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What Are the Limitations of ChatGPT?

Published on April 20, 2023 by Tegan George . Revised on August 15, 2023.

ChatGPT is a popular chatbot released by OpenAI in late 2022. Chatbots, or computer programs that simulate human interactions via artificial intelligence (AI) and natural language processing (NLP), can help answer many academic questions.

While using ChatGPT for your studies can be really useful, particularly for help with exam preparation, homework assignments, or academic writing , it is not without its limitations. It’s essential to keep in mind that AI language models like ChatGPT are still developing technologies and are far from perfect. Current limitations include:

• Incorrect answers
• Biased answers
• Lack of human insight
• Overly long (wordy) answers

Table of contents

Chatgpt limitation 1: incorrect answers, chatgpt limitation 2: biased answers, chatgpt limitation 3: lack of human insight, chatgpt limitation 4: overly long or wordy answers, other interesting articles, frequently asked questions about chatgpt.

Because ChatGPT is a constantly evolving language model, it will inevitably make mistakes . It’s critical to double-check your work while using it, as it has been known to make grammatical, mathematical, factual, and reasoning errors (using fallacies).

It’s not always reliable for answering complicated questions about specialist topics like grammar or mathematics, so it’s best to keep these types of questions basic. Double-check the answers it gives to any more specialized queries against credible sources .

Perhaps more concerningly, the chatbot sometimes has difficulty acknowledging that it doesn’t know something and instead fabricates a plausible-sounding answer. In this way, it prioritizes providing what it perceives as a more “complete” answer over factual correctness.

Some sources have highlighted several instances where ChatGPT referred to nonexistent legal provisions that it created in order to avoid saying that it didn’t know an answer. This is especially the case in domains where the chatbot may not have expertise, such as medicine or law, or anything that requires specialized knowledge in order to proceed beyond a general language understanding.

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ChatGPT, like all language models, is at risk of inherent biases , and there are valid concerns that widespread usage of AI tools can perpetuate cultural, racial, and gender stigma. This is due to a few factors:

• How the initial training datasets were designed
• Who designed them
• How well the model “learns” over time

If biased inputs are what determines the pool of knowledge the chatbot refers to, chances are that biased outputs will result, particularly in regards to how it responds to certain topics or the language it uses. While this is a challenge faced by nearly every AI tool, bias in technology at large represents a significant future issue.

While ChatGPT is quite adept at generating coherent responses to specific prompts or questions, it ultimately is not human. As such, it can only mimic human behavior , not experience it itself. This has a variety of implications:

• It does not always understand the full context of a topic, which can lead to nonsensical or overly literal responses .
• It does not have emotional intelligence  and does not recognize or respond to emotional cues like sarcasm, irony, or humor.
• It does not always recognize idioms, regionalisms, or slang . Instead, it may take a phrase like “raining cats and dogs” literally.
• It does not have a physical presence and cannot see, hear, or interact with the world like humans do. This makes it unable to understand the world based on direct experience rather than textual sources.
• It answers questions very robotically , making it easy to see that its outputs are machine-generated and often flow from a template.
• It takes questions at face value and does not necessarily understand subtext. In other words, it cannot “read between the lines” or take sides. While a bias for neutrality is often a good thing, some questions require you to choose a side.
• It does not have real-world experiences or commonsense knowledge and cannot understand and respond to situations that require this kind of knowledge.
• It can summarize and explain a topic but cannot offer a unique insight . Humans need knowledge to create, but lived experiences and subjective opinions also are crucial to this process—ChatGPT cannot provide these.

ChatGPT’s training datasets encourage it to cover a topic from many different angles, answering questions in every way it can conceive of.

While this is positive in some ways—it explains complicated topics very thoroughly—there are certainly topics where the best answer is the most direct one, or even a “yes” or “no.” This tendency to over-explain can make ChatGPT’s answers overly formal, redundant, and very lengthy .

If you want to know more about ChatGPT, using AI tools , fallacies , and research bias , make sure to check out some of our other articles with explanations and examples.

• ChatGPT vs human editor
• ChatGPT citations
• Is ChatGPT trustworthy?
• Deep learning
• Sunk cost fallacy
• Straw man fallacy
• Slippery slope fallacy
• Red herring fallacy
• Ecological fallacy
• Logical fallacy

Research bias

• Implicit bias
• Framing bias
• Cognitive bias
• Optimism bias
• Hawthorne effect
• Unconscious bias

No, ChatGPT is not a credible source of factual information and can’t be cited for this purpose in academic writing . While it tries to provide accurate answers, it often gets things wrong because its responses are based on patterns, not facts and data.

