Artificial Intelligence Use, Adoption & Governance Policy

1. PREAMBLE

Glorious Vision University recognises Artificial Intelligence (AI), including Generative Artificial Intelligence (GenAI), as an important technological development with significant implications for higher education, research, administration and professional practice.

AI can support teaching, learning, research, academic writing, data analysis, administrative efficiency, student support, institutional planning, innovation and staff development. It can also create risks relating to academic dishonesty, inaccurate information, fabricated references, intellectual property, privacy, confidentiality, bias, unequal access, inappropriate automation and the weakening of independent learning.

The University therefore adopts a human-led and responsibly governed approach to AI. AI shall be treated as an assistive technology and not as a substitute for human judgement, academic responsibility, professional accountability or independent intellectual work.

This Policy provides the institutional framework for the responsible adoption, use, evaluation, monitoring and development of AI across the University. It is intended to promote innovation while protecting academic standards, research integrity, staff and student rights, institutional data and the reputation of the University.

2. TITLE

This Policy shall be known as the Glorious Vision University Artificial Intelligence Use, Adoption and Governance Policy, 2026.

3. POLICY PURPOSE

The purpose of this Policy is to establish clear, practical and enforceable rules for the use and adoption of AI within Glorious Vision University.

The Policy seeks to ensure that:

• AI is used in ways that improve the quality and efficiency of academic and administrative work.
• Human judgement and accountability remain central to all significant University decisions.
• Academic integrity, originality and independent learning are protected.
• Research involving AI remains transparent, reproducible, ethical and scientifically defensible.
• Staff and students develop appropriate AI literacy and digital competence.
• University information, personal data and confidential materials are protected.
• AI tools are selected and used according to their educational, research, administrative, ethical and security suitability.
• AI adoption is monitored through evidence-based institutional measures rather than unsupported claims of technological achievement.

• 4. SCOPE

This Policy applies to the use of AI in all University activities, whether conducted on campus, remotely, through University systems, or in connection with University work.

It covers, among other activities:

• teaching and learning;
• lectures, tutorials and practical exercises;
• assignments, presentations and projects;
• examinations and other assessments;
• dissertations, theses and research projects;
• research design, literature work, data processing and scholarly communication;
• staff development and professional training;
• academic planning and quality assurance;
• student support services;
• examinations and records administration;
• finance, procurement, human resource and general administration;
• communication, marketing and digital content;
• information and library services;
• software development, coding and data analysis;
• University consultancy and externally funded projects.

The Policy applies to both University-provided AI systems and external AI services used for University purposes.

5. DEFINITIONS

For the purpose of this Policy:

Artificial Intelligence (AI) means computational systems capable of performing tasks that ordinarily require aspects of human intelligence, including prediction, classification, reasoning, language processing, generation and decision support.

Generative Artificial Intelligence (GenAI) means AI systems capable of generating new text, images, audio, video, software code, summaries, designs or other content from user instructions.

AI-assisted work means work in which AI contributes to some part of the process while the human user retains responsibility for the final product.

AI-generated content means content produced directly by an AI system.

AI literacy means the ability to understand the capabilities, limitations, risks and appropriate uses of AI and to use AI critically and responsibly.

AI tool means any software, platform, model or digital service incorporating AI functionality.

Approved AI tool means an AI tool formally authorised for specified University use following institutional review.

Conditional AI tool means an AI tool that may be used only for specified purposes or subject to stated restrictions.

Restricted AI tool means an AI tool whose use is prohibited or limited for specified University activities because of significant academic, legal, ethical, security or privacy concerns.

AI disclosure means a statement showing when and how AI has been used in academic, research or professional work.

6. GUIDING PRINCILES

The University’s use and adoption of AI shall be guided by the following principles:

6.1 Human Responsibility

AI may assist human work but shall not replace human accountability. A member of staff or student remains responsible for the accuracy, integrity, legality, originality and quality of work submitted under their name.

