Basic rules for searching, choosing, analyzing, and using information:
*Seek information, not affirmation
AI and LLMs —
- Right now, AI is a mess
- The perils of AI-generated deepfake videos
- AI and Large Language Models: shortcomings and mistakes
- 5 ways data centers endanger their local communities and the country as a whole
Algorithms —
- Algorithms: what are they? What can they do?
- Algorithms: impact on searching and finding academic information
- More sources
*Understanding
- Do you truly understand the information you have found? Be honest. Is the information clear? Does it make sense? Could you describe the topic to another person?
- If the information is not understandable, then it is not worth much to you. There are ways to make information more understandable (secondary sources are a good start) and there is no shame in using them!
*Original source + motivation/context + how you want to use = value
*Dig deeper–do not rely on just one source
Other issues:
Predatory publishers
- “A vast ecosystem of predatory publishers is churning out “fake science” for profit …”
- Fake papers are contaminating the world’s scientific literature, fueling a corrupt industry and slowing legitimate lifesaving medical research
- How to identify fake academic publications?
Nonsense papers
- “… this strange episode brings to mind broader questions about academic publishing, including whether way too much subpar research is being pumped out each year and whether peer review is all it’s cracked up to be … He asked a question that’s worth contemplating: “Is anyone actually reading this journal?”
Evaluation examples (class activity)
- Worksheet to use
- Group #1
- Group #2
- Group #3
Questions? Please let me know (engelk@grinnell.edu).
**updated August 11, 2026**

