Aptem roadmap

We’re exploring a Model Context Protocol (MCP) connector for Aptem Apprentice. It will securely connect your Aptem data to the AI assistants your organisation already uses. You could then ask questions of live data in plain English, such as which learners have the most activity awaiting review, without manual exports or bespoke integrations.

The connector would follow the permissions people already have in Aptem, so each user only sees the data they can already access.

We’re working closely with providers throughout the development to understand which data matters most and how they would get maximum value from this development.

To support a better learner experience, we’re exploring a dedicated mobile app that delivers a simpler, more intuitive interface. This will make it easier for apprentices to access essential features on their phone and meet the expectations of today’s mobile-first users.

Employers can view learning plan progress and key details. This allows providers to confidently grant access while giving employers stronger oversight of learners.

A dedicated admin centre to centralise all administrative and configuration tasks.

Aptem Apprentice is updated for the 2026/27 apprenticeship funding rules and ILR specification, so providers stay compliant. Training plans now include a three-party completion sign-off, and platform terminology, Trackers and the funding dashboard reflect the revised rules. Aptem supports the new ILR data fields and reporting requirements, so compliance and MIS teams can capture the right data, generate compliant exports and meet submission deadlines without disruption.

Aptem Apprentice now includes a dedicated programme type for apprenticeship units, covering programme set-up, delivery hour planning, and a compliant unit training plan with three-party sign-off at programme start.

A new self-service data import tool for ILR data, designed to reduce the manual reconciliation and review involved in fixing data issues during import. An early access program will be set up to provide access when this feature is available.

The Aptem virtual assistant (AVA) supports learners throughout their apprenticeship, using their own programme data as well as their apprenticeship standard. Learners can ask what’s next on their learning plan, when their next review is or which review actions are still outstanding. If they’re not sure whether an activity counts as off-the-job training, AVA can tell them and help them draft the log entry. For questions about using Aptem, it points them to the relevant Help Centre articles.

AVA is also integrated with checkpoint, so after finishing a checkpoint, learners can explore a topic further while it’s still fresh. Learners can choose how AVA communicates with them, including accessibility options such as plain language.

Providers can add context at organisation, programme and employer level, in their own words, so AVA’s answers reflect how they actually work rather than offering generic information. Administrators can also report on how AVA is used across the organisation.

A new dashboard surfaces learners’ virtual assistant conversations when moderation flags that they need attention. Learners receive a supportive response with their provider’s designated safeguarding lead contact details. Permitted staff receive a notification, a permission-gated review area and an export they control, supporting providers’ safeguarding and Prevent duties.

Admins will have the ability to attach the right standard or qualification to a programme, including the ability to search by title or reference number and link it to the programme.

We’re extending Aptem’s virtual assistant to provider staff, giving your tutors, admin and compliance teams a way to efficiently access information from the platform. It can help tutors to prepare for reviews, respond to queries about which learners need attention, and suggest next steps.

We’re exploring how AI can make off-the-job training easier to record, review and evidence. The aim is to help learners get their logs right first time, free up tutors to spend less time checking entries and more time supporting learners, and give providers greater confidence that their off-the-job evidence is accurate and ready for audit.

Tutors will be able to set up a manual checkpoint once and apply it to one learner, a selection of learners or a whole cohort. This preserves the tutor judgement that makes manual checkpoints valuable while removing the per-learner repetition. Learners get timely checks on what they’ve just been taught, and tutors spend less time on set-up.

Aptem’s marking aid, developed in partnership with Graide, is designed specifically for apprenticeship programmes. It speeds up the learner submission process and helps you meet your KPIs around learner response times. By cutting tutor marking time by up to 50%, it gives tutors more time for personalised support. Administrators manage and maintain marking aid from one central area, which makes configuration quicker and gives a simpler, more controlled set-up for marking workflows.

Enhanced reviews is a set of AI-powered features that reduces the admin around progress reviews, so tutors can focus on productive conversations and one-to-one support. Enhanced reviews summarisation documents the key discussion points, themes, feedback and action points from each review. This improves accuracy and shortens feedback loops.

Tutors can also upload the review transcript as evidence, giving a complete record of the meeting for quick reference if questions come up later.

To support better-informed reviews, this feature will provide a summary of key points, including achievements, outstanding actions and previous review summarise to ensure that both the tutor and the learner can effectively prepare for a structured review.

This feature adds more value to reviews by generating specific actions and goals for learners that are measurable and time limited. These goals can be tracked through the system to allow tutors to monitor progress and support where required. 

The review summarisation eliminates the trade-off between valuable conversations and accurate record-keeping, by automatically generating the key discussion points and themes from each review. As the conversation progresses, crucial feedback and action points will be documented, helping to improve engagement, boost accuracy and shorten feedback loops.

Achieve significant time savings without compromising feedback quality. By reducing the time taken to provide repetitive feedback, it allows for more personalised guidance that improves learner outcomes.

