GMU Publishes Institutional Framework for AI in Teaching and Assessment

The GlobalMind University has published a comprehensive Institutional Framework for Artificial Intelligence in Teaching and Assessment, effective immediately for all faculties. The framework establishes clear expectations for how generative AI tools may be used in coursework, research, and faculty-developed materials — balancing innovation with the academic integrity standards our accreditors and employers expect.

Developed over twelve months through consultation with faculty senate, student union representatives, library services, and external ethics advisors, the framework replaces ad-hoc departmental policies that varied widely in strictness and clarity. A single public document now defines permitted, restricted, and prohibited uses, with programme-level supplements where disciplinary norms require nuance.

For students, the core principle is transparency. When AI assists with brainstorming, drafting, coding, or translation, students must acknowledge the tool, describe how it was used, and retain authorship of critical analysis and final conclusions. Modules include sample acknowledgement statements and rubric language that rewards thoughtful AI collaboration while penalising undisclosed substitution of human reasoning.

For faculty, the framework encourages intentional design. Assessments that measure memorisation of easily generated content are discouraged in favour of tasks requiring personal reflection, local case application, oral defence, or iterative drafts with documented revision history. The Centre for Learning Innovation offers template assignment briefs and workshop recordings on "AI-resilient assessment design."

Research ethics receive extended guidance. Doctoral candidates must disclose AI use in literature synthesis, data coding assistance, and graphical abstract preparation. Supervisors review disclosure statements at each milestone review. Funded research projects follow funder-specific rules where stricter than institutional baseline.

GMU does not ban AI tools outright. Instead, we teach critical AI literacy — evaluating hallucinations, identifying bias, protecting personal data in prompts, and understanding environmental costs of large model inference. A new optional micro-module, "Working Wisely with AI," launches in GMU Academy this term and will integrate into orientation for all new degree students by January.

Technology services deployed an approved-tools list for institutional subscriptions where data processing agreements protect student FERPA/GDPR-class information. Free public tools may be used for low-risk tasks but not for uploading confidential peer work or unreleased exam content.

Enforcement relies on education first, investigation when necessary. Academic misconduct procedures were updated to distinguish careless non-disclosure from deliberate fraud. First-time minor breaches may receive remedial training rather than immediate expulsion, except where professional accreditation rules require stricter action.

Accreditation reviewers welcomed the framework during recent candidacy consultations, noting alignment with emerging DEAC guidance on academic integrity in digitally mediated environments. GMU will review the document annually as tools and regulations evolve.

Students and faculty may download the full framework from the policies section of the portal. Questions should be directed to learning.innovation@gmuniv.com or raised through programme leaders during induction.