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 the most trusted, accurate Credit for Prior Learning platform.
ArchieCPL is AI-supported and human-led, built for institutional control:
- Powered by AI with a human in the loop
- Humans make final credit approval decisions
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
ArchieCPL for Veterans
ArchieCPL is designed to help Veterans and military-connected learners understand how military training, service experience, credentials, academic learning, and work experience may translate into postsecondary credit and shorter pathways to completion.
Combined with KnowMeQ’s skills and career intelligence, the same evidence can also be articulated into civilian skills, occupations, employment pathways, and targeted upskilling to support career mobility.
Why this matters
Veterans often leave military service with substantial learning and capability that can be difficult to see in civilian systems. Military occupations, rank, training, Joint Services Transcript (JST) records, credentials, and service experience can contain evidence relevant to postsecondary programs and civilian careers, but that evidence is frequently expressed in different language and data structures.
ArchieCPL is designed to create a bridge between those systems by:
- ingesting military and civilian evidence
- structuring the information into skills and learning outcomes
- comparing it with institutional programs and courses
- surfacing potential CPL opportunities for authorized human review
Veteran capability overview
| Capability | What it enables for Veterans |
|---|---|
| Military learning translation | Structures MOS identifiers, rank, training, JST information, credentials and experience into transferable skills, learning evidence and civilian-language descriptions. |
| Postsecondary pathway discovery | Compares prior learning with programs, courses and learning outcomes to identify potential CPL and shorter pathways to completion. |
| Career articulation | Uses the structured skills profile to support civilian career and job matching, skill-gap identification and targeted upskilling. |
| Evidence-backed recommendations | Shows supporting evidence, confidence indicators and match factors rather than producing unexplained recommendations. |
| Human institutional authority | Prepares recommendations for faculty, registrar, CPL or other authorized institutional review; AI does not make the final credit decision. |
| Reusable learner profile | Allows the profile to evolve as a Veteran adds a new JST, credential, resume, assessment or other evidence. |
Core functional workflow (Veteran education and credit recognition)
| Stage | Function |
|---|---|
| 1. Evidence intake | The Veteran uploads or connects available evidence such as a JST, resume, credentials, academic transcript or other documentation. |
| 2. Experience extraction | ArchieCPL extracts and structures military training, occupations, responsibilities, credentials, tools and other evidence. |
| 3. Skills and learning articulation | Military and civilian experience is represented through a common layer of skills, competencies, tasks and evidence. |
| 4. Program and course comparison | The platform compares the structured evidence against participating institutions' programs, courses and learning outcomes. |
| 5. Potential credit identification | ArchieCPL surfaces potential CPL opportunities, including the supporting evidence, confidence and relevant match factors. |
| 6. Human review | Authorized institutional reviewers can examine, accept, redirect, reject or request additional evidence, with rationale recorded. |
| 7. Ongoing profile | New JST information, credentials, employment or learning can be appended and recommendations can be re-evaluated as curricula or source information changes. |
Potential institutional outcomes
- More consistent interpretation of military learning across programs and reviewers.
- Earlier visibility into potential CPL before a Veteran commits to a program.
- Reduced manual effort spent locating, comparing and documenting evidence.
- Better auditability through evidence traces, confidence indicators and decision rationale.
- Reusable precedents that can support future reviews where institutional policy permits.
- Shorter, clearer pathways for Veterans whose prior learning already meets course or program outcomes.
Skills taxonomy and military-to-civilian translation
A central challenge in Veteran mobility is that military, postsecondary and workforce systems describe capability differently. KnowMeQ’s underlying skills intelligence bridges those systems through a shared language of skills, competencies, tasks and evidence.
| Military evidence | Common skills layer | Civilian destinations |
|---|---|---|
| MOS / occupation | Skills, competencies, tasks | College programs and courses |
| Rank and responsibilities | Leadership, decision-making, technical and operational capabilities | Civilian occupations and jobs |
| JST and military training | Learning evidence and educational outcomes | CPL / PLA review |
| Credentials and certifications | Verified capabilities and knowledge | Upskilling and credential pathways |
| Service and work experience | Recency, duration and applied evidence | Resume and career articulation |
Connecting education recognition to career mobility (Johari Suite)
Veterans often need to answer two questions at the same time:
- “What can my military experience count for in college?”
- “What can my military experience qualify me to do in the civilian workforce?”
KnowMeQ’s architecture is designed so the same underlying profile can support both.
Johari creates an adaptive skills and capability profile using information from resumes, military experience, credentials, employment history and validated skill and competency assessments. The platform can identify transferable skills, compare an individual’s profile with civilian occupations and employment opportunities, identify skill gaps, and recommend careers and educational programs that are accessible now or through targeted upskilling.
| Veteran question | KnowMeQ capability | Possible output |
|---|---|---|
| What civilian roles align with my experience? | Skills profile + career matching | Compatible occupations and jobs, with match factors. |
| Where are my gaps? | Gap analysis + assessments | Skills to strengthen and targeted upskilling. |
| What education can I shorten? | ArchieCPL | Potential CPL and shorter program pathways. |
| How do these pathways connect? | Reusable person profile | Career → education → credit recognition → upskilling pathway. |
| How do I explain my experience? | Military-to-civilian translation | Civilian-language skills, capabilities and evidence. |
Representative Veteran journey
Transitioning service member: career, education and credit recognition- The user begins with a resume, JST, MOS, rank, training record, credentials or a combination of these sources.
