Essential skills

Developing AI Literacy for Academics

Essential skills and knowledge for using AI responsibly, critically and effectively in academic work.

What is AI literacy?

AI literacy encompasses the knowledge, skills, and critical thinking abilities needed to understand, evaluate, and effectively use AI technologies. For academics, this includes understanding how AI models work, their capabilities and limitations, and how to integrate them ethically and effectively into scholarly work.

To gauge your own understanding, you can take The Generative AI Literacy Assessment Test.

A practical rule: fluency is not evidence. AI outputs can sound polished even when they are incomplete, biased or wrong. Treat AI as a collaborator for generating, organising and critiquing ideas, not as a source of evidence. Verification should be built into the task from the start: check sources, inspect assumptions, record how AI was used, and keep human responsibility for the final judgement.

Core skills

Core AI literacy skills

Effective prompt engineering

Writing clear, specific instructions and breaking down complex requests to yield better AI outputs. This involves understanding how to structure prompts, provide context, and iterate on instructions.

Example: Instead of "Help with my essay," try "Act as an academic writing tutor. Review my thesis statement about climate change impacts on agriculture and suggest three ways to make it more specific and arguable."

Critical evaluation of AI outputs

Developing the ability to identify biases, limitations, and factual errors in AI-generated content. This includes fact-checking information and comparing AI outputs with trusted sources.

Practice: Always verify academic sources suggested by AI models. Check if the source exists and genuinely supports the claims the AI is making.

AI-human collaboration

Viewing AI as a tool for inspiration and starting points, not as a replacement for expertise or critical thinking. Learning to work with AI rather than being replaced by it.

Approach: Use AI for brainstorming and initial drafts, then apply your academic expertise to refine, critique, and enhance the output.

Data privacy and ethics

Understanding the importance of data protection when using AI tools, especially with confidential or sensitive academic materials. Knowing when and how to use local models or privacy-focused alternatives.

Warning: Never upload client, patient, or confidential institutional data to public AI models without proper safeguards.

Principles

Guiding principles for AI use

Nine commitments that underpin responsible AI use in academic settings.

Human agency & AI partnership Appropriate integration in assessment Transparency Mitigation of inaccuracy & bias Equity Accessibility Data protection Employability agility Knowledge sharing & collaboration

Framework

Learning vs production

It is important to be explicit with students about the permissible and prohibited uses of AI for any given assessment. A key aspect is exploring how they can use AI for the purpose of learning, rather than solely for production.

AI for learning

  • Explaining complex concepts
  • Generating practice questions
  • Providing feedback on drafts
  • Brainstorming ideas
  • Checking understanding

AI for production (with caution)

  • Generating draft material where the assessment brief explicitly allows it
  • Formatting and structure
  • Language translation
  • Data analysis assistance
  • Citation formatting, followed by manual checking

Put these skills into practice on the prompting page, or see how permitted use is made explicit in the assessment framework.

Companion guides

Related AI literacy sites

Research Guide

Secure, verification-first AI use across the research workflow.

Student AI Literacy

Student-facing guidance on evidence, verification and judgement.

AI for Beginners and Experts by Experience

Plain-language support for people unfamiliar with AI.