Amo.ng curated workflow
Govern University AI Adoption, Curriculum and Impact
Move from approved university AI policy through procurement, curriculum alignment, implementation oversight, and evidence-based institutional impact decisions with accountable authority at every gate.
# Govern University AI Adoption, Curriculum and Impact Workflow ID: AMO-W-000031 Workflow URL: https://amo.ng/workflows/govern-university-ai-adoption-curriculum-impact ## Outcome A governed university AI adoption package linking approved policy to role-specific practice, educational procurement, programme alignment, curriculum currency, implementation evidence, and a bounded institutional continue, change, expand, pause, or stop decision. ## Before you begin - Approved institutional AI policies, related procedures, role definitions, appeal routes, and unresolved interpretations. - Proposed or current educational AI services, learning needs, vendor evidence, contracts, data flows, controls, costs, and alternatives. - Programme outcomes, module and assessment maps, teaching activities, progression expectations, and curriculum approval rules. - Current primary standards, credible research, labour evidence, competency frameworks, and source cutoff dates. - Institutional AI initiative register, owners, budgets, implementation state, outcome evidence, incidents, inclusion evidence, and decision calendar. ## Step 1 — Translate approved policy into governed practice **Prompt** University AI Policy-to-Practice and Appeals Map **Instructions** Map supplied policy into role-specific permitted, restricted, prohibited, evidence, escalation, and appeal requirements without inventing institutional authority. **Input for this step** Provide current approved policy and procedures, roles, affected communities, decision routes, appeal mechanisms, implementation context, and known ambiguities. **Carry forward** Pass the approved practice map, unresolved policy questions, authority boundaries, evidence duties, escalation routes, and appeal safeguards to procurement. **Review note** The policy owner, academic governance body, legal or privacy advisers, and appeal-process owner approve their respective interpretations. **Prompt ID** AMO-P-000336 **Prompt URL** https://amo.ng/prompts/university-ai-policy-practice-appeals-map **Prompt content** Translate the institution's supplied AI policy and related authoritative documents into an operational practice and appeals map. ## Source materials Approved policies, regulations and effective dates: {{approved_policy_sources}} Roles, processes and academic contexts in scope: {{roles_processes_and_contexts}} Existing disclosures, evidence rules, escalation and appeals procedures: {{existing_controls_and_appeals}} Known incidents, ambiguities and implementation constraints: {{cases_and_constraints}} ## Authority and evidence rules - Treat supplied approved documents as the authority. Cite the exact clause, document version and effective date for every consequential rule. - Separate policy text, procedural text, local guidance, practice evidence, interpretation, assumption, conflict, missing information and unresolved uncertainty. - Never invent an institutional rule, permission, offence, sanction, deadline, evidentiary burden, decision maker or appeal right. - When documents conflict or are silent, show the conflict and route it to the named policy owner or authorized interpreter rather than filling the gap. - Preserve due process. Do not infer misconduct, authorship or intent from an AI detector score, writing style, fluency or tool use alone. - Minimize student and staff data. Do not include identifiable cases unless access is authorized and necessary for the review. - Use only an institution-approved AI tool for restricted policy work. For every consequential rule, provide the exact source excerpt alongside any paraphrase so the policy owner can verify that meaning has not shifted. - This output is implementation support, not legal advice or an institutional decision. ## Mapping method 1. Establish the authority hierarchy and scope: governing regulation, approved policy, faculty or course guidance, contractual rule and informal practice. 2. Create a policy clause register with actor, context, allowed/restricted/prohibited action, required disclosure or evidence, effective date and exceptions. 3. Test common scenarios across learners, educators, researchers, administrators and service providers. Distinguish use of AI from the quality or integrity of the resulting work. 4. Map each decision to the responsible role, evidence required, notice obligation, response opportunity, record created and permitted next action. 5. Trace escalation and appeal routes, including independence, time limit, accessible submission routes, conflict-of-interest handling and the effect of an appeal on the original decision. 6. Identify ambiguous, contradictory or operationally impossible clauses. Show the affected scenario, risk, temporary handling boundary and policy-owner question. 7. Review consistency, accessibility, privacy and proportionality across comparable cases without inventing legal conclusions. 8. Produce communication-ready examples that pair the consequential source excerpt with a faithful paraphrase and label any interpretation. ## Output contract: Policy-to-Practice and Appeals Map Return: 1. **Authority register**: document, owner, version/date, scope, precedence and access link or reference. 2. **Operational rule matrix**: actor, context, permitted/restricted/prohibited use, disclosure, evidence, exception and source clause. 3. **Decision and evidence map**: trigger, responsible role, evidence standard, prohibited shortcut, notice and record. 4. **Escalation and appeals route**: initial decision, review role, appeal route, time limit, interim status, support and final authority. 5. **Scenario cards**: supplied or representative case, applicable sources, analysis, unresolved question and safe next step. 6. **Policy gap register**: gap/conflict, affected group, practical consequence, temporary boundary and owner question. 