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Related Concept Videos

Random and Systematic Errors01:20

Random and Systematic Errors

Scientists always try their best to record measurements with the utmost accuracy and precision. However, sometimes errors do occur. These errors can be random or systematic. Random errors are observed due to the inconsistency or fluctuation in the measurement process, or variations in the quantity itself that is being measured. Such errors fluctuate from being greater than or less than the true value in repeated measurements. Consider a scientist measuring the length of an earthworm using a...
Types of Errors: Detection and Minimization01:12

Types of Errors: Detection and Minimization

Error is the deviation of the obtained result from the true, expected value or the estimated central value. Errors are expressed in absolute or relative terms.
Absolute error in a measurement is the numerical difference from the true or central value. Relative error is the ratio between absolute error and the true or central value, expressed as a percentage.
Errors can be classified by source, magnitude, and sign. There are three types of errors: systematic, random, and gross.
Systematic or...
Systematic Error: Methodological and Sampling Errors01:15

Systematic Error: Methodological and Sampling Errors

In the case of systematic errors, the sources can be identified, and the errors can be subsequently minimized by addressing these sources. According to the source, systematic errors can be divided into sampling, instrumental, methodological, and personal errors.
Sampling errors originate from improper sampling methods or the wrong sample population. These errors can be minimized by refining the sampling strategy. Defective instruments or faulty calibrations are the sources of instrumental...
Non-equilibrium in the Cell01:16

Non-equilibrium in the Cell

An important concept in studying metabolism and energy is that of chemical equilibrium. Most chemical reactions are reversible. They can proceed in both directions, releasing energy into their environment in one direction, and absorbing it from the environment in the other direction. The same is true for the chemical reactions involved in cell metabolism, such as the breaking down and building up of proteins into and from individual amino acids, respectively. Reactants within a closed system...
Fundamental Attribution Error01:14

Fundamental Attribution Error

According to some social psychologists, people tend to overemphasize internal factors as explanations—or attributions—for the behavior of other people. They tend to assume that the behavior of another person is a trait of that person, and to underestimate the power of the situation on the behavior of others. They tend to fail to recognize when the behavior of another is due to situational variables, and thus to the person’s state. This erroneous assumption is called the fundamental attribution...
Propagation of Uncertainty from Systematic Error01:10

Propagation of Uncertainty from Systematic Error

The atomic mass of an element varies due to the relative ratio of its isotopes. A sample's relative proportion of oxygen isotopes influences its average atomic mass. For instance, if we were to measure the atomic mass of oxygen from a sample, the mass would be a weighted average of the isotopic masses of oxygen in that sample. Since a single sample is not likely to perfectly reflect the true atomic mass of oxygen for all the molecules of oxygen on Earth, the mass we obtain from this particular...

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Related Experiment Videos

Leveraging generative artificial intelligence errors to teach appropriate citation usage.

Rishita Shah1, Jj L Miranda1

  • 1Department of Biology, Barnard College, Columbia University, New York, New York, USA.

Journal of Microbiology & Biology Education
|May 27, 2026
PubMed
Summary
This summary is machine-generated.

Generative artificial intelligence (AI) tools often create incorrect citations. Educators can use these AI errors to teach students proper scientific writing and citation practices.

Keywords:
artificial intelligenceinformation literacystudents

Related Experiment Videos

Area of Science:

  • Education Technology
  • Artificial Intelligence
  • Scientific Writing

Background:

  • Large language models (LLMs) are increasingly used by students.
  • LLMs frequently generate inaccurate citations, including fabricated references (hallucinations) and misinterpretations of real sources.
  • These errors present a pedagogical opportunity to enhance understanding of citation practices.

Purpose of the Study:

  • To propose a framework for using generative AI errors in educational settings.
  • To leverage AI-generated citation mistakes for teaching rigorous scientific writing.
  • To improve students' information literacy and understanding of source types.

Main Methods:

  • Repurposing generative AI's citation errors as teaching examples.
  • Designing classroom exercises focused on correcting AI-generated citation mistakes.
  • Implementing a conceptual framework for AI-assisted learning in scientific writing.

Main Results:

  • Demonstrates a key weakness in current generative AI capabilities regarding factual accuracy and citation.
  • Provides students with practical experience in identifying and correcting citation errors.
  • Offers a structured approach for students to practice appropriate source type usage.

Conclusions:

  • Generative AI errors can be effectively utilized as a pedagogical tool in scientific writing education.
  • This approach enhances both AI literacy and information literacy among students.
  • The proposed framework facilitates the integration of AI-generated examples into classroom instruction for improved writing skills.