AI That Works Like Your District Does.

From drafting letters to surfacing career matches to preparing counselors before a meeting — SchooLinks agents do the setup work so your staff can focus on students.
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Recommender Agent
Generating draft letter…
"Jordan has consistently demonstrated initiative and intellectual curiosity throughout their time at Westfield High. Their leadership in the Environmental Action Club reflects…"
Alert — High Priority
FAFSA Incomplete
15 seniors haven't submitted their FAFSA — deadline in 8 days.
Send Reminder →
Career Rec Agent
JM
Jordan M.
4/4 assessments complete
Biomedical Engineering 94% match
View all recommendations →

Turn CCR Data Into Action—Automatically

These aren't general-purpose AI features. Every agent in SchooLinks operates within a bounded context — a purpose-built data scope scoped to a specific task and user role. The agent can't access what it shouldn't see. And it cites what it surfaces.

Generate What Staff Actually Writes

Draft recommendation letters and other high-effort content using student context already in SchooLinks—then review, edit, and submit with confidence.

Alert You Before Students Fall Behind

Surface time-sensitive gaps and milestones across CCR (think: incomplete steps, missing submissions, off-track signals) so teams can intervene earlier.

Nudge the Next Best Action

Recommend practical next steps—follow up, schedule, remind, or guide—based on student activity and district workflows already in place.

Analyze Progress in Plain English

Move from “Where do I even start?” to clear insights by making data easier to interpret and act on—without living in spreadsheets.

See Agentic AI in SchooLinks

From drafting to triage to nudges—watch how SchooLinks helps teams take the next best action, faster.

Recommend
Career Recommendation Agent
Surfaces personalized pathway suggestions
  • Matches students to careers using assessment data
  • Weighs labor market signals and local opportunities
  • Ranks pathways by fit score and feasibility
How it works →
Analyze
Student Insights Agent
Synthesizes assessments into actionable profiles
  • Aggregates scores across multiple instruments
  • Flags anomalies and engagement patterns
  • Generates counselor-ready summary reports
How it works →
Generate
Recommendation Letter Agent
Drafts personalized letters from structured data
  • Pulls verified student accomplishments
  • Adapts tone for scholarship vs. employment
  • Outputs counselor-editable drafts
How it works →
Complete
Form Completion Agent
Pre-fills applications with student-verified data
  • Maps student profile fields to form requirements
  • Flags missing or inconsistent information
  • Queues completed forms for counselor review
How it works →
Alert
Alerts
Proactively flags milestones and risks
  • Monitors deadline proximity and inactivity
  • Surfaces students falling off track
  • Routes urgent alerts to the right counselor
How it works →
Nudge
Nudges
Delivers timely, personalized outreach
  • Triggers messages based on student state
  • Personalizes tone and channel per student
  • Tracks open rates and response patterns
How it works →
Hover cards to explore

How Our AI Actually Works

The ChatBot Is Not the
Decision Engine

A lot of AI tools in edtech hand the LLM the wheel. It decides what to recommend. It decides what to say. Districts are right to be uncomfortable with that — and so are we.

Every agent in SchooLinks is built on a two-sided model:

Deterministic Side

Handles all logic, scoring, and matching

Assessments scored with Cronbach's alpha and z-scores. Recommendations mapped through validated frameworks. Data grounded in authoritative sources:

SOC CIP O*NET Labor Market
Auditable.  Reproducible.  Not vibes.
LLM-Bounded Side

Does three specific things with those outputs

  • 1Writes the copy
  • 2Synthesizes across multiple data sources
  • 3Resolves text to known taxonomies

It never computes the alignment. It never makes the recommendation. If it gets something wrong, the framing might be off — the underlying match is not.

The blast radius of an AI error is proportional to how much the AI controls. We built the system so that radius is small.

Career Recommendation Agent

Career Matches Rooted in
Assessment Science, Not Guesswork

The Career Recommendation Agent reviews a student's completed assessments — Find Your Path, Would You Rather, Student Focus, and Top Skills — and generates four career suggestions with explicit reasoning and next steps.

