Software Signal · Research
Questions worth investigating. Evidence worth challenging.
Research is Software Signal's evidence engine: it monitors developments, frames useful questions, compares supporting and contradictory evidence, tests where practical, and feeds learning back into the Framework.
Active investigation
AI in Teaching Workflows
Where can AI improve teaching preparation and delivery without weakening educator judgment, learner trust, privacy, or accountability?
Open the investigation- Status
- Active · evidence gathering
- Question
- How should AI participate in teaching workflows responsibly?
- Evidence posture
- Supporting and contradictory evidence are both in scope.
- Practical output
- Findings may shape guidance, experiments, and learning practice.
How investigations work
From signal to a justified update.
- Observe and frame
Separate a durable engineering question from short-lived tool noise.
- Gather and compare
Use research, standards, practitioner evidence, existing engineering knowledge, and critical counter-evidence.
- Test where useful
Use software experiments or prototypes when they can expose real behavior or constraints.
- Synthesise honestly
Distinguish what appears supported, conditional, uncertain, contradicted, or still unexplored.
- Feed learning back
Update the Framework, practical work, or confidence—not merely the publication queue.
Research becomes useful when it connects.
An investigation may lead to writing, an experiment, a reusable artifact, a Framework update, or a clearer question. None is manufactured simply to fill a category.