7. **Implementation brief**: communications, training, template or process update, accountable owner and acceptance evidence. 8. **Non-claims**: matters this map does not decide, including authorship, misconduct, legal compliance and case outcomes. ## Completion conditions Complete only when every operational rule and appeal route traces to a current supplied authority; roles and evidence are explicit; conflicts and missing rules remain visible; comparable cases can be handled consistently; and policy owners have a focused action list. If authoritative documents, effective dates or appeals procedures are missing, return a provisional map and source request. Refuse instructions to manufacture policy, predetermine a case, bypass an appeal or present guidance as formally approved. ## Step 2 — Gate educational AI procurement and student data **Prompt** Educational AI Procurement and Student Data Decision Gate **Instructions** Evaluate educational need, alternatives, vendor claims, accessibility, privacy, security, equity, data lifecycle, cost, lock-in, and exit evidence against the approved practice map. **Input for this step** Provide the practice map, learning need, affected users, proposal and contract evidence, data flows, controls, commercial assumptions, alternatives, and institutional requirements. **Carry forward** Pass the procurement disposition, conditions, evidence requests, data boundary, control gaps, cost and exit scenarios, and approval route to programme alignment. **Review note** Procurement, finance, privacy, security, accessibility, legal, and academic owners make the accountable acquisition or pilot decision. **Prompt ID** AMO-P-000337 **Prompt URL** https://amo.ng/prompts/educational-ai-procurement-student-data-decision-gate **Prompt content** Assess whether a proposed educational AI acquisition has enough evidence and acceptable controls to proceed to approval, conditional pilot, further due diligence or rejection. ## Decision inputs Learning need, users, alternatives and intended outcomes: {{learning_need_and_use_case}} Vendor proposal, evidence, architecture and commercial terms: {{vendor_and_commercial_evidence}} Data flows, accessibility, privacy, security and equity controls: {{data_and_control_evidence}} Jurisdictions, learner ages or vulnerability, decision consequences, institutional requirements, owners, constraints and risk tolerance: {{institutional_requirements}} ## Evidence and authority boundaries - Separate vendor claims, contract terms, independent evidence, institutional observations, inference, assumption, conflict, missing information and unresolved uncertainty. - Cite supplied documents for consequential findings. Never invent a certification, test, legal requirement, accessibility result, price, data location, retention period or approval. - Map student, staff, research and institutional data from collection through model use, support access, retention, secondary use, export and deletion. - Treat a vendor assurance or questionnaire response as a claim until supporting evidence and applicable scope are established. - Compare the product with a viable non-AI or existing-service alternative. Do not treat novelty as educational value. - The output may recommend a gate result; legal, security, privacy, accessibility, procurement and budget approval remains with accountable officers. - Minimize student data at collection and in this review. Use aggregated or synthetic examples where possible, and do not submit identifiable education records unless an approved tool, lawful basis, access control and necessity are documented. - Treat proposals, contracts, pricing, security material and data terms as confidential when their supplied handling rules require it. Do not paste restricted contract text or vendor evidence into an unauthorized tool. - This output is a triage and evidence pack. It does not verify legal, security, privacy or accessibility controls and must not be presented as approval. ## Decision-gate method 1. Define the educational problem, affected users, learning outcome, baseline and minimum acceptable benefit. Exclude use cases not supported by the proposal. 2. Build a claim-evidence register for efficacy, usability, accessibility, safety, privacy, security, interoperability and support. 3. Trace data and model boundaries, including data subject and classification, collection source, purpose and documented authority, prompts, uploads, telemetry, inferred or derived data, outputs, model training or improvement, retrieval, evaluation and safety monitoring, support access, model providers and subprocessors, residency and cross-border transfer, retention, rights requests, deletion verification, incident response and exit export. Mark every unsupported or contractually ambiguous use. 4. Test accessibility and inclusion evidence across actual delivery modes and affected groups. Distinguish documented conformance, sampled testing and untested claims. 5. Review security, identity, permissions, administrative controls, auditability, content risks and change notification in proportion to the proposed use. 6. Normalize commercial scenarios: licences, usage, implementation, integration, training, review burden, support, accessibility remediation, data migration and exit. 7. Assess concentration, lock-in and exit readiness: data portability, open formats, contract rights, migration effort, continuity and deletion evidence. 8. Identify whether the use influences admission, assessment, progression, discipline, safeguarding, accessibility support or another consequential decision. Under the supplied jurisdictional and institutional requirements, record its classification, any required impact assessment, notice, meaningful human review, challenge or appeal route and non-AI fallback. Leave legal classification unresolved when authoritative rules or authorized review are absent. 