The matching logic is deterministic: distance minimization, z-scores, and statistically validated frameworks built over years. The LLM doesn't pick the careers. It interprets how the assessments align, where they agree, and where they diverge — then presents results in plain language students can act on.

Interactive preview — sample data only

Find Your Path
Would You Rather
Student Focus
Top Skills
Deterministic Scoring z-scores  ·  distance minimization  ·  validated frameworks
LLM Interprets Alignment Writes the language. Doesn't pick the career.
Biomedical Engineer
Strong alignment with Top Skills (analytical) and Find Your Path (science + helping). High labor demand in your region.
Health Informaticist
Bridges healthcare and data — matches Student Focus (structured work) and Top Skills (systems thinking).
Data Scientist
High z-score match on analytical reasoning across three assessments. Growing field with accessible entry paths.
Occupational Therapist
Would You Rather and Student Focus both flag a preference for hands-on, people-centered work. Strong regional demand.

Results update automatically when a student retakes any assessment

No prompting required

Results generate automatically once a student completes at least two assessments.

Updates as students grow

Retakes one assessment three months later? Results recalculate to reflect where they are now.

Short assessments, deep output

Eight short, repeatable assessments deliver the interpretive depth that used to require an hour-long session.

Student Insights Agent

Everything a Counselor Needs
Before a Meeting — In One View

The Student Insights Agent lives inside the student Casefile. It brings together academics, goals, assessments, college plans, and activity into a single snapshot — and lets counselors ask natural-language questions to go deeper.

"Does this student have a balanced college list?" "What careers are they targeting?" Counselors get answers in seconds instead of clicking across six sections.

Interactive preview — sample data only

Student Casefile — Insights View
GPA3.8 / 4.0View source →
Assessments4 of 5 completedView source →
College List8 schools addedView source →
Top Career MatchBiomedical EngineeringView source →
Last Activity2 days agoView source →
Student Insights Agent

Suggested prompts

Ask another question ↺
No student PII included in requests or responses
Cited, verifiable answers

Every data point links back to the source record — counselors can verify, not just trust.

Privacy enforced at the system level

No student PII included in requests or responses — names, IDs, addresses never leave the protected layer.

Guided interaction

Suggested prompts meet counselors where they are — no AI training required.

Recommender Letter Agent

A Strong First Draft, Built From What You Already Know About the Student

Writing recommendation letters is one of the most time-intensive tasks on a counselor's plate. The Recommender Agent uses student profile data and brag sheet inputs already in SchooLinks to generate a well-structured draft — so staff spend their time refining and personalizing, not starting from a blank page.

Every draft is a starting point, not a final product. Staff review, edit, and submit with full control over what goes out under their name.

Interactive preview — sample data only

Student Profile Inputs
Student Brag Sheet Included
Academic Record Included
Activity & Involvement Included
Recommender Agent — Draft Output

Draft will appear here…

Context-aware drafts

Pulls from student profile and brag sheet inputs to generate tailored letters educators can make their own.

Human-in-the-loop by design

Staff review, edit, and approve before anything is submitted — AI supports the work, not replaces it.

District-controlled enablement

Can be toggled on or off in District Settings for the right level of access and governance.

Form Completion Agent

Bulk-Fill Placement Forms in Minutes, Not Afternoons

For districts managing high volumes of work-based learning placements, the Form Completion Agent auto-fills free-response fields using information already stored in SchooLinks — then surfaces everything in a preview table before a single record is saved.

No more opening placements one by one. No more copying the same information into the same fields. Generate across your entire cohort, review in one place, save with confidence.

Interactive preview — sample data only

Form Completion Agent — Placement Progress
5 placements · 3 pending form completion
Student Employer Program Form Status
Built for scale

Generate responses across many placements at once to eliminate repetitive manual entry across your entire cohort.

Review before anything finalizes

All outputs render in a table preview — staff validate before submission, not after.

Works where teams already live

Appears directly in Placement Progress → Form Progress workflows with a single "Generate responses with AI" action.

