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MCB 143: Evolution of Genomes, Cells, and Development

AI in literature searching and writing

AI Literacy: Uses and limitations of AI tools

As when using any tool, it's important to understand the strengths and limitations of AI tools before choosing to use them. Some considerations to be aware of from the start:

  • "Artificial Intelligence" tools are not intelligent: Large Language Models (LLMs) ingest a vast text corpus and are then trained to produce text based on statistical predictions of word probability and order based on context. Although they generate text that sounds authoritative, these agents do not, and cannot, evaluate the accuracy of their own output, and can produce responses that are biased, false, or harmful.
  • Free ChatGPT-based tools currently have a knowledge cutoff of 2021: Nothing published or posted to the web after 2021 was included in training corpus for the free version of the ChatGPT LLM, so any information it references will be from 2021 or earlier.
  • ChatGPT and other AI resources are not supported tools at UC Berkeley: They have not been reviewed for accessibility, privacy, or security.

Outlined below are some possible uses of AI tools in literature searching and writing, along with limitations to consider:

Brainstorming topics and research questions

brainstorming diagram

Image: Drtonymc, CC BY-SA 3.0, via Wikimedia Commons

AI tools can:

  • provide a quick overview of a research field
  • suggest topics that might be appropriate for a writing assignment
  • describe relevant keywords
  • cite initial sources for further exploration.


  • Generative AI tools are based on LLMs that are typically trained on text scraped from the open web during a fixed time period (as noted, for free ChatGPT tools the time period ended in 2021). So generated query answers may not reflect the most current information.
  • LLMs may reflect biases or errors in their source material or training process.
  • AI tools generally provide better answers when prompts are more specific and precise. At the beginning of a research project it can be difficult to formulate precise queries because you are just beginning to learn about a domain.
  • AI tools may provide better answers when the LLM has specifically been trained on a relevant corpus. ChatGPT and many other tools were trained on text scraped from the open web, which is not domain-specific and includes misleading, biased, false, and harmful information.

Searching for information

Millet, The Gleaners

Image (detail): Jean-François Millet, Des glaneuses (The Gleaners), 1857. Source: Musée d'Orsay

AI tools can:

  • provide citations to scientific journal articles and other sources on a topic
  • suggest similar sources
  • show citation, authorship, or topic networks among sources.


  • The process of iterative searching in databases helps you to learn more about a research domain and to focus your results on the most relevant sources; AI tools bypass those processes for the user.
  • Free ChatGPT-based tools currently have a knowledge cutoff of 2021—nothing published or posted to the web after 2021 was included in training corpus for the free version of the LLM.
  • Generative AI tools can "hallucinate" false citations. As with any other form of research, it's important to verify all sources and the information they contain.
  • Relevancy rankings on some AI tools rely on article citation metrics and journal impact factors, neither of which is necessarily an indication of article quality, timeliness or relevance to the query.
  • AI tools are often proprietary black boxes that do not indicate how or why the results were obtained. Database searches can be revised to improve results, saved, and shared. Systematic reviews and evidence synthesis projects generally supply the exact searches employed in the chosen databases so that others can check the strategy and reproduce the results; neither is possible with AI tools.
  • Databases have powerful features such as automated search expansion, filters, and search and citation alerts, that can help focus and simplify your search process.

Synthesizing information

Rail tracks map

Image (detail): ButuCC, CC BY-SA 3.0, via Wikimedia Commons

AI tools can:

  • provide summaries of single papers, related groups of papers, or research areas
  • help you identify information sources for deeper reading and exploration
  • provide explanations of specific terms or concepts to help you understand technical or specialized sources.


  • Critical evaluation and assessment of relevance are important skills involved in synthesizing information into new knowledge. AI tools are notoriously poor at those tasks.
  • Summaries generated by AI tools often rely on the language in the source itself. This will not necessarily clarify the content.
  • See the limitations for brainstorming.



Image (detail): Subhashish Panigrahi, Wikimedia Commons, CC-BY-SA 4.0

AI tools can:

  • make suggestions for changes to word choice, grammar, and sentence structure
  • generate short- to medium-length texts responding to prompts


  • One purpose of courses like this one is to help you become a better writer. Over-reliance on AI tools undermines that goal.
  • The process of writing helps you to clarify and deepen your understanding of a topic (just as the process of teaching someone else helps you to learn). Over-reliance on AI means that you don't get that benefit.
  • The output of AI tools can sound authoritative, but as noted above, generative AI can provide misleading, false, biased, and/or harmful information, including citations to sources that don't exist.
  • Copying text without citation from any source, including AI tools, is considered plagiarism: "If a student uses text generated from ChatGPT and passes it off as their own writing, without acknowledging or citing the influence of ChatGPT in their process, they are in violation of the university’s academic honor code" (UC Berkeley Center for Research, Teaching and Learning).