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Education meets possibility

Better learning.
Smarter systems.
Human at heart.

Make AI useful for your university. We design learning experiences, train your people and build practical systems—so the technology makes sense, and the benefits can be measured.

Find your service Explore the approach

Designed around your people. Built around your goals.

WorldWise Learning symbol: two ribbons and orbiting spheres around a central figure
WorldWise Learning

A considered approach to AI in education

What we can help you do

Complex possibilities.
Clear, practical services.

Each service stands on its own, or connects into a bigger plan.

English practice systemsFaculty trainingAI systems engineeringAI strategy

Service / 01 · English practice systems

More English practice.
More confident students.

An AI-supported practice lab gives students somewhere to speak, listen and try again, aligned to what their teachers already teach.

English practice labsSpeaking & listeningRole-playCEFR / IELTS-aligned
What the practice lab includes

Discipline-specific speaking and role-play scenarios matched to student levels, with formative feedback against your rubrics and a second attempt at reduced support.

You receive a pilot design, scenario library, student workflow, teacher resources and an evaluation plan.

Service / 02 · Faculty training

Help your people
feel confident with AI.

Workshops and coaching turn uncertainty into everyday skill, using your faculty’s own tasks and materials.

Faculty developmentHands-on workshopsReusable templatesSix-level pathway
What the six-level pathway covers

Responsible AI foundations, prompting and content workflows, rubric-linked assessment, discipline-specific use, automation and agent systems, then train-the-trainer. Delivered as 60–90 minute micro-workshops on real faculty tasks.

You receive a role-specific pathway, working templates and an evidence artefact per level.

Service / 03 · AI systems engineering

Less repetitive work.
More room for what matters.

Connect your tools and simplify everyday work with purpose-built AI systems engineering, data integration and automation.

AI systems engineeringLLM & agent systemsData integrationVendor-neutral
What we design and build

Applications built around one workflow. Retrieval-grounded knowledge assistants answering from approved curriculum, policy and rubrics with a visible source trace. Data integration between systems that do not talk to each other. Agent systems with human review at anything consequential.

Automate what is repetitive, rules-based and reviewable. Keep human consequential academic judgment.

You receive a documented workflow, a working prototype, testing criteria and a handover plan.

Service / 04 · AI strategy

A clear plan for AI.
A responsible way forward.

Decide where AI belongs, set boundaries staff can actually apply, and know whether it works before any wider rollout.

Institutional strategyResponsible useImpact measurementGovernance
What we help you define

Use cases ranked by learning need and feasibility. Allowed, limited and prohibited rules defined per task. Data minimisation, retention, role-based access and audit trails, with a named owner for every consequential workflow and a roadmap ending in scale, adapt or stop.

You receive a prioritised roadmap, governance checklist, responsibility map and evaluation framework.

You own what we build together. Every engagement ends with artefacts your institution keeps and can run without us: the documented use case and workflows, the scenario and template library, the measurement rubric with baseline evidence, training materials, a governance checklist and an architecture recommendation.

Capability stack

Pedagogy at the base.
Institutional systems on top.

Most AI projects begin at the upper layers and fail at the lower ones. This work starts at the base, with one problem designed alongside faculty, and builds upward only where evidence supports it.

  1. Institutional scalegovernance · analytics · operating models · adoption
  2. Applications & automationdashboards · workflows · APIs · rapid prototypes
  3. AI systemsLLMs · agents · retrieval · evaluation · guardrails
  4. Faculty developmenttraining · modelling · coaching · implementation
  5. Curriculum & assessmentoutcomes · rubrics · moderation · progression
  6. Pedagogylanguage learning · inquiry · feedback · differentiation

Layers 01–05: demonstrated background   Layer 06: proposed with the university

Systems in practice

A production system,
built and run in the open.

A system Nathaniel Jay Adams designed, built and operates himself, for his own teaching. Not a university deployment and not offered as one — a working example of the data architecture, integration discipline and operating standard described above.

