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Mentor Matching and Schedule Optimizer

Run the Mentor Matching and Schedule Optimizer MicroSim fullscreen

About This MicroSim

Two jobs people hope an agent will do -- pair mentors with students, and pick a meeting time -- turn out to be the same job: score several candidates against stated criteria, then hand a person a shortlist.

The upper two columns are the matching graph. Every line carries a shared-interest score, and the line's thickness is that score. One pairing scores 2 and the rest score 1, which is exactly the situation a ranking is good at surfacing and bad at resolving: three students tie, and nothing on the graph breaks the tie.

The bottom row is the scheduler. Each slot is checked against three constraints -- mentor availability, venue hours, and the holiday calendar -- and two of the three slots fail exactly one check. The slot that fails on venue hours is the instructive one: a scheduler that models only people would have recommended it.

Press Best pairing for the combined answer. Read the third paragraph of that panel too -- the pairings the score is least sure about are the ones a human should look at, and a system that hides its ties is less useful than one that shows them.

How to Use

  • Click a mentor or a student to read their interests, their best match, and where the score is ambiguous.
  • Click a slot to see its three-constraint checklist, with the failing check named.
  • Press Best pairing to select the strongest match and the only fully satisfying slot together.
  • Compare line thickness: the one thick line is the only score above 1.
  • Hover a line to enlarge its score label; drag a node to untangle a crowded area.
  • Use the navigation buttons to zoom and pan, or open fullscreen for mouse-wheel zoom. Reset restores the layout.

Lesson Plan

Bloom level: Analyze (L4) -- examine

Learning objective: Given a mentor-student matching graph and a set of candidate schedule slots, the learner examines the scores to identify the strongest mentor-student pairing and the schedule slot that satisfies every stated constraint.

Before the sim (5 min). Ask how the club currently pairs mentors with students and picks its meeting time. Usually the answer is whoever was standing there and the time we have always used. Neither is written down, which is why neither can be checked.

With the sim (10 min). Find the thick line first, then ask what makes it thick. Then work the slots: have the group predict which slot wins before clicking, and pay attention to the Wednesday slot -- every mentor is free and it still fails.

After the sim (20 min). Have the group add a sixth mentor and a sixth student on paper, score the new pairings by shared tags, and see whether the top-ranked pairing changes. Then add a fourth constraint of their own -- transport, a rival activity, a caretaker's finishing time -- and re-check the three slots.

Check for understanding. Point at two students who both score 1 and ask which should be matched first. There is no answer on the graph, and saying so -- and naming what a person would need to know instead -- is the objective met.

Embedding This MicroSim

Paste this into any page of the book, adjusting the relative path to docs/sims/ for the page's depth:

<iframe src="../../sims/mentor-matching-schedule-optimizer/main.html" width="100%" height="782px" scrolling="no"></iframe>

Specification

The full specification below is extracted from Chapter 33: AI-Customized Curriculum and Rolling Out New Agents.

Type: graph-model
**sim-id:** mentor-matching-schedule-optimizer<br/>
**Library:** vis-network<br/>
**Status:** Specified
**Template:** https://github.com/dmccreary/organizational-analytics/tree/main/docs/sims/mentor-matching-network

Purpose: Let a learner examine a bipartite mentor-student matching graph alongside a small set of schedule-slot nodes, to see how both AI assisted mentor matching and AI scheduling optimization score multiple candidates before a person picks one.

Bloom Taxonomy: Analyze (L4)
Bloom Taxonomy Verb: examine

Learning objective: Given a mentor-student matching graph and a set of candidate schedule slots, the learner examines the scores to identify the strongest mentor-student pairing and the schedule slot that satisfies every stated constraint.

Node types:
1. Mentor nodes (blue circles, left column): five mentors, each labeled with one or two interest tags (e.g., "Robotics, Python")
2. Student nodes (green circles, right column): five students, each labeled with one or two interest tags
3. Schedule slot nodes (amber squares, bottom row): three candidate slots (Mon 5pm, Tue/Thu 4:30pm, Wed 6pm), each labeled with which constraint it violates, if any

Edges:
- Mentor-to-student edges weighted by shared-interest score (thicker edge = stronger match), click an edge to reveal the matching score and shared tags
- Dashed lines from each schedule slot to a small "constraints" icon, click to reveal which constraint (mentor availability, venue hours, holiday calendar) that slot satisfies or fails

Interactive features:
- Click any mentor or student node to highlight its strongest-scoring match and dim all other edges
- Click any schedule slot node to reveal its full constraint checklist in a side panel, with failed constraints marked in red and satisfied ones in green
- Button: "Show Top-Ranked Pairing and Slot" -- highlights the single best mentor-student match and the one fully-satisfying schedule slot simultaneously
- Zoom with the mouse wheel, pan by dragging the background

Visual styling: Blue for mentors, green for students, amber for schedule slots; edge thickness maps to match strength

Legend: Node color key (mentor, student, schedule slot) and edge-thickness meaning

Implementation: vis-network JavaScript library, bipartite layout with mentors and students in two columns and schedule slots in a row beneath, canvas size 800x480px.

References