[{"_1":2,"_3":-5,"_4":-5},"loaderData",{"_5":6,"_7":8},"actionData","errors","root",{"_26":27,"_28":29,"_30":12,"_31":12,"_32":12,"_33":29,"_34":12,"_35":12,"_36":12,"_37":12,"_38":-5,"_39":40},"routes/canvas.shared.$sharedTextdocId",{"_9":10,"_11":12},"sharedTextdoc",{"_13":14,"_15":-7,"_16":17,"_18":19,"_20":21,"_22":23,"_24":12,"_25":12},"isAuthenticated",false,"sharedTextdocId","6a0bd0ffcf40819199c84f6bfe0f20f4","versionInt","title","What Prompts More Faculty Pilot App","type","code/react","content","import React, { useMemo, useState } from \"react\";\nimport { Download, RotateCcw, Sparkles, Brain, Leaf, Users, BarChart3, Lightbulb, Eye, EyeOff } from \"lucide-react\";\nimport { Card, CardContent } from \"@/components/ui/card\";\nimport { Button } from \"@/components/ui/button\";\n\nconst TASKS = [\n {\n id: \"rpm\",\n label: \"RPM-guided AI writing task\",\n emoji: \"🧭\",\n unit: \"task\",\n baseIterations: 3.5,\n baseMinutes: 11,\n agency: 95,\n friction: 82,\n footprintWh: 1.4,\n note: \"Anchored to your current TAPs finding: 3.5 iterations on average.\",\n },\n {\n id: \"oneshot\",\n label: \"One-shot AI prompt\",\n emoji: \"⚡\",\n unit: \"prompt\",\n baseIterations: 1,\n baseMinutes: 2,\n agency: 45,\n friction: 22,\n footprintWh: 0.4,\n note: \"Fast, but usually less rhetorically controlled.\",\n },\n {\n id: \"google\",\n label: \"Google-style web search\",\n emoji: \"🔎\",\n unit: \"search\",\n baseIterations: 0.25,\n baseMinutes: 1,\n agency: 65,\n friction: 30,\n footprintWh: 0.3,\n note: \"Useful baseline for search-oriented digital behavior.\",\n },\n {\n id: \"email\",\n label: \"Drafting a professional email\",\n emoji: \"✉️\",\n unit: \"email\",\n baseIterations: 1.5,\n baseMinutes: 6,\n agency: 78,\n friction: 44,\n footprintWh: 0.2,\n note: \"A familiar faculty writing behavior.\",\n },\n {\n id: \"slides\",\n label: \"Making 3 slides with AI support\",\n emoji: \"🖥️\",\n unit: \"mini-deck\",\n baseIterations: 5,\n baseMinutes: 18,\n agency: 83,\n friction: 70,\n footprintWh: 3.2,\n note: \"Often involves structure, visuals, and revision loops.\",\n },\n {\n id: \"zoom\",\n label: \"Attending a 30-minute Zoom meeting\",\n emoji: \"👥\",\n unit: \"meeting\",\n baseIterations: 0,\n baseMinutes: 30,\n agency: 50,\n friction: 35,\n footprintWh: 9,\n note: \"Useful comparison for routine faculty work.\",\n },\n {\n id: \"netflix\",\n label: \"Watching 30 minutes of streaming video\",\n emoji: \"🍿\",\n unit: \"session\",\n baseIterations: 0,\n baseMinutes: 30,\n agency: 25,\n friction: 8,\n footprintWh: 18,\n note: \"A non-work baseline for digital consumption.\",\n },\n {\n id: \"tiktok\",\n label: \"Scrolling short-form video for 15 minutes\",\n emoji: \"📱\",\n unit: \"scroll\",\n baseIterations: 0,\n baseMinutes: 15,\n agency: 20,\n friction: 5,\n footprintWh: 7,\n note: \"A contrast case: high digital activity, low writing agency.\",\n },\n];\n\nconst MODES = {\n simple: { label: \"Simple\", multiplier: 0.8 },\n standard: { label: \"Standard\", multiplier: 1 },\n complex: { label: \"Complex / reasoning-heavy\", multiplier: 1.6 },\n};\n\nfunction calculate(task, qty, mode) {\n const multiplier = MODES[mode].multiplier;\n const aiWeight = task.id === \"rpm\" || task.id === \"oneshot\" || task.id === \"slides\" ? multiplier : 1;\n return {\n iterations: task.baseIterations * qty * aiWeight,\n minutes: task.baseMinutes * qty * aiWeight,\n agency: task.agency,\n friction: task.friction,\n footprintWh: task.footprintWh * qty * aiWeight,\n };\n}\n\nfunction RatioLine({ label, a, b, suffix = \"\" }) {\n const higher = a === b ? \"tie\" : a > b ? \"left\" : \"right\";\n const ratio = Math.max(a, b) / Math.max(Math.min(a, b), 0.01);\n return (\n

