AI_is_here_to_support_the_literature_review__1_ (1).pptx
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The presentation was delivered to the Bagwell College of Education faculty and students. The outline: 1. Using generative #AI to develop a research question and topic, build a conceptual framework 2. AI-based search engines and the use of generative AI technology for searching 3. Analysis and synthesis of the literature 4. Pros and cons of using generative AI to review literature
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AI_is_here_to_support_the_literature_review__1_ (1).pptx
- 1. AI is here to support the literature review! http://bit.ly/AI4LR
- 3. Literature Review phases Research Question and Topic Development Searching for Evidence Analysis & Synthesis
- 4. Research Question Developing research questions using ChatGPT prompts Using Elicit to generate a research question Hippo from Sage RQ generator
- 5. Topic and Concepts Development Concept Paper Conceptual Learning Topic Literature or Knowledge Mapping Research Question Research Planning https://libguides.kennesaw.ed u/Research_Plan/checklist
- 6. Concept & Literature Mapping • Concept Mapping - VOSViewer, Open Knowledge Map, Elicit • Citation Mapping - co- citation, citation chaining, reference analysis: LitMAp, Research Rabbit, Inciteful
- 9. Semantic Search Engines • AI algorithms use vector comparisons and NLP.
- 10. Extract data and Concepts Citation Analysis Search (NLP) Elicit Summary
- 11. Review Steps Summarization • Smmry • Paper-Digest • Scholarcy • Explainpaper • RobotReviewer DataSeer (data extraction) Meta-synthesis • Nvivo and Atlas.ti • RapidMiner Meta-analysis • DataRobot • ASReview • Network analysis/ • Visualization • Citation analysis Analysis Review •Elicit: The AI Research Assistant •Iris.ai •Laser.ai •Lateral •Paper Wizard . •Snowball •PaperDigest •Scite Research Assistant (Beta)
- 12. Pro/Cons Pros Cons Time efficiency: AI-generated summaries save researchers time, allowing them to focus on analysis and interpretation. Comprehensive coverage: AI can review vast literature quickly, ensuring researchers stay informed of the latest developments. Improved accessibility: AI- generated summaries can make complex research more digestible, promoting knowledge sharing and interdisciplinary understanding. Accuracy: AI-generated content not always accurately represent the original research (hallucinations or false citations) Transparency: Sources are not clear or checked. AI-generated content and sources for training large learning models are hidden. Coverage is limited to open-access sources. Synthesis and interpretation: technical flows – technology is not available yet
- 13. Citing generative AI • APA Style: How to Cite ChatGPTInformation about how to quote AI-generated text, create references to AI models and software, and related issues. • Chicago Style Online: Citing Content Developed or Generated by AIThis site features questions and answers about how to cite AI in text and how to create formal citations for footnotes or endnotes. • MLA Style Center: How Do I Cite Generative AI in MLA Style?This site provides information about using the MLA template, paraphrasing and quoting text, citing creative visual works, and more. • Citing AI-Generated Images Guidance from the U. of Victoria Libraries on citing images from some of the most popular AI image generators.
Editor's Notes
- Based on the website I created, learn more about the topic there – url on the title page and at the end of the presentation. I used AI-based Beatiful.ai automatic presentation maker platform, which connected with DALL-E and text generator, but mostly the content was based on my experience as a systematic review methodologist, developer, and tester for AI search and lit review tools.
- As a technologist, I am always open to learning more about innovations; as a researcher and a librarian, I am also skeptical about tools in the Hype stage (Gartner diagram). This duality helps me in the evaluation of the tools
- I will focus on three stages of the research and literature review process where AI tools could be or already integrated.
- While use of AI can make qualitative research more efficient, explicatory, and equitable, there are two pitfalls that need to be underscored. First, AI is a tool to enhance researcher’s capabilities and not to replace her. AI cannot be accorded with ownership, and authorship of research. In other words, AI cannot be treated as an independent and objective interpreter of social field. The task of interpretation shall always remain with human researchers.