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Welcome to Phoenix University AI Policy

PHOENIX UNIVERSITY, AGWADA, NIGERIA

POLICY ON THE USE OF ARTIFICIAL INTELLIGENCE (AI)

Document Control 
Policy titlePolicy on the Use of Artificial Intelligence (AI)
Version[1.0]
Approved byGoverning Council
Date of approval14th November, 2025
Policy ownerOffice of the Vice Chancellor
Next review date13th November, 2030

TABLE OF CONTENTS

INTRODUCTION AND DEFINITIONAL ISSUES. 5

1.1 Definitions. 5

1.2 Policy objectives. 6

1.3 Guiding principles. 6

PERMISSIBLE USES OF AI FOR CATEGORIES OF STAFF AND STUDENTS. 8

2.1 Acceptable uses by academic staff. 8

2.2 Acceptable uses by students. 8

2.3 Acceptable uses in research. 9

2.4 Acceptable uses in administration and professional services. 10

2.5 Prohibited uses. 10

2.6 Assessment-specific rules. 11

APPROVED TOOLS, DISCLOSURE AND CITATION.. 12

3.1 Tool selection and approval 12

3.2 AI-use categories. 14

3.3 Required disclosure. 15

3.4 Citing AI. 15

3.5 Authorship and accountability. 15

DATA PROTECTION, SECURITY AND RISK MANAGEMENT. 16

4.1 Protection of personal and confidential information. 16

4.2 Human review and consequential decisions. 16

4.3 Bias, accessibility and reliability. 17

4.4 Intellectual property and licensing. 17

4.5 AI risk assessment 17

MONITORING, ASSESSMENT AND ACADEMIC INTEGRITY.. 18

5.1 Monitoring principles. 18

5.2 Approved Detection Tools. 18

5.3 Limits of AI-detection tools. 19

5.4 Fair investigation. 19

COMPLIANCE, ACCOUNTABILITY AND DISCIPLINE.. 20

6.1 Compliance. 20

6.2 Responses to breaches. 20

6.3 Reporting concerns. 20

6.4 Review of this Policy. 21

RESPONSIBILITIES AND GOVERNANCE.. 22

7.1 University Management 22

7.2 Policy owner / AI governance function. 22

7.3 Deans, Heads of Department and course leaders. 22

7.4 Academic staff and researchers. 23

7.5 Students. 23

7.6 ICT, Library, Quality Assurance and support units. 23

IMPLEMENTATION AND SUPPORT SYSTEMS. 25

8.1 Communication and awareness. 25

8.2 Training and AI literacy. 25

8.3 Curriculum integration. 25

8.4 Access and equity. 26

8.5 Support and feedback. 26

8.6 Implementation plan. 26

8.7 Support systems. 27

APPENDIX A: AI-USE DISCLOSURE TEMPLATES. 28

A1. General disclosure. 28

A2. Research disclosure. 28

A3. Student project or thesis declaration. 28

APPENDIX B: QUICK GUIDE FOR STUDENTS AND STAFF. 29

PHOENIX UNIVERSITY, AGWADA

POLICY DOCUMENT GUIDING THE USE OF ARTIFICIAL INTELLIGENCE (AI) IN PHOENIX UNIVERSITY, AGWADA

Introduction

Phoenix University, Agwada (the University) has as its Vision “Phoenix University shall mobilize the best human and material resources available to provide highly qualitative and affordable education with the philosophical orientation, distinct leadership competence and lifelong skills that will equip students adequately to solve contemporary and emerging challenges in their societies.”. Its Mission is “To be a leading centre of excellence renowned and respected for the generation, dissemination, application and advancement of knowledge and committed to character development in the service of humanity”. The University, being desirous of leveraging Artificial Intelligence to enhance its research, teaching, and administrative processes, hereby prepares this AI Policy to guide the responsible and ethical use of AI technologies in the University. This Policy outlines the principles, governance structures, and implementation strategies to ensure that AI is leveraged in a manner that aligns with the University’s values, enhances its core functions, and benefits the broader community.

This Policy applies to all staff and students within the University.

SECTION ONE

INTRODUCTION AND DEFINITIONAL ISSUES

1.1 Definitions

Artificial Intelligence (AI): Computer-based systems that generate predictions, recommendations, classifications, content or decisions from data or encoded rules to achieve specified objectives.

