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Doctor of Ministry Research: Artifical Intelligence

Databases organized by the sections in the Literature Review for Doctor of Ministry Theses

Artificial Intelligence

Large language models of artificial intelligence are trained on massive amounts of text data from books, articles, and websites. They produce responses based on probability rather than understanding. They are not search engines, and their output may be incorrect or biased. Claude and ChatGPT are examples of large language models that do not reveal their training data.

Perplexity uses large language models to search and summarize current information on the internet; it includes source links. 

Notebook LM, Elicit, and Undermind are research assistants. Google's NotebookLM organizes, summarizes, and provides notes on resources you upload. Elicit searches academic papers in Semantic Scholar using natural language to match concepts even without exact keywords. Undermind uses an iterative process to locate, extract, organize, and rank the most relevant papers primarily from Semantic Scholar.

EBSCO has introduced natural language into their search queries. EBSCO, ProQuest, and JSTOR provide beta versions of AI Research Assistants within the results screen. These tools may highlight the focus of an article, chapter, or book so you can assess its relevance to your research. Results vary by database, so check the specific features available in each.

If you choose to use AI programs or features always fact-check, use critical thinking to assess results, and read excerpts in context.

Possible Uses

  • Use AI as a conversational partner to explore, refine, and expand potential research topics.
  • Discover possible sources that you verify using trusted library databases.

Cautions

  • Privacy - Do not provide information to an open large language model that you would not publish on the internet. There are significant questions about how the data is used, stored, and protected.
  • Accuracy - Always verify and evaluate results using trusted academic sources.
  • Bias - Evaluate results for race, gender, class, and other biases.
  • Academic Integrity - See the Denver Seminary Policy.
  • Environmental Impact - Data centers providing artificial Intelligence require substantial energy and water usage, producing significant wastewater and emissions. See "We Did the Math on AI's Energy Footprint: Here's the Story You Haven't Heard," MIT Technology Review, May 20, 2025.
  • Copyright - Providing text to an AI for analysis may be a copyright violation. Works generated solely by AI are not eligible for copyright protection; those involving human creativity may qualify (Copyright Office, May 2025). The use of copyrighted material to train AI continues to be litigated. The courts, not the Copyright Office, will decide what qualifies as fair use and what requires licensing (Copyright Office, May 2025).