Turning Assessment into Action with TeacherMatic

“I will give you a refund.”

These statements, taken from a fictional learner’s customer service response in November’s Deep Dive, reveal two useful teaching opportunities: checking facts before explaining a problem and recognising the limits of someone’s authority.

Feedback can identify both issues. The next challenge is helping the learner respond differently.

In From Feedback to Progress, Esam Baboukhan demonstrated how connecting TeacherMatic generators can turn reviewed feedback into targeted learning activities. The session encouraged educators to push beyond each generator’s individual purpose and explore how one output could inform the next.

The embedded recording shows the process. The wider opportunity is to make assessment evidence the starting point for more personalised, relevant and contextualised learning.

Follow the learning priority between generators

The webinar’s Level 2 assignment involved a customer whose paid booking was missing when a fitness class was already full. The receptionist could check records and explain options, but a supervisor needed to approve refunds and exceptions.

A reviewed rubric established expectations. The Advanced Feedback Generator used the saved rubric as a reference, with the educator reviewing the feedback against the learner’s work.

From there, the same priorities informed multiple-choice questions, classroom questions and scenarios.

The important connection was the learning need carried between tools. The learner had promised a refund without authority. A follow-up activity could therefore help them recognise that boundary and practise a suitable response.

For example, an educator might ask:

A customer requests a refund. What could the receptionist say while seeking a supervisor’s decision?

After discussing possible responses, the learner could revise the original promise:

“I can ask my supervisor to review your refund request.”

This illustrative revision gives the learner something concrete to explain: what changed, why it changed and how it affects the customer’s expectations.

Choose the activity that addresses the difficulty

Different generators can support different teaching decisions.

If a learner struggles to recognise an appropriate response, a focused multiple-choice question can help distinguish it from plausible mistakes. The explanations matter: they should make clear why an attractive answer might be inappropriate.

If the learner selects correctly but cannot explain the choice, classroom questions can explore the reasoning. For example: why might promising a refund create further frustration if approval is later refused?

If the learner explains the principle but struggles to apply it elsewhere, a new scenario can provide practice in a changed context.

These distinctions offer a starting point rather than a fixed sequence. Where understanding is missing, a short model or further explanation may be needed before independent practice.

Connecting generators works best when the educator chooses the next activity for a reason.

Make “Any Additional Information” work harder

The “Any Additional Information” field gives educators space to communicate that reason.

A topic such as “customer complaints” leaves much open to interpretation. A short teaching brief can specify the learner’s context, evidence from their work, reviewed priorities and relevant constraints.

For example:

Create six multiple-choice questions for a Level 2 customer service learner. Their response promised a refund, although only a supervisor can approve one. The reviewed learning priority is recognising authority boundaries while responding helpfully. Use realistic situations and plausible alternatives. Include answer explanations and two questions applying the principle in a different context.

This adaptable example separates what the learner wrote from the educator’s interpretation of it. It also tells the generator what thinking the activity should require.

The same brief could inform classroom questions or a scenario. Educators can request a model before practice, adjust the language or increase the challenge through less obvious choices.

A concise brief also avoids carrying an entire feedback report into every generator, helping educators leave out unnecessary learner information.

Make personalisation manageable

Educators have always adapted teaching in response to assessment. Creating suitable follow-up resources across a full workload takes time.

TeacherMatic can reduce the drafting effort, making targeted practice more feasible. Personalisation can involve an individual task, but it can also mean grouping learners around a shared need.

If several learners make unsupported assumptions, a common discussion activity may be useful. A learner who understands that principle but struggles with authority boundaries may need a different question.

The opportunity is to use assessment evidence flexibly. Producing more resources has value when those resources address an identified need.

Check each connection through SAFE

The Deep Dive situated this approach within TeacherMatic’s SAFE AI framework:

• Safeguarding Data and Privacy: include only necessary learner information and follow organisational requirements.
• Augmenting Professional Judgement: check feedback against the work, select priorities and refine resources.
• Fairness and Inclusion: review whether judgements, language and activities are appropriate and accessible.
• Ethical and Transparent Practice: take responsibility for AI use and communicate its role appropriately.

Review becomes especially important when connecting generators. An inaccurate interpretation in feedback could influence a quiz and reappear in a scenario. Several consistent resources could reinforce the same original error.

Check the foundation before building on it. Confirm that the feedback reflects the evidence, then review each subsequent output.

Fairness needs similar attention. Complex wording could obscure whether a learner understands the customer service principle. Clear language helps keep the challenge focused on the intended learning.

Look for improvement in the learner’s response

The demonstration introduced a second situation: a customer had travelled to a leisure centre for a swimming lesson cancelled without notification. The cause was unknown, and refunds required supervisor approval.

The context changed while the learning priorities remained relevant. Could the learner establish facts without inventing an explanation and offer appropriate next steps?

After practice, learners can revise their original work and explain their changes. Applying the principle in a fresh situation with less prompting gives the educator further evidence.

Together, these responses provide a stronger basis for deciding what support comes next than activity completion alone.

Take the next step with feedback

Connecting generators creates possibilities beyond the first useful output. Educators carry a reviewed learning priority between tools, shape the practice and examine what changes for the learner.

The recording offers a demonstration to build on. The value comes from applying that thinking to a genuine teaching need.

Further reading and resources

• TeacherMatic SAFE AI Framework: Guidance on responsible AI-supported feedback.
• Shared prompt collection: A live document with prompts to adapt for the “Any Additional Information” field.
• TeacherMatic webinars: Register for future Deep Dive Wednesdays and other free webinars offering practical CPD.