Hello AI Enthusiasts 👋

Welcome to the latest edition of ‘T3’ – AI Tech, Tools & Trends in Higher Education.

Thanks for indulging me in a much-needed break over Easter, and then allowing me to take an additional week to deal with an AI-related existential crisis (anyone else experiencing similar feelings ?!) 🙂 

In this edition, I’m sharing a publication I co-authored that just came out, which details a method for evidencing an AI-augmented approach to conducting your next literature review.

AI Literature Reviews – Cite this paper to evidence your AI-informed approach :)

Paper: “Insights into the application of AI-augmented research methods for informing accounting practice: the development – through AI - of accountability-related prescriptions pertaining to seasonal work”, authored by Eager, Deegan, and Fiedler.

The method section of the paper details an innovative approach to conducting a scoping study, augmented by artificial intelligence (AI). It’s written in the context of researching accountability to seasonal workers but is applicable across multiple domains/topics.

Here's an overview of the method and its research application:

Defining Focus and Scope: In the paper, we first clarify research questions and objectives, establish search parameters, define the language, publication date, and disciplinary domain, and identify key concepts and keywords for guiding their search queries.

Creating the Knowledge Base: Using AI tools like Scite and Elicit, we conducted searches based on the identified keywords to retrieve relevant sources, forming a knowledge base. This base included full-text versions of sources, with additional ones added to enhance theoretical and conceptual depth.

Analysis and Synthesis: The knowledge base was imported into an AI tool with multi-document analysis functionality (Petal was used in this case). This tool conducted a thematic analysis of the knowledge base to identify key themes and generate summaries or answers to research questions.

Communicating Findings: The AI-generated outputs were reviewed, edited as necessary, and disseminated as study findings.

Application for Other Researchers: Researchers can use this approach to augment traditional research methods, improving efficiency and depth of analysis in literature reviews. By leveraging AI tools for searching, retrieving, and analyzing literature, researchers can handle vast amounts of data and identify patterns or themes that may not be immediately evident. This method is useful in fields with extensive literature, allowing for a comprehensive review and synthesis of existing knowledge. The process is iterative, allowing for the refinement of the search and analysis as the study progresses.

To apply this method, researchers could:

• Clearly define their research questions and scope.

• Use AI tools to systematically search and compile a relevant knowledge base.

• Employ AI for analyzing and synthesizing the data, and identifying key insights.

• Critically review and refine AI-generated outputs to ensure they align with the research objectives and questions.

• Use AI as a complementary tool, but never a replacement :)

This AI-augmented approach suggests the potential for AI to enhance the capacity to conduct thorough literature reviews, offering a scalable and efficient method for exploring complex research areas, especially those with abundant existing research.

Download a copy of the paper here.

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Take care and chat soon,

Bron