# Assurance sources and calibration

Canonical citations for the frameworks the assurance audit invokes, and an honest
statement of which parameters are research-derived vs. house-calibrated. When a
recommendation or the report names a framework, cite it from this file so the move
reads as evidence, not assertion. When it states a numeric threshold, label it a
calibrated default, not a finding.

Two tiers are tracked deliberately: (1) sources the July 2026 Assessment Design
Accelerator program itself assembled (its Research & Resources page), and (2)
sources this tool imports that are NOT on that page. Keep the distinction visible;
do not present tier 2 as program-endorsed.

## Framework citations (attach inline to the named move)

| Framework (as we use it) | Cite as | Evidence strength | Program-cited? |
|---|---|---|---|
| **TILT** -- pair any verify/critique-the-AI move with the rubric and annotated model examples up front | Winkelmes, M-A., et al. (2016). "A Teaching Intervention that Increases Underserved College Students' Success." *Peer Review* 18(1/2). AAC&U. | Strongest: intervention study, 1,180 students / 7 minority-serving institutions, statistically significant gains (medium-to-large), largest for first-generation and low-income students. | Yes |
| **Two-Lane** -- the Security axis (a controlled/secured anchor lane vs. an open/AI-permitted lane) | Two-Lane approach, University of Sydney; adapted per University of Bath and University of Auckland (TeachWell) guidance; EDL online/async brief (v2). | Practitioner framework, widely adopted; not an empirical study. | Yes |
| **Validity over cheating** -- the "security is not the same as done" separation; the grade's validity as a signal | Dawson, P., Bearman, M., Dollinger, M., & Boud, D. (2024). "Validity matters more than cheating." *Assessment & Evaluation in Higher Education*. | Peer-reviewed. | Yes |
| **Assurance-by-design stance** -- design AI-aware by default rather than police after | Lodge, J., Howard, S., Bearman, M., Dawson, P., & Associates (2023). "Assessment reform for the age of artificial intelligence." TEQSA. | Authority principles paper. | Yes |
| **AIAS levels (1-5)** -- the AI-use-in-instructions read | Perkins, M., Furze, L., Roe, J., & MacVaugh, J. (2024). "The AI Assessment Scale (AIAS)." *Journal of University Teaching & Learning Practice*. | Published scale. | **No -- our import, not on the program page.** Flag as such. |
| **Bloom's revised taxonomy** -- the cognitive-demand read | Anderson, L. W., & Krathwohl, D. R. (Eds.) (2001). *A Taxonomy for Learning, Teaching, and Assessing.* | Standard reference. | No -- foundational, not program-specific. |

HPI/ID frameworks used elsewhere in the plugin (Gilbert BEM, Mager & Pipe, Merrill,
ARCS, SDT, CLT, Hattie) come from `standards/instructional-design.md`, a different
lineage than the Accelerator's cited base. Do not attribute them to the program.

## Securing classification is AI-led

Which assessments count as secured is now an AI classification (read the prompt,
rubric, and syllabus; judge identity control or authorship evidence; cite the
evidence), not a regex match. See `docs/plan-assurance-ai-led.md` and
`standards/assessment-assurance-design.md` lever 1. The 40%/0.75/band parameters
below remain house-calibrated arithmetic run over that classification -- the
numbers did not change, only what feeds them did.

## Calibrated thresholds (house defaults, NOT research-derived)

State these as our defaults when they appear. No cited source sets these numbers;
they are conservative operating choices and can be tuned.

- **Assurance tier cut** -- "Assured" at >= 40% of grade weight on secured anchors;
  "Partially Assured" below that but > 0%; "Unassured" at 0%; "Counterfeit" when a
  high-weight item claims security it does not have. The 40% line is a house default.
- **Substitutability threshold** -- the 0.75 cutoff on the AI-substitutability score
  is a house default.
- **AI-forward rating bands** -- "Needs redesign / Use with safeguards / AI-resistant /
  AI-forward" are house-defined bands, not a validated scale.
- **Course-level Bloom expectation** -- the 100-200 / 300-400 / 500+ expected-ceiling
  bands (`scripts/course_level.py`) are a house calibration for reading low Bloom as
  level-appropriate, not a defect. Not from a cited instrument.

## How to surface in the report

- Recommendations: when a move names a framework, append the short cite in the move
  text, e.g. "... shared up front (TILT; Winkelmes 2016)."
- Report `method.sources`: a `[{label, cite}]` list rendered as a compact references
  block in section 06, drawn from this file for the frameworks the report actually used.
- Report `method.calibration`: one line noting the tier cut, substitutability cutoff,
  and AI-forward bands are house-calibrated defaults, not research-derived findings.
