Diagnostic Instrument · SARDC v1.0

School AI Readiness &
Digital Citizenship Diagnostic

What this is, and what it is not. SARDC is the earlier diagnostic whose scoring conventions the AIP Baseline inherits — perception and evidence weighted 60/40, normalisation, red-flag override, maturity bands. It is not a second, competing measurement system, and it is not what is delivered in an engagement. The instrument used in every AIP engagement is the AIP Baseline: nine instruments, 205 items, scored on 0–5 across seven readiness dimensions, nine governance dimensions and five practice dimensions. Download it free →

A multi-stakeholder, evidence-weighted framework for assessing a school's capacity to govern artificial intelligence responsibly — and to develop students who use it wisely.

11
Domains
5
Stakeholder Groups
110+
Survey Items
4
Maturity Levels
10
Report Sections
About the Instrument
What is the SARDC?

The School AI Readiness & Digital Citizenship Diagnostic (SARDC) is a structured assessment that gives school leadership a clear, evidence-informed picture of where their institution stands across eleven critical domains of responsible AI adoption.

Unlike single-stakeholder surveys, SARDC triangulates perceptions from five groups — teachers, students, administrators, coaches, and teacher-leaders — then weights them against observable evidence (policy documents, lesson plans, incident logs). Where stakeholders disagree, the instrument flags misalignment rather than averaging it away.

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Purpose
Baseline assessment at programme start; repeated at 6-month intervals to measure growth.
Time Required
10–15 min per stakeholder group. Full cycle (all groups + evidence review) completes in one week.
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Output
Domain heatmap, maturity profile, stakeholder alignment index, and 30/90/180-day action roadmap.
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Confidentiality
All responses anonymised. Aggregate scores only. No individual responses shared with leadership.
Evidence-Weighted Scoring

Perception data (60%) is combined with evidence scoring (40%) so that strong survey responses cannot mask the absence of written policies, training records, or governance artefacts. This prevents "optimism inflation" common in self-report instruments.

Assessment Domains
11 Domains of AI Readiness

Each domain is independently scored (0–100) then combined using the weights below to produce a School AI Readiness Index (SARI) score out of 100.

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1. Policy & Governance
Weight: 0.12 · Priority domain ★
Existence, quality, and communication of an AI use policy. Roles and accountability structures. Review cadence and version control.
AI Policy Document Governance Body Review Schedule Staff Awareness
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2. Teaching & Learning
Weight: 0.12 · Priority domain ★
Integration of AI-augmented pedagogy. Lesson design that develops critical thinking alongside AI use. Balance between AI support and independent student work.
Lesson Plans Metacognition Differentiation Formative Assessment
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3. Safeguarding & Safety
Weight: 0.12 · Priority domain ★
Protection of students from harmful AI outputs, manipulation, and inappropriate content. Incident reporting pathways. Designated safeguarding lead involvement.
Incident Log Content Filters DSL Training Student Reporting
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4. Data Privacy & Compliance
Weight: 0.10
FERPA/GDPR/local compliance for AI tool data processing. Vendor data agreements. Student data minimisation practices. Parental consent frameworks.
DPA Register Consent Forms Vendor Audits Data Map
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5. AI Literacy & Critical Thinking
Weight: 0.10
Student and staff ability to understand how AI works, identify bias, evaluate AI-generated content, and resist metacognitive laziness when AI is readily available.
Metacognitive Checks Bias Awareness Fact-Checking AI Explainability
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6. Infrastructure & Access
Weight: 0.08
Device availability, network reliability, and equitable access to AI tools. Digital equity across student populations, including EAL/SEND learners.
Device Ratio Network Uptime Equity Audit SEND Access
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7. Leadership & Culture
Weight: 0.09
Senior leadership commitment to responsible AI adoption. Culture of ethical experimentation, psychological safety for raising AI concerns, and shared values around human-centredness.
Leadership Buy-in Psych Safety Values Statement Staff Voice
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8. Professional Development
Weight: 0.08
Quality and coverage of AI-related CPD. Training log completeness. Coaching structures. Staff confidence and self-efficacy with AI tools in the classroom.
Training Log Coverage % Coaching Hours Confidence Survey
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9. Student Wellbeing & Agency
Weight: 0.09
Impact of AI use on student stress, autonomy, and sense of self. Prevention of over-reliance. Student voice in AI decisions affecting them. Digital wellbeing monitoring.
Wellbeing Data Over-Reliance Student Voice Agency Metrics
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10. Digital Citizenship
Weight: 0.08
Student understanding of ethical AI use, academic integrity, digital footprint, and responsible online behaviour in AI-mediated environments. Explicit curriculum coverage.
Curriculum Map Academic Integrity Digital Footprint Ethics Units
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11. Community & Communication
Weight: 0.02
Transparency with parents, carers, and the wider community about how AI is used in school. Communication quality, frequency, and inclusivity across language groups.
Parent Comms Translated Docs Community Events Feedback Channels
Priority Domains