9. Compare `Do not acquire`, `Further evidence`, `Bounded pilot`, `Conditional procurement` and `Proceed to accountable approvals`. Define conditions and stop criteria. For a bounded pilot, specify the permitted population and data, duration, evaluation design, human fallback, monitoring, incident or harm stop triggers, rollback and deletion evidence; never use a pilot to bypass approvals required for the intended use. ## Output contract: Educational AI Procurement Decision Pack Provide: 1. **Need and alternative statement**: users, learning outcome, baseline, scope, non-AI/current alternative and success evidence, labelled as procurement triage rather than control verification. 2. **Claim-evidence register**: claim, source, independence, scope, freshness, contradiction, confidence and evidence request. 3. **Data lifecycle and consequential-use map**: data subject/group, age or vulnerability, data class, source, purpose and documented authority, flow, access, model use, provider/subprocessor, location or transfer, retention, secondary use, rights or contest route, deletion evidence and owner. 4. **Control decision matrix**: accessibility, privacy, security, equity, safeguarding and operational control, with evidence and gap. 5. **Commercial and exit scenarios**: assumptions, total-cost components, sensitivity, lock-in, transition burden and unresolved terms. 6. **Conditions and evidence requests**: blocker, smallest evidence/control needed, vendor or institutional owner and due point. 7. **Gate recommendation**: one bounded disposition, rationale, residual risk, required approvals and expiry/review date. 8. **Decision record template**: evidence version, reviewers, conditions accepted, dissent, next action and later validation. ## Verification and completion Complete only when the learning need and baseline are explicit; material vendor claims have an evidence status; data flows and retention are mapped; consequential uses, affected groups, jurisdictional questions, meaningful human review and contest routes are resolved or explicitly blocked; accessibility, privacy, security and equity gaps have owners; costs include implementation and exit; and the decision route names every required approver. If contracts, data terms, accessibility evidence, price or ownership are missing, return a provisional gate and targeted due-diligence requests. Refuse to certify legal compliance, security, accessibility or procurement approval. ## Step 3 — Align programmes, teaching, and assessment **Prompt** Programme Learning Outcome Coverage and Constructive Alignment Audit **Instructions** Trace programme outcomes through modules, teaching activities, assessments, progression, duplication, and gaps in light of approved policy and procurement boundaries. **Input for this step** Provide programme outcomes, module and assessment maps, teaching activities, approval rules, the practice map, and any conditionally approved AI capabilities. **Carry forward** Pass the constructive-alignment matrix, underassessment and duplication findings, progression risks, proposed changes, owners, and approval needs to curriculum-currency review. **Review note** Programme leaders, module owners, assessment boards, accessibility leads, and formal curriculum committees approve consequential changes. **Prompt ID** AMO-P-000332 **Prompt URL** https://amo.ng/prompts/programme-learning-outcome-coverage-constructive-alignment-audit **Prompt content** Audit how programme learning outcomes are introduced, practised and independently assessed across the supplied curriculum evidence. ## Programme evidence Programme outcomes and qualification or accreditation framework: {{programme_outcomes_and_framework}} Module specifications and sequence: {{module_specifications_and_sequence}} Teaching activities, assessments, rubrics and sample evidence: {{teaching_assessment_and_evidence}} Learner pathways, delivery modes and review constraints: {{learner_pathways_and_constraints}} ## Evidence boundary - Use actual module, assessment and rubric evidence. Do not infer outcome coverage from a module title or generic description alone. - Separate documented coverage, interpretation, assumption, conflict, missing evidence and unresolved uncertainty. - Distinguish introduced, practised, formatively checked and independently summatively assessed. Repetition is not progression unless complexity or learner independence changes. - Do not invent accreditation requirements, module content, student attainment, external-examiner conclusions or approval. - Preserve route, elective, placement, online and accessibility differences. Do not assume one pathway represents every learner. Minimize learner-level evidence, reconcile document versions and process restricted evidence only in an approved environment. - The programme team and authorized academic bodies own curriculum change and approval. ## Audit method 1. Normalize each programme outcome into observable knowledge, skill, judgment or performance while preserving the approved wording. 2. Inventory modules, prerequisites, level, sequence, pathway status, delivery mode, teaching activity, assessment and available attainment evidence. Reconcile document versions before mapping and identify stale or conflicting specifications. 3. Map each outcome to where it is introduced, practised and assessed. Cite the module and assessment evidence that supports the classification. For a large programme, work in version-controlled batches and reconcile all batches before a programme-level disposition. 4. Test constructive alignment between outcome, teaching activity, learner practice, assessment task and rubric criterion. 5. Review progression across levels for complexity, integration, authenticity, learner independence and feedback opportunity. 6. Identify underassessment, isolated one-point dependency, excessive duplication, premature assessment, missing prerequisite, elective-path gap and construct mismatch. 7. Examine whether allowed formats and accommodations preserve equivalent opportunity to demonstrate the outcome. 