Specifically, the CRAAP test for evaluating sources includes five criteria: currency , relevance , authority , accuracy , and purpose . ChatGPT fails to meet at least three of them:

• Currency: The dataset that ChatGPT was trained on only extends to 2021, making it slightly outdated.
• Authority: It’s just a language model and is not considered a trustworthy source of factual information.
• Accuracy: It bases its responses on patterns rather than evidence and is unable to cite its sources .

So you shouldn’t cite ChatGPT as a trustworthy source for a factual claim. You might still cite ChatGPT for other reasons—for example, if you’re writing a paper about AI language models, ChatGPT responses are a relevant primary source .

ChatGPT is an AI language model that was trained on a large body of text from a variety of sources (e.g., Wikipedia, books, news articles, scientific journals). The dataset only went up to 2021, meaning that it lacks information on more recent events.

It’s also important to understand that ChatGPT doesn’t access a database of facts to answer your questions. Instead, its responses are based on patterns that it saw in the training data.

So ChatGPT is not always trustworthy . It can usually answer general knowledge questions accurately, but it can easily give misleading answers on more specialist topics.

Another consequence of this way of generating responses is that ChatGPT usually can’t cite its sources accurately. It doesn’t really know what source it’s basing any specific claim on. It’s best to check any information you get from it against a credible source .

ChatGPT can sometimes reproduce biases from its training data , since it draws on the text it has “seen” to create plausible responses to your prompts.

For example, users have shown that it sometimes makes sexist assumptions such as that a doctor mentioned in a prompt must be a man rather than a woman. Some have also pointed out political bias in terms of which political figures the tool is willing to write positively or negatively about and which requests it refuses.

The tool is unlikely to be consistently biased toward a particular perspective or against a particular group. Rather, its responses are based on its training data and on the way you phrase your ChatGPT prompts . It’s sensitive to phrasing, so asking it the same question in different ways will result in quite different answers.

No, it is not possible to cite your sources with ChatGPT . You can ask it to create citations, but it isn’t designed for this task and tends to make up sources that don’t exist or present information in the wrong format. ChatGPT also cannot add citations to direct quotes in your text.

Instead, use a tool designed for this purpose, like the Scribbr Citation Generator .

But you can use ChatGPT for assignments in other ways, to provide inspiration, feedback, and general writing advice.

It’s not clear whether ChatGPT will stop being available for free in the future—and if so, when. The tool was originally released in November 2022 as a “research preview.” It was released for free so that the model could be tested on a very large user base.

The framing of the tool as a “preview” suggests that it may not be available for free in the long run, but so far, no plans have been announced to end free access to the tool.

A premium version, ChatGPT Plus, is available for $20 a month and provides access to features like GPT-4, a more advanced version of the model. It may be that this is the only way OpenAI (the publisher of ChatGPT) plans to monetize it and that the basic version will remain free. Or it may be that the high costs of running the tool’s servers lead them to end the free version in the future. We don’t know yet.

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George, T. (2023, August 15). What Are the Limitations of ChatGPT?. Scribbr. Retrieved August 30, 2023, from https://www.scribbr.com/ai-tools/chatgpt-limitations/

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Research Study Limitations

Usually the limitation of the study is located at the beginning of the discussion section of your document so that the reader knows and understands the limitation before reading your document. But of course, do make an excuse the limitation for not doing a thorough research. If there is so much limitation on your research, it means that your topic is too narrow so it means you should find another topic that is specific but not too narrow. Also, you can consult your professor or mentor the possible ways to revise your study.