Domains 1 (Policy & Governance), 2 (Teaching & Learning), and 3 (Safeguarding) are weighted highest at 0.12 each. These three domains together account for 36% of the composite SARI score, reflecting their foundational role in responsible AI adoption.

Weight Distribution
Domain Weight Relative Contribution
Policy & Governance 0.12
12%
Teaching & Learning 0.12
12%
Safeguarding & Safety 0.12
12%
Data Privacy & Compliance 0.10
10%
AI Literacy & Critical Thinking 0.10
10%
Leadership & Culture 0.09
9%
Student Wellbeing & Agency 0.09
9%
Infrastructure & Access 0.08
8%
Professional Development 0.08
8%
Digital Citizenship 0.08
8%
Community & Communication 0.02
2%
Stakeholder Surveys
Five Surveyed Groups

Each stakeholder group receives a tailored survey instrument (10–12 items per domain, 3 domains per group per cycle). Questions use a 5-point Likert scale for perception items and multiple-choice for knowledge items.

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Teachers
n = all classroom teachers
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Students
n = sample Y7–Y13
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Administrators
n = SLT + HoDs
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IT / Coaches
n = IT team + CPD leads
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Teacher-Leaders
n = AI champions, HoDs
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Teacher Survey — Sample Items (Policy & Literacy domains)
Likert 1–5 unless noted · Full instrument: 110+ items across all domains
1I am aware of the school's current policy on student AI use in the classroom.1 Strongly Disagree → 5 Strongly Agree
2I feel confident discussing AI-related safeguarding concerns with the designated lead.1–5
3I design lesson activities that require students to think critically rather than simply accept AI output.1–5
4I have observed signs of metacognitive laziness (reduced independent thinking) in my students since AI tools became available.1–5
5When AI gives you an answer, what should you do first? (knowledge item)Multiple Choice
6I have received professional development on responsible AI integration in the past 12 months.Yes / No / Partial
7Students in my class understand the concept of "human-in-the-loop" and can explain why it matters.1–5
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Student Survey — Sample Items (Literacy & Wellbeing domains)
Age-appropriate language · Administered with teacher present
1I know when it is and isn't appropriate to use AI to help with my schoolwork.1–5
2I check AI answers before trusting them or sharing them.Always / Usually / Sometimes / Rarely / Never
3Using AI tools makes me feel like I don't need to think as hard myself.1–5
4I know how to report it if an AI app shows me something that makes me uncomfortable.Yes / No / Not sure
5My teachers help me understand the limits and risks of AI, not just how to use it.1–5
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Administrator Survey — Sample Items (Governance & Data domains)
Senior Leadership Team + Heads of Department
1Our school has a written, dated AI policy that has been reviewed in the past 12 months.Yes / No / In progress
2We have a designated person responsible for AI governance decisions.Yes / No
3We have data processing agreements in place with all AI tool vendors used in school.All / Most / Some / None / Unsure
4Our budget includes dedicated allocation for AI professional development in the current academic year.Yes / No / Partial
5Staff would feel comfortable raising concerns about AI misuse without fear of professional repercussion.1–5
Scoring Methodology
5-Step Scoring Model
1
Perception Scoring
Each stakeholder group's responses are normalised to a 0–100 scale per domain. Likert items: (response − 1) ÷ 4 × 100. Knowledge items: % correct × 100. Group scores are averaged within each stakeholder type.
P_group = mean(normalised_items) for each domain
2
Cross-Stakeholder Weighting
Group scores are combined using differential weights reflecting data quality and stake in the domain. Administrator & Teacher-Leader responses carry the most weight in governance domains; Student responses carry the most weight in literacy and wellbeing domains.
P_domain = Σ(w_g × P_group) / Σw_g
3
Evidence Scoring
A trained reviewer scores observable artefacts (policy documents, training logs, lesson plans, incident records) on a 0–3 rubric per domain. This prevents perception scores from inflating results where no documentation exists.
E_domain = (artefact_score / 3) × 100
4
Composite Domain Score
Perception and evidence are combined at a 60/40 ratio. Evidence carries significant weight so that schools cannot score highly on perception alone without artefacts to support it.
D_score = (0.60 × P_domain) + (0.40 × E_domain)
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Modifiers & Final SARI Score
Two modifiers are applied before computing the final weighted composite: Misalignment Penalty (if stakeholder standard deviation > 0.8 across groups, domain score is reduced by 10 points) and Red Flag Override (any red flag condition caps the domain score at 40 regardless of survey results). The 11 domain scores are then combined using the domain weights.
SARI = Σ(domain_weight × adjusted_D_score)