8. Prioritize the smallest curriculum or evidence repair, including where the right action is better documentation rather than new assessment. ## Output contract: Programme Alignment Evidence Map Return: 1. **Programme and pathway boundary**: versions, routes, levels, exclusions, evidence coverage and unresolved inputs. 2. **Outcome definition register**: approved outcome, observable interpretation, framework source, ambiguity and owner. 3. **Coverage matrix**: outcome by module with `Introduced`, `Practised`, `Formative evidence`, `Summatively assessed`, or `No evidence supplied`, plus source reference and document version. 4. **Constructive alignment table**: outcome, learning activity, assessment, rubric criterion, alignment status and gap. 5. **Progression map**: level, expected complexity/independence, actual evidence, break and consequence. 6. **Pathway and delivery comparison**: route, missing/duplicated coverage, accessibility or modality issue and affected outcome. 7. **Risk register**: gap type, evidence, learner or quality risk, severity, confidence and dependency. 8. **Prioritized repair plan**: change, affected module, owner, approval route, implementation evidence and recheck date. 9. **Review disposition**: `Adequately evidenced`, `Conditionally aligned`, or `Material alignment gaps`, with limitations. ## Completion conditions Complete when every matrix cell has an evidence status and every material gap has an owner and next action; use `Unassigned` when no owner is supplied. Completion does not mean every outcome is successfully evidenced. Reconcile all document versions and mapping batches, keep learner evidence minimized, and mark incomplete cells `No evidence supplied` rather than aligned. Do not claim accreditation, programme approval, educational quality or student attainment. Those judgments remain with authorized academic and external review processes. ## Step 4 — Test curriculum currency and competency gaps **Prompt** AI Curriculum Currency and Graduate Competency Gap Audit **Instructions** Compare the aligned programme with dated primary standards, credible research, labour evidence, and durable competency needs while separating stable capability from vendor fashion. **Input for this step** Provide the alignment matrix, current curriculum, approved source set and cutoff date, competency frameworks, graduate destinations, labour evidence, and institutional priorities. **Carry forward** Pass the dated evidence register, durable and short-lived competency findings, curriculum gap priorities, implementation dependencies, and review dates to impact governance. **Review note** Academic subject owners and curriculum governance decide which evidence warrants change; source discovery never substitutes for curriculum approval. **Prompt ID** AMO-P-000338 **Prompt URL** https://amo.ng/prompts/ai-curriculum-currency-graduate-competency-gap-audit **Prompt content** Audit whether an academic programme develops current, durable and assessable AI-related graduate competencies using supplied curriculum evidence and dated external sources. ## Audit inputs Programme outcomes, module specifications and assessment evidence: {{programme_and_assessment_evidence}} Learner profile, discipline, graduate destinations and institutional mission: {{programme_context}} Primary standards, credible research and labour evidence: {{external_evidence}} Review scope, resource constraints and accountable owners: {{review_constraints_and_owners}} ## Evidence discipline - Separate current curriculum evidence, direct external evidence, stakeholder claim, reviewer inference, assumption, conflict, missing information and unresolved uncertainty. - Record source title, author or issuing body, jurisdiction or population, version or status, publication date, access date, method, direct URL or identifier and applicability for every consequential external claim. Check whether a standard or framework has been withdrawn or superseded. Never invent a standard, citation, labour forecast or employer requirement. - Prefer primary standards, competency frameworks, systematic evidence and transparent labour data over vendor marketing or isolated job advertisements. - Treat job-posting counts, employer surveys, occupational data and forecasts as different evidence types. Record their coverage, sampling and limitations; do not infer durable labour demand from vacancy frequency alone or claim that a curriculum change will improve graduate employment without suitable outcome evidence. - Distinguish durable competencies, such as evidence judgment or data stewardship, from product-specific operation that may decay quickly. - Do not infer teaching or learner attainment from a module title. Require teaching, practice and assessment evidence. - Preserve academic governance: the review proposes evidence-backed changes, while authorized programme bodies approve outcomes, assessments and curriculum. - For consequential external evidence, record the original-source URL or DOI, publication date and access date, then require manual verification in the original source. Do not treat a search summary or generated citation as evidence. - Exclude learner records and sensitive attainment data unless the review explicitly requires aggregated evidence and the institution has approved the handling method. ## Audit method 1. Define the graduate roles, discipline context, time horizon and decision the audit will inform. 2. Normalize external evidence into competency statements with source version or status, publication and access dates, population, method, direct locator, applicability, confidence, limitation and expected shelf life. Keep observed demand, stakeholder preference and forecast demand distinct. 3. Map each programme outcome to modules, teaching activity, practice opportunity, assessment, performance standard and progression stage. 4. Test coverage for technical use, critical evaluation, evidence and data literacy, domain application, ethics/safety, accessibility, collaboration and accountable decision-making only where relevant. 