- The platform normalizes military experience and maps it to a transparent skills and evidence profile. Optional KnowMeQ assessments can refine the baseline.
- Career intelligence identifies civilian occupations and jobs that are accessible now, accessible with targeted upskilling, or that require a larger transition.
- ArchieCPL compares the Veteran’s JST, academic, work and volunteer evidence against participating college or university programs and course learning outcomes.
- The Veteran can see potential education pathways and possible prior-learning credit before beginning a formal institutional review process.
- An institution receives the evidence package, match rationale and confidence information for authorized human review.
- The Veteran’s profile remains reusable as new credentials, learning, work experience or military records are added.
- Receive a structured recommendation linked to the relevant source evidence.
- See the course/program learning outcomes and the military or experiential evidence contributing to the match.
- Review confidence and evidence indicators rather than a black-box recommendation.
- Accept, reject, redirect or request further evidence based on institutional policy.
- Record rationale and maintain an auditable decision trail.
- Where permitted, reuse approved precedents to support consistent future reviews.
Explainability, human review and academic integrity
ArchieCPL is designed to make AI-supported recommendations transparent and reviewable. ArchieCPL is not intended to make binding academic decisions on behalf of institutions.
| Control | How it supports trust |
|---|---|
| Evidence traces | Recommendations can show the military, academic, work or credential evidence contributing to a match. |
| Confidence indicators | Reviewers can see the strength of the comparison and focus attention where uncertainty is higher. |
| Source information | The platform can preserve source context and support re-evaluation when curricula or evidence changes. |
| Human authority | Authorized reviewers retain the ability to approve, reject, redirect or request further evidence. |
| Decision rationale | Review actions and reasons can be recorded for auditability and future governance. |
| Precedent reuse | Approved institutional decisions can support consistency where policy permits, without replacing new human review. |
Responsible AI principles
- Ground recommendations in identifiable source evidence and institutional curriculum data.
- Use constrained AI functions for interpretation and matching rather than allowing an unconstrained model to issue binding decisions.
- Maintain model, prompt, configuration and decision traceability where applicable.
- Support human correction, override and escalation.
- Evaluate quality and monitor unsupported or low-confidence outputs.
- Keep high-impact academic decisions under authorized human control.
Security, privacy and accessibility
Veteran education workflows can involve personally identifiable information, military records, academic records and other sensitive evidence. Security and privacy controls therefore need to be built into the architecture, data model and operating procedures.
| Control area | Representative approach |
|---|---|
| Data minimization | Collect and retain only the information required for defined features, recommendations or review workflows. |
| Identity and access | Role-based access, least privilege, appropriate authentication and separation of duties. |
| Encryption and secrets | Encryption in transit and at rest, managed secrets and environment separation. |
| Audit and traceability | User/system activity, recommendation evidence, configuration versions, human decisions and override reasons can be logged. |
| Privacy and retention | Consent, purpose limitation, policy-aligned retention, archival/deletion rules and privacy risk assessment. |
| Security assurance | Threat modelling, vulnerability management, penetration testing, incident response and partner/security review. |
| Accessibility | Target accessible digital experiences, including keyboard operation, screen-reader support, plain language, accessible documents and error recovery. |
Integration and deployment
ArchieCPL is designed as reusable intelligence infrastructure that can be integrated with enterprise platforms or delivered as a white-labelled cloud solution.
Representative integration points
- Student information systems (SIS) and institutional records.
- Admissions, advising, registrar and CPL/PLA workflows.
- Learning management systems and course/program catalogues.
- Credential and transcript sources, including JST and academic records.
- Career and workforce platforms.
- APIs and data feeds for program, course, learning-outcome and equivalency information.
- Reporting, audit and analytics environments.
Existing functional foundations
| Foundation | Representative capability |
|---|---|
| Document and evidence intake | Resume/document parsing, experience extraction and structured evidence. |
| Skills intelligence | Skills profiling, taxonomy crosswalks and military-to-civilian translation. |
| Career intelligence | Career/job/upskilling recommendation and skill-gap identification. |
| CPL intelligence | Course/program matching, evidence and confidence display, precedent support. |
| Reviewer workflow | Role-aware review patterns, human override and decision traceability. |
| Deployment | Responsive, white-labelled experiences and API-oriented integration. |
What success looks like
A successful Veteran skills-recognition workflow should make military learning easier to understand, easier to evaluate and easier to carry forward into both education and employment decisions.
