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slug: "cpl-assessments"
title: "CPL Assessments"
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template_version: "v2"
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# CPL Assessments
We are KnowMeQ, and **ArchieCPL** is our AI-powered Credit for Prior Learning (CPL) / Prior Learning Assessment and Recognition (PLAR) platform.

ArchieCPL is designed to help colleges and universities:

- **Scale PLAR/CPL** to handle high volumes of applications without proportionally expanding staff workload
- **Make faster, more consistent, well-documented credit decisions** while keeping academic control with the institution
- **Support enrolment priorities** by showing adult learners and other applicants how their prior learning can count toward a credential

## White-label, public-facing experience (optional)

ArchieCPL can be deployed as a **white-label solution** for colleges and universities.

Depending on your implementation goals, it can:

- **Reside on your institution’s website** as a publicly accessible experience
- Help individuals explore **programs to enroll in programs at the institution and for current students, to earn prior learning credits**
- Commonly available resources are used to articulate the individual's skills and experiences: **resumés, transcripts, work experience.**   
- **ArchieCPL presents potential courses and programs that they may pursue at the institution and confidence scores act as a supportive guide for the individual to gauge the likelihood (via %) that they will qualify for a course credit application**
- **Applicants receive course and program recommendations for free.  They only pay when they process formal CPL / PLAR course applications**
- **KnowMeQ and Colleges split ArchieCPL course application revenue**; helping institutions recover subscription costs
- ArchieCPL as a state or regional marketplace: networks of colleges can use ArchieCPL and Eddie/Ed-T (Credit Transfer) for geographic credit mobility networks; applicants and students can identify where their credits can transfer, and where they may earn CPL credits (different programs or courses)
- Individuals' work, military and education experiences align to **course and program outcomes**, then organize the file for staff and faculty review

Our goal is to help institutions reinforce a simple learner message: **“anyone can start here.”**

## How an ArchieCPL assessment flows

Our assessment moves through four stages, and each stage has a defined hand-off to a human reviewer.

- **Submission:** The student logs in, uploads a resumé, and picks the courses they want assessed for prior learning credit. College staff (registrar, credit transfer officers, or advisors) can request additional proof or pull in course-specific tests and quizzes.
- **AI analysis:** Our AI automatically assesses eligibility for course credit by comparing the resumé against the institution's own curriculum and learning outcomes. The assessment takes minutes.
- **Recommendation Report:** The AI generates a report for staff review that includes a confidence score, a written rationale explaining the matches between the student's evidence and the course outcomes, and color-coding so reviewers can triage strong, marginal, and weak cases quickly.
- **Review and decision:** College staff (and, depending on the institution, faculty and chair-level reviewers) accept, reject, or request further evidence. Approved credits are pushed to the institution's Student Information System (SIS) for posting on the student transcript.

## Scoring rubrics for prior learning

Our scoring is not a single pass/fail check. Instead, our AI matches the student's evidence against comprehensive skill and competency taxonomies tied to each institution's stated learning outcomes, and it returns a confidence score plus a detailed rationale that tells reviewers why a particular score was assigned.

To protect the integrity of the score, we include built-in safeguards in the validation step:

- **Color-coded outputs** to flag strong matches versus weak matches for triage.
- **AI-generated content detection** so reviewers can see when uploaded artifacts may have been synthesized rather than produced by the learner.
- **Request-for-further-evidence pathways** that let staff trigger follow-up artifacts when the initial evidence is not enough to support a credit decision, including:
  - reflection essays
  - reference letters
  - video submissions
  - follow-up quiz questions aligned with course outcomes

When you (as an institutional customer) onboard, we configure the rubric to your specific programs so the AI is comparing evidence against your own learning outcomes, not a generic national standard.

## Validation workflows with universities

We don't replace faculty review; we route submissions to it faster.

Our public case examples include three validation models that partner colleges use with ArchieCPL:

- **Hybrid review (Fleming College):** Fleming uses a hybrid approach where AI handles speed and scale while subject matter experts (SMEs) supply the human judgment that protects assessment integrity. Fleming's white-labeled implementation is called **FastPass**, launched in November 2024. In its first phase, FastPass generated more than 5,000 unique course recommendations.

Across all of these models, our AI’s role is to surface the recommendation and rationale; the human reviewer’s role is to decide and sign off.

## Credential equivalency mapping

Equivalency mapping is the step that converts an assessment into an actual credit decision.

ArchieCPL maps the skills, certifications, and work history it identifies against standardized learning outcomes and against your institution-specific learning outcomes. Each match is captured with enough detail that faculty can accept it, reject it, or request further evidence on its merits.