Generative AI: AI systems that generate text, code, images, audio, video, summaries or other content in response to prompts or other inputs.

AI-assisted work: Work in which AI contributes to one or more tasks, while a person remains responsible for the final content, judgement and submission.

Hallucination: An AI output that appears plausible but is inaccurate, unsupported, fabricated or inconsistent with reliable evidence.

Academic integrity: The principles of honesty, fairness, trust, respect, responsibility and proper acknowledgement in learning, assessment and research.

Sensitive information: Personal data, student records, staff records, credentials, unpublished research, confidential institutional documents, security information or other information requiring protection.

Human oversight: Meaningful review by a competent person who can question, correct, reject or stop an AI-assisted process.

AI disclosure: A clear statement describing the AI tool used and the material tasks for which it was used.

High-impact AI use: Use that may materially affect admission, grades, progression, employment, discipline, access to services, safety, rights or significant institutional decisions.

1.2 Policy Objectives

  1. Promote AI literacy and responsible innovation across the University community.
  2. Support teaching, learning, research and administration without undermining independent thought or professional judgement.
  3. Set clear expectations for disclosure, attribution, verification and assessment.
  4. Protect personal data, confidential information, intellectual property and research participants.
  5. Provide proportionate governance, risk assessment, oversight and routes for raising concerns.
  6. Ensure that AI adoption is inclusive, accessible and attentive to bias, language, disability and unequal access to technology.

1.3 Guiding Principles

  1. Human responsibility: a person remains accountable for decisions and submitted work, even where AI has been used.
  2. Transparency: AI use must be disclosed when required by this Policy, a course brief, a supervisor, a funder or a publisher.
  3. Accuracy and verification: outputs must be checked against reliable evidence; AI-generated references must never be accepted without verification.
  4. Fairness and inclusion: AI must not be used to discriminate, harass or unfairly disadvantage individuals or groups.
  5. Privacy and security: information may be entered into an AI service only where the user is authorised and the service is suitable for that information.
  6. Proportionality: controls should reflect the likely impact and risk of the intended use.
  7. Academic freedom and innovation: responsible experimentation and learning about AI are encouraged within appropriate safeguards.

SECTION TWO

PERMISSIBLE USES OF AI FOR CATEGORIES OF STAFF AND STUDENTS

2.1 Acceptable Uses by Academic Staff

Subject to course requirements, confidentiality rules and professional judgement, academic staff may use AI to:

  1. Develop lesson plans, illustrative examples, discussion prompts, practice questions and draft learning resources.
  2. Explain difficult concepts in alternative ways and prepare accessible or differentiated learning materials.
  3. Improve grammar, readability, translation and document structure.
  4. Support coding, simulation, modelling, data exploration, transcription and visualisation, with verification.
  5. Brainstorm research questions, identify search terms, organise notes and assist with literature discovery.
  6. Develop rubrics or draft feedback, provided the lecturer reviews the material and makes the final assessment judgement.
  7. Explore AI applications in teaching and research and design learning activities that build AI literacy.

2.2 Acceptable Uses by Students

  1. Use AI as a supplementary tutor to explain concepts, generate practice questions or provide feedback on understanding.
  2. Brainstorm ideas, plan a project, identify search terms and locate potential academic sources for independent verification.
  3. Receive language, grammar, accessibility or formatting assistance where permitted by the assessment instructions.
  4. Use AI for coding assistance, debugging, data analysis, translation or visualisation where the task allows it and the contribution is disclosed as required.
  5. Generate practice examples or simulations that support learning, provided fabricated examples are not presented as real observations or evidence.
  6. Use AI tools approved for accessibility support, while respecting assessment rules and privacy requirements.

2.3 Acceptable Uses in Research

  1. Assist with literature discovery, search strategy, coding support, data cleaning, transcription, translation and exploratory analysis.
  2. Support drafting or editing, provided authors verify accuracy, originality, citations and the integrity of the final work.
  3. Use synthetic data for testing or demonstration only when clearly labelled and when its use is methodologically appropriate.
  4. Use AI in research design or modelling with adequate documentation of tools, versions, parameters, data sources and validation procedures.
  5. Use AI-based methods as part of a research contribution when methods, limitations and reproducibility requirements are reported.