  1. Problem

    Teaching across several programmes produced the same load weekly: records scattered across documents, progress tracked by hand, nowhere a term’s evidence could be reviewed as a whole.

  2. System architecture

    A server-rendered application on managed cloud hosting over a relational database, deployed continuously from source control, reachable only behind authentication. Vendor-neutral by construction: the data model is the asset, not the provider.

    Application
    Next.js 16 · React · TypeScript
    Data
    PostgreSQL via Prisma · Zod-validated boundaries
    Runtime dependencies
    12, deliberately
    Access
    Authenticated; no public surface

  3. What was built

    A ten-table domain model covering the whole teaching operation rather than one feature, with a command centre for weekly planning on top.

    • Students · Instructors · Lessons · Materials
    • Assessments · Payments
    • Source documents · Review queue
    • Integration health · Scheduled run history

  4. Data integration and AI readiness

    Two scheduled jobs run daily: calendar events are reconciled into lessons, and teaching records are mirrored into Notion. A separate local source-file pipeline tracks files by content hash, so changes, duplicates and disappearances are detected rather than assumed.

    The system does not call a language model, deliberately. Source-document tracking and a human review queue are precisely the grounding and oversight substrate a retrieval-grounded assistant requires. The data discipline comes first; the model is added afterwards, if evidence justifies it.

  5. Operational use

    It runs unattended on a schedule, as working infrastructure rather than a demonstration. The scheduler guarantees only hourly precision and does not retry a failed invocation, so the design accepts both: the two jobs have start windows an hour apart (not a completion dependency), every run is recorded durably, and a missed run is surfaced rather than assumed. Scheduled requests are authenticated and fail closed if the secret is absent. Failures in credential expiry, host permissions and third-party API changes were diagnosed and fixed in production — the part that never appears in a prototype.

  6. Evidence

    Diagrams generated from the system’s own schema and scheduling configuration.
    No student, staff or institutional data appears in any of them.

    Architecture in practice
    One system, from teaching to evidenceTeaching records are updated by scheduled jobs. Run history and connector health make outcomes inspectable. These pathways show process flow, not database foreign keys.
    01 / Architecture

    Two planes,
    one system.

    Teaching is what happens.
    Operations hold the evidence.

    Teaching core Teaching relationshipsLesson connects to Student, Instructor, Material and Assessment. Student also connects to Assessment and Payment. Payment has no direct Lesson relationship.
    StudentLearner
    InstructorEducator
    LessonLearning experience
    AssessmentProgress
    MaterialResources
    PaymentStudent billing
    Operations plane
    SourceDocument Source filesReviewItem Review queueIntegrationHealth Connector healthCronRun Run history
    View architecture evidence

    Six teaching models, four operations models. Lesson belongs to a Student and Instructor; Material is optional. Assessment belongs to Student and can reference Lesson. Payment belongs to Student, not Lesson.

    ReviewItem can reference SourceDocument. IntegrationHealth and CronRun are independent tables. The two planes are conceptual groupings, not a database relationship.

    02 / Automation

    Two daily jobs.
    A deliberate sequence.

    Bring the calendar in.
    Then mirror teaching records out.

    11:00 UTCCalendar → Lessons
    1. Calendar
    2. Reconcile
    3. Lessons
    1 hour separation
    12:00 UTCTeaching records → Notion
    1. Database
    2. Synchronise
    3. Notion
    Each authorised job invocation
    starts a CronRun record.
    View scheduling evidence

    0 11 * * * → /api/cron/daily-sync
    0 12 * * * → /api/cron/notion-sync

    Start windows are one hour apart; completion order is not locked. The noon job mirrors Students, Instructors, Lessons, Payments and Assessments into Notion.

    Source-file hashing and review are a separate local pipeline, not the noon cloud job. Requests require the cron secret; unauthorised requests are rejected before creating a run.

    03 / Observability

    Nothing fails
    silently.

    See what ran, what worked,
    and what needs attention.

