Responsible Use of Generative AI

An introduction to responsible AI use, with an applied research workshop

Course At A Glance

  • Level: Foundation to intermediate
  • Duration: Two 60-minute introductory lessons; modular applied workshop (allow approximately 2 hours and confirm the selected agenda with your instructor)
  • Best for: Researchers, research staff, doctoral researchers, and professional services colleagues who need clearer judgement about AI use
  • Main themes: research change, prompting, literature reviews, analysis, grant applications, automation, and research integrity
  • Format: Two linked introductory sessions followed by an applied workshop; sessions can be delivered separately
  • Outcome: More confident, source-aware, and defensible use of generative AI in research settings

Why This Course Matters

Generative AI can reduce friction in research work, but it also creates new checking work, new governance questions, and new opportunities to over-trust persuasive output. Many people are already experimenting with AI, yet still feel unsure about where it helps, where it creates risk, and what responsible use actually looks like in practice.

This course is designed to close that gap. It helps learners understand how AI is changing research activity and how to use AI tools more selectively, with clearer boundaries, stronger validation, and better records.

What You Will Actually Do

This is not a hype session and it is not a tool demo dressed up as policy. Learners work through prompt design, source checking, critique, documentation, and workflow decisions using realistic research examples.

Across the course you will:

  • examine where AI genuinely changes research work
  • identify opportunities without ignoring risk relocation
  • practise stronger prompting with source boundaries and uncertainty cues
  • validate AI outputs against real documents
  • decide when AI use should stay ad hoc and when it can become a workflow or agent
  • apply these habits to literature reviews, research design, qualitative or quantitative analysis, and grant preparation

Who This Is For

This course is especially useful if you:

  • already use AI occasionally but want clearer rules for responsible use
  • need to explain or defend AI-assisted work in a research setting
  • want better habits for validation, documentation, and disclosure
  • support researchers and need a stronger framework for advising on AI practice

No programming background is required.

You Will Build Confidence In

Judging Where AI Helps

Separate high-value support tasks from situations where AI mainly increases risk or checking burden.

Prompting More Deliberately

Use clearer task framing, source boundaries, output formats, and follow-up prompts.

Checking Outputs Properly

Verify claims, quotations, summaries, and workflow outputs against actual sources and research expectations.

Recording AI Use

Keep lightweight notes, validation steps, and disclosure decisions that make AI use easier to explain later.

Learning Journey

Lesson What you will tackle Why it matters
01 Introduction to AI for Research Opportunities, risks, governance, data security, and how AI changes research work Builds judgement before tool use becomes routine
02 Prompting for Research Prompt design, validation, source-grounded workflows, and practical AI exercises Turns broad principles into concrete, defensible practice
03 Applied AI for Research Choosing tools, literature reviews, research design, analysis, agents, grants, and integrity Builds checked, reusable workflows across the research lifecycle

Suggested Delivery

The first two lessons provide 120 minutes of teaching and activities. They can run as:

  • one combined session, with any break added to the 120-minute learning time
  • two linked 1-hour sessions delivered separately

A simple structure is:

  1. Hour 1: 01 Introduction to AI for Research
  2. Hour 2: 02 Prompting for Research

Follow these with 03 Applied AI for Research, a modular workshop for which approximately two hours is a useful planning guide. It uses eight brief discussion pauses, with a choice of qualitative or quantitative material for the analysis pause. Confirm the selected topics, discussion time and break with the teaching team; the full lesson provides more material than every cohort needs to cover.

What Learners Typically Leave With

By the end of the course, learners should be more able to:

  • explain where AI changes research work and where human judgement must remain central
  • write stronger prompts with clearer boundaries and verification steps
  • spot when AI output needs source checking, clarification, or rejection
  • document AI-assisted work in a way that supports transparency later
  • decide when a repeated AI task is suitable for templating, workflow design, or simple automation

How To Prepare

It helps to arrive with:

  • access to a browser and, if available, an approved institutional AI tool
  • one low-risk research task or prompt you want to improve
  • one public or non-sensitive document you can use for critique or comparison
  • one question about AI use, governance, or disclosure that feels unresolved in your context

Detailed preparation guidance is available in Setup.

Course Materials

Learners

  • Setup: what to bring, what to access, and how to prepare
  • Additional Material: source-checking practice, an AI use record, and a workflow review checklist
  • Reference: glossary, prompt templates, validation guidance, and documentation habits
  • Discussion: prompts for breakout work and end-of-session reflection

Instructors

Optional Next Steps

These are separate courses, not additional sessions or prerequisites for this course.

For support applying these skills to a specific permitted task, explore Digital Skills Clinics.

Community and Course Information