Learning Experience Platform

Learning that adjusts to the learner — not the other way around.

Manresa CATER is an AI-guided LXP built on a simple premise: every learner is different, so their path should be too. It personalises in real time, measures mastery as it happens, and puts an AI co-tutor beside every learner, at every step.

Mia — AI co-tutor

MMia

Nice work on Concept 3 — that was a tricky one. Want to try something a little harder, or revise first?

Let's try something harder.

MMia

On it. I noticed you learn best with worked examples first — I'll lead with one.

app.manresa.ai / cater / live Adaptive
Question 8 / 30 Projectile · harder ↑ 00:00

A projectile is launched at angle θ. Which angle gives the maximum range?

A30°
B45°
C60°
D90°

Adaptive learning path

3 4 5
Foundations Currently: Concept Mastery Revision

Learner mastery dashboard

Concept mastery
Time on task
Confidence rating
Retention risk

Adapts

Difficulty ↑ · new question

Difficulty

Captures

Mastery ↑ granular Strength · Kinematics Next · harder item

Mastery scale

Novice Developing Proficient Master

Why we personalise

Learners are individuals. They arrive with wildly varying knowledge gaps, metacognition, motivation, self-directedness, and self-efficacy — the learning solutions we design should reflect that.

Knowledge gapsMetacognitionMotivationSelf-directednessSelf-efficacy Knowledge gapsMetacognitionMotivationSelf-directednessSelf-efficacy

A single fixed path can only ever be right for the average learner — which means it's wrong for almost everyone. Real inclusion in learning isn't a diversity statement; it's a design decision: build the system to notice difference, and respond to it, one learner at a time.

The technology

Five things happening behind every learner's screen.

A path that adapts continuously — not a static curriculum with a personalised label on top.

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Model updating

Every interaction updates the learner's profile in real time, gathering more evidence to personalise what comes next.

Learning paths

Highly efficient, personalised path recommendations through the course — combining direct and inferred evidence.

Content objects

Within a session, CATER optimises which content to recommend and in what sequence, to drive engagement and flow.

Metacognition

Rich, accurate information delivered back to the learner about their own progress — building self-awareness and goal-oriented motivation.

Memory decay

CATER detects when a learner is at risk of forgetting a previously mastered concept, and recommends a revision strategy for interleaved practice.

The result

A path that adapts continuously — not a static curriculum with a personalised label on top.

Structure, underneath the adaptivity Three learning strategies, working together.

Diagnostic placement

An adaptive assessment module used to benchmark learners and give an initial understanding of their strength across the course concepts.

Subject mastery

The core learning experience — designed to teach and assess concept mastery, highly configurable by institution, subject, and even individual instructor.

Revision & exam prep

A dedicated learning strategy to focus on a full course, or a subset, to prepare with confidence.

MMia · AI Co-Tutor

Ever-ready. Ever-patient. Always exactly where the learner is.

Mia is CATER's virtual subject expert — a conversational co-tutor trained on an institution's own content, available around the clock. She doesn't just answer questions; she notices how a learner learns best, and adjusts how she teaches accordingly.

  • Realtime, automated guidance — available 24/7, not bound to office hours or a fixed class schedule.
  • Trained on the institution's or organization's own content, so guidance stays accurate and relevant.
  • Notices individual learning preferences — leading with worked examples, analogies, or practice, depending on what works for that learner.
  • Frees instructors from repetitive guidance, so they can focus on the complex, human parts of teaching.

A conversation with Mia

MMia

I noticed you've revisited Concept 5 twice this week — want to walk through it together, one more time?

Yes please, I keep mixing up the two frameworks.

MMia

Totally fair — they're easy to conflate. Let's start with a side-by-side example from your own coursework.

Measuring what matters

Mastery and traits — tracked continuously, not guessed at once a semester.

Learner mastery

The depth of understanding or proficiency a learner has achieved in a topic, concept, or skill — continuously measured, and used to decide what comes next: repeat, skip ahead, or dive deeper. Represented on a scale from Novice to Master.

Quiz scores Time on task Confidence ratings Concept maps & skill progression trees

Learner traits

The cognitive, behavioural, and emotional characteristics that influence how a learner learns best.

CognitiveLearning style (visual, verbal), working memory, logical reasoning ability
BehaviouralAttention span, persistence, time-on-task, self-regulation
EmotionalMotivation, anxiety, confidence level
MetacognitiveAwareness of one's own learning, self-assessment habits

Built for corporate L&D too

Proficiency at your own pace. A path that's entirely your own.

In a workforce setting, adaptive learning changes what upskilling feels like. Instead of a fixed cohort pace that leaves some behind and bores others, every employee moves at the pace that's right for them — spending real time where they need it, and moving quickly through what they already know.

Own paceNo employee is held back by a cohort, or rushed past something they haven't yet mastered.
Complete privacyAn employee's learning path and progress are theirs — a personal, judgment-free space to close a gap.
A personalised environmentGuided by Mia, reinforced by an adaptive path — learning that feels built for one person, not deployed to a thousand.
What institutions gain Insight that reaches lecturers before, during, and after the semester.

Diagnostics

Individual and group insight into mastery and traits before a single student enters the room.

Learner data

An accurate, constantly updating picture of the intake class, at scale and in real time.

Remediation

Struggling learners, detected earlier

Struggling learners detected earlier, with intervention strategies to get them back on track.

Content analytics

Insight into the instructional efficacy and diagnostic power of course content itself.

Passion

Automated grading and recommendations free instructors to focus on teaching's more complex, human parts.

CATER in practice

A couple of ways this comes together.

MBA skilling ecosystem

A full two-year architecture

A structured two-year course design with a dual skill-tagging framework across seven generic skills, IRT-based mastery aggregation, and three-tier certification — culminating in a Learner Achievement Dossier recruiters can trust.

Blended delivery

Digital Skills Enablement

For institutions without in-house digital skills faculty — Manresa practitioners deliver in person, CATER reinforces on-demand, and CURA verifies proficiency at the end.

"Learn in person. Reinforce on-demand. Prove it with data."

See CATER guide a real learner, start to finish.

Walk through the adaptive path, a conversation with Mia, and what mastery looks like on the other end — with your own content as the example.