6.2 Academic Integrity

AI shall not be used to obtain an unfair academic advantage, misrepresent authorship, fabricate evidence, generate false references or circumvent assessment requirements.

6.3 Transparency

Significant use of AI in academic, research or professional work shall be disclosed in accordance with the applicable requirements.

6.4 Accuracy and Verification

AI-generated information may contain errors, bias, fabricated references or misleading conclusions. Users shall independently verify material facts, references, quotations, calculations, data interpretation and other consequential outputs before relying on them.

6.5 Data Protection and Confidentiality

Personal, confidential, commercially sensitive, unpublished or restricted University information shall not be entered into an AI system unless the system has been authorised for that category of information and the required institutional safeguards are in place.

6.6 Fairness and Inclusion

AI adoption shall not create unjustified barriers for staff or students who have limited access to technology, connectivity, devices or specialist tools.

6.7 Intellectual Property

Users shall respect copyright, licensing requirements, ownership rights, research agreements, database restrictions and other intellectual property obligations when using AI.

6.8 Academic and Professional Judgement

AI shall support rather than replace scholarly judgement, critical thinking, teaching expertise, professional discretion and institutional decision-making.

6.9 Proportionality

The University shall distinguish between low-risk and high-risk uses of AI and apply controls proportionate to the nature and potential consequences of the use.

6.10 Continuous Learning

AI governance shall be reviewed as technology, academic practice, regulation and evidence develop.

PART I; INSTITUTIONAL AI ADOPTION

7. INSTITUTIONA APPROACH TO AI ADOPTION

Glorious Vision University shall adopt AI through a phased institutional approach:

Stage 1: AI Literacy

The University shall establish basic AI awareness among staff and students, covering responsible use, verification, privacy, academic integrity, ethical issues and appropriate tool selection.

Stage 2: Assisted Practice

Approved AI tools may be used to support routine academic, research and administrative activities under defined controls.

Stage 3: Enhanced Academic and Research Practice

Departments and relevant units may adopt AI for advanced teaching, research, data analysis, academic support, assessment design, institutional planning and scholarly productivity.

Stage 4: Institutional Innovation

The University may develop or adopt specialised AI solutions for institutional processes, research innovation, student support and quality improvement where evidence demonstrates educational, administrative or research value.

Adoption at each stage shall depend on institutional capacity, infrastructure, staff competence, risk assessment and demonstrated value.

8. PERMITTED GNERAL USE OF AI

Subject to this Policy, AI may be used to support:

• brainstorming and generation of ideas;
• language improvement and proofreading;
• translation where appropriate;
• lesson and instructional planning;
• generation of practice questions and learning activities;
• preparation of administrative drafts;
• summarisation of user-provided materials;
• coding and software development;
• data cleaning and exploratory analysis;
• research planning and methodological support;
• literature discovery and thematic organisation;
• accessibility and learning support;
• communication and productivity;
• development of non-sensitive templates and documents.

Permission to use AI for one purpose does not automatically permit its use for another purpose.

9. PROHIBITED GENERAL USES

The following uses are prohibited:

• Submission of AI-generated work as wholly independent human work where such use is not permitted or disclosed.
• Use of AI to impersonate a staff member, student, examiner, supervisor or institutional officer.
• Fabrication or manipulation of academic, research, administrative or financial records.
• Generation of fabricated references, quotations, data, findings or evidence and presenting them as genuine.
• Uploading confidential, personal, sensitive, proprietary or restricted University information into unauthorised AI systems.
• Use of AI to circumvent examinations, tests, admission requirements or other academic controls.
• Use of AI to create fraudulent documents, false identities or deceptive institutional communications.
• Use of AI in a manner that breaches applicable law, University regulations, ethical standards or contractual obligations.
• Automated decision-making in high-impact University matters without appropriate human review and institutional authority.
• Any other use designated as prohibited by the University.

