Harness the power of ethical AI usage in your academic journey. Develop digital literacy skills for the future.
All content on this page is general guidance only. Your specific assignment briefs take definitive precedence over any advice provided here. Assignment requirements, AI permissions, and academic integrity standards may vary by module, school, and assessment type. Always consult your assignment brief first and contact your module leader directly for clarification if you are unsure.
AI Literacy Foundation
The rapid emergence of generative AI tools marks a watershed moment in higher education. As a UK university student, you must engage with these powerful tools using integrity, critical judgement, and transparency.
At its core, ethical AI use in academia rests on one fundamental principle: the work you submit must be a true reflection of your own intellectual effort. This means developing sophisticated AI literacy that goes far beyond knowing how to write prompts.
Your university education represents an investment in your skills and knowledge. Using AI to bypass the learning process makes you vulnerable and actively undermines the intrinsic value of your degree. The goal is not to ban AI, but to train you to be the expert who can assess, guide, and take accountability for intellectual work.
Generative AI offers clear opportunities to enhance learning as a brainstorming partner, Socratic tutor, or language assistant. However, this power comes with inherent risks of over-reliance and misconduct that can fundamentally detract from your academic development.
Cognitive Offloading Risk
When you delegate core intellectual tasks to AI, such as synthesising complex readings or structuring arguments, you engage in cognitive offloading that saves time but fundamentally detracts from your academic development.
True AI literacy requires deep, critical understanding of what these tools are, what they can and cannot do, and the moral implications of their use. Your university is committed to supporting this crucial skill development through guidance and clear expectations.
Essential skills compromised by over-reliance on AI:
Core academic capabilities requiring personal development:
Long-term academic and professional consequences:
Core Academic Contract
Academic integrity means completing studies honestly, respecting others' work, and ensuring assessment fairly measures your knowledge. When submitting work, the university expects it to be wholly your own effort.
Academic integrity is the bedrock of scholarship in UK higher education. This represents a commitment not only to yourself but to your peers and the wider academic community. The Student Charter explicitly outlines your responsibility to maintain high standards of honesty.
This expectation is non-negotiable and applies regardless of assessment format, whether essay, code, portfolio, or presentation. Before submitting any assessment, ask: Does this work demonstrate my analysis, my critical thinking, and my application of knowledge?
Submitting machine-generated content and claiming authorship:
Using AI when explicitly forbidden by module guidelines:
Cardiff Met's official position on artificial intelligence use in assessments is unambiguous and forms part of the university's formal academic misconduct framework. Students must understand and comply with these institutional requirements.
Cardiff Metropolitan University has established clear institutional guidelines regarding AI use in academic assessments. The Cardiff Metropolitan University Academic Handbook explicitly addresses AI use under Section 8 - Academic Misconduct
For complete details of Cardiff Met's AI policies and academic misconduct procedures, students should reference:
All Cardiff Met students are expected to be familiar with university policies. Lack of awareness of these AI policies will not be accepted as a defence in misconduct proceedings. When in doubt, always seek written clarification from your module leader before using any AI tools.
What may be productive AI use for private study can be catastrophic if applied uncritically to assessment. Understanding this distinction is essential for ethical academic practice.
AI use that fosters engagement and builds knowledge (only when assignment briefs explicitly permit AI use):
AI use that compromises assessment integrity:
Transparency Imperative
Responsible use requires careful consideration of motivation and, most importantly, transparency. Move beyond viewing AI as a secret cheat code, treat it as a tool requiring methodological rigour and honest disclosure.
The most common errors occur when crossing the line between using AI for process support and using it for content generation. Clear boundaries help maintain academic integrity whilst allowing beneficial AI engagement when explicitly permitted by assignment briefs.
Supports learning and development (only when assignment briefs explicitly permit):
Outsources learning or constitutes misconduct:
If you are ever questioned about work authenticity, your best defence is a robust, transparent paper trail demonstrating your personal intellectual journey from prompt to submission.
If AI was used in preparation of assessed work (only when explicitly permitted by assignment briefs), include a clear statement detailing the specific tool, purpose, and extent of input.
Human-in-the-Loop
The goal is to train you to be the 'human in the loop', the expert who can assess, guide, and ultimately take accountability for the final intellectual product whilst maintaining critical human judgement.
AI is not going away. Your time in higher education is about learning to engage with this technology critically and positively. The future workplace demands professionals who can use these tools ethically and effectively, without losing their human critical edge.
By adhering to principles of literacy, integrity, and transparency, you ensure that you develop authentic skills required to succeed both in studies and future career. Embrace the tools, but never outsource your mind!
This section provides a comprehensive list of all key terms used throughout this AI literacy guide. Hover over any term to see its definition.
AI literacy cognitive offloading academic integrity false authorship unauthorised assistance transparency imperative human-in-the-loop AI hallucination