Course-aware learning assistants
Help learners explore approved concepts, find relevant material and ask follow-up questions. Ground responses in the correct course version and explain when the available material does not support an answer.
Business platforms
AI-powered learning platforms
Support learners and educators with guided learning, knowledge access and feedback tools.
Built around a specific business need.
Learners often need help at the moment they become stuck, while educators need time to understand misconceptions and give thoughtful feedback. Generic AI answers may sound helpful but use the wrong terminology, reveal an assessment answer or explain a topic at a level that does not fit the learner.
We design AI-assisted learning around educational intent and approved material. The assistant’s role, knowledge boundaries and feedback style are defined with educators, then tested on realistic learning situations. Its purpose is to support understanding and practice while keeping assessment authority and sensitive decisions with the appropriate people.
Who this is for
- Training providers adding guided support to existing learning programmes.
- Employers developing contextual learning for staff roles and procedures.
- Educators exploring AI-assisted practice, feedback and content preparation.
Where this can make a practical difference.
Practice and formative feedback
Provide hints, worked reasoning or feedback on low-stakes practice. Adapt the assistance to the learning objective without presenting automated judgement as an assessor’s final decision.
Educator preparation tools
Assist with draft explanations, practice questions and alternative examples. Educators review accuracy, accessibility and alignment before material reaches learners, retaining responsibility for the published learning experience.
How we approach the work.
Set educational boundaries
Agree what learners should understand or practise and what assistance would undermine that aim. Define the distinction between teaching support, practice and formal assessment.
Prepare approved knowledge
Organise source material by course, version and learner access. Record terminology and common misconceptions so retrieval supports the intended curriculum rather than a generic answer.
Design supportive interactions
Create prompts and interfaces that invite reasoning, clarify confusion and offer manageable next steps. Avoid assuming that more generated content means a better learning experience.
Evaluate with educators
Test factual accuracy, instructional usefulness and handling of ambiguous questions. Include assessment requests, unsupported claims and situations where the assistant should defer to a tutor.
Pilot and review impact
Run a limited learning activity with appropriate oversight. Examine learner understanding, help-seeking patterns and tutor feedback before expanding the assistant’s role.
The sequence is tailored to the agreed scope, dependencies and risk. It describes our delivery approach, not a fixed timeline or performance guarantee.
What the engagement can deliver.
- Educational use-case and assistance-boundary specification.
- Approved knowledge structure and source-aware response design.
- Learner or educator interfaces for the selected workflows.
- Evaluation examples reviewed by subject specialists.
- Monitoring, feedback and escalation procedures.
The proposal identifies the deliverables, acceptance evidence, responsibilities and support arrangements included in your engagement.
Measure what matters to this workflow.
Instructional usefulness
Have educators review whether responses help learners reason and progress towards the intended objective.
Grounding accuracy
Check whether substantive claims are supported by the correct approved materials and course version.
Appropriate escalation
Measure whether uncertain or unsuitable requests reach a human rather than receiving confident unsupported guidance.
These are measures to discuss and baseline. Targets are agreed for the specific system and data; no universal improvement percentage is assumed.
What to prepare for discovery.
- Defined learning objectives and approved source materials.
- Educators available to review examples and pilot responses.
- Clear rules for learner privacy and assessment boundaries.
Questions worth asking.
Will AI mark all learner work?
We distinguish formative assistance from consequential assessment. Automated support may help with selected tasks, but final decisions require the review and authority appropriate to your educational setting.
Can the assistant adapt to individual learners?
Adaptation should use relevant, permitted information and a clear educational purpose. We avoid inferring diagnoses or treating a short interaction as a reliable assessment of someone’s abilities.
Does this require replacing our LMS?
Not necessarily. A focused assistant can complement an existing platform if content access, learner identity and integration permissions are suitable. Discovery establishes the least disruptive useful option.