5. Identify underassessment, duplication, premature advanced content, progression breaks, obsolete tool-specific content and unsupported claims of coverage. 6. Challenge each proposed addition: why it matters, supporting evidence, likely durability, prerequisites, opportunity cost and how attainment would be assessed. 7. Compare the smallest viable options: refresh examples, revise an assessment, strengthen progression, add a module element, retire content, or gather better evidence. 8. Create an evidence-refresh schedule based on source volatility rather than an arbitrary annual rewrite. ## Output contract: Curriculum Currency and Competency Dossier Return: 1. **Scope and evidence register**: programme context, decision horizon, source title and issuing body, version or status, publication/access date, direct URL or identifier, jurisdiction/population, method, applicability, supersession check and reliability limit. 2. **Competency architecture**: competency, durable/vendor-specific classification, rationale, prerequisites and observable attainment. 3. **Curriculum-to-assessment map**: outcome, module, teaching, practice, assessment, standard, progression and evidence status. 4. **Gap and redundancy register**: issue, evidence, consequence, affected learners, priority and uncertainty. 5. **Change options**: smallest intervention, intended learning outcome, assessment evidence, workload/resource effect, dependency and owner. 6. **Vendor-fashion challenge**: proposed content, supporting source, durability risk, portable alternative and decision. 7. **Review recommendation**: `Maintain`, `Targeted refresh`, `Programme redesign evidence phase`, or `Insufficient evidence`, with rationale. 8. **Currency plan**: volatile evidence to monitor, trigger, cadence, source owner and next formal review. ## Verification and completion Complete only when external evidence is directly traceable, dated, checked for current or superseded status and qualified for method and applicability; claimed curriculum coverage has teaching and assessment support; durable and vendor-specific competencies are distinguished; changes have assessable outcomes and opportunity costs; and approval authority remains explicit. For each completion condition, record the expected curriculum or external evidence, the actual supplied evidence, `Met`, `Not met`, or `Blocked` status, and the unresolved programme-owner action. If programme documents or credible external evidence are incomplete, return a provisional map and prioritized evidence plan. Refuse requests to fabricate labour demand, declare a programme compliant or present proposed changes as approved. ## Step 5 — Reconcile implementation and institutional impact **Prompt** University AI Initiative Portfolio and Academic Impact Review **Instructions** Reconcile teaching, research, and administrative AI initiatives against mission outcomes, costs, risk, duplication, inclusion, implementation evidence, and the preceding governance conditions. **Input for this step** Provide the initiative register, prior gate outputs, owners, budgets, implementation status, outcome and baseline evidence, incidents, adoption and inclusion measures, dissent, and decision horizon. **Carry forward** Produce the institutional portfolio decision with evidence quality, policy and procurement compliance status, curriculum and implementation effects, remediation, owners, and continue, change, expand, pause, or stop decisions. **Review note** The authorized institutional body makes portfolio decisions after academic, procurement, privacy, legal, accessibility, finance, staff, and student evidence is represented. **Prompt ID** AMO-P-000339 **Prompt URL** https://amo.ng/prompts/university-ai-initiative-portfolio-academic-impact-review **Prompt content** Review a university's AI initiative portfolio to determine which activities have credible academic or institutional value and which require redesign, consolidation, evidence development or closure. ## Portfolio inputs Initiative register, owners, users and lifecycle states: {{initiative_register}} Outcome, adoption, cost and operational evidence: {{outcome_cost_and_adoption_evidence}} Risk, inclusion, data, control and dependency evidence: {{risk_and_dependency_evidence}} Institutional mission, constraints and decision authority: {{mission_constraints_and_authority}} ## Portfolio evidence rules - Separate approved objective, observed result, initiative-owner claim, financial estimate, inference, assumption, conflict, missing information and unresolved uncertainty. - Do not invent costs, saved time, learning gains, research impact, adoption, risk acceptance, approvals or causal attribution. - Distinguish activity and output from outcome and impact. Do not treat licences purchased, users registered or content generated as realized value. - Use causal language only when a suitable evaluation design and its assumptions support it. Otherwise report an observed change, association or plausible contribution, not impact caused by the initiative, and retain alternative explanations. - Compare initiatives using shared decision dimensions without forcing unlike teaching, research and administrative outcomes into one opaque score. - Surface distributional effects, accessibility, staff/student burden, data rights and who bears risk or unpaid review work. - This review prepares a decision pack. Portfolio allocation, employment, academic, legal and risk decisions remain with the named governing owners. - Make cost calculations reproducible by recording source values, units, time periods, allocation rules, formulas and sensitivity assumptions. Label shared-cost allocation and causal attribution limits explicitly, and require finance validation before use. - Aggregate learner and staff evidence and suppress small cells where disclosure or re-identification risk exists. Do not infer individual performance from portfolio data. ## Review method 1. Normalize the portfolio: initiative purpose, sponsor, affected group, stage, dependencies, committed resources, decision date and claimed outcome. 