• PART II: AI IN TEACHING, LEARNING AND ASSESSMENT

• AI IN TEACHING AND LEARNING

Academic staff may use approved AI tools to support curriculum planning, teaching preparation, generation of examples, formative learning activities, feedback, accessibility and other pedagogical purposes.

Teaching staff shall review AI-generated materials before use and shall ensure that content supplied to students is academically accurate, relevant and appropriate to the course.

Students may use AI as a learning support tool where the lecturer or assessment instructions permit such use.

AI shall not be used to remove opportunities for students to develop essential subject knowledge, reasoning, writing, analytical or practical skills.

11. AI IN ACADEMIC ASSEEMENT

Every assessment involving possible AI use shall fall within one of the following four categories.

Category A: AI Prohibition

AI use is not permitted.

This category shall apply where the assessment is intended to measure independent knowledge, reasoning, writing, practical competence or other abilities that must be demonstrated without AI assistance.

Category B: Limited AI Assistance

AI may be used only for specifically stated functions, such as brainstorming, language correction, formatting or generation of practice material.

The assessment instructions shall clearly define the permitted and prohibited uses.

Category C: Declared AI Assistance

AI may be used as part of the assessment process, but the student shall disclose the tool used, the purpose of use and the extent of assistance.

The submitted work must remain the student’s own intellectual work.

Category D: AI-Integrated Assessment

AI is deliberately incorporated into the assessment task as part of the learning outcome.

In this category, students may be required to interact with, evaluate, critique or compare AI outputs and demonstrate their ability to exercise human judgement.

RESPONSIBILITY OF THE LECTURER IN AI-ENABLED ASSESSMENT

Where AI is permitted, the lecturer shall state in the assessment instructions:

• the applicable AI category;
• what AI use is permitted;
• what AI use is prohibited;
• whether disclosure is required;
• the required format of disclosure;
• whether students may use external AI systems;
• any restrictions on data entered into AI systems;
• how the student’s independent contribution will be evaluated.

AI requirements shall be communicated before the assessment is undertaken.

ACADEMIC INTEGRITY AND UNAUTHORISED AI USE

Unauthorised AI use may constitute academic misconduct where it results in deception, misrepresentation of authorship, unfair advantage or violation of assessment instructions.

Possible evidence may include the submitted work, drafts, notes, version histories, references, oral explanation, process evidence, code history, examination records and other relevant material.

An AI-detection system shall not by itself be treated as conclusive evidence of academic misconduct.

Where concern arises, the relevant academic procedures of the University shall apply.

PART III: AI CONTENT AND DECLARATION

14. PRINCIPLE OF AI CONTENT DECLARATION

The University shall not treat a fixed percentage of AI-generated text as a scientific test of authorship or academic integrity.

The University recognises that AI-detection technologies cannot reliably establish authorship solely by estimating the proportion of text that may have been generated by AI.

The primary basis for determining compliance shall therefore be:

• whether AI use was permitted;
• whether required disclosure was made;
• whether the student’s or researcher’s intellectual contribution is demonstrable;
• whether the work is accurate and properly referenced;
• whether institutional assessment requirements were followed.

15. OPTIONAL QUALITIATIVE AI INVOLEMENT DELARATION

Where a School, Institute, College, department, research supervisor, examination process or institutional reporting mechanism requires a quantitative declaration, the following bands may be used as a self-declared estimate of AI involvement:

These bands are administrative disclosure categories and shall not be interpreted as scientifically verified measurements of machine-generated text.

The existence of a declared percentage shall not excuse unauthorised AI use.

16. AI USE DECLARATION

Where disclosure is required, the following information shall be provided:

AI Use Declaration

“I declare that Artificial Intelligence tools were / were not used in the preparation of this work.