2.4 Acceptable Uses in Administration and Professional Services

  1. Draft routine correspondence, meeting summaries, reports, schedules and non-confidential internal guidance.
  2. Assist with document classification, search, summarisation and workflow automation using authorised systems.
  3. Support analysis of operational data after appropriate access control, data minimisation and privacy review.
  4. Improve accessibility, translation and plain-language communication.
  5. Assist service teams with frequently asked questions where responses are reviewed and escalation to a human officer is available.

2.5 Prohibited Uses

The following uses are prohibited unless a formally approved, lawful and narrowly defined exception applies:

  1. Submitting AI-generated work as entirely one’s own where this misrepresents authorship, effort, knowledge or skill.
  2. Using AI during examinations, tests or restricted assessments without explicit permission from the responsible lecturer or examination authority.
  3. Using AI to fabricate, alter or selectively suppress research data, results, participant responses, images, citations or evidence.
  4. Inventing references, quotations, sources, credentials, recommendation letters, transcripts or other official records.
  5. Using AI to impersonate another person, falsify attendance, evade identity checks or misrepresent participation in coursework or research.
  6. Uploading personal, sensitive, confidential or restricted University information to a public AI service without authorisation and appropriate safeguards.
  7. Using AI to harass, threaten, discriminate against, defame or create deceptive content about members of the University community.
  8. Using AI to make consequential decisions about students, staff or applicants without authorised governance, relevant human review, an opportunity to challenge outcomes and documented safeguards.
  9. Using AI-generated output without checking for plagiarism, copyright infringement, confidentiality breaches or factual error.
  10. Using AI to bypass academic-integrity controls, conceal prohibited assistance or interfere with University systems.

2.6 Assessment-Specific Rules

The lecturer or assessment setter must state whether AI use is prohibited, limited or permitted for each assessment where this distinction matters. Students must follow the stated rule. Where instructions are silent and the permitted extent is unclear, students should seek clarification before submitting work. Assessment design should, where practicable, evaluate understanding through explanations, demonstrations, drafts, oral discussion, practical work or other evidence in addition to a final written product.

SECTION THREE

APPROVED TOOLS, DISCLOSURE AND CITATION

3.1 Tool Selection and Approval

The University should maintain a current register of AI services approved for specified purposes. Approval should consider terms of service, data retention, privacy, security, accessibility, cost, age restrictions, intellectual-property terms and whether institutional accounts or contractual protections are available. Listing a tool does not guarantee that every use of it is appropriate.

Until a formal register is established, users must apply this Policy and any applicable ICT, data-protection, research-ethics and assessment rules. Users should prefer institutionally managed or privacy-protective services for University work and must not assume that a free service is suitable for confidential information.

The following table lists approved AI tools for each area, along with their respective URLs. These tools have been evaluated and deemed appropriate for use in academic work as of the time of framing this Policy (September, 2026).

AreaApproved AI ToolURL
Literature SearchElicitelicit.com
 Typeset.io (SciSpace)typeset.io
 PubMedpubmed.ncbi.nlm.nih.gov
 Google Scholarscholar.google.com
 Semantic Scholarsemanticscholar.org
Knowledge SearchResearch Rabbitresearchrabbit.ai
 Litmapslitmaps.com
 Connected Papersconnectedpapers.com
 Wolfram Alphawolframalpha.com
 IBM Watsonibm.com/watson
 NotebookLMnotebooklm.google
 Perplexityperplexity.ai
Writing ImprovementGrammarlygrammarly.com
Referencing/CitationMendeleymendeley.com
 Zoterozotero.org
 EndNoteendnote.com
 RefWorksrefworks.proquest.com
 Paperpilepaperpile.com
 JabRefjabref.org
 BibMebibme.org
 EasyBibeasybib.com
 Citavicitavi.com
AdministrationOtterotter.ai
 ChatGPTchatgpt.com
 Microsoft Copilotcopilot.microsoft.com
 DeepSeekdeepseek.com
 Claudeclaude.ai
 Geminigemini.google.com
Personal TutorLlama Tutorllamatutor.together.ai

This list is subject to revision at any time within the life span of this Policy. Tools not listed may be used only with the written approval of the AI Policy Committee.

