3a. Importance of Evaluating GenAI and Other Sources

3a. Importance of Evaluating GenAI and Other Sources#

Introduction#

In academic activity and industry, good decisions and solid designs depend on accurate, up-to-date, and trustworthy information. Throughout your thesis, you’ll rely on information from articles, websites, reports, and maybe even AI tools. But not all of these sources are equally reliable, and using weak or incorrect information can easily lead to poor results.

Step 1

Why Evaluating Information Matters
What are main reasons to evaluate sources?

Step 2

Limitations of GenAI Output
What are some major concerns for GenAI output?

Step 1: Why Evaluating Information Matters#

Evaluating information matters because:

  • It prevents technical errors. Using outdated data, incorrect formulas, or unverified claims can lead to serious mistakes. For example, choosing the wrong material specifications, misinterpreting a dataset, or designing an inefficient algorithm.

  • It saves you time and effort. When you learn to spot low-quality or irrelevant sources early, you can focus on the material that actually supports your project.

  • It improves the credibility of your work. Projects or reports that are based on reliable, well-chosen sources are more convincing and show that you understand your topic deeply.

  • It strengthens your critical thinking. Engineers and scientists constantly have to assess whether data, methods, or solutions are valid. Evaluating information sources develops that same analytical mindset.

  • It prepares you for real-world problem solving. In professional settings, you’ll often need to judge the reliability of information, from technical manuals to software documentation or industry reports, before making decisions that have real consequences.

In short, evaluating information helps you build accurate, efficient, and credible work both in your studies and your future career.

Step 2: Limitations of GenAI Output#

GenAI tools can be helpful during the research process if they are used correctly. At the start of your project, you should check whether you are allowed to use these tools, and in what way. For more information on acknowledging your use of AI you can also have a look at this section.

THESIS SUPERVISOR

It is strongly suggested that you discuss your planned AI use with your thesis supervisor and check what is allowed.

In the Overview of Information Skills chapter, we briefly addressed general concerns and consideration about GenAI use during your thesis journey. When using GenAI output, you should keep in mind specifically that GenAI tools are prone to make mistakes and have biases embedded, which means you always should critically evaluate the output of a tool, as well as the need to use a tool.

AI tools can produce incorrect or misleading information. The output of AI systems can, for example, include citations that look realistic, but don’t really exist. Ask any AI to find you some academic sources on a topic and you are presented with a plausible list of seemingly high quality scientific sources. However, these references don’t always exist. For example; In July 2026, research from the Dutch magazine De Groene Amsterdammer revealed that, since the release of ChatGPT, the proportion of scientific articles containing non-existing references has increased sevenfold (read about the research in English here). You should always verify the output of AI with reliable sources.

GenAI tools are trained with real-life data. The output can therefore reflect and amplify biases from human thinking. For example, research has shown that outputs of GenAI tools often include biases against women and people of colour. Other biases, for example confirmation bias (the tendency to search for and favour information that confirms your beliefs), can also affect the data that is used for training the GenAI tool, and therefore also affect the output. When you ask an AI tool to find you research, to summarise or synthesise information, these biases influence also your analysis.

Moreover, there is also a bias in the data that AI tools access. For example, academic AI tools like Elicit or Consensus, while they search existing sources, draw on open access research output (often from Semantic Scholar). Sources that are behind a paywall are not included and thus missing from your analysis. So, also from the perspective of potential biases, it is important to always critically check GenAI output.