Where AI was used:

AI tool(s): ______________________________

Purpose of use: __________________________

Extent/type of assistance: ________________

AI involvement estimate, where required: __________

I confirm that I reviewed and verified the output and accept full responsibility for the final work submitted.”

The University may prescribe a standard electronic or printed AI Use Declaration Form for use across departments.

PART IV: AI IN RESEARCH

17. AI IN RESEARCH

AI may be used in research to support activities such as:

• topic exploration and research planning;
• literature discovery;
• organisation of scholarly materials;
• language editing;
• transcription;
• coding assistance;
• qualitative and quantitative data processing;
• visualisation;
• development of analytical frameworks;
• translation;
• technical support;
• preparation of research materials.

Researchers shall independently verify AI-assisted research outputs before inclusion in a scholarly work.

AI shall not be relied upon as an unquestioned source of factual evidence.

18. RESEARCH INTEGRITY

Researchers shall not use AI to:

• fabricate or falsify data;
• create false participants or responses;
• manufacture citations or references;
• misrepresent AI-generated analysis as independently conducted analysis;
• manipulate research images or results in a deceptive manner;
• breach confidentiality or research-participant protections;
• disclose restricted research materials to unauthorised systems;
• conceal significant AI assistance where disclosure is required;
• compromise the reproducibility or integrity of the research process.

Where AI materially contributes to research design, analysis, generation of content or other substantive activities, the researcher shall disclose such use in accordance with disciplinary and publication requirements.

19. AI AND RESEARCH AUTHORHIP

AI tools shall not be recognised as authors, co-authors or independent academic contributors.

Authorship shall remain with accountable human researchers who have made genuine intellectual contributions and who can accept responsibility for the work.

AI assistance shall be acknowledged or disclosed where required by the University, funder, research ethics requirements, publisher or relevant professional standard.

20. RESEARCH DATA AND AI SYSTEMS

Researchers shall assess the data risks associated with any AI tool before uploading research information.

Particular caution shall apply to:

• personal data;
• identifiable participant information;
• interview transcripts;
• unpublished datasets;
• examination materials;
• confidential institutional information;
• proprietary data;
• commercially sensitive information;
• restricted research outputs.

Research involving sensitive data shall use only approved systems and procedures.

22. USE OF AI IN PEER REVIEW AND SCHOLARLY EVALUATION

Staff shall not submit confidential manuscripts, grant applications, examination scripts, unpublished research reports or other restricted scholarly materials to public AI systems unless expressly authorised and appropriate safeguards are in place.

The confidentiality obligations of peer review, examination, supervision and research evaluation remain fully applicable where AI tools are used.

PART V: AI TOOL ACCESS, APPROVAL AND CONTROL

22. APPROVED AI TOOLS REGISTER

The University shall maintain an Approved AI Tools Register administered through the appropriate institutional structures.

AI tools shall be classified as:

Approved

Tools authorised for specified low- or moderate-risk University purposes.

Conditional

Tools that may be used only for specified purposes, user groups, information types or controlled environments.

Restricted

Tools that shall not be used for specified University activities or categories of information.

The register shall be reviewed periodically because AI tools, terms of service, privacy arrangements and technical capabilities may change.

23. AI TOOL EVALUATION CRITERIA

Before institutional approval, an AI tool may be assessed against:

• educational or institutional value;
• research usefulness;
• accuracy and reliability;
• data protection;
• confidentiality;
• intellectual property;
• accessibility;
• cost and sustainability;
• interoperability;
• information security;
• vendor terms and conditions;
• suitability for the intended user group;
• potential academic or operational risks.

The University shall not adopt an AI system solely because of popularity or technical sophistication.

24. ACCESS TO AI TOOLS

Access to institutional AI tools may be provided through:

• University-approved institutional accounts;
• ICT-supported platforms;
• Library-supported access;
• approved external services;
• licensed software;
• authorised departmental subscriptions.

The University may define different access rights for students, academic staff, non-teaching staff, researchers and administrators according to the level of risk associated with particular uses.