3.2 AI-Use Categories

CategoryExamplesMinimum expectation
Low-risk assistanceGrammar, brainstorming, practice explanations, formattingCheck outputs; disclose where assessment or publication rules require.
Moderate-risk useCoding, data analysis, research synthesis, draft feedbackVerify results; document tool and task; protect data; obtain supervisor/lecturer guidance where needed.
High-impact useAdmission, grading, discipline, employment, student risk profiling, safety-related decisionsPrior approval, documented risk review, privacy/security assessment, meaningful human oversight and an appeal route.

3.3 Required Disclosure

Students and staff must disclose material AI assistance in assessed work, theses, dissertations, research outputs, grant applications or official documents whenever required by the assignment, supervisor, department, publisher, funder or University rule. Disclosure should identify the tool, the task for which it was used and the human verification undertaken. Routine spelling or accessibility assistance need not be separately disclosed unless a specific rule requires it.

3.4 Citing AI

AI-generated output is not a substitute for citing the original source of factual claims. Users must verify sources independently and cite original publications, datasets or other evidence. Where the AI tool itself is material to the method or output, describe it in the format required by the relevant discipline, journal, publisher or style guide. Record the tool name, provider, version or access date where relevant, and the task performed. Follow current publisher guidance for AI-assisted manuscripts.

3.5 Authorship and Accountability

AI systems must not be listed as authors where the relevant publisher or academic standard requires authors to take responsibility for the work. Human authors remain responsible for the accuracy, originality, permissions, citations and integrity of all submitted material, including AI-assisted portions.

SECTION FOUR

DATA PROTECTION, SECURITY AND RISK MANAGEMENT

4.1 Protection of Personal and Confidential Information

  1. Do not enter passwords, authentication tokens, financial details, medical records, student grades, disciplinary records, identifiable research-participant data or confidential personnel information into a public AI tool.
  2. Use anonymised, pseudonymised or synthetic data where feasible and appropriate, while recognising that re-identification may remain possible.
  3. Share only the minimum information necessary for the task and confirm that the intended use is authorised.
  4. Follow applicable Nigerian data-protection requirements, University ICT rules, research-ethics approvals and contractual obligations.
  5. Report suspected exposure, leakage or misuse of data promptly through the University’s designated ICT, data-protection or security reporting channel.

4.2 Human Review and Consequential Decisions

AI output must not be the sole basis for a consequential decision about a person’s admission, academic result, discipline, employment, access to support or rights. An authorised person must review relevant evidence, consider context and limitations, document the rationale where appropriate, and provide a meaningful route for correction or appeal. Automated scores and classifications must not be treated as inherently objective.

4.3 Bias, Accessibility and Reliability

Users must consider whether an AI system performs differently across language groups, genders, disabilities, socio-economic groups or other relevant contexts. Important outputs should be tested against reliable evidence and suitable local conditions. AI must not replace reasonable accommodations or established professional standards.

4.4 Intellectual Property and Licensing

Users must respect copyright, licences, confidentiality, ownership agreements and research-participant permissions. Do not upload unpublished manuscripts, third-party copyrighted content, code, examination questions or proprietary materials to an external service unless authorised. AI-assisted work should be checked for close imitation, unlicensed reproduction and conflicts with project or funder agreements.

4.5 AI Risk Assessment

Before deploying an AI system in a University service or using it for high-impact purposes, the responsible unit should document its purpose, data sources, expected benefits, foreseeable harms, affected persons, security controls, bias testing, human oversight, fallback procedure, vendor terms and review date. The system must not be deployed until the designated University authority has approved the assessment.

SECTION FIVE

MONITORING, ASSESSMENT AND ACADEMIC INTEGRITY

5.1 Monitoring Principles

The University may use proportionate and lawful methods to review compliance, including examination procedures, oral verification, review of drafts and working files, code demonstrations, source checking and approved similarity or AI-analysis tools. Monitoring must respect privacy, procedural fairness and applicable University rules.

5.2 Approved Detection Tools

The following tools are approved for detecting AI use:

  1. Turnitin: primarily known for plagiarism detection; it also offers an AI-writing indicator, the accuracy and reliability of which are subject to ongoing debate.
  2. Originality.ai: detects AI-generated text from large language models and also offers plagiarism detection.
  3. Winston AI: identifies AI-generated text, with a built-in plagiarism checker and multi-language support.
  4. GPTZero: analyses text to detect AI generation, including by measuring perplexity and burstiness, and provides a confidence score.
  5. Copyleaks AI Content Detector: detects AI-generated content, including paraphrased or mixed text, in multiple languages.
  6. Sapling AI Detector: a freely available tool for detecting AI-generated text.
  7. Drillbit: a similarity-detection tool that helps educators, researchers and students maintain originality by identifying copied or closely matched text.
  8. Supervisors’ Visual Inspection: AI content generation tools are becoming increasingly sophisticated, and AI can now “humanise” content, making it harder to distinguish from human-written material. Supervisors must therefore remain vigilant and employ robust methods, including oral defence and review of drafts, to detect AI-generated written content.

