AI workspace tracks adult learning with controlled help and evidence
LearnAdapt Praxis: Controlled AI Assistance and Evidence Traces for Adult Workplace Learning
Computers and SocietyHuman-Computer Interaction
Summary
Workplace learning with AI can make it hard to see what someone truly understands because AI might do a lot of work for them. The authors built a workspace called LearnAdapt Praxis that helps adults learn by controlling when AI can help and keeping detailed records of what was done and when. It organizes learning in five steps and makes sure that help is restricted during important parts. Their tests on lots of fake work projects showed the system works as expected for tracking and controlling assistance. However, the system’s effect on actual learning or quality of AI outputs still needs more research with real learners.
What this means in practice
- •For workplace training teams: Manage adult learner projects with controlled AI help and detailed activity records to support assessment and coaching.
- •For corporate compliance officers: Use detailed records from AI-assisted learning to verify process adherence and support audit trails in employee training.
Authors
Nizam Kadir
Abstract
AI can help adults produce plausible workplace artefacts while leaving their independent reasoning difficult to inspect. LearnAdapt Praxis addresses this design problem through a five-stage learning workspace: Frame, Learn, Build, Validate and Transfer. The application stores an initial response, versioned specifications, coaching records, validation observations, a separate transfer response and facilitator feedback. Project-level server controls restrict coaching, access to earlier work and learner export during an active transfer attempt; they do not establish that a learner avoided assistance outside the application. This technical report describes the implemented architecture and evaluates selected workflow, authority, failure-recovery and provenance properties. A reproducible synthetic rehearsal exercised 120 serial project episodes across three fictional workplace contexts and four injected provider conditions. All 3,990 recorded checks met their specified expectations. The resulting records contained 240 specification versions, 1,200 activity events and 60 stored synthetic hints; 60 injected provider failures produced no fabricated hints. Separate authentication regressions exposed and resolved a session-expiry defect, and limited staging and production checks verified account access and live model connectivity. The evidence supports the tested engineering properties of the recorded release. It does not establish learning gains, model-output quality, unaided assessment validity, population usability or production capacity. The contribution is an implemented arrangement for separating assisted work, project-level assistance withdrawal and inspectable evidence, accompanied by executable tests and an explicit account of what remains to be validated with adult learners.