Stakeholder Misalignment Detection

When the standard deviation of stakeholder group scores within a domain exceeds 0.8, this signals that different groups have very different lived experiences of that domain. A 10-point reduction is applied and the misalignment is flagged in the report with targeted recommendations.

Scoring Example

Policy & Governance domain:

P_domain = 72
E_domain = 60
D_raw = (0.6×72)+(0.4×60) = 67.2
SD check: 0.6 → no penalty
Red flag: none
D_final = 67.2
Maturity Rubric
4-Level Maturity Framework

Each domain score maps to one of four maturity levels. Levels are consistent across all 11 domains to enable cross-domain comparison.

Level Score Range Label Description Typical Characteristics
Level 1 0 – 39 Emerging Little or no evidence of deliberate practice. Ad-hoc responses to AI-related situations. No written policy; individual staff act independently; no training record; incidents addressed reactively.
Level 2 40 – 64 Developing Initial structures in place but inconsistently applied. Awareness exists but not embedded in practice. Draft policy exists; some staff trained; isolated good practice in pockets; no systematic monitoring.
Level 3 65 – 84 Established Consistent practice across most of the school. Systems documented and followed by the majority of staff and students. Approved policy; most staff trained; monitoring in place; student voice included; annual review cycle.
Level 4 85 – 100 Leading Exemplary, evidence-rich practice. The school models good practice and contributes to sector-wide learning. Policy reviewed termly; all staff CPD-certified; students co-create AI literacy content; external partnerships; published case studies.
Maturity Ceiling Effect

A school may score Level 4 (Leading) in Digital Citizenship while remaining at Level 1 (Emerging) in Safeguarding. The SARDC deliberately shows this granularity — a high composite SARI score can still mask critical gaps. Reviewers are instructed to flag any priority domain at Level 1 regardless of overall score.

Override Conditions
Red Flag Criteria

The following conditions automatically cap a domain score at 40 (Emerging) regardless of survey perception scores. Red flags represent minimum standards where no amount of positive sentiment can substitute for their absence.