2. Build an evidence bridge from resources and activity to observable outputs, outcomes and mission contribution. For each outcome record its definition, baseline, target, denominator, affected population, measurement window, data completeness, evaluation design, comparison or counterfactual where defensible, plausible alternative explanations and attribution limit. Do not construct a counterfactual that the evidence cannot support. 3. Reconcile cost, including licences, infrastructure, integration, support, review, training, remediation, compliance and exit. Provide reproducible formulas, units and ranges, state causal and allocation limits, and flag all figures pending finance validation. 4. Assess adoption quality: intended versus actual use, workflow displacement or augmentation, friction, exclusion, work shifted to other roles and persistence over time. 5. Map material privacy, security, academic integrity, research integrity, accessibility, equity, vendor and operational dependencies with current control evidence. 6. Identify overlap, shared infrastructure, incompatible approaches, duplicated evaluation, concentration risk and opportunities for responsible consolidation. 7. Apply explicit `Scale`, `Redesign`, `Hold`, `Stop` and `Insufficient evidence` gates without collapsing unlike outcomes into one score. Recommend `Scale` only when the evidence supports the outcome, implementation fidelity, safeguards, full cost, operating capacity, sustainability and transferability to the proposed scale; otherwise state the smallest evidence or control gap preventing that disposition. 8. Sequence decisions and dependencies, including evidence work, consultation, contractual windows, continuity and reversible exit. ## Output contract: University AI Portfolio and Impact Dossier Provide: 1. **Portfolio register**: initiative, sponsor, purpose, users, stage, cost range, dependencies and next decision. 2. **Academic outcome and impact evidence bridge**: input, activity, output, outcome definition, baseline/target, population and denominator, measurement window and completeness, observed result, mission contribution, evaluation design or comparison, source, alternative explanation, attribution limit and confidence. 3. **Adoption and burden map**: beneficiary, actual use, friction, review workload, excluded group and corrective owner. 4. **Risk and control evidence map**: exposure, affected group, evidence, control, gap, risk owner, acceptance authority and status, and review date. 5. **Duplication and dependency map**: shared job, overlapping asset, concentration, consolidation option, transition risk and constraint. 6. **Initiative disposition cards**: evidence supporting consideration of `Scale`, `Redesign`, `Hold`, `Stop`, or `Insufficient evidence`, with gate evidence, conditions, dissent and authorized decision owner. 7. **Portfolio sequence**: immediate reversible action, dependent decision, contractual/academic timing, owner and completion evidence. 8. **Governing-body brief**: decisions requested, evidence strength, unresolved uncertainty, distributional impact and matters outside this review. ## Verification and completion Complete only when every initiative has an owner and lifecycle state; value claims trace to supplied evidence and distinguish observed outcomes, contribution and causally supported impact; outcome definitions, baselines, denominators, time windows, missingness and attribution limits are visible; full cost and shifted burden are visible; risks and dependencies are mapped; dispositions use explicit gates; and the governing decision remains with authorized owners. If the initiative register, outcome data, costs or decision rights are materially incomplete, issue an evidence-development plan instead of a portfolio recommendation. Refuse to fabricate impact, approve investment, make employment decisions or present suggested dispositions as adopted policy. ## Completion criteria Complete when: - Policy requirements, role authority, evidence duties, escalation, and fair appeal routes are approved and traceable. - Each procurement decision maps the learning need, alternatives, student-data lifecycle, accessibility, security, equity, cost, exit conditions, and named approvers. - Programme outcomes and curriculum changes have evidence, ownership, governance status, and compatibility with approved policy. - Implementation and impact measures distinguish observed outcomes from assumptions, preserve dissent and uncertainty, and name remediation owners. - Academic, procurement, privacy, legal, accessibility, finance, and institutional leaders retain their formal decision authority. # Govern University AI Adoption, Curriculum and Impact Workflow ID: AMO-W-000031 Workflow URL: https://amo.ng/workflows/govern-university-ai-adoption-curriculum-impact Use this Amo.ng workflow with your preferred AI tool. Complete the steps in order and carry the specified output forward. Outcome: A governed university AI adoption package linking approved policy to role-specific practice, educational procurement, programme alignment, curriculum currency, implementation evidence, and a bounded institutional continue, change, expand, pause, or stop decision. Required inputs: - Approved institutional AI policies, related procedures, role definitions, appeal routes, and unresolved interpretations. - Proposed or current educational AI services, learning needs, vendor evidence, contracts, data flows, controls, costs, and alternatives. - Programme outcomes, module and assessment maps, teaching activities, progression expectations, and curriculum approval rules. - Current primary standards, credible research, labour evidence, competency frameworks, and source cutoff dates. - Institutional AI initiative register, owners, budgets, implementation state, outcome evidence, incidents, inclusion evidence, and decision calendar. ## Step 1 — Translate approved policy into governed practice **Instructions** Map supplied policy into role-specific