Alerts

Catch the Gaps Before They Become Outcomes

The Alert capability surfaces time-sensitive signals across the platform — incomplete milestones, missing submissions, off-track indicators — before counselors discover them at the end of the semester. The goal isn't to generate more notifications. It's to flag the things that actually warrant a conversation.

Alerts are tied to what staff can do next, not just what's wrong.

Interactive preview — sample data only

Active Alerts — CCR Milestones 4 new
Proactive, not reactive

Identify emerging gaps early in the cycle, not at reporting time when it's too late to act.

Connected to CCR workflows

Every alert links directly to the relevant record or action — no hunting required.

Built for district oversight

Helps leaders maintain visibility without requiring manual data pulls or overriding role-based access.

Nudges

The Next Best Action, Surfaced Before You Have to Ask

Nudges recommend practical next steps — follow up, schedule, remind, guide — based on real student activity already tracked in SchooLinks. The difference between a nudge and a generic reminder is context: nudges are tied to what's actually happening with a specific student, not a calendar-based blast.

Staff see what's recommended, decide what to act on, and execute in a click.

Interactive preview — sample data only

Recommended Actions Based on recent student activity. 3
Action recommendations, not busywork

Suggest next steps counselors and advisors can take immediately — no extra research required.

Designed for adoption

No new tools, no prompting skills, no AI training — nudges appear in the workflows staff already use.

Staff stay in control

All recommendations are reviewable before any action is taken — AI surfaces options, humans decide.

Analyze

Plain-English Answers to District-Level Questions

The Analyze capability makes CCR data easier to interpret at scale — so directors and counselors can quickly understand who needs help, where gaps are concentrated, and what to prioritize — without living in spreadsheets or waiting on a data pull.

Ask in plain English. Get a clear answer scoped to your access level.

Interactive preview — sample data only

Analyze — District Insights
View full report →
Plain-English analysis

Move from "I need to export and filter this" to an actionable insight in seconds.

District-friendly visibility

Built with role-based access in mind — users see what their permissions allow, nothing more.

Purpose-limited by design

Student data is used only for authorized educational purposes — never for profiling or external model training.

Frequently Asked Questions

Everything you wanted to know about how our AI actually works.

SchooLinks agents are embedded directly into CCR workflows — they generate, flag, recommend, and surface insights where staff already work. There's no prompt engineering required, no blank chat box, and no separate AI tool to learn. Each agent has a specific job, a bounded data scope, and a defined role in the workflow.
The matching logic is entirely deterministic — built on statistically validated frameworks, distance minimization, and z-scores developed over years. The LLM receives those outputs and interprets them in plain language. It doesn't select the careers. It explains what the data already computed.
Each SchooLinks agent operates within a purpose-built data scope — only the student data relevant to the specific task, scoped to the user's access level. The agent can't reach into records it wasn't given. When data doesn't exist in that context, the agent says so instead of guessing.
Yes. SchooLinks AI features are designed to operate in compliance with FERPA, using role-based access controls and purpose-limited educational use. Student data is never used to train models outside SchooLinks' infrastructure.
No. PII is not used or is masked, and it is never used to train models outside SchooLinks' infrastructure. Student names, IDs, email addresses, phone numbers, and dates of birth are excluded from all agent requests and responses at the system level.
No — and that's intentional. Staff generate a draft, then review and edit before anything is submitted. The agent is designed for educator oversight, not to replace it.
It generates responses using existing data in SchooLinks related to the candidate, sponsor, and program. All outputs appear in a preview table for staff review before anything is saved.
No. Every agent is built into existing SchooLinks workflows. Staff interact through the same interfaces they already use — the AI capability is embedded, not bolted on as a separate tool.
The agent says so. When data doesn't exist in the bounded context, it reports that clearly rather than generating a guess. Those gaps are also logged so the SchooLinks team can determine whether to build them into a future agent context.
Yes. Agent capabilities like the Recommender Letter Agent can be enabled or disabled at the district level in District Settings, giving administrators governance over what staff and students can access.

