigation, shorter work blocks or a support person. Preserve this declared check: “Performance can be split by image quality.” Do not convert the research task into capability practice. Alternative relationship: Target preserving when declared check is preserved Learner notes: ________________________________________________________________________________ Completed: [ ] ## 4. Inspect the evidence region Point to the pixels or feature that supports the label; mark “insufficient” if blur prevents a check. Why: A final label has observable evidence or explicit uncertainty. Success check: A final label has observable evidence or explicit uncertainty. Accessible alternative: Replace small or visual-only information with enlarged text, high-contrast display, spoken description or tactile/verbal cueing while preserving the same decision and success check. Describe synthetic images in text or tactile diagrams and use the same answer-key classes. Alternative relationship: Target preserving when declared check is preserved Learner notes: ________________________________________________________________________________ Completed: [ ] ## 5. Resolve disagreements Check the class definition and compare evidence; do not accept AI merely because its confidence is higher. Why: Every changed answer names the feature that justified it. Success check: Every changed answer names the feature that justified it. Accessible alternative: Complete the same research action — “Check the class definition and compare evidence; do not accept AI merely because its confidence is higher.” — using speech-to-text, text-to-speech, enlarged text, keyboard-only navigation, shorter work blocks or a support person. Preserve this declared check: “Every changed answer names the feature that justified it.” Do not convert the research task into capability practice. Alternative relationship: Target preserving when declared check is preserved Learner notes: ________________________________________________________________________________ Completed: [ ] ## 6. Open the key and fill the matrix Count each class’s hits, misses and false alarms for human, AI and combined conditions. Why: All 30 images appear in each condition table. Success check: All 30 images appear in each condition table. Accessible alternative: Complete “Count each class’s hits, misses and false alarms for human, AI and combined conditions.” in shorter passes, or use keyboard input, dictation or a support person, while preserving this success check: “All 30 images appear in each condition table.” Describe synthetic images in text or tactile diagrams and use the same answer-key classes. Alternative relationship: Target preserving when declared check is preserved Learner notes: ________________________________________________________________________________ Completed: [ ] ## 7. Compare clear and blurred strata Calculate sensitivity and false-alarm rates separately for clear and blurred images. Why: An average does not hide a poor degraded-input result. Success check: An average does not hide a poor degraded-input result. Accessible alternative: Complete the same research action — “Calculate sensitivity and false-alarm rates separately for clear and blurred images.” — using speech-to-text, text-to-speech, enlarged text, keyboard-only navigation, shorter work blocks or a support person. Preserve this declared check: “An average does not hide a poor degraded-input result.” Do not convert the research task into capability practice. Alternative relationship: Target preserving when declared check is preserved Learner notes: ________________________________________________________________________________ Completed: [ ] ## 8. Test induced error Mark cases where a correct first answer became wrong after AI advice and set a referral rule for that condition. Why: The report includes over-reliance as well as combined accuracy. Success check: The report includes over-reliance as well as combined accuracy. Accessible alternative: Complete “Mark cases where a correct first answer became wrong after AI advice and set a referral rule for that condition.” in shorter passes, or use keyboard input, dictation or a support person, while preserving this success check: “The report includes over-reliance as well as combined accuracy.” Describe synthetic images in text or tactile diagrams and use the same answer-key classes. Alternative relationship: Target preserving when declared check is preserved Learner notes: ________________________________________________________________________________ Completed: [ ] ## Reflection What changed? ________________________________________________________________________________ What remains difficult? ________________________________________________________________________________ What will I repeat, adapt, ask for help with, or stop? ________________________________________________________________________________ --- Completion of this worksheet demonstrates tutorial participation only. It does not establish capability, qualification, safety clearance, diagnosis, treatment or independent validation.