permitted, restricted, prohibited, evidence, escalation, and appeal requirements without inventing institutional authority. **Input for this step** Provide current approved policy and procedures, roles, affected communities, decision routes, appeal mechanisms, implementation context, and known ambiguities. **Carry forward** Pass the approved practice map, unresolved policy questions, authority boundaries, evidence duties, escalation routes, and appeal safeguards to procurement. **Review note** The policy owner, academic governance body, legal or privacy advisers, and appeal-process owner approve their respective interpretations. **Prompt** University AI Policy-to-Practice and Appeals Map **Prompt ID** AMO-P-000336 **Prompt URL** https://amo.ng/prompts/university-ai-policy-practice-appeals-map ## Step 2 — Gate educational AI procurement and student data **Instructions** Evaluate educational need, alternatives, vendor claims, accessibility, privacy, security, equity, data lifecycle, cost, lock-in, and exit evidence against the approved practice map. **Input for this step** Provide the practice map, learning need, affected users, proposal and contract evidence, data flows, controls, commercial assumptions, alternatives, and institutional requirements. **Carry forward** Pass the procurement disposition, conditions, evidence requests, data boundary, control gaps, cost and exit scenarios, and approval route to programme alignment. **Review note** Procurement, finance, privacy, security, accessibility, legal, and academic owners make the accountable acquisition or pilot decision. **Prompt** Educational AI Procurement and Student Data Decision Gate **Prompt ID** AMO-P-000337 **Prompt URL** https://amo.ng/prompts/educational-ai-procurement-student-data-decision-gate ## Step 3 — Align programmes, teaching, and assessment **Instructions** Trace programme outcomes through modules, teaching activities, assessments, progression, duplication, and gaps in light of approved policy and procurement boundaries. **Input for this step** Provide programme outcomes, module and assessment maps, teaching activities, approval rules, the practice map, and any conditionally approved AI capabilities. **Carry forward** Pass the constructive-alignment matrix, underassessment and duplication findings, progression risks, proposed changes, owners, and approval needs to curriculum-currency review. **Review note** Programme leaders, module owners, assessment boards, accessibility leads, and formal curriculum committees approve consequential changes. **Prompt** Programme Learning Outcome Coverage and Constructive Alignment Audit **Prompt ID** AMO-P-000332 **Prompt URL** https://amo.ng/prompts/programme-learning-outcome-coverage-constructive-alignment-audit ## Step 4 — Test curriculum currency and competency gaps **Instructions** Compare the aligned programme with dated primary standards, credible research, labour evidence, and durable competency needs while separating stable capability from vendor fashion. **Input for this step** Provide the alignment matrix, current curriculum, approved source set and cutoff date, competency frameworks, graduate destinations, labour evidence, and institutional priorities. **Carry forward** Pass the dated evidence register, durable and short-lived competency findings, curriculum gap priorities, implementation dependencies, and review dates to impact governance. **Review note** Academic subject owners and curriculum governance decide which evidence warrants change; source discovery never substitutes for curriculum approval. **Prompt** AI Curriculum Currency and Graduate Competency Gap Audit **Prompt ID** AMO-P-000338 **Prompt URL** https://amo.ng/prompts/ai-curriculum-currency-graduate-competency-gap-audit ## Step 5 — Reconcile implementation and institutional impact **Instructions** Reconcile teaching, research, and administrative AI initiatives against mission outcomes, costs, risk, duplication, inclusion, implementation evidence, and the preceding governance conditions. **Input for this step** Provide the initiative register, prior gate outputs, owners, budgets, implementation status, outcome and baseline evidence, incidents, adoption and inclusion measures, dissent, and decision horizon. **Carry forward** Produce the institutional portfolio decision with evidence quality, policy and procurement compliance status, curriculum and implementation effects, remediation, owners, and continue, change, expand, pause, or stop decisions. **Review note** The authorized institutional body makes portfolio decisions after academic, procurement, privacy, legal, accessibility, finance, staff, and student evidence is represented. **Prompt** University AI Initiative Portfolio and Academic Impact Review **Prompt ID** AMO-P-000339 **Prompt URL** https://amo.ng/prompts/university-ai-initiative-portfolio-academic-impact-review Completion criteria: Complete when: - Policy requirements, role authority, evidence duties, escalation, and fair appeal routes are approved and traceable. - Each procurement decision maps the learning need, alternatives, student-data lifecycle, accessibility, security, equity, cost, exit conditions, and named approvers. - Programme outcomes and curriculum changes have evidence, ownership, governance status, and compatibility with approved policy. - Implementation and impact measures distinguish observed outcomes from assumptions, preserve dissent and uncertainty, and name remediation owners. - Academic, procurement, privacy, legal, accessibility, finance, and institutional leaders retain their formal decision authority.Copy workflow includes every step and the full linked Prompt content. Use with AI copies a shorter guide with Prompt links; neither action runs the Workflow.
Outcome
A governed university AI adoption package linking approved policy to role-specific practice, educational procurement, programme alignment, curriculum currency, implementation evidence, and a bounded institutional continue, change, expand, pause, or stop decision.