Users shall comply with the terms of authorised systems and shall not share institutional accounts or access credentials improperly.

PART VI: DATA PROTECTION, SECURITY, AND INTELLECTUAL PROPERTY

25. DATA PROTECTION AND CONFIDENTIALITY

Users shall protect University information when interacting with AI systems.

The following shall not be entered into an unauthorised AI system:

• student records;
• staff records;
• examination questions and scripts;
• confidential committee documents;
• passwords or authentication information;
• financial information;
• personal identifiers;
• confidential research materials;
• proprietary University documents;
• restricted communications;
• information protected by law, contract or institutional policy.

Anonymisation or removal of identifiers shall be used where appropriate.

26. INTELLECTUAL PROPERTY AND CONYRIGHT

AI use shall comply with applicable copyright, licensing and intellectual property requirements.

Users remain responsible for determining whether AI-generated or AI-assisted material can lawfully be reproduced, published, submitted or distributed.

Where the source, ownership or licensing status of AI-generated material is uncertain, the user shall seek appropriate guidance before use.

PART VII: EVALUATION OF AI-ASSISTED WORK

27. EVALUATION PRINCIPLES

Evaluation of AI-assisted work shall focus on the student’s or researcher’s demonstrated knowledge, judgement, originality, understanding and ability to verify and defend the work.

Assessment shall not be based solely on an alleged AI percentage.

Relevant evaluation methods may include:

• written output;
• drafts and development history;
• oral questioning or viva-style verification;
• explanation of methods and decisions;
• examination of references and source materials;
• practical demonstrations;
• process logs or version history;
• code review;
• comparison of submitted work with demonstrated competence;
• relevant AI disclosure records.

28. STUDENT VERIFICATION OF AI-ASSISTED WORK

Where AI has materially assisted an assessment, a lecturer may require a student to explain:

• the purpose for which AI was used;
• the prompts or instructions used, where relevant;
• how the output was evaluated;
• which portions were modified or independently developed;
• the sources used to verify factual claims;
• the student’s understanding of the submitted work.

The University may require an oral defence where the authorship, understanding or authenticity of submitted work is reasonably questioned.

29. AI DETECTION SYSTEMS

AI-detection tools may be used as supplementary indicators where considered appropriate.

A detection score shall not, by itself, constitute proof that a student or staff member used AI improperly.

Academic decisions shall be based on the totality of relevant evidence and the applicable University procedures.

PART VIII: STAFF DEVELOPMENT AND WORK PERFORMANCE

30. AI LITERACY FOR STAFF

The University shall progressively develop staff competence in responsible AI use.

Staff development shall cover:

• AI fundamentals;
• responsible prompting;
• verification of AI outputs;
• academic integrity;
• research integrity;
• data protection;
• copyright and intellectual property;
• AI-supported teaching;
• AI-supported research;
• AI-supported administration;
• AI risks and limitations;
• discipline-specific applications.

Participation in designated AI capacity-development activities may form part of staff professional development requirements.

31. AI CHAMPIONS

The University may establish an AI Champions Network comprising trained representatives from faculties, departments, Library, ICT and administrative units.

AI Champions may support:

• staff awareness;
• responsible experimentation;
• identification of useful applications;
• reporting of emerging risks;
• peer support;
• collection of evidence on AI adoption;
• communication of approved practices.

AI Champions shall not override existing academic, professional or institutional authorities.

32. AI AND WORK PERFORMANCE

AI may be used to improve administrative productivity, including:

• document preparation;
• scheduling and workflow support;
• data summarisation;
• report preparation;
• information retrieval;
• routine communication;
• planning and monitoring;
• data analysis;
• service improvement.

AI-assisted administrative work shall be reviewed by the responsible officer before official use.

The use of AI shall not remove accountability from the officer responsible for the activity.

