1 Purpose and Objectives
- Promote Responsible Adoption: Encourage the use of AI tools (such as generative text, analysis models, and domain-specific assistants) to enhance teaching, science instruction, and medical/technical training.
- Safeguard Academic Integrity: Clearly distinguish between authorized AI-assisted learning and unauthorized plagiarism or contract cheating.
- Build Local Context: Foster research into inclusive datasets and AI solutions tailored to Nigerian languages, culture, and socioeconomic realities.
2 Guidelines for Students
A. Academic Writing and Assignments
- Transparency & Disclosure: Students must explicitly declare when and how generative AI tools (e.g., ChatGPT, Claude, etc.) were used in completing assignments, projects, or theses.
- Prohibited Actions: Submitting unedited AI-generated text, code, or data as one's own original work is strictly classified as plagiarism.
Tiered Assignment Framework
Course lecturers will explicitly state on every course outline which of the following AI tiers applies:
Tier 1
Prohibited
No AI tools allowed for the specific task.
Tier 2
Assisted / Permitted
AI allowed for brainstorming, outline creation, or grammar checks with mandatory citation.
Tier 3
Fully Integrated
AI use encouraged as a core analytical or creative component with a reflection appendix.
B. Examinations and Assessments
- Generative AI tools, smart devices, and unauthorized large language models are strictly banned during physical and remote-proctored university examinations unless explicitly authorized by the Senate for specific computer-based tests.
3 Guidelines for Academic Staff and Teaching
- Curriculum Integration: Departments (especially in Science, Computing, and Medical Laboratory Science) are encouraged to embed prompt literacy, critical evaluation of AI outputs, and ethical AI use into course units (e.g., building upon GST modules).
- Assessment Redesign: Lecturers should shift toward authentic assessments-such as viva voce, localized case studies, reflective journals, and in-class problem-solving-that reduce heavy reliance on generic AI output.
- AI Detection Tools: The university recognizes the limitations of automated AI detectors. Detection flags will never be used as sole proof of misconduct; they must be paired with holistic review and student dialogue.
4 Research and Data Ethics
- Ethical Boundaries: Research involving or utilizing AI must comply with UNIOSUN research ethics guidelines, ensuring data privacy, consent, and avoidance of algorithmic bias.
- Contextual Relevance: Scholars and postgraduate researchers are urged to develop local datasets that reflect Nigerian and African languages, health profiles, and infrastructure constraints rather than acting as passive consumers of Western models.
5 Governance and Capacity Building
- University AI Governance Committee: Establishment of a multidisciplinary committee comprising representatives from the Faculty of Computing and Information Technology, Academic Planning, Library Services, and Student Affairs to review and update guidelines annually.
- Digital Infrastructure & Training: Management will periodically partner with stakeholders to improve campus broadband, digital equipment, and staff/student workshops on AI readiness.
- Non-Compliance: Violations of this policy will be routed through the standard UNIOSUN Student/Staff Disciplinary Committee processes.