    1. RunWork begins
    2. RecordedHistory saved
    3. Health updatedConnector state
    4. Failure surfacedAction is visible
    Run historyJob · timing · outcome · errorCronRun
    Connector healthHealth · last sync · last error

    A reachable connector does not prove a scheduled job succeeded. Both signals matter.

    View observability evidence

    CronRun
    job · startedAt · finishedAt · ok · error · trigger

    IntegrationHealth
    healthy · lastSyncAt · lastError

    Derived status, not stored health fields: ok, failed, missed, blocked, none. “Missed” means the last successful run is stale; it is not a measured count. Connector health is persisted by the Notion integration separately from run completion.

    This illustrates the recording design, not a live monitor or a guarantee against every infrastructure failure. No sample records or invented metrics are shown.

    Source-derived architecture. No live records.Understand the system. Inspect the evidence.

  7. Transferability

    The pattern moves to institutional work unchanged: a defined data model, a scheduled reconciliation pipeline, grounded assistance under human review, and observability that survives real failure. What changes for a university is governance, scale and where review sits — not the architecture.

Nathaniel Jay Adams, Founder and AI Systems Architect at WorldWise Learning
Leadership

Nathaniel Jay Adams

Founder & AI Systems Architect, WorldWise LearningCo-founder, Jurassic English™

An interdisciplinary academic, technologist, AI systems architect and education strategist working across artificial intelligence, LLMs, agent systems, educational technology, curriculum architecture and digital learning infrastructure.

His work combines academic practice, software and AI system design, courseware engineering and strategic innovation, designing AI-enabled educational systems that connect pedagogy, data, intelligent automation and human decision-making into practical learning environments.

  • AI systems engineering
  • Curriculum & courseware architecture
  • Research & development
  • University & enterprise partnerships

Two brands, one practice

WorldWise LearningInstitutional work: consulting, AI systems, education technology, partnerships and organisational solutions.
Jurassic English™The student-facing academy, where the teaching practice behind this work happens.
Professional bases
United States 1995–2011
The Netherlands 2001–2008
China 2010–2026
Vietnam Hanoi · Ho Chi Minh City · 2026–present
Portrait of Nathaniel Jay Adams, Founder and AI Systems Architect at WorldWise Learning

Nathaniel Jay Adams

Founder & AI Systems Architect

WorldWise Learning

Co-founder, Jurassic English™

Path 01

Information Technology Career

Three decades of technology, from enterprise systems and digital transformation to applied AI, across the United States, the Netherlands, China and Vietnam.

IBMOracleSymantecPrior enterprise experience includes roles with IBM, Oracle and Symantec. Named as past employers only. No endorsement or current partnership is implied.

From enterprise computing to agent systems

  1. Enterprise Computing1995–2000 · United States

    Programming, technical support and network team leadership, including contract work for IBM Global Services, then systems analysis and pre-sales support.

  2. Network & Distributed Systems2001–2008 · The Netherlands

    IT support leadership, service delivery and network administration for multinational organisations in Nieuwegein, Amsterdam and Rotterdam.

  3. Application Systems2008–2011 · United States · China

    Lead systems analysis and senior technical engineering, including Symantec, working between Salt Lake City and Shanghai.

  4. Cloud Architecture2011–2017 · China

    Enterprise product roles with IBM and Oracle in Shenzhen, followed by graduate research on cloud-based solutions in English-language education.

  5. Digital Transformation

    Service management and delivery practice, with ITIL Foundation and PRINCE2 Foundation certification: the discipline behind changing how organisations work, not only what they run.

  6. Artificial Intelligence

    Research on AI-enhanced language acquisition within the University of Warwick iPGCE, connecting language pedagogy to AI tools.

  7. Agent Systems

    Designing LLM and agent workflows with human review, and shipping them: Jurassic AI Speaking scores IELTS-style speaking with the learner's own words as evidence.