Here are some tips when writing limitation of your research study:

• Discuss each limitation in concise terms but I detailed
• Explains reason for each limitation
• Knows the impact of your limitation to your findings and conclusion of your study
• Describe why it is need further research of study because of these limitations
• Clarifies why each limitation exists

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Limitations of study thesis writing

How to structure the Research Limitations section of your dissertation

There is no “one best way” to structure the Research Limitations section of your dissertation. However, we recommend a structure based on three moves. the announcing. reflecting and forward looking move. The announcing move immediately allows you to identify the limitations of your dissertation and explain how important each of these limitations is. The reflecting move provides greater depth, helping to explain the nature of the limitations and justify the choices that you made during the research process. Finally, the forward looking move enables you to suggest how such limitations could be overcome in future. The collective aim of these three moves is to help you walk the reader through your Research Limitations section in a succinct and structured way. This will make it clear to the reader that you recognise the limitations of your own research, that you understand why such factors are limitations, and can point to ways of combating these limitations if future research was carried out. This article explains what should be included in each of these three moves :

We use the word potential impact because we often do not know the degree to which different factors limited our findings or our ability to effectively answer our research questions and/or hypotheses. For example, we know that when adopting a quantitative research design, a failure to use a probability sampling technique significantly limits our ability to make broader generalisations from our results (i.e. our ability to make statistical inferences from our sample to the population being studied). However, the degree to which this reduces the quality of our findings is a matter of debate. Also, whilst the lack of a probability sampling technique when using a quantitative research design is a very obvious example of a research limitation, other limitations are far less clear. Therefore, the key point is to focus on those limitations that you feel had the greatest impact on your findings, as well as your ability to effectively answer your research questions and/or hypotheses.

Overall, the announcing move should be around 10-20% of the total word count of the Research Limitations section.

the reflecting move focuses on explaining the nature of these limitations and justifying the choices that you made during the research process. This part should be around 60-70% of the total word count of the Research Limitations section.

It is important to remember at this stage that all research suffers from limitations, whether it is performed by undergraduate and master’s level dissertation students, or seasoned academics. Acknowledging such limitations should not be viewed as a weakness, highlighting to the person marking your work the reasons why you should receive a lower grade. Instead, the reader is more likely to accept that you recognise the limitations of your own research if you write a high quality reflecting move. This is because explaining the limitations of your research and justifying the choices you made during the dissertation process demonstrates the command that you had over your research.

We talk about explaining the nature of the limitations in your dissertation because such limitations are highly research specific. Let’s take the example of potential limitations to your sampling strategy. Whilst you may have a number of potential limitations in sampling strategy, let’s focus on the lack of probability sampling ; that is, of all the different types of sampling technique that you could have used [see Types of probability sampling and Types of non-probability sampling ], you choose not to use a probability sampling technique (e.g. simple random sampling. systematic random sampling. stratified random sampling ). As mentioned, if you used a quantitative research design in your dissertation, the lack of probability sampling is an important, obvious limitation to your research. This is because it prevents you from making generalisations about the population you are studying (e.g. Facebook usage at a single university of 20,000 students) from the data you have collected (e.g. a survey of 400 students at the same university). Since an important component of quantitative research is such generalisation, this is a clear limitation. However, the lack of a probability sampling technique is not viewed as a limitation if you used a qualitative research design. In qualitative research designs, a non-probability sampling technique is typically selected over a probability sampling technique.

And this is just part of the puzzle?

Even if you used a quantitative research design, but failed to employ a probability sampling technique, there are still many perfectly justifiable reasons why you could have made such a choice. For example, it may have been impossible (or near on impossible) to get a list of the population you were studying (e.g. a list of all the 20,000 students at the single university you were interested in). Since probability sampling is only possible when we have such a list, the lack of such a list or inability to attain such a list is a perfectly justifiable reason for not using a probability sampling technique; even if such a technique is the ideal.

As such, the purpose of all the guides we have written on research limitations is to help you: (a) explain the nature of the limitations in your dissertation; and (b) justify the choices you made.

In helping you to justifying the choices that you made, these articles explain not only when something is, in theory. an obvious limitation, but how, in practice. such a limitation was not necessarily so damaging to the quality of your dissertation. This should significantly strengthen the quality of your Research Limitations section.

Finally, the forward looking move builds on the reflecting move by suggesting how the limitations you have discuss could be overcome through future research. Whilst a lot could be written in this part of the Research Limitations section, we would recommend that it is only around 10-20% of the total word count for this section.

There are many possible limitations that your research may have faced. However, is not necessary for you to discuss all of these limitations in your Research Limitations section. After all, you are not writing a 2000 word critical review of the limitations of your dissertation, just a 200-500 word critique that is only one section long (i.e. the Research Limitations section within your Conclusions chapter). Therefore, in this first announcing move. we would recommend that you identify only those limitations that had the greatest potential impact on: (a) the quality of your findings; and (b) your ability to effectively answer your research questions and/or hypotheses.