For institutions that want a public-facing equivalent, we can support practices that pair a transfer-rule database with an online equivalency tool on the partner's own website. We can configure ArchieCPL to support either an internal-only mapping workflow or a hybrid that also publishes approved equivalents to students and advisors.

## Transcript-level reporting

Once a credit decision is approved, our workflow produces the artifacts your registrar needs to post credit on a transcript:

- **Recommendation Reports** generated by the AI, which record the evidence reviewed, the matched learning outcomes, and the confidence score at the time of the decision.
- **Decision records** stored in your institution's credit transfer database so the same prior learning can be re-evaluated consistently across terms and across applicants.
- **SIS push** of approved credits so they appear on the student transcript without manual data entry by the registrar.
- **Automated transcript request and retrieval** support for institutions that want CPL students to have their existing transcripts pulled and matched as part of intake.

The intended end state is that credit decisions and the evidence behind them are reproducible: another reviewer, working the same file in a later term, can see exactly what was considered and why.

## Strategic fit: PLAR/CPL as an enrolment priority

ArchieCPL is a scalable credit recognition platform that helps colleges and universities expand PLAR/CPL as a strategic enrolment priority.

It’s especially relevant for:

- Adult learners
- Workers and mid-career learners
- Newcomers
- Career changers
- Learners with prior education or significant prior experience

By helping applicants see that their previous learning, work, and life experience can count toward their next credential, institutions can improve **application confidence**, **conversion**, and **persistence**.

## Integration-ready

ArchieCPL can be designed to connect with institutional systems based on your implementation priorities, including:

- **Student Information Systems (SIS)** for transcript posting
- **CRM platforms** to support enrolment pipelines
- **Application workflows** and other institutional intake processes
- **Credential/credit transfer tools** (where applicable)

## Core value (most important points)

1. **Scalable PLAR/CPL capacity:** Expand PLAR/CPL volumes without proportionally expanding staff workload, while keeping academic review and final decisions in institutional hands.
2. **Labour efficiency:** Reduce manual file review by 90% -  organizing resumés, transcripts, evidence, work experience, and learning outcome alignment in one place.  
3. **Trust and academic control:** We do not position ArchieCPL as an automatic credit-awarding system. It supports the review process with structured evidence, alignment indicators, confidence signals, and audit trails, while the institution retains academic authority.
4. **Cost-effective enrolment strategy:** Use PLAR/CPL as a student access tool and an enrolment conversion tool by helping adult learners see that their previous learning has value.
5. **Student access and confidence:** Reduce uncertainty around fit, recognition of experience, and time commitment by giving learners a clearer starting point and a more navigable, responsive experience.
6. **Workforce alignment:** Support employer partnerships, upskilling pathways, workforce development, and mid-career recruitment by translating workplace learning and experience into academic pathways.

## Pairing with transfer credit workflows (Eddie/Ed‑T)

ArchieCPL supports prior learning credit. It can also work alongside **Eddie/Ed‑T** for transfer credit and equivalency workflows.

Together, they can support a broader **credit mobility** strategy for learners entering college from work, other institutions, military experience, or non-linear education pathways.

## What we ask institutions to provide

When a college adopts ArchieCPL, we work with you to configure the rubric and the validation workflow. To do that effectively, we need:

- Your current course catalog and learning outcomes (the AI matches against these).
- Your decision rights matrix — who reviews, who approves, and at what stage.
- Your SIS environment and the integration method you want for posting approved credits.
- Any institution-specific credential lists you already maintain (e.g., provincial certifications, trade credentials).
- The hosts/process owners for any follow-up evidence steps (who fields reference letters, who authors quiz questions).

## Operational facts at a glance

- **Assessment turnaround:** minutes per application, after initial setup.
- **Scale:** designed to process thousands of credit reviews.
- **Throughput benchmark:** the FastPass deployment at Fleming College generated more than 5,000 unique course recommendations in its first phase.
- **Output per assessment:** confidence score, detailed rationale, color-coded match quality, and a list of matched learning outcomes.
- **Reviewer hand-off:** every AI recommendation goes to a human reviewer before any credit is posted.
- **Evidence types supported:** resumés, transcripts, certifications, work history, portfolios, plus follow-up artifacts (reflection essays, reference letters, video submissions, follow-up quiz questions) when staff request them.
- **Funding acknowledgement:** our publicly released Fall 2025 white paper on CPL and PLAR was developed with funding support from ONCAT (Ontario Council on Articulation and Transfer) and the Province of Ontario.

## Getting started

If your college wants to run a CPL pilot on ArchieCPL, the next step is a working session with our team. We walk you through configuration of the rubric to your outcomes, set up the validation routing for your faculty, and map your SIS handoff.

- Product page: https://knowmeq.com/archie-cpl
- Request a demo: https://calendly.com/matt-foran-knowmeq-sm