Check the overview
Look at active learners and completion. Note anyone inactive for more than a week.
Built around the work
Know where to help. Leave room to learn.
An AI training manager dashboard should show who is progressing and which skills need support, without turning practice into monitoring. The planned Tosheo.ai dashboard shows completion and skill levels by role, never raw learner chat logs.
tosheo.ai / Your workspaceIllustrative preview
A path for your work.
Marketing- 01 / Give AI the right contextCompleted in this example
- 02 / Write a useful first draftCompleted in this example
- 03 / Make it sound like your brandPractice task / in progressYour next move
Rewrite a launch email. Keep the promise specific. Remove the claim you cannot prove.
- 04 / Check before you shareUp next / evidence and judgement
Where does the team need a hand?
| Example learner | Current skill | Next useful step |
|---|---|---|
| Marketing learner | Brand voice | Review a revised email |
| HR learner | Interview questions | Check against role criteria |
| Finance learner | Explaining a variance | Check the source figures |
Fictional examples. No customer data or measured outcomes.
01 / Built around the work
What does the manager dashboard show?
The planned dashboard has a team overview and a per-person skill view. The overview shows active learners, the completion rate and the average skill level. The skill view is a heatmap with one row per person and one column per skill in that person's role skill graph.
Those are two different signals. Completion tells you someone is doing the practice; skill level tells you how their submitted work has scored against rubrics. A person can finish every lesson and still be developing on output checking, and that is the gap the heatmap is meant to show.
Because skills come from each role's own graph, a mixed team does not get one blended score. A Sales rep is measured on account research and grounded outreach, a Support agent on policy-faithful replies. Comparing people across roles on a single number would hide more than it shows, so the planned view keeps them apart.
02 / Built around the work
How do you read the skill heatmap?
Read across a row to see one person's strengths and gaps, and down a column to see a team-wide gap. The example below is a fictional four-person marketing team at Pinecrest Studio. The level labels are illustrative; the final scale is still being designed.
Here the column tells the story. Three of four people are still developing on output checking, so the useful action is a team conversation about checking claims, not four separate reminders. Sam's row suggests a different need: strong drafting, but brand voice has not yet been practised.
| Learner | Content drafting | Brand-voice control | Research synthesis | Output checking |
|---|---|---|---|---|
| Asha | Strong | Steady | Steady | Developing |
| Ben | Steady | Developing | Not started | Developing |
| Maya | Steady | Strong | Strong | Strong |
| Sam | Strong | Not started | Steady | Developing |
03 / Built around the work
What does the dashboard not show?
The design principle is manager visibility without surveillance. The planned manager view does not show raw learner chat logs, so a manager cannot read how someone phrased their prompts or what they asked the AI along the way.
Some details still need a decision, such as whether a manager can open a learner's final submitted task. We will publish the exact fields and permissions before launch rather than describe them loosely now. No screenshot on this website represents a live team or an audited permission model.
04 / Built around the work
What is in the weekly digest and the nudge?
The weekly digest is a planned email to the manager that summarises the same signals as the dashboard, so a manager does not need to log in to see whether the team is moving. The nudge button sends a reminder to a learner who has gone inactive.
A reminder works best when it comes with a reason. Before nudging, ask whether the person has had time protected for practice and whether their path connects to work they care about. Sending more reminders is not a substitute for a relevant task.
05 / Built around the work
How should a manager use the dashboard each week?
A short, regular routine is more useful than a detailed monthly audit. This is a suggested habit, not a measured method.
Find one column
Pick the skill where the most people are still developing. That is the team conversation for the week.
Talk about one piece of work
Ask one learner to explain an unsupported claim they removed or a figure they checked.
Protect time
Agree a slot for practice before sending a nudge.
06 / Built around the work
When is the dashboard the wrong tool?
The dashboard is a training view, not a performance management record. Do not use it as the basis for a disciplinary decision or a pay review; a practice score reflects a few short tasks, not someone's whole job. It also does not show whether a person is ready for high-risk work, which still needs your normal review.
It is not a hiring tool either. Tosheo.ai is for building skills in the team you already have, not for assessing candidates before they join.
Before you decide
A few good questions.
Can managers read employees' AI chats?
No. The planned dashboard shows skill progress and completion, not raw learner chat logs. The exact fields will be published before launch.
What is the difference between completion and skill level?
Completion shows whether someone has finished lessons. Skill level reflects how their submitted work scored against rubrics. A person can complete a path and still be developing on a skill.
How often does the manager hear about progress?
A weekly email digest is planned, summarising activity and progress across the team. The dashboard is available at any time.
How does the dashboard handle a team with several roles?
Skill levels are planned to be organised by each role's own skill graph, so Sales and Support skills are shown against their own graphs rather than blended into one team score.
Should practice scores be used in performance reviews?
We advise against it. Scores reflect short practice tasks, and treating them as performance evidence can discourage the honest attempts that practice depends on.
Keep exploring.
Back to the Tosheo.ai product overview
Take a closer look ↗How the adaptive path produces skill levels
Take a closer look ↗How scenarios are scored
Take a closer look ↗What the certificate proves
Take a closer look ↗AI guidance for managers
Take a closer look ↗Measuring AI skills across a team
Take a closer look ↗Data boundaries and planned controls
Take a closer look ↗See planned pricing
Take a closer look ↗A useful next step
Give your team a path, not another playlist.
Explore the planned role-specific lessons, practical tasks and manager view.
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