| For Veterans | For institutions and partners |
|---|---|
| See how military learning and experience translate into civilian skills. | Receive structured, evidence-backed comparisons instead of manually reconstructing a Veteran's experience. |
| Discover potential college credit and shorter pathways before investing significant time in an application. | Reduce repetitive interpretation and evidence-gathering work. |
| Compare education and career pathways from the same underlying profile. | Improve consistency and transparency of CPL review. |
| Understand why a recommendation was made and what evidence supports it. | Preserve academic authority through human review and documented rationale. |
| Add new evidence over time without starting over. | Build reusable precedents and auditable workflows where policy permits. |
| Identify targeted upskilling when a desired career or program requires additional skills. | Create stronger bridges among Veterans, postsecondary education and the workforce. |
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)
- Scalable PLAR/CPL capacity: Expand PLAR/CPL volumes without proportionally expanding staff workload, while keeping academic review and final decisions in institutional hands.
- Labour efficiency: Reduce manual file review by 90% - organizing resumés, transcripts, evidence, work experience, and learning outcome alignment in one place.
- 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.
- 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.
- 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.
- 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.
International research: CPL practices in Canada and the USA
KnowMeQ has completed international Credit for Prior Learning (CPL) research outlining practices of key institutions in Canada and the USA.
How ArchieCPL helps institutions (benefits and outcomes)
Increases revenue and enrolment
- Nearly 4 in 10 employees (37%) are concerned their current skills will become obsolete in the future, and 64% of respondents say upskilling and reskilling has been useful for their job security.
- Employers anticipate that 44% of workers’ skills will be disrupted in the next five years, with 6 in 10 employees requiring training and upskilling before 2027.
- 71% of adult learners highlight flexibility as a crucial factor in their educational pursuits.
- Students allocated credit for prior learning saved an average of 9 to 14 months in earning their degrees and between $1,500 and $10,200 in tuition costs (depending on the institution type).
Improves student retention
- Adult students with prior learning credits had a credential completion rate of 49%, compared to 27% for non-PLA adult students.
- Receipt of CPL is associated with a 17% increase in the likelihood of completing a credential.
- Adult students who earned credit through Prior Learning Assessment (PLA) took, on average, 17.6 more course credits from their institution compared to those without PLA credits.
Optimizes institutional resources
- AI usage in higher education has surged 2.3x over the past year, with 93% of leaders expecting even greater use over the next two years.
- 80% of higher-education respondents are adopting AI to boost efficiency and productivity across their institutions.
- Over 80% of administrators predict significant growth in AI use for student success, enrollment management, and marketing.
Strengthens workforce alignment
- CPL recipients have a 17% higher employment rate within six months of graduation and earn 10–20% more annually than non-CPL peers.
Executive summary (higher-education context)
Higher education is facing a financial crisis that is driving job losses, program cuts, and projected deficits. At the same time, technological advances are making reskilling and upskilling more important than ever.
According to the World Economic Forum’s Future of Jobs Report 2025, employers anticipate that 39% of workers’ core skills will need to change by 2030.
Implementing ArchieCPL: a roadmap for success
Phase 1: Organizational alignment
- Conduct cost-benefit analysis and define institutional goals.
- Secure stakeholder buy-in through faculty, administration, and student engagement.
- Ensure compliance with privacy regulations (FERPA, GDPR, PIPEDA).
Phase 2: Pilot and integration
- Integrate with existing student information systems (SIS).
- Run a pilot program with a department or faculty.
- Optimize workflows and train admissions teams.
Phase 3: Full-scale adoption and growth
- Expand AI-powered credit recognition campus-wide.
- Develop marketing and recruitment campaigns targeting experienced learners.
- Establish feedback loops to continuously improve system performance.
Introducing ArchieCPL
What is ArchieCPL?
Built with Canada’s Vector Institute for AI, ArchieCPL is informed by skill and competency taxonomies that are internationally recognized and creates through-lines between occupations, postsecondary programs, and learning outcomes.
ArchieCPL’s AI platform assesses postsecondary students’ Credit for Prior Learning applications quickly, reducing bias. It allows higher-education institutions to process 10x the number of course-credit applications without increasing headcount, creating valuable new revenue opportunities for universities and colleges.
How it works
- Applicant uploads their resume or transcript to Archie.
- Archie’s AI evaluates skills, education, and experience vs. postsecondary programs and course learning outcomes for credit recognition.
- Student receives prior learning credit recommendations, along with a confidence / predictor of credit application success percentage instantly.
- Student meets with a college administrator to review the application and receive credit approval.
- Student’s course credits received through ArchieCPL are added to the academic transcript.
- Student begins a faster, less expensive postsecondary journey.
Why KnowMeQ
- Deep higher-education expertise — our solutions are designed with academic rigor and industry best practices.
- Unmatched AI-powered accuracy — built on proven machine learning models trained on education and workforce data.
- Seamless integration — colleges have the option to plug ArchieCPL into their existing SIS, CRM, and LMS platforms, and complement their workflows.
- Dedicated implementation and support — our team provides white-glove service to ensure seamless adoption and continuous success.
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/meet-archie
You can also reach us at hello@knowmeq.com.