Building a Data Collection Plan Crews Will Use
Collect the minimum credible evidence at natural workflow points, test burden, and close the feedback loop with participants.
KNOWLEDGE ARCHIVE / RELEASE 01
Practical methods for AI, instructional design, maritime workforce education, applied research, educational technology, and R&D—written to move from idea to evidence to responsible operation.
SELECT A CHANNELSix connected domains. Each channel opens a focused field archive.
Methods, decision tools, and implementation notes from across the archive.
Collect the minimum credible evidence at natural workflow points, test burden, and close the feedback loop with participants.
Move beyond document acknowledgment by combining vessel familiarization, coached tasks, evidence, and explicit sign-off authority.
Create scenarios around consequential decisions, realistic cues, recoverable errors, and structured debriefs.
Build a small, versioned set of realistic questions that tests accuracy, refusal, citation, inclusion, and instructional usefulness.
Make each gate test the uncertainties appropriate to the stage, with decisions to continue, redirect, pause, transfer, or stop.
Choose standards by the interoperability problem: launching packaged content, connecting tools, or recording learning experiences.
Combine transparent descriptive data with purposeful qualitative evidence when small numbers make precision claims fragile.
Engineer simulations around team decisions and observable cues, then make the debrief reconstruct how the bridge team understood the situation.
Translate expert stories and tacit judgments into actions, conditions, standards, and common failure cues.
Specify where people inspect AI-supported work, what evidence they need, and when they have authority to halt the process.