Having identified the most important limitations to your dissertation in the announcing move. the reflecting move focuses on explaining the nature of these limitations and justifying the choices that you made during the research process. This part should be around 60-70% of the total word count of the Research Limitations section.

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• How to Write a Thesis or Dissertation Conclusion

How to Write a Dissertation Conclusion | Checklist and Examples

Published on 9 September 2022 by Tegan George and Shona McCombes. Revised on 10 October 2022.

The conclusion is the very last part of your thesis or dissertation . It should be concise and engaging, leaving your reader with a clear understanding of your main findings, as well as the answer to your research question .

In it, you should:

• Clearly state the answer to your main research question
• Summarise and reflect on your research process
• Make recommendations for future work on your topic
• Show what new knowledge you have contributed to your field
• Wrap up your thesis or dissertation

Table of contents

Discussion vs. conclusion, how long should your conclusion be, step 1: answer your research question, step 2: summarise and reflect on your research, step 3: make future recommendations, step 4: emphasise your contributions to your field, step 5: wrap up your thesis or dissertation, full conclusion example, conclusion checklist, frequently asked questions about conclusion sections.

While your conclusion contains similar elements to your discussion section , they are not the same thing.

Your conclusion should be shorter and more general than your discussion. Instead of repeating literature from your literature review , discussing specific research results , or interpreting your data in detail, concentrate on making broad statements that sum up the most important insights of your research.

As a rule of thumb, your conclusion should not introduce new data, interpretations, or arguments.

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Depending on whether you are writing a thesis or dissertation, your length will vary. Generally, a conclusion should make up around 5–7% of your overall word count.

An empirical scientific study will often have a short conclusion, concisely stating the main findings and recommendations for future research. A humanities topic or systematic review , on the other hand, might require more space to conclude its analysis, tying all the previous sections together in an overall argument.

Your conclusion should begin with the main question that your thesis or dissertation aimed to address. This is your final chance to show that you’ve done what you set out to do, so make sure to formulate a clear, concise answer.

• Don’t repeat a list of all the results that you already discussed
• Do synthesise them into a final takeaway that the reader will remember.

An empirical thesis or dissertation conclusion may begin like this:

A case study –based thesis or dissertation conclusion may begin like this:

In the second example, the research aim is not directly restated, but rather added implicitly to the statement. To avoid repeating yourself, it is helpful to reformulate your aims and questions into an overall statement of what you did and how you did it.

Your conclusion is an opportunity to remind your reader why you took the approach you did, what you expected to find, and how well the results matched your expectations.

To avoid repetition , consider writing more reflectively here, rather than just writing a summary of each preceding section. Consider mentioning the effectiveness of your methodology , or perhaps any new questions or unexpected insights that arose in the process.

You can also mention any limitations of your research, but only if you haven’t already included these in the discussion. Don’t dwell on them at length, though – focus on the positives of your work.

• While x limits the generalisability of the results, this approach provides new insight into y .
• This research clearly illustrates x , but it also raises the question of y .

You may already have made a few recommendations for future research in your discussion section, but the conclusion is a good place to elaborate and look ahead, considering the implications of your findings in both theoretical and practical terms.

• Based on these conclusions, practitioners should consider …
• To better understand the implications of these results, future studies could address …
• Further research is needed to determine the causes of/effects of/relationship between …

When making recommendations for further research, be sure not to undermine your own work. Relatedly, while future studies might confirm, build on, or enrich your conclusions, they shouldn’t be required for your argument to feel complete. Your work should stand alone on its own merits.

Just as you should avoid too much self-criticism, you should also avoid exaggerating the applicability of your research. If you’re making recommendations for policy, business, or other practical implementations, it’s generally best to frame them as ‘shoulds’ rather than ‘musts’. All in all, the purpose of academic research is to inform, explain, and explore – not to demand.

Make sure your reader is left with a strong impression of what your research has contributed to the state of your field.

Some strategies to achieve this include:

• Returning to your problem statement to explain how your research helps solve the problem
• Referring back to the literature review and showing how you have addressed a gap in knowledge
• Discussing how your findings confirm or challenge an existing theory or assumption

Again, avoid simply repeating what you’ve already covered in the discussion in your conclusion. Instead, pick out the most important points and sum them up succinctly, situating your project in a broader context.

