Methodology case study · Assessment-to-Instruction Cycle (AIC)
Finding the
instructional boundary.
Designing an evidence-guided assessment-to-instruction system for a community literacy cohort.
The contribution
For a 12-student reading cohort spanning Grades 2–9, I developed and implemented a structured process for translating broad assessment results into specific, testable instructional decisions.
The assessment results identified relative strengths and weaknesses. They did not, by themselves, tell a tutor where to begin. My role was to connect the evidence to a question that could change instruction, test it, and translate the result into a teaching plan.
“Collect the smallest amount of new evidence that could change the instructional decision.”
Four decisions that organize the work
Interpret the sources independently.
I audited the records and developed the academic and cognitive models independently before comparing them. Cognitive findings suggested conditions to investigate. Direct academic performance guided the teaching target.
Ask a question that can change instruction.
I treated a discrepancy as a question with competing explanations. The next probe had to distinguish between explanations that would lead to different instruction.
Locate the instructional boundary.
I used targeted contrasts to find where otherwise available knowledge became unstable. I then selected a short formative measure tied to the most useful skill the learner could currently access.
Teach, monitor, and revise.
I reviewed formative results alongside the tutor’s session observations, revised the interpretation, and translated it into an activity packet. If the evidence changed the target, the instruction changed with it.
The full article details the 12-phase architecture, including source auditing, independent DORA and MindPrint analyses, evidence reconciliation, probe design, formative monitoring, tutor translation, and longitudinal updating.
Inside the full article
- Aggregate cohort descriptives and the limits of interpreting placement indicators.
- Probe contrasts for alphabetic transfer, word-form precision, output mode, listening versus reading comprehension, and orthographic retention.
- Three de-identified examples showing how new evidence changed the instructional model.
- An account of the tutor-ready administration packets, progress reviews, activity banks, and mini-lessons the project produced.
The locally constructed probes were designed to resolve instructional questions. The article distinguishes them from psychometrically equivalent tests and preserves the difference between observations, hypotheses, and decisions.
How the work was developed
Human–AI collaboration
This project was developed through an educator-directed collaboration with OpenAI’s ChatGPT. I defined the instructional problem, contextualized the evidence, established constraints, reviewed and corrected analyses, and retained responsibility for instructional decisions.
ChatGPT contributed as an analytic and design collaborator, including structuring analyses, comparing hypotheses, designing probes and decision rules, and translating findings into materials. Its outputs remained provisional and subject to review. The full article includes a detailed collaboration disclosure.
Related research interests