33. AI AND ACADEMIC PLANNING

The Directorate of Academic Planning may use approved AI tools to support:

• institutional data analysis;
• academic planning;
• programme monitoring;
• quality assurance;
• accreditation preparation;
• institutional reporting;
• trend analysis;
• performance monitoring;
• policy analysis;
• strategic planning.

AI-generated analyses used for formal institutional decisions shall be checked against the underlying data and validated by the responsible officers.

PART IX: GOVERNANCE AND INSTITUTIONAL RESPONSIBILITY

34. UNIVERSITY AI GOVERNANCE STRUCTURE

The University shall establish an AI Governance Committee or designate an equivalent existing institutional committee to oversee implementation of this Policy.

The Committee shall operate under the authority of University Management and within the University’s established governance framework.

Its membership may include representatives responsible for:

• Academic Planning;
• Information and Communication Technology;
• Library and Information Services;
• academic quality assurance;
• teaching and learning;
• research;
• student affairs;
• examinations;
• administration;
• other relevant professional or academic functions.

The Committee shall advise Management on AI adoption, governance, risks, institutional standards and implementation priorities.

35. RESPONSIBILITIES OF THE DIRECTATE OF ACADEMIC PLANNING

The Directorate of Academic Planning shall:

• coordinate institutional implementation and monitoring of this Policy;
• develop institutional AI performance indicators;
• coordinate periodic assessment of AI adoption across academic units;
• maintain institutional evidence of AI-related capacity development and adoption;
• support integration of AI into academic planning and quality assurance;
• coordinate reporting required for institutional governance, accreditation and other legitimate regulatory purposes;
• present periodic AI implementation reports to Management or the appropriate University authority;
• coordinate policy review in consultation with relevant units.

The Directorate of Academic Planning shall not be treated as the technical owner of the University’s AI infrastructure.

36. RESPONSIBILITIES OF ICT

The ICT unit shall:

• provide technical advice on AI systems;
• support secure access to approved systems;
• advise on information security;
• support institutional account management;
• assist in evaluating technical risks;
• maintain relevant technical records;
• provide user support within available institutional capacity.

37. RESPONSIBILITIES OF THE LIBRARY

The University Library shall support:

• AI literacy;
• information literacy;
• source verification;
• research discovery;
• referencing and citation practices;
• copyright awareness;
• responsible use of AI in scholarly communication.

The Library may develop guidance for students and researchers on evaluating AI-generated information.

38. RESPONSIBILITIES OF FACULTIES AND DEPRTMENTS

Faculties and departments shall:

• implement this Policy within their programmes and activities;
• define discipline-appropriate AI requirements;
• specify assessment categories where applicable;
• monitor compliance;
• support staff and student AI literacy;
• report significant AI-related concerns;
• encourage appropriate innovation.

39. RESPONSIBIITIES OF STAFF

Staff shall:

• use AI responsibly and within authorised purposes;
• verify significant AI-generated information;
• protect University data;
• disclose AI use where required;
• comply with assessment and research requirements;
• avoid fabricated references, data and evidence;
• maintain professional and academic judgement;
• report serious AI-related risks or incidents through the appropriate channels.

40. REPONSIBILITIES OF STUDENTS

Students shall:

• comply with the AI requirements stated for each assessment;
• disclose AI use where required;
• verify information produced by AI;
• maintain their own intellectual contribution;
• protect personal and University information;
• avoid academic misconduct;
• retain sufficient knowledge of submitted work to explain and defend it.

PART X: INSTITUTIONAL AI MATURITY AND PERFORMANCE

41. INSTITUTIONAL AI MATURITY FRAMEWORK

For internal planning and monitoring, the University may assess its AI maturity across five dimensions:

These percentages are an internal management framework for monitoring institutional development. They are not presented as a national ranking formula or external regulatory standard.