The end is near! Once you’ve finished writing your conclusion, it’s time to wrap up your thesis or dissertation with a few final steps:

• It’s a good idea to write your abstract next, while the research is still fresh in your mind.
• Next, make sure your reference list is complete and correctly formatted. To speed up the process, you can use our free APA citation generator .
• Once you’ve added any appendices , you can create a table of contents and title page .
• Finally, read through the whole document again to make sure your thesis is clearly written and free from language errors. You can proofread it yourself , ask a friend, or consider Scribbr’s proofreading and editing service .

Here is an example of how you can write your conclusion section. Notice how it includes everything mentioned above:

V. Conclusion

The current research aimed to identify acoustic speech characteristics which mark the beginning of an exacerbation in COPD patients.

The central questions for this research were as follows: 1. Which acoustic measures extracted from read speech differ between COPD speakers in stable condition and healthy speakers? 2. In what ways does the speech of COPD patients during an exacerbation differ from speech of COPD patients during stable periods?

All recordings were aligned using a script. Subsequently, they were manually annotated to indicate respiratory actions such as inhaling and exhaling. The recordings of 9 stable COPD patients reading aloud were then compared with the recordings of 5 healthy control subjects reading aloud. The results showed a significant effect of condition on the number of in- and exhalations per syllable, the number of non-linguistic in- and exhalations per syllable, and the ratio of voiced and silence intervals. The number of in- and exhalations per syllable and the number of non-linguistic in- and exhalations per syllable were higher for COPD patients than for healthy controls, which confirmed both hypotheses.

However, the higher ratio of voiced and silence intervals for COPD patients compared to healthy controls was not in line with the hypotheses. This unpredicted result might have been caused by the different reading materials or recording procedures for both groups, or by a difference in reading skills. Moreover, there was a trend regarding the effect of condition on the number of syllables per breath group. The number of syllables per breath group was higher for healthy controls than for COPD patients, which was in line with the hypothesis. There was no effect of condition on pitch, intensity, center of gravity, pitch variability, speaking rate, or articulation rate.

This research has shown that the speech of COPD patients in exacerbation differs from the speech of COPD patients in stable condition. This might have potential for the detection of exacerbations. However, sustained vowels rarely occur in spontaneous speech. Therefore, the last two outcome measures might have greater potential for the detection of beginning exacerbations, but further research on the different outcome measures and their potential for the detection of exacerbations is needed due to the limitations of the current study.

Checklist: Conclusion

I have clearly and concisely answered the main research question .

I have summarized my overall argument or key takeaways.

I have mentioned any important limitations of the research.

I have given relevant recommendations .

I have clearly explained what my research has contributed to my field.

I have  not introduced any new data or arguments.

You've written a great conclusion! Use the other checklists to further improve your dissertation.

In a thesis or dissertation, the discussion is an in-depth exploration of the results, going into detail about the meaning of your findings and citing relevant sources to put them in context.

The conclusion is more shorter and more general: it concisely answers your main research question and makes recommendations based on your overall findings.

While it may be tempting to present new arguments or evidence in your thesis or disseration conclusion , especially if you have a particularly striking argument you’d like to finish your analysis with, you shouldn’t. Theses and dissertations follow a more formal structure than this.

All your findings and arguments should be presented in the body of the text (more specifically in the discussion section and results section .) The conclusion is meant to summarize and reflect on the evidence and arguments you have already presented, not introduce new ones.

For a stronger dissertation conclusion , avoid including:

• Generic concluding phrases (e.g. “In conclusion…”)
• Weak statements that undermine your argument (e.g. “There are good points on both sides of this issue.”)

Your conclusion should leave the reader with a strong, decisive impression of your work.

The conclusion of your thesis or dissertation shouldn’t take up more than 5-7% of your overall word count.

The conclusion of your thesis or dissertation should include the following:

• A restatement of your research question
• A summary of your key arguments and/or results
• A short discussion of the implications of your research

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George, T. & McCombes, S. (2022, October 10). How to Write a Dissertation Conclusion | Checklist and Examples. Scribbr. Retrieved 30 August 2023, from https://www.scribbr.co.uk/thesis-dissertation/conclusion/

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