  8. WorldWise Learning

    Enterprise systems thinking applied to teaching and learning: practical AI, measured against a baseline, with humans making the decisions.

Capabilities

  • Enterprise Systems
  • Network & Distributed Systems
  • Software & Application Development
  • Cloud Architecture
  • Systems Integration
  • Technology Strategy
  • Digital Transformation
  • Automation
  • Applied AI
  • LLM Systems
  • Agent Systems
  • Retrieval & Knowledge Systems
  • Human-in-the-loop AI Architecture

Path 02

Educational Career

International education, academic leadership, curriculum architecture and AI-enabled learning systems.

Career chronology

  1. 2018–2022 · International schools & language centres, ChinaESL Teacher
    • CEFR-aligned materials and IELTS preparation programmes
    • Communicative speaking curricula and digital learning activities
    • Primary, secondary and adult learners
  2. 2022–2023 · Walton Foreign Language School, ChinaHead ESL Teacher & Curriculum Coordinator
    • Coordinated an ESL programme serving 300+ students
    • Speaking examination scores up 20% through CLT and TBLT
    • CEFR-aligned digital project, 500 students: 40% better vocabulary retention
  3. 2023–2024 · Walton Foreign Language School, International High School, ChinaForeign Director — ESL Programme
    • Led a 12-instructor team serving 400+ students; 90% teacher retention
    • Curriculum and assessment redesign: Cambridge results up 25%
    • 100% Cambridge Economics pass rate
  4. 2025–2026 · Wuhan Haidian Foreign Language Shiyan School, ChinaSenior English & Technology Educator
    • AP Computer Science A (Java), CS fundamentals and applied AI
    • Advanced IELTS for 60+ students: 1.0–1.5 band gains
    • CEFR-aligned digital tools across the ESL programme
  5. 2026–present · Independent · Hanoi, VietnamAI & English Education Consultant / Professional Trainer
    • AI-supported English learning and IELTS speaking development
    • Training professionals to use generative AI within their industries
    • AI-enabled practice systems that extend learning beyond class time
  6. Present · Hanoi · Ho Chi Minh City, VietnamCo-founder, Jurassic English™
    • Built the Jurassic English™ literature-based academic English and reasoning pathway
    • Launched Jurassic AI Speaking, AI-scored IELTS-style speaking practice
    • Business and technical English for tech teams in Ho Chi Minh City and Hanoi

Built at Jurassic English™

Co-founder · jurassicenglish.com · Services live on jurassicenglish.com, designed and built as part of the same practice.

Academic foundation

  • M.S. Information Technology ManagementWestern Governors UniversityAwarded 2017 · graduate research on cloud-based solutions in ESL education
  • International PGCE (iPGCE)University of WarwickNear completion · expected 2026 · research: AI-enhanced language acquisition and CEFR-aligned instruction
  • Advanced TEFL CertificationInternational TESOL Academy120 hours
  • Professional certificationsITIL Foundation · PRINCE2 Foundation · Google Certified Educator (Levels 1 & 2) · Microsoft Innovative Educator

Educational expertise

Pedagogy+Technology+Evidence+Human decision-making

  • Academic English & IELTS

    Advanced IELTS preparation and academic English within Cambridge- and CEFR-referenced curricula.

  • Curriculum Architecture

    Building and revising ESL and subject curricula so goals, lessons and assessment line up.

  • Teacher Development

    Workshops, coaching and AI-workflow training; led a 12-instructor department with 90% teacher retention.

  • Assessment & Evidence

    Tracking student progress with varied evaluation tools, and judging new methods against a baseline.

  • Computer Science & AI Education

    AP Computer Science A (Java), CS fundamentals and applied AI, informed by decades of engineering practice.

  • Institutional Learning Systems

    Designing the systems around teaching: platforms, data, workflows and governance.

Two careers. One systems philosophy.