⚑ Policy & Governance — Red Flags
  • No written AI policy exists (or none updated in the last 24 months)
  • No designated person holds accountability for AI decisions
  • Staff have not been informed of any AI guidelines this academic year
⚑ Safeguarding — Red Flags
  • No AI-related incidents have been logged (suggesting no reporting culture, not zero incidents)
  • Designated Safeguarding Lead has not received AI-specific safeguarding training
  • Students do not know how to report AI-related harm or discomfort
  • Content filtering is absent or not reviewed in the current academic year
⚑ Data Privacy — Red Flags
  • Any AI tool in use has no data processing agreement with the school
  • Student personal data is known to be processed by an AI tool outside the approved vendor list
  • No parental consent mechanism exists for AI tool use involving student data
⚑ Teaching & Learning — Red Flags
  • No lesson plans include any metacognitive checkpoint or critical thinking task alongside AI use
  • AI tools are used for student work generation with no human review or assessment follow-up
Output
10-Component Report Structure

Each SARDC cycle produces a structured report delivered within 5 working days of data collection closing. The report is available in both an executive summary format (4 pages) and a full evidence annex.

01
Executive Summary
SARI composite score, overall maturity level, top 3 strengths, top 3 critical gaps, and recommended immediate actions. Board-ready language.
02
Domain Profiles
Full score breakdown for all 11 domains with maturity level classification, contributing factors, and evidence quality rating.
03
Readiness Heatmap
Visual 11×4 grid mapping each domain against maturity levels. Colour-coded (red/amber/blue/green). Single-page, suitable for governor presentations.
04
Stakeholder Alignment Index
Cross-stakeholder agreement analysis per domain. Flags where teacher, student, and administrator perceptions diverge significantly (SD > 0.8).
05
Strengths Analysis
Narrative on highest-scoring domains with specific examples from evidence artefacts and survey data. Suitable for staff recognition and external communication.
06
Risk Register Entries
Pre-formatted risk entries for the school's governance risk register. Each red flag condition and misalignment finding is presented as a risk with likelihood, impact, and owner.
07
AI-Citizenship Connection
Narrative linking governance findings to student digital citizenship outcomes. Shows how structural gaps translate to student behaviour and competency gaps.
08
Prioritised Recommendations
20–30 specific, actionable recommendations ranked by impact and urgency. Each includes the responsible role, required resources, and success indicator.
09
30 / 90 / 180-Day Roadmap
Phased action plan: quick wins (30 days), structural changes (90 days), and sustained embedding (180 days). Aligned to the school's academic calendar.
10
Next Diagnostic Date
Recommended date for the next SARDC cycle (typically 6 months). Tracking table to monitor score changes across domains over multiple cycles.
Implementation Guide
Pilot Administration Protocol

The SARDC is administered by a trained Togno consultant in five phases over approximately 10 working days.

1
Preparation
Days 1–2
Agree scope with SLT. Set up anonymous survey links per stakeholder group. Collect evidence artefacts (policy docs, training logs, lesson plan samples, incident register). Brief staff on purpose and confidentiality.
2
Survey Administration
Days 3–5
Online surveys distributed to all five stakeholder groups. Minimum response targets: 80% of staff, 20% student sample (stratified by year group), 100% of SLT/HoDs. Reminder issued at Day 4 for non-responders.
3
Evidence Review
Days 5–6
Consultant scores all submitted artefacts using the evidence rubric (0–3 per domain). Gaps in artefacts are recorded. No artefact = 0 score for evidence component of that domain.
4
Scoring & Analysis
Days 7–8
All five scoring steps applied. Red flag and misalignment checks run. Domain scores computed. SARI composite calculated. Report drafted including heatmap, domain profiles, alignment analysis, risk register entries, and roadmap.
5
Action Planning & Handover
Days 9–10
Report delivered to SLT. 90-minute debrief session with leadership team to walk through findings and co-create the 30/90/180-day roadmap. Report finalised and added to governance record. Next SARDC date confirmed.

Review Cycle

SARDC is designed for administration at three points during the Togno AIP programme:

Schools may optionally commission annual SARDC cycles independently after programme completion.

Commission a SARDC for Your School

The diagnostic is included as standard in the Togno AIP Governance Programme. Schools can also commission a standalone SARDC as part of a readiness assessment before committing to the full programme.