Draft Recommendation Letters in Minutes—Not Hours

Writing recommendations is one of the most time-intensive tasks for counselors and teachers. The Recommender Agent creates a well-structured first draft using student profile data and brag sheets—then staff can refine and personalize before submitting.

  • Context-aware drafts: Uses student profile + brag sheet inputs to generate tailored letters educators can edit.
  • Human-in-the-loop by design: Staff review, edit, save, and submit—AI supports decisions; it doesn’t replace them.
  • District-controlled enablement: Can be enabled/disabled in District Settings for the right balance of access and governance.

Auto-Fill Repetitive Placement Forms at Scale

For districts managing high volumes of work-based learning placements, the Form Completion Agent bulk-fills free-response fields using information already stored in SchooLinks—then presents a preview table for staff review before saving.

  • Built for scale: Generate responses across many placements to reduce repetitive manual entry.
  • Review before anything is finalized: Outputs render in a table preview for staff to validate before submission.
  • Works where teams already live: Appears in placement workflows (Placement Progress → Form Progress) with a “Generate responses with AI” action when enabled.

Catch the “Quiet Misses” Before They Become Outcomes

The Alert capability is designed to flag important student actions and behaviors across the platform—so counselors and directors don’t have to discover problems at the end of the semester.

  • Proactive signals: Identify incomplete milestones and emerging gaps early—not just at reporting time.
  • Built for CCR workflows: Alerts are tied to what staff can do next, not just what’s wrong.
  • Built for district oversight: Helps leaders maintain visibility while protecting role-based access controls.

From Insight to Intervention—With One Click

Predictive nudges recommend practical next steps (follow up, schedule, remind, or guide) based on real student activity—so staff can take action faster, with less guesswork.

  • Action recommendations, not busywork: Suggest next steps counselors and advisors can execute quickly.
  • Designed for adoption: No new tools, no “AI training,” no complicated prompting required.
  • Keeps humans in control: Staff can review and approve actions—AI supports decisions, it doesn’t replace them.

Ask Better Questions. Get Clearer Answers.

As part of the Agentic AI roadmap, “Analyze” focuses on making district data easier to interpret—so teams can quickly understand who needs help and why, without manual slicing and exporting.

  • Plain-English analysis: Reduce time spent translating reports into action items.
  • District-friendly visibility: Built with district-level governance and role-based access in mind.
  • Purpose-limited by design: Student info used only for authorized educational purposes—not marketing or profiling.

Privacy Built In. Human First.

SchooLinks designed its AI workflows to minimize data exposure, keep actions auditable, and operate within the same role-based access controls districts already rely on.

  • PII is not used or is masked and is never used to train models outside SchooLinks’ infrastructure.
  • Encryption in transit + at rest with segmented, access-controlled environments.
  • FERPA-aligned controls: AI does not expand access; it operates within existing permissions and approved educational purposes.

FAQ's

Q. Is SchooLinks Agentic AI FERPA compliant?

SchooLinks AI features are designed to operate in compliance with FERPA, using role-based access controls and purpose-limited educational use.

Q. Does SchooLinks AI use student PII to train external models?

No—PII is not used or is masked, and it is not used to train models outside SchooLinks’ infrastructure.

Q. Is the Recommendation Letter Agent fully automated?

No—staff generate a draft, then review/edit before submitting. It’s designed for educator oversight.

Q. Where does the Form Completion Agent pull answers from?

It generates responses using existing data in SchooLinks related to the candidate, sponsor, and program, then shows a review table before saving.

‍

Q. Do staff need to learn prompting to use these agents?

The agentic approach is designed to be integrated into workflows and not require staff training in AI/prompting skills.

Q. What makes this “agentic” instead of a chatbot?

SchooLinks emphasizes context + workflow integration—AI outputs are embedded where staff can review, approve, and act.

See Agentic AI in the SchooLinks College and Career Readiness Platform

Fast setup. Real workflows. Built for districts.
Get in touch today to see a modern CCR platform that meets all your district needs, and then some.
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