5.3 Limits of AI-Detection Tools

AI-text detectors and automated indicators are imperfect and may produce false positives or false negatives. A detector score alone must not be treated as conclusive proof of misconduct or be the sole basis for a penalty. Any concern should be examined alongside the assessment instructions, the student’s explanation, drafts or notes where relevant, source evidence and other fair indicators.

5.4 Fair Investigation

  1. Explain the concern to the person affected and identify the rule that may have been breached.
  2. Give the person a reasonable opportunity to respond and provide relevant evidence.
  3. Ensure the reviewer considers alternative explanations and does not rely solely on an automated score.
  4. Keep records confidential and limit access to authorised persons.
  5. Apply existing University procedures for academic misconduct, staff discipline and appeals.


SECTION SIX

COMPLIANCE, ACCOUNTABILITY AND DISCIPLINE

6.1 Compliance

All members of the University community are expected to comply with this Policy and with applicable course, research, ICT, privacy, intellectual-property and professional requirements. Users are responsible for checking the conditions attached to the AI tools they use and for verifying material outputs.

6.2 Responses to Breaches

A breach may lead to education or corrective action, reassessment, withdrawal of access, a formal investigation or disciplinary proceedings, depending on seriousness, intent, impact, repetition and applicable University rules. Any sanction must be imposed only through the University’s authorised procedures, with due process and applicable rights of review or appeal. This Policy does not create penalties that are inconsistent with the University’s statutes or existing regulations.

6.3 Reporting Concerns

Concerns about unsafe, deceptive, discriminatory or unauthorised AI use should be reported to the relevant lecturer, Head of Department, Dean, supervisor, ICT/security officer, data-protection contact, research-ethics body or other designated University authority. The University should provide a clear reporting route and protect reports from unnecessary disclosure or retaliation, subject to due process.

6.4 Review of this Policy

The Policy should be reviewed at least every three years and sooner where there are material changes in AI capability, law, regulation, institutional systems or academic practice. Amendments must be approved through the University’s formal governance process.

SECTION SEVEN

RESPONSIBILITIES AND GOVERNANCE

7.1 University Management

  1. Approve the institutional approach, designate the policy owner and provide proportionate resources for implementation.
  2. Ensure AI adoption is aligned with the University’s mission, strategic priorities and applicable obligations.
  3. Approve high-impact AI deployments and ensure that risks are reviewed before implementation.

7.2 Policy Owner / AI Governance Function

  1. Maintain the policy, guidance notes, tool register and record of approved high-impact uses.
  2. Coordinate consultation among academic, ICT, library, legal/compliance, data-protection, research-ethics and student-support functions.
  3. Review reported incidents, monitor implementation and recommend policy updates.
  4. Publish practical guidance and ensure that staff and students know where to seek advice.

7.3 Deans, Heads of Department and Course Leaders

  1. Communicate assessment-specific AI rules and ensure that they are clear before work is submitted.
  2. Support discipline-appropriate use of AI and design assessments that measure learning outcomes.
  3. Refer suspected misconduct through established procedures rather than making unsupported assumptions based on detector scores.
  4. Identify training needs and report recurring implementation issues.

7.4 Academic Staff and Researchers

  1. Verify AI-assisted material, protect research and student data, and disclose material AI use when required.
  2. Set transparent rules for assessments and explain acceptable and unacceptable assistance.
  3. Ensure that research outputs remain methodologically sound, reproducible where appropriate and honestly reported.
  4. Do not delegate final academic judgement or responsibility for feedback and grading to AI.

7.5 Students

  1. Follow assessment instructions and disclose AI assistance as required.
  2. Check factual claims, calculations, code, quotations and references before relying on them.
  3. Keep appropriate records of AI assistance where required by the assessment or supervisor.
  4. Protect personal and confidential information and ask for clarification when rules are uncertain.