{label}

\n {higher === \"tie\" ? \"About equal\" : `${ratio.toFixed(1)}× higher`}\n

\n {a.toFixed(a < 10 ? 1 : 0)}{suffix}\n

\n {b.toFixed(b < 10 ? 1 : 0)}{suffix}\n

\n );\n}\n\nfunction SelectTask({ title, value, setValue, quantity, setQuantity, onAnyChange }) {\n const task = TASKS.find((t) => t.id === value);\n return (\n \n \n

{title}

\n {\n setValue(e.target.value);\n onAnyChange();\n }}\n >\n {TASKS.map((task) => (\n \n ))}\n \n

\n \n Quantity\n {quantity} {task.unit}{quantity === 1 ? \"\" : \"s\"}\n \n {\n setQuantity(Number(e.target.value));\n onAnyChange();\n }}\n className=\"mt-3 w-full\"\n />\n

{task.note}

\n \n \n );\n}\n\nexport default function WhatPromptsMorePilot() {\n const [left, setLeft] = useState(\"rpm\");\n const [right, setRight] = useState(\"oneshot\");\n const [leftQty, setLeftQty] = useState(1);\n const [rightQty, setRightQty] = useState(4);\n const [mode, setMode] = useState(\"standard\");\n const [guess, setGuess] = useState(\"\");\n const [participant, setParticipant] = useState(\"\");\n const [records, setRecords] = useState([]);\n const [showEnergy, setShowEnergy] = useState(false);\n\n const leftTask = TASKS.find((t) => t.id === left);\n const rightTask = TASKS.find((t) => t.id === right);\n const leftCalc = useMemo(() => calculate(leftTask, leftQty, mode), [leftTask, leftQty, mode]);\n const rightCalc = useMemo(() => calculate(rightTask, rightQty, mode), [rightTask, rightQty, mode]);\n\n const winner = leftCalc.iterations === rightCalc.iterations ? \"tie\" : leftCalc.iterations > rightCalc.iterations ? \"left\" : \"right\";\n const agencyWinner = leftCalc.agency === rightCalc.agency ? \"tie\" : leftCalc.agency > rightCalc.agency ? leftTask.label : rightTask.label;\n const footprintWinner = leftCalc.footprintWh === rightCalc.footprintWh ? \"tie\" : leftCalc.footprintWh > rightCalc.footprintWh ? leftTask.label : rightTask.label;\n const footprintLoser = leftCalc.footprintWh === rightCalc.footprintWh ? \"tie\" : leftCalc.footprintWh > rightCalc.footprintWh ? rightTask.label : leftTask.label;\n const footprintRatio = Math.max(leftCalc.footprintWh, rightCalc.footprintWh) / Math.max(Math.min(leftCalc.footprintWh, rightCalc.footprintWh), 0.01);\n const footprintDifference = Math.abs(leftCalc.footprintWh - rightCalc.footprintWh);\n\n function hideEnergyAnswer() {\n setShowEnergy(false);\n }\n\n function saveRecord() {\n const record = {\n participant: participant || `Participant ${records.length + 1}`,\n timestamp: new Date().toISOString(),\n energyPrediction: guess,\n revealedEnergyAnswer: footprintWinner,\n mode: MODES[mode].label,\n leftTask: leftTask.label,\n leftQty,\n leftIterations: leftCalc.iterations.toFixed(2),\n leftMinutes: leftCalc.minutes.toFixed(2),\n leftAgency: leftCalc.agency,\n leftFriction: leftCalc.friction,\n leftFootprintWh: leftCalc.footprintWh.toFixed(2),\n rightTask: rightTask.label,\n rightQty,\n rightIterations: rightCalc.iterations.toFixed(2),\n rightMinutes: rightCalc.minutes.toFixed(2),\n rightAgency: rightCalc.agency,\n rightFriction: rightCalc.friction,\n rightFootprintWh: rightCalc.footprintWh.toFixed(2),\n };\n setRecords([record, ...records]);\n setGuess(\"\");\n setParticipant(\"\");\n setShowEnergy(false);\n }\n\n function downloadCSV() {\n const headers = Object.keys(records[0] || { participant: \"\", timestamp: \"\" });\n const rows = records.map((r) => headers.map((h) => `\"${String(r[h] ?? \"\").replaceAll('\"', '\"\"')}\"`).join(\",\"));\n const csv = [headers.join(\",\"), ...rows].join(\"\\n\");\n const blob = new Blob([csv], { type: \"text/csv;charset=utf-8;\" });\n const url = URL.createObjectURL(blob);\n const a = document.createElement(\"a\");\n a.href = url;\n a.download = \"what-prompts-more-pilot-results.csv\";\n a.click();\n URL.revokeObjectURL(url);\n }\n\n function reset() {\n setLeft(\"rpm\");\n setRight(\"oneshot\");\n setLeftQty(1);\n setRightQty(4);\n setMode(\"standard\");\n setGuess(\"\");\n setShowEnergy(false);\n }\n\n return (\n

\n Faculty pilot activity\n

What Prompts More?