Before you begin
Have all or some of the following available before you start. The more relevant context you can provide, the stronger the workflow output will be.
- Approved institutional AI policies, related procedures, role definitions, appeal routes, and unresolved interpretations.
- Proposed or current educational AI services, learning needs, vendor evidence, contracts, data flows, controls, costs, and alternatives.
- Programme outcomes, module and assessment maps, teaching activities, progression expectations, and curriculum approval rules.
- Current primary standards, credible research, labour evidence, competency frameworks, and source cutoff dates.
- Institutional AI initiative register, owners, budgets, implementation state, outcome evidence, incidents, inclusion evidence, and decision calendar.
Ordered sequence
Workflow steps
Complete the steps in order. For each step, provide the listed context, carry its result into the next step, and pause wherever a review note is shown.
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Step 1 Translate approved policy into governed practice
Map supplied policy into role-specific permitted, restricted, prohibited, evidence, escalation, and appeal requirements without inventing institutional authority.
Prompt: University AI Policy-to-Practice and Appeals MapInput for this step
Provide current approved policy and procedures, roles, affected communities, decision routes, appeal mechanisms, implementation context, and known ambiguities.
Carry forward
Pass the approved practice map, unresolved policy questions, authority boundaries, evidence duties, escalation routes, and appeal safeguards to procurement.
Review note
The policy owner, academic governance body, legal or privacy advisers, and appeal-process owner approve their respective interpretations.
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Step 2 Gate educational AI procurement and student data
Evaluate educational need, alternatives, vendor claims, accessibility, privacy, security, equity, data lifecycle, cost, lock-in, and exit evidence against the approved practice map.
Prompt: Educational AI Procurement and Student Data Decision GateInput for this step
Provide the practice map, learning need, affected users, proposal and contract evidence, data flows, controls, commercial assumptions, alternatives, and institutional requirements.
Carry forward
Pass the procurement disposition, conditions, evidence requests, data boundary, control gaps, cost and exit scenarios, and approval route to programme alignment.
Review note
Procurement, finance, privacy, security, accessibility, legal, and academic owners make the accountable acquisition or pilot decision.
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Step 3 Align programmes, teaching, and assessment
Trace programme outcomes through modules, teaching activities, assessments, progression, duplication, and gaps in light of approved policy and procurement boundaries.
Prompt: Programme Learning Outcome Coverage and Constructive Alignment AuditInput for this step
Provide programme outcomes, module and assessment maps, teaching activities, approval rules, the practice map, and any conditionally approved AI capabilities.
Carry forward
Pass the constructive-alignment matrix, underassessment and duplication findings, progression risks, proposed changes, owners, and approval needs to curriculum-currency review.
Review note
Programme leaders, module owners, assessment boards, accessibility leads, and formal curriculum committees approve consequential changes.
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Step 4 Test curriculum currency and competency gaps
Compare the aligned programme with dated primary standards, credible research, labour evidence, and durable competency needs while separating stable capability from vendor fashion.
Prompt: AI Curriculum Currency and Graduate Competency Gap AuditInput for this step
Provide the alignment matrix, current curriculum, approved source set and cutoff date, competency frameworks, graduate destinations, labour evidence, and institutional priorities.
Carry forward
Pass the dated evidence register, durable and short-lived competency findings, curriculum gap priorities, implementation dependencies, and review dates to impact governance.
Review note
Academic subject owners and curriculum governance decide which evidence warrants change; source discovery never substitutes for curriculum approval.
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Step 5 Reconcile implementation and institutional impact
Reconcile teaching, research, and administrative AI initiatives against mission outcomes, costs, risk, duplication, inclusion, implementation evidence, and the preceding governance conditions.
Prompt: University AI Initiative Portfolio and Academic Impact ReviewInput for this step
Provide the initiative register, prior gate outputs, owners, budgets, implementation status, outcome and baseline evidence, incidents, adoption and inclusion measures, dissent, and decision horizon.
Carry forward
Produce the institutional portfolio decision with evidence quality, policy and procurement compliance status, curriculum and implementation effects, remediation, owners, and continue, change, expand, pause, or stop decisions.
Review note
The authorized institutional body makes portfolio decisions after academic, procurement, privacy, legal, accessibility, finance, staff, and student evidence is represented.
Completion criteria
Complete when:
- Policy requirements, role authority, evidence duties, escalation, and fair appeal routes are approved and traceable.
- Each procurement decision maps the learning need, alternatives, student-data lifecycle, accessibility, security, equity, cost, exit conditions, and named approvers.
- Programme outcomes and curriculum changes have evidence, ownership, governance status, and compatibility with approved policy.
- Implementation and impact measures distinguish observed outcomes from assumptions, preserve dissent and uncertainty, and name remediation owners.
- Academic, procurement, privacy, legal, accessibility, finance, and institutional leaders retain their formal decision authority.
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