42. INSTITUTIONAL AI PERFORMANCE INDICATORS

The University may monitor indicators such as:

• proportion of academic and administrative staff receiving AI training;
• proportion of students receiving AI literacy orientation;
• number of departments implementing AI guidance;
• number of assessments classified under the University’s AI assessment categories;
• number of approved AI tools available;
• number of research projects reporting responsible AI use;
• examples of AI-supported institutional process improvement;
• documented AI-related incidents;
• staff and student access to approved AI tools;
• evidence of AI-supported research and innovation;
• quality and outcome measures associated with AI adoption;
• annual expenditure and resource implications of AI adoption.

The University shall favour evidence of educational, research and institutional value rather than reporting adoption solely in terms of tool usage.

PART XI: RESEARCH AND INNOVATION

43. AI RESEARCH AND INNOVATION

The University shall encourage responsible research into AI and emerging technologies within relevant academic disciplines.

AI-related institutional innovation may include:

• AI-supported research projects;
• development of specialised academic applications;
• intelligent student-support systems;
• institutional analytics;
• adaptive learning;
• digital knowledge services;
• administrative decision-support systems;
• interdisciplinary research on AI and society.

Research and innovation initiatives shall be subject to relevant ethical, academic, data protection and institutional approval requirements.

44. PILOT PROJECTS

Colleges, Institutes, departments and units may propose AI pilot projects where there is a clearly defined institutional, academic or research need.

A pilot proposal should, where applicable, identify:

• the problem being addressed;
• the intended users;
• the proposed AI tool or system;
• expected benefits;
• resources required;
• risks;
• data requirements;
• evaluation criteria;
• duration;
• responsible officer or unit.

Successful pilots may be considered for wider institutional adoption based on evidence.

PART XII: EQUITY, ACCESS, AND RESPONSIBLE USE

45. EQUITY OF ACCESS

The University shall seek to avoid unnecessary disadvantage arising from unequal access to AI tools, devices, connectivity or subscriptions.

Where AI use is required for academic work, departments should consider the availability of reasonable access for students.

Where alternative arrangements are necessary because of legitimate access constraints, relevant academic authorities may provide appropriate alternatives.

46. DISABILITY AND ACCESSIBILITY

The University shall encourage AI applications that improve accessibility for students and staff with disabilities, while ensuring that such applications do not compromise privacy, independence or academic standards.

47. PROFESSIONAL ND ETHICAL ACCOUNTABILITY

Use of AI does not transfer responsibility from a University employee or student to the technology provider.

A person who approves, signs, submits, publishes or relies upon AI-assisted work remains responsible for that work to the extent prescribed by their role.

Managers and supervisors shall ensure that AI adoption does not introduce unacceptable risks to University operations.

PART XIII: INCIDENTS, COMPLIANTS AND REVIEW

48. AI-RELATED INCIDENTS

AI-related incidents that may involve academic misconduct, data breaches, security problems, fabricated records, discriminatory outcomes, serious misinformation or unauthorised disclosure shall be reported through the relevant University procedure.

The University may establish a standard AI Incident Reporting Form.

49. COMPLAINTS AND APPEALS

A staff member or student affected by an AI-related decision may use the normal complaint, appeal, examination, disciplinary or academic review procedures of the University.

No AI system shall be treated as the final authority in a disputed academic or administrative matter where human review is required.

PART XIV: IMPLEMENTATION

50. IMPLEMENTATION PHASES

Implementation shall occur progressively.

Phase I: Policy Establishment

• approval and publication of the Policy;
• designation of responsible institutional structures;
• establishment of the Approved AI Tools Register;
• preparation of standard AI declaration and assessment templates.

• Phase II: Capacity Development
• staff awareness and training;
• student orientation;
• AI Champions development;
• development of guidance for teaching, assessment, research and administration.

• Phase III: Controlled Adoption
• departmental and unit-level adoption of approved AI tools;
• implementation of AI assessment categories;
• introduction of AI-supported administrative and research pilots;
• collection of institutional evidence.