\n Compare the Rhetorical Prompting Method average of 3.5 iterations with other digital behaviors. The goal is to make invisible writing behaviors visible, measurable, and discussable.\n

RPM baseline

3.5

iterations per task

\n\n

\n \n \n

\n\n \n \n

Assumption layer

Prompt complexity

Use this when the AI-supported task involves more planning, reasoning, or revision.

\n {\n setMode(e.target.value);\n hideEnergyAnswer();\n }}\n className=\"rounded-2xl border border-slate-200 bg-white p-3 shadow-sm\"\n >\n {Object.entries(MODES).map(([key, value]) => (\n \n ))}\n \n

\n \n \n\n \n \n

Before revealing the answer

Faculty prediction

\n setParticipant(e.target.value)}\n placeholder=\"Name or table #\"\n className=\"rounded-2xl border border-slate-200 p-3 shadow-sm\"\n />\n setGuess(e.target.value)}\n placeholder=\"Which behavior uses more estimated energy? Why?\"\n className=\"rounded-2xl border border-slate-200 p-3 shadow-sm\"\n />\n \n

\n \n \n

\n\n

\n \n \n

\n \n

Energy reveal

Which behavior takes up more energy?

\n Ask participants to predict first, then click reveal. The numbers are pilot placeholders meant to prompt discussion, not definitive environmental claims.\n

\n\n {!showEnergy ? (\n \n ) : (\n

Estimated higher-energy behavior

\n {footprintWinner === \"tie\" ? \"About equal\" : footprintWinner}\n

\n {footprintWinner === \"tie\"\n ? `Both selected behaviors are estimated at about ${leftCalc.footprintWh.toFixed(1)} Wh.`\n : `${footprintWinner} uses about ${footprintRatio.toFixed(1)}× more estimated energy than ${footprintLoser}, a difference of ${footprintDifference.toFixed(1)} Wh.`}\n

{leftTask.label}

{leftCalc.footprintWh.toFixed(1)} Wh

{rightTask.label}

{rightCalc.footprintWh.toFixed(1)} Wh

\n \n

\n )}\n \n \n\n \n \n

\n \n

Comparison result

{leftTask.emoji}

{leftTask.label}

{leftCalc.iterations.toFixed(1)}

iterations

{rightTask.emoji}

{rightTask.label}

{rightCalc.iterations.toFixed(1)}

iterations

\n {winner === \"tie\" ? \"These behaviors show about the same iteration load.\" : `${winner === \"left\" ? leftTask.label : rightTask.label} prompts more by iteration count.`} Agency is highest for {agencyWinner}. Estimated footprint is higher for {footprintWinner}.\n

\n \n \n\n

\n \n \n \n \n \n

\n\n

\n \n \n \n

What this measures

Iteration count, estimated time, rhetorical agency, productive friction, and rough energy estimates. These are pilot measures for discussion, not settled instruments.

\n \n \n \n \n \n

How to use it live

Ask faculty to predict which behavior takes more energy, click reveal, then discuss what the comparison shows and what it oversimplifies.

\n \n \n \n \n \n

Caveat

Footprint values are rough placeholders. Replace them with vetted local or published estimates before making environmental claims.

\n \n \n

\n\n

\n \n

Debrief prompt

When does “more prompting” mean more waste, and when does it mean more learning, revision, and human judgment?

\n \n \n

\n\n {records.length > 0 && (\n

Saved pilot responses

\n \n \n \n \n \n \n \n \n \n \n \n \n {records.map((r, i) => (\n \n \n \n \n \n \n \n \n ))}\n \n

Participant	Energy prediction	Revealed answer	First	Second	Mode
{r.participant}	{r.energyPrediction \|\| \"—\"}	{r.revealedEnergyAnswer}	{r.leftTask}: {r.leftFootprintWh} Wh	{r.rightTask}: {r.rightFootprintWh} Wh	{r.mode}

\n )}\n

\n );\n}\n","access","public","isModerationBlocked","isAnonifyApiKeyDetected","dd",{"_41":42,"_43":44},"disablePrefetch",true,"shouldPrefetchAccount","shouldPrefetchUser","shouldPrefetchSystemHints","manageRootRouteStylesheet","mobileWebHandoffEnabled","promoteCss","disableSSR","stripModulepreloadImports","paidCampaignAppRedirect","statsigGateEvaluationsPromise",["P",40],"traceId","1006763981554564022","traceTime",1787838283502]