AI for Research Postmortems on Failed Aims: Documenting What Didn't Work

End-of-lesson check

15 questions · take it digitally for instant feedback at tendril.neural-forge.io/learn/quiz/end-research-ai-research-postmortem-failed-aim-creators

1. What is the primary purpose of documenting a failed research aim as a postmortem?

   1. To demonstrate that the lab has abandoned promising directions
   2. To prove the researcher made a genuine effort before giving up
   3. To create paperwork that satisfies grant requirements
   4. To capture institutional memory so future researchers avoid repeating the same mistakes

2. Which task is AI well-suited to assist with when creating a research postmortem?

   1. Generating a structured template with sections for aim, approach, and hypotheses
   2. Replicating the failed experiment to verify results
   3. Conducting the bench work to investigate the failure
   4. Determining the exact scientific reason an experiment failed

3. Why should a postmortem avoid 'sanitizing' or softening the description of what failed?

   1. Because reviewers penalize labs that document failures
   2. Because sanitized reports look unprofessional
   3. Because funding agencies require dramatic failure narratives
   4. Because the next researcher needs to know what actually happened to avoid repeating the mistake

4. A comprehensive research postmortem should include which of the following elements?

   1. Original aim, approach taken, observable outcome, hypotheses for failure, what was tried next, and why the work is stopping
   2. Only the final data and conclusions
   3. A summary of all successful experiments in the lab
   4. A list of publications that resulted from the work

5. What does the lesson mean by 'institutional memory' in a research lab context?

   1. The collective knowledge about what has been tried and what failed, passed between lab members over time
   2. Physical records stored in filing cabinets
   3. Published papers from the laboratory
   4. The lab's budget and spending history

6. A student joins a lab and wants to try a particular experimental approach. How would a well-documented postmortem help them?

   1. It would list all the equipment they need
   2. It would show them exactly how to get published quickly
   3. It would reveal whether someone had already tried that approach and what happened
   4. It would confirm the approach always works

7. Why is a 'tag-friendly summary' mentioned as a useful AI output for postmortems?

   1. Because tags are required by funding agencies
   2. Because AI generates better tags than humans
   3. Because tags make the document look more professional
   4. Because searchable tags help future researchers find relevant failed attempts when planning new experiments

8. What distinguishes the AI's role from a researcher's role in the postmortem process?

   1. There is no distinction—they can do the same things
   2. AI decides what to try next while researchers document the past
   3. AI organizes and structures information while researchers provide scientific interpretation
   4. AI conducts the experiments while researchers write the report

9. The lesson states that AI cannot 'replace the bench investigation.' What does this mean?

   1. AI cannot help design new experiments
   2. AI cannot be used in laboratory settings
   3. AI cannot physically work at the laboratory bench
   4. AI cannot determine the actual cause of experimental failure through direct observation and testing

10. When documenting hypotheses for failure, how should they be ordered?

    1. In reverse chronological order of when they were considered
    2. Randomly to avoid bias
    3. In priority order, with the most likely explanations listed first
    4. Alphabetically by researcher name

11. Why does the lesson emphasize that documenting failed aims helps 'reviewers see honest progression'?

    1. Because reviewers only care about failures, not successes
    2. Because funding agencies pay researchers more for failed experiments
    3. Because reviewers penalize labs that have no failures
    4. Because showing what didn't work demonstrates the researcher's thorough and honest approach to the scientific process

12. What is wrong with the phrase 'AI can decide why something failed scientifically'?

    1. AI should not be used in research at all
    2. AI can organize data but cannot interpret scientific meaning or draw conclusions about causation
    3. Nothing is wrong with this phrase—AI can definitely do this
    4. AI is not advanced enough to write in scientific language

13. What information should the postmortem include about why the work is stopping or pausing?

    1. A list of other labs working on similar problems
    2. Only the PI's personal opinion
    3. A clear explanation that helps future researchers understand whether this is a temporary pause or permanent abandonment
    4. Nothing—stopping reasons are not relevant

14. What does the lesson say about the relationship between AI-generated postmortems and a Principal Investigator (PI) debrief?

    1. AI should write the PI debrief automatically
    2. The PI debrief is unnecessary when AI helps
    3. AI can replace the PI debrief entirely
    4. AI cannot substitute for a PI debrief—human conversation is still needed

15. A postmortem that says 'the experiment didn't work' without details would fail to help future researchers because it lacks:

    1. Specific information about what was tried, what happened, and what should be different next time
    2. Approval from the department chair
    3. Proper formatting and citations
    4. Colorful charts and graphs