# AI-Forward Framework Reference

> Source: the faculty AI-Forward / AI-Resistant assessment rubric tool. This file mines that rubric into the assurance-audit skill. It is NOT emitted as a spreadsheet; the skill produces the scorecard and report.

## Two orthogonal axes

The assurance-audit produces two ratings that answer different questions:

| Axis | Question | Enum | Where |
|---|---|---|---|
| Assurance tier | Can the course certify learning despite AI? (resistance) | Assured / Partially Assured / Unassured / Counterfeit | `tier`, Dimension D |
| AI-Forward rating | Does the design teach and assess ethical, judgment-based AI use? (forwardness) | Needs redesign / Use with safeguards / AI-resistant / AI-forward | `ai_forward_rating`, Dimension F |

They are independent. A course can be **Assured but Needs-redesign** (locked down, teaches no AI literacy) or **AI-forward but Unassured** (good AI pedagogy, no certifying anchor). Report both; do not collapse them.

The rubric itself keeps both categories because "they solve different problems." AI-resistant limits offload for the target Bloom level; AI-forward intentionally teaches and assesses ethical AI use.

## Anchor scale

Each dimension is scored on four categorical anchors. Use the category that best describes the **current** design, not the aspirational intent:

`0 Needs redesign | 1 Use with safeguards | 2 AI-resistant | 3 AI-forward`

## The six dimensions (4 anchors each)

### D1. AI-use transparency and ethical boundaries
- 0: AI prohibited, ignored, or governed only by a blanket course statement.
- 1: Basic policy exists, but task-level permitted use is unclear or hard to operationalize.
- 2: Task states where AI is not allowed and why; integrity expectations are visible and enforceable.
- 3: Task specifies ethical AI use, disclosure, verification, and student accountability for AI-mediated work.
- Cue: AIAS is a practical vocabulary; Ateskan flags construct drift (frequency of AI use confused with degree of AI authority).
- **Deterministic mapping**: undeclared policy = 0; declared policy present = 2; declared + task-level AI-integration evidence (AI appendix, prompt log, verification log) = 3. AIAS level 1-5 itself is AI-judged and reported separately; it never changes the deterministic anchor.

### D2. Bloom / outcome alignment
- 0: Label claims higher-order learning but mostly rewards recall, template following, or volume.
- 1: Partly matches the outcome; Bloom level and evidence type are mixed.
- 2: Cleanly measures the intended Bloom level with constraints suited to that level.
- 3: Asks students to use AI while preserving the intended construct and cognitive demand.
- **Deterministic mapping**: counterfeit objective items present = 1; otherwise 2. Reaching 3 (preserve construct while using AI) is AI-judged.

### D3. AI-resistant assurance anchor
- 0: High-stakes grade rests on an unsupervised product AI could produce or heavily complete.
- 1: Some anchor exists, but it is soft, low-weight, or easily outsourced.
- 2: A meaningful certifying moment limits AI offload (timed/randomized quiz, oral defense, observed performance, controlled exam, or unique/local evidence).
- 3: Students may use AI, but the assessment secures human judgment through defense, critique, process trail, or situated decisions.
- **Deterministic mapping**: mirrors the assurance tier (Counterfeit/Unassured = 0, Partially Assured = 1, Assured = 2).

### D4. Human judgment and AI literacy
- 0: Success depends mainly on output polish, answer retrieval, or reproducing examples.
- 1: Some reasoning required, but criteria do not clearly reward judgment or AI evaluation.
- 2: Students must justify choices, compare options, solve contextual problems, or defend reasoning.
- 3: Students critique, verify, improve, or ethically direct AI output and are graded on the quality of that judgment.
- **Deterministic mapping** (per item, grade-weight-weighted): judgment verb + AI reference = 3; judgment verb = 2; weak reasoning keyword = 1; none = 0.

### D5. Process, feedback, and authorship evidence
- 0: Final product only; no drafts, logs, feedback cycles, reflection, or check-ins.
- 1: One process artifact, but it does not meaningfully inform grading or authorship.
- 2: Process evidence supports authorship and learning (drafts, version history, short defense, feedback cycle, reflection).
- 3: AI use is documented through prompts, revision decisions, verification moves, and reflection on limits/bias.
- **Deterministic mapping**: AI-documentation keyword (AI appendix, prompt log, verification log) = 3; process-trail keyword = 2; single reflection/draft signal = 1; none = 0.