  • Enterprise Technology
  • AI Systems Architecture

WorldWise LearningIntegrated Educational Systems

  • International Education
  • Curriculum & Academic Practice

WorldWise Learning brings together enterprise technology architecture and international academic practice to design educational systems in which pedagogy, data, intelligent automation and human professional judgment operate as one connected environment.

How we work

Start focused.
Build something useful.

A pilot runs 8–12 weeks: one cohort, one semester, faculty-led, with a documented baseline. No institution-wide rollout before evidence.

  1. 01 / Understand · weeks 1–2

    Find the real need.

    Listen to your team, observe classes and agree what a useful result looks like.

    You get: a brief, a baseline and success criteria.
  2. 02 / Design · weeks 2–4

    Make the plan tangible.

    Map the experience, build the scenario bank, agree responsibilities and safeguards.

    You get: a pilot plan and a working environment.
  3. 03 / Put into practice · weeks 4–9

    Build. Train. Refine.

    Introduce the solution with a small group and support your people through weekly review cycles.

    You get: a working pilot and practice evidence.
  4. 04 / Evaluate · weeks 9–12

    Let evidence guide you.

    Review learning, staff workload and system quality against the baseline.

    You get: findings and a scale / adapt / stop brief.

Five ways to structure the work, and they can be sequenced

  1. Pilot adviser & designer — the pilot, baseline and measurement framework.
  2. Training & implementation — the pilot alongside the faculty coaching loop.
  3. Prototype system build — interface, dashboard and supporting workflows.
  4. Ongoing systems consultant — architecture, governance and roadmap over time.
  5. Train-the-trainer — internal capability, so the institution owns it.
Evidence framework

Four measures,
with student learning first.

Where feasible: matched cohorts, common tasks, blinded moderation, documented baseline. Satisfaction is useful, but it is not a measure of learning.

Primary

Student learning

  • Pre / post comparable task
  • Transfer to unfamiliar scenarios

Did students demonstrably improve?

Student behaviour

  • Practice frequency and completion
  • Independent practice

Did practice volume actually rise?

Faculty impact

  • Workload and sustainability
  • Confidence with AI workflows

Is this sustainable for faculty?

System quality

  • Output accuracy and failure rate
  • Safety and integrity incidents

Is the system reliable enough to trust?

Academic integrity

A practical allowed, limited
and prohibited framework.

Rules are defined per task, not in one vague statement. This is the starting template; your academic board owns the final version.

Allowed

  • Brainstorming
  • Language practice
  • Formative quizzes
  • Draft feedback where course policy allows

Limited · disclosed

  • Editing
  • Translation
  • Research synthesis
  • Code assistance

Prohibited where unaided work is required

  • Submitting AI output as one’s own
  • Fabricated sources
  • Bypassing examination conditions
A little more clarity

Good questions.
Straight answers.

Do we need to understand AI already?

No. We start with your goals and explain options in everyday language. Technical choices follow your needs, staff readiness and existing systems.

Can we start with just one service?

Yes. Begin with a training programme, a single workflow or a focused practice pilot. Services combine when there is a clear benefit.

How do you know whether the work helps?

We agree a baseline and success criteria before the pilot: student performance, practice completion, faculty workload and the accuracy of system outputs.

How much does an engagement cost?

Pricing depends on scope, people and technical requirements. A discovery conversation establishes the work so deliverables, timing and costs are agreed before anything begins.

How is student information handled?

Data requirements are agreed with your institution: collect only what is necessary, restrict access, set retention periods, and keep consequential decisions under human review.

Let’s find your starting point

What would you like
to make possible?

Tell us what you are trying to achieve, where the current challenge is, and what kind of support you may need. WorldWise Learning will review your inquiry and respond within two business days.

  1. What is the biggest problem you are trying to solve?
  2. Who uses the facility today, and how?
  3. How do you currently measure improvement?
  4. Which group would make the most useful first pilot?
  5. What result would make one semester count as successful?
Start your 3-minute inquiry (opens in a new tab)

Seven short questions · normally a reply within two business days · what we ask

Prefer email

info@jurassicenglish.com