• Phase IV: Review and Institutional Improvement
• annual assessment of AI maturity;
• review of incidents and emerging risks;
• assessment of measurable benefits;
• revision of institutional guidance and controls.

51. IMPLEMENTATION INSTRUMENTS

The following supporting instruments shall be developed or adopted to operationalise this Policy:

• AI Use Declaration Form
• AI Assessment Classification Template
• Approved AI Tools Register
• Staff and Student AI Competency Framework
• AI Incident and Academic Integrity Reporting Form
• Annual Institutional AI Adoption Scorecard

These instruments shall be reviewed periodically and may be amended without changing the fundamental principles of this Policy, subject to the appropriate University approval process.

PART XV: MONITORING, REPORTING, AND REVIEW

52: MONITORING

Implementation shall be monitored through periodic reports from faculties, departments, ICT, Library, research structures and relevant administrative units.

Monitoring shall consider:

• compliance;
• usage patterns;
• staff and student competence;
• institutional benefits;
• emerging risks;
• resource implications;
• academic outcomes;
• research outcomes;
• quality assurance evidence.

53: ANNUAL AI REPORT

The Directorate of Academic Planning, in consultation with relevant University units, shall coordinate an annual institutional AI report for the appropriate University authority.

The report may include:

• progress against implementation targets;
• staff and student training;
• AI tool adoption;
• teaching and assessment practices;
• research and innovation activities;
• administrative applications;
• incidents and corrective measures;
• infrastructure and resource needs;
• institutional AI maturity assessment;
• priorities for the succeeding year.

54. POLICY REVIEW

This Policy shall be reviewed at least once every two years, or earlier where there are significant developments in:

• AI technology;
• national regulation;
• higher education standards;
• data protection requirements;
• research ethics;
• academic integrity standards;
• institutional priorities.

Amendments shall follow the University’s established policy approval procedures.

PART XVI: INSTITUTIONAL COMMITMENT

55. UNIVERSITY COMMITMENT

Glorious Vision University is committed to becoming an institution in which Artificial Intelligence is adopted responsibly, strategically and inclusively to strengthen teaching, learning, research, administration, staff development and institutional effectiveness.

The University shall pursue AI adoption that is:

AI-enabled, human-led, ethically governed, evidence-based and educationally purposeful.

AI shall be used to strengthen human capability rather than diminish it. The University shall promote innovation while preserving academic integrity, observe regulations governing referencing, plagiarism, and research credibility, professional responsibility, confidentiality and the human values that underpin higher education.

56. EFFECTIVE DATE

This Policy shall take effect upon approval by the appropriate governing authority of Glorious Vision University and shall remain in force until amended or replaced in accordance with the University’s approved policy process.

Policy Year: 2026

Glorious Vision University, Ogwa, Edo State, Nigeria

The University recognises that the central objective is to maximise the educational, research and institutional value obtained from AI while preserving human judgement, academic integrity, privacy, equity and institutional accountability.

Approved by:

Vice Chancellor: Professor Ezekiel S. Asemah

Effective Date: _________________________

Review Date: ___________________________

POLICY REFERENCE FRAMEWORK

This Policy is informed by internationally recognised principles for responsible AI in education and research, particularly human agency, inclusion, equity, privacy, ethical validation, AI competency development, institutional governance and evidence-based adoption. UNESCO’s guidance provides a human-centred framework for AI in education and research, while the OECD’s 2026 policy work identifies responsible use, compliance and procurement, AI competencies, pilot evaluation and specialised institutional tools as important areas of policy development.

Reference 1

UNESCO. (2023). Guidance for generative AI in education and research. UNESCO. Updated January 2026.

Reference 2

OECD. (2026). Policies supporting responsible and systematic GenAI adoption in higher education. OECD Education Spotlights, No. 23. OECD Publishing. https://doi.org/10.1787/c4e5621f-en