7.6 ICT, Library, Quality Assurance and Support Units

  1. ICT should advise on security, access management, service procurement, integration and incident response.
  2. The Library should support information literacy, source verification, citation practice and responsible AI-assisted literature searching.
  3. The Directorate of Quality Assurance and Student Services should coordinate policy monitoring and collect feedback on implementation.
  4. The Registry and staff-development unit should include relevant AI literacy and responsible-use training in staff development.
  5. Student-support services should provide accessible guidance and help users understand the policy.


SECTION EIGHT

IMPLEMENTATION AND SUPPORT SYSTEMS

8.1 Communication and Awareness

The University should publish this Policy through official channels, include it in staff and student orientation, and make it available in an accessible format. Course handbooks, research guidance and administrative procedures should refer to the Policy where relevant.

8.2 Training and AI Literacy

  1. Provide introductory training on AI capabilities, limitations, hallucinations, bias, privacy, security and academic integrity.
  2. Offer discipline-specific workshops for teaching, research, coding, data analysis and administrative use.
  3. Provide guidance on disclosure, citation, copyright, source verification and assessment rules.
  4. Refresh training as tools and institutional requirements change.

8.3 Curriculum Integration

Departments should consider how AI literacy and discipline-specific AI competence can be integrated into curricula. Students should learn not only how to use AI tools, but also how to evaluate their outputs, identify limitations, protect data and explain the reasoning behind their own work.

8.4 Access and Equity

Where feasible, the University should provide equitable access to suitable tools, training and library resources. No student should be unfairly disadvantaged because a paid AI subscription is unavailable, unless access to a specified tool is an approved and clearly communicated requirement. Alternative arrangements should be considered where needed.

8.5 Support and Feedback

The designated policy owner should establish a contact point for questions, incident reporting and suggestions. Feedback from students, staff, researchers and administrators should inform periodic review and improvement.

8.6 Implementation plan

PhaseKey actionResponsible leadIndicative timing
1. ApprovalConsult stakeholders; revise and approve policyUniversity Management / designated committeeBefore commencement
2. Set-upName policy owner; establish contacts and tool-review processUniversity Management / ICTFirst 1–2 months
3. CommunicationPublish policy and assessment guidanceRegistry / Deans / DepartmentsFirst 3 months
4. TrainingDeliver staff and student AI-literacy sessionsPolicy owner / Library / ICTFirst 6 months; then annually
5. ReviewCollect feedback, incidents and implementation evidenceQuality Assurance / policy ownerAnnually; formal review at least every 3 years

8.7 Support Systems

  1. Library support for literature searching, citation management and research skills.
  2. Academic writing, tutoring and peer-learning support to strengthen independent learning.
  3. ICT support for safe tool selection, account security and incident reporting.
  4. Staff-development opportunities for responsible integration of AI into teaching, research and administration.
  5. Accessible routes for requesting clarification, reporting problems and appealing decisions under existing University procedures.

APPENDIX A: AI-USE DISCLOSURE TEMPLATES

A1. General Disclosure

“I used [name of AI tool and, where available, version] to assist with [specific task(s), such as brainstorming, language editing, coding support or data visualisation]. I reviewed and verified the relevant outputs and remain responsible for the accuracy, originality and final content of this work.”

A2. Research Disclosure

“During this research, [tool/provider] was used for [specific task and stage of the research]. The use of the tool was limited to [scope]. Outputs were checked using [verification method, source data or independent review]. The authors take responsibility for the methods, interpretation, conclusions and integrity of the reported work.”

A3. Student Project or Thesis Declaration

“This submission is my own work except for the AI assistance declared here: [tool and purpose]. I have checked the accuracy of material claims and references, followed the assessment and University rules, and retained responsibility for the submitted work.”

These templates should be adapted to the requirements of the department, supervisor, publisher, funder or assessment. A disclosure does not make otherwise prohibited AI use acceptable.

APPENDIX B: QUICK GUIDE FOR STUDENTS AND STAFF

Before using AIBefore submitting or acting on AI output
  Check the assessment, supervisor or workplace rules.Confirm that the tool is appropriate for the task.Do not enter confidential or personal data without authorisation.Decide whether disclosure is required.  Verify facts, calculations, quotations and references.Review for bias, errors, omissions and inappropriate content.Check copyright, originality and privacy implications.Disclose material assistance where required and retain accountability.