To support better-informed reviews, this feature will provide a summary of key points, including achievements, outstanding actions and previous review summaries to ensure that both the tutor and the learner can effectively prepare for a structured review.

To support initial assessment and track progress from day one, tutors can now create checkpoints for learners during onboarding.

The actions section lets tutors record, track and manage the tasks agreed with learners during a progress review. This gives continuity between reviews, stronger evidence of progress and better collaboration between tutors, learners and employers.

Auto review actions uses AI to turn the review summary into trackable goals and actions for the learner, which tutors check and edit before saving. Learners can also ask the virtual assistant about their outstanding actions.

Providers would be able to better customise programmes by selecting learning plan components from a library of modules. This approach aims to create a personalised learning experience, maintain learner engagement, and incorporate prior learning from the start.

The first phase of development focused on efficiently housing information about the submission within Aptem, enabling learners and tutors to easily access it via the Learning Plan. A streamlined process that encourages clarity of expectations and standardised assessment criteria for consistency, fairness and efficiency.

Learn more: https://www.aptem.co.uk/aptem-news/aptem-and-graide-launch-first-ai-driven-integrated-marking-aid-for-apprenticeships/

This new feature allows organisations to pinpoint and address any quality issues relating to marking and also to implement improvements in a syllabus or learning materials where learner performance is not as expected across a cohort.

The clone feature will allow admins to clone existing exercise for reuse across different programmes or leaning plan components, without impacting the original exercise. This saves time and maintains consistency across similar exercises and programmes. The AI learning from the original exercise is carried over, providing faster and more accurate feedback for cloned exercises.

Expanding on the initial iteration of Aptem’s marking aid, this feature introduces intelligent KSB criteria mapping to assist tutors during the marking process. The tool learns how you mark, every time you mark. With this human-in-the-loop approach, it helps to drive accuracy and efficiency, freeing up tutors’ time and allowing them to focus on personalised guidance that supports learner progress.

Enhanced marking efficiency with AI-powered suggestions that are programmed to align with apprenticeship standards’ knowledge, skills and behaviours (KSB) criteria.
The tool learns how tutors mark, every time they mark. With this human-in-the-loop approach, it helps to drive accuracy and efficiency, freeing up tutors’ time and allowing them to focus on personalised guidance that supports learner progress.
Tutors retain full control to enrich feedback while significantly reducing marking time.

Modular learning plans will provide a foundation for flexible, responsive learner journeys. This new addition will empower both providers and learners with scalable, engaging and personalised learning pathways.

For more intuitive and efficient OTJT logging, the new activity log makes it easier for learners to record all of their learning hours in a way that meets compliance requirements. This supports clearer progress tracking and reporting.

Select customers are currently participating in an early adopter trial.

This new automation will help employers and providers achieve timely document signing and improved compliance by sending reminders to any outstanding signatories.

This enhanced functionality will also offer improved oversight and reporting for providers. We’re actively engaging with customers through design review sessions, and there will be further opportunities to get involved and provide your valuable feedback as development progresses.

You can now easily drill down into detailed learner information by clicking on any metric on the right-hand side of every tab in the Power BI Caseload dashboard, eliminating the need for manual data interrogation. This allows you to access the information you need faster and more intuitively. Additionally, cancelled learners have been added to the dashboard, giving you a complete and comprehensive view of all learner data.

Key enhancements overview:

1. 8-month minimum duration management:

   • Introduces validation for a minimum apprenticeship duration of 8 months (242 days) starting 1 August 2025.
   • Impact areas include the apply programme screen, programme builder, and automation trackers.
   • Pre-August programmes remain unaffected.

2. OTJT management upgrades:

   • Removes 6-hour-per-week rule and introduces published minimum hours by standard and Recognised Prior Learning (RPL).
   • Adds an RPL field across screens, batch uploads, and APIs.
   • Automated validation ensures planned OTJT hours meet calculated minimums.

3. Enhanced English & Maths functional skills evidence:

   • Adds distinct fields for planned EM hours and supports separate logging for EM and OTJT hours.
   • Included in updated training plan V2 to simplify audit readiness.

Help Centre article: Preparing Aptem for the 2025/2026 Apprenticeship funding rules

Quality teams will have the ability to review marking and tutoring across a cohort to assess for consistency with the goal delivering continuous improvement of quality standards. This new feature allows organisations to pinpoint and address any quality issues relating to marking and also to implement improvements in a syllabus or learning materials where learner performance is not as expected across a cohort.

This development will allow organisations to personalise the style and tone of the virtual assistant based on their teaching practices and brand values. The update will also allow for the addition of supplementary knowledge such as employer practices and proprietary learning materials.

Aptem has integrated its virtual assistant with the Checkpoint functionality. As a result, after completing a Checkpoint, the virtual assistant will be available to the learner, providing them with the opportunity to delve deeper into the topic. This timely intervention allows learners to enhance their understanding by engaging in targeted conversations right at the moment they are assessing their knowledge.