Before you dive into finding scholarly articles, it’s essential to spend time on preliminary reading. Think of preliminary reading as front-loading your learning: giving yourself the background knowledge and vocabulary you need before tackling more complex content. The topics you’re working with — from microbial consortia to machine learning — are discipline-specific and full of specialized language. If you try to jump straight into primary research articles, you’re likely to feel overwhelmed and may miss the big picture. Preliminary reading is a solid investment of your time.
Start exploring your topic with easier-to-read sources: Wikipedia, encyclopedia entries, even chatbots. Your goal isn’t to find your actual sources yet — it’s to build a firm foundation that facilitates the finding (and comprehension) of your sources. Use this stage to:
Define the major concepts in your topic (What exactly is a life cycle assessment? How does catalytic conversion work?).
Collect useful vocabulary and search terms that will help later in databases.
Notice what kinds of questions researchers and practitioners are asking.
Find simple examples that make abstract ideas more concrete.
When we regroup next week, I want you to be able to define your topic and explain it to me in plain language. That’s how we’ll know you’ve done the front-loading that makes scholarly sources easier to handle — and more meaningful to use.
Use Tools for Preliminary Reading
General Topic Overviews
Wikipedia – Quick, accessible introductions to individual concepts within complex research questions. Helpful for scoping out the “big picture” and picking up vocabulary.
Specialized Encyclopedias (e.g., Gale eBooks) – Concise, reliable topic summaries written by experts. Especially useful for discipline-specific definitions and context.
ChatGPT, Claude, Deepseek, etc. – Provide Wikipedia-style overviews without citations.
Perplexity AI – Wikipedia-style overviews with citations (but typically to popular/substantive sources, not scholarly).
Specific Research Overviews
NotebookLM (Google) – Podcast feature lets you “listen while learning” from individual articles.
Elicit.org – Pulls key points, summaries, and related works from research papers.
ChatGPT (PDF upload) – Upload a paper and ask for section summaries, key takeaways, or “teach this to me like I’m new to the field.”
1. Get an overview of your topic
Use your favorite chatbot to get a general introduction to your topic, a perfect way to frontload your learning. A starter prompt might look like this (notice how it borrows directly from the topic list):
I’m a freshman college student conducting research on machine learning for predictive modeling of waste-to-product pathways. I’m at the beginning of the project and need a basic primer on machine learning in general, and then a specific understanding of how ML methods are applied to predicting waste conversion outcomes. Please avoid jargon since I don’t yet have a firm background on this topic.
(I’ve copied two sample responses from popular chatbots into the attached document.)
2. Generate keywords
Ask your chatbot to generate useful search terms. You’ll start with the basic terms from your assigned topic, but effective research demands branching out. Next week, you’ll jump into databases and Google Scholar to start finding sources, and having a variety of useful keywords will make a big difference. As you research, you will continuously add to your list of keywords.
(See the attached document for an example of how I prompted ChatGPT for search terms.)
3. Try “listening while learning”
Use Google Scholar to locate a relevant article on your topic, then upload it into NotebookLM to generate an audio overview. This feature is especially helpful for those who learn well by listening. For example, NotebookLM created both a podcast-style summary and a one-paragraph overview of the article “Integrating Life Cycle and Quantitative Methods for Sustainability Assessment.” I've linked the results below...have a listen and see what you think.
Having tools that help you penetrate complex journal articles makes the reading process less overwhelming. We’ll also cover strategies for how to read a scholarly article, but doing some frontloading first can make the scholarly text much easier to understand.
Your homework is simple. Come back next week:
1. Ready to explain your topic to your classmates. A real-world, plain-language of what you will be researching and why it matters in terms of the circular bioeconomy.
2. With a list of search terms in hand to plug into our research databases and Google Scholar
3. With an "AI Experience" to share. A favorite source, or maybe even something that didn't work so well. Aim to broaden your horizons: If you are a ChatGPT devotee, branch out to another bot like Perplexity to compare responses. If you've never used AI to summarize an article, try it this week to see how it helped and how it didn't.