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Using AI for Research

Stefan Schutt

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Coming soon

Using AI for Research is designed to support students and staff to confidently and ethically integrate artificial intelligence into their research and study. Beginning with core skills for new researchers, including benefits and limitations of AI, ethical use and citation and introductory prompt engineering, the guide progresses to practical applications such as idea generation, literature searches and summaries, transcription and academic writing support and journal metrics evaluation. It concludes with advanced topics for experienced researchers.

Designed for undergraduates, postgraduates and academic staff, the resource offers clear practical advice and can be embedded into LMS subjects or used as a standalone resource. Developed in response to the growing need for reliable, hands-on AI guidance in higher education, it provides tips for enhancing research without compromising integrity.

Section outlines

This resource will include the following sections. Select each to view the topics that are currently planned to be covered.

Introduction

  • Using AI in research: the good, the bad, and the pitfalls
    • Why use AI?
    • What are the drawbacks of using AI in research?

Part 1: AI for student researchers

  • How to use AI responsibly
  • Some ethical considerations
  • Tips on starting out with AI
  • AI prompts: the basics

Part 2: Using AI in research projects

  • Ethical considerations for professional researchers
    • Introduction: What AI can and can’t do
    • Data privacy and confidentiality
    • Transparency and integrity
    • Bias and fairness
    • Informed consent
    • Attribution and intellectual property
    • Equity and access
    • Quality and validity
  • Best practice tips
  • Working on your research project: let’s get started
    • Generating ideas
    • Academic writing
    • Evaluating journal metrics
    • Technical problem solving
    • Streamlining workflows
    • Collaboration and team research
    • Literature searches, summaries, document analyses
    • Sharpening the Research Question
    • Systematic/scoping reviews and meta-analyses
    • Research ethics applications
    • Working with Methodology and Methods
    • Data collection and management
    • Analysis
    • Research translation/dissemination

Part 3: Advanced AI for experienced researchers

  • Advanced prompt engineering
  • AI model fine-tuning
  • Integration of AI with specific methodologies
    • Qualitative Methodologies
    • Quantitative Methodologies

Toolbox

This section will feature a list of AI tools/platforms and details of how they can be used in research. It will be updated regularly.

Details

Publication date: TBA
Publisher: La Trobe eBureau
ISBN: 978-1-7641666-1-4
DOI: https://doi.org/10.26826/1029