ThoughtAmp

About ThoughtAmp

Last updated: 12/22/2025

ThoughtAmp is an AI‑powered discussion engine for classes, workshops, and talks. Instructors write a short pre‑prompt (the teaching intent), and ThoughtAmp delivers guided, personalized conversations to every learner—then gives you pull‑through visibility into what happened so you can teach better in real time.

Modes

Class Mode (roster‑linked)

Attribute chats to students, track engagement, and pick up threads in office hours. Great for assignments and graded activities.

  • Roster + analytics
  • Pull up transcripts in office hours
  • Export to LMS / CSV / PDF

Single‑Prompt Mode (anonymous)

Frictionless, privacy‑first interactions—ideal for live talks, guest lectures, outreach, or sensitive topics. Aggregate insights without names.

  • No login required
  • Link or QR in slides
  • Aggregate themes for debrief

What ThoughtAmp Does

Guided Conversations

Create focused pre‑prompts that scaffold discussion. Students explore with structure and personalization.

  • Preloaded papers & cases
  • Guided participation
  • Personalized by interest
  • Open‑ended surveys
  • After‑hours tutor/coach

Insight & Analysis

Observe conversations live, then summarize, synthesize, and assess understanding—at a student or class level.

  • Engagement dashboards
  • Reasoning trace review
  • Targeted feedback
  • Theme extraction
  • AI literacy & usage checks
Use Case 1

Pre‑Prompted Conversations

Prompts give structure, focus, and personalization to discussions. Click any prompt to copy it.

Preloaded Readings & Papers

Guided Classroom Participation

Customized Learning Paths

Open-Ended Surveys

After-Hours Tutor / Coach

Use Case 2

Insight & Analysis for Organizers

Surface patterns, gauge engagement, and review reasoning—instantly.

Measure Engagement

Understand Thought Processes

Feedback & Coaching

AI-Assisted Summaries

AI Literacy & Usage

Program-Level Analytics

See It In Action (Example Conversations)

Use Case 1

Pre‑Prompted Conversations → Personalized Dialogs

Teaching intent: “Walk through the basics of supply and demand. Start by asking for an industry.”

Student A — Sneaker Resale

sample dialog
TA:Pick an industry you care about.
Student:Sneaker resale.
TA:Name one factor that could shift demand for a limited‑edition release.
Student:A celebrity endorsement.
TA:Is that a movement along or a shift of the demand curve? Why?
Student:Shift right—at every price, more pairs are desired.
TA:Propose a supply shock unique to this market and predict the price/quantity effect.

Student B — Concert Tickets

sample dialog
TA:Pick an industry you care about.
Student:Concert tickets.
TA:Dynamic pricing is introduced. Does that change demand, supply, or the clearing mechanism?
Student:The clearing mechanism—given demand.
TA:Give a scenario that still leads to scalping and one policy to reduce it.
Use Case 2

TA Off‑Hours (Focused Tutoring)

Teaching intent: “Be a TA for Linear Programming basics. Keep on target and do not go more advanced.” Assign students to chat before office hours; pull up transcripts during your meeting.

Conversation A — Model Structure

sample dialog
Student:I’m lost—what *is* an LP?
TA:An optimization model with a linear objective and linear constraints. Choose a context: diet, blending, or staffing?
Student:Staffing.
TA:Define your decision variables in one sentence.
Student:x_Mon, x_Tue … scheduled employees per day.
TA:Great. Write one linear constraint enforcing a minimum staffing level.

Conversation B — Feasibility vs. Optimality

sample dialog
Student:My solver fails—feasible or not?
TA:Paste one constraint set. We’ll isolate conflicting bounds. Try relaxing the Wed minimum by 10%—what happens?

Conversation C — Sensitivity (Right Level)

sample dialog
Student:What if wage rate rises by $1?
TA:We can discuss reduced costs and shadow prices as defined for this model. First, identify the binding constraint.
Use Case 3

Assessment via Dialog (Porter’s Five Forces)

Teaching intent: “Have a conversation that requires the student to justify each force with evidence.” Includes rubric checkpoints and argue‑both‑sides prompts to reduce plagiarism.

Conversation — Mid‑Market Cloud CRM

sample dialog
TA:Pick a company and define its industry boundaries.
Student:Mid‑market cloud CRM providers.
TA:For threat of substitutes, give a non‑obvious substitute and a metric to compare.
Student:In‑house spreadsheets; compare 12‑mo churn and onboarding cost.
TA:Rate that threat (H/M/L) with a short justification, then argue the opposite—what would make it High?
Use Case 4

Live Class Ideation → Surface Best Examples (TSP)

Teaching intent: “Guide students into a cool Traveling Salesman Problem application. Start from an industry; co‑develop a concrete instance.” In class, ask ThoughtAmp to rank the most interesting examples and call on those students.

Student Thread — Food Rescue

sample dialog
TA:Pick an industry.
Student:Nonprofit food rescue.
TA:Define the “cities.”
Student:Daily pickup sites.
TA:Constraints?
Student:Perishable windows; vehicle capacity.
TA:Suggest an objective beyond distance—what and why?
Use Case 5

Conferences & Talks (QR‑Powered Sidekick)

Teaching intent: “Pre‑load my CV and paper; let the audience query the AI live.” You get a post‑talk digest of questions and interests.

Audience Q&A — Methods

sample dialog
Student:How does your method differ from Smith (2023) on data leakage?
TA:Key differences: (1) blocked time split leakage tests; (2) feature‑group ablations. See Sec. 4.2 for metrics.

Additional Scenarios

Corporate Training

Practice soft skills with safe simulations, then analyze transcripts for skill growth.

Workshops & Conferences

Capture participant dialogue and instantly surface the “room’s brain”.

Peer‑to‑Peer Learning

Use ThoughtAmp as a first‑draft partner; bring refined ideas to group discussion.

Why ThoughtAmp? (Benefits)

  • Deeper engagement: Every student participates via a 1:1 guided dialog.
  • Time saved: Offload first‑pass explanations; use human time for coaching.
  • Better office hours: Pull up transcripts and start where the student got stuck.
  • Visibility & evidence: Search, filter, and showcase exemplars; export artifacts.
  • Inclusive participation: Quiet students contribute privately; surface their best ideas.
  • Flexible privacy: Run activities anonymously or roster‑linked.

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