### D6. Equity, workload, and 7.5-week feasibility
- 0: Depends on default proctoring, high-bandwidth/private-space assumptions, heavy manual review, or inaccessible tools.
- 1: Feasible for some students/sections, but access, accommodations, workload, or timing remain unresolved.
- 2: Protects integrity with scalable, accessible constraints and alternatives.
- 3: Builds AI literacy and assurance using reusable templates, short check-ins, rubrics, and accessible workflows.
- **Deterministic mapping**: from Dimension E feasibility band (high_burden = 1, moderate_burden = 2, low_burden = 3).

## Overall rating

The headline `ai_forward_rating` is the **weakest of the three forward dimensions** (D1 transparency, D4 judgment, D5 authorship), conservative per "best describes current design, not aspiration." D2/D3/D6 are reported for completeness but do not pull the rating up.

**Auto-rule**: high-stakes tasks with no assurance anchor are automatically flagged. When the tier is Unassured or Counterfeit and a high-stakes item (>= 20% weight) is unanchored, the rating floors to Needs redesign.

The rating is deterministic. AIAS level and the D4/D5 AI refinements (`needs_ai_review`) never alter it; they enrich the narrative only, consistent with the "AI never feeds the deterministic rating" contract used by the assurance tier.

## Bloom calibration (the "Avoid" rules)

Per high-weight assessment (>= 20% weight), `bloom_calibration_findings` applies the rubric's Bloom Guide, using only signals the inventory carries (Bloom level by title, quiz/objective type, proctoring signal, rubric presence, process-trail keyword, weight):

| Bloom level | Avoid (fires a finding when) | Rule id |
|---|---|---|
| Remember / Understand | high-stakes auto-graded quiz, no proctoring signal (AI-pasteable recall) | `recall_unprotected` |
| Apply | high-stakes auto-graded item, reusable problems AI can solve | `apply_reusable` |
| Analyze / Evaluate | high-stakes item with no rubric (graded on fluency, not criteria) | `highorder_no_criteria` |
| Create | high-stakes item with no staged-process signal (one-shot, no milestones/defense) | `create_oneshot` |

Each finding cites assessment + points + grade_weight_pct. Question-bank randomization and attempt limits are not exposed by the inventory, so recall findings name that as a caveat (verify in Canvas) rather than asserting it.

Recall-level work can be AI-resistant without being AI-forward; Create-level work usually needs process evidence, judgment, and disclosure. Higher Bloom labels are not protection; the rating rewards fit to the target level, not the label.

## Evidence base and cautions

This rubric is a **design diagnostic, not a psychometrically validated instrument**. Much of the supporting research is perception-heavy or conceptual. Treat scores as conversation starters, not proof. Key project sources and their limits:

- Perkins et al. (AIAS, 2024/2025): permitted AI-use levels as a task-level communication vocabulary. Framework, not validated efficacy evidence.
- Ateskan (2026): AIAS implementation and construct drift. Define AI authority, not just frequency. Very small sample.
- Kofinas, Tsay & Pike (2025): detection limits; do not rely on human detection or authentic tasks alone. Small sample, GPT-3.5, exploratory.
- Mizori et al. (2025): open-book vs closed-book in the LLM era; match format to competency, avoid unprotected recall/open-book items. Pre-LLM studies, high heterogeneity.
- Proctoring cluster (Marano 2024, Whitlow & Metts 2024, Duncan & Joyner 2022, Felsinger 2023, Sakhipov 2025): proctoring carries privacy, anxiety, and access costs; treat it as a flagged exception with alternatives, not a default. Equity evidence partly indirect; efficacy contested.
- Carbonel et al. (2025), Martin et al. (2025), Xia et al. (2024): converge on staged/process-focused design (oral/viva, process trails, declared AI options). Perceptions and design propositions, not effectiveness evidence.

Do not use this tool to rank faculty or automate compliance. It is a per-course design diagnostic; human review and local calibration come before any program-wide reporting.
