Statistics problem: Comparing written work with & w/out use of AI

  • #1
TULC
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I want to compare performance on written work under different conditions, for example with and without the use of AI, according to some specified criteria. Assume the written work is a critical analysis of specific content.

The written work will be scored on a number of dimensions, such as creativity etc. The goal is to gain some understanding - based on a large sample of written samples - of the extent to which AI can improve the written work. This will be a way to develop a benchmark against which we can compare individual written samples. If the correlation b/w individual written work with and w/out use of AI is sig. weaker than expected based on the analysis of a larger sample of written work, then one could argue that this warrants a question: is AI being overused by the individual?

Given the above, would calculating correlation coefficients be a good choice here? I want something simple that can be used with ease by almost anyone. At the same time, I acknowledge the fact that I am manipulating some variables, so a correlational approach may not be ideal. If so, what alternatives, if any, would you suggest?
 
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  • #2
You might have more luck posting this in the statistics group. The moderators might move it if you ask them. (If you post it there yourself they will complain about a duplicate.) You can make this request by hitting the "Report" button and typing it in.
 
  • #3
Thread closed for Moderation...
 
  • #4
TULC said:
I want to compare performance on written work under different conditions, for example with and without the use of AI, according to some specified criteria.
This is not allowed at PF for two reasons: first, we don't allow discussion of personal research; and second, we don't allow discussions based on AI-generated content.

Thread will remain closed.
 
  • #5
Hornbein said:
You might have more luck posting this in the statistics group.
Not this topic, no. See previous post.
 

1. What are the key statistical methods used to compare written work with and without the use of AI?

To compare written work with and without the use of AI, several statistical methods can be employed. Commonly, t-tests or ANOVA are used to compare means of metrics such as word count, readability scores, or error rates between two or more groups. Regression analysis might also be used to control for additional variables, such as the writer's experience or the topic's complexity. Additionally, qualitative analyses like thematic analysis might be used to assess differences in content or style.

2. How can you ensure the validity and reliability of your findings in such a study?

Ensuring validity and reliability involves several steps. First, it's crucial to have a clear operational definition of what constitutes "AI-assisted" and "non-AI-assisted" writing. Second, using a randomized controlled trial design can help establish causality by minimizing selection bias. Third, reliability can be improved by using standardized assessment tools and having multiple raters evaluate the written work. Lastly, validating the results through replication studies or cross-validation with different datasets can also strengthen the findings.

3. What are the ethical considerations when comparing written work with and without AI?

There are several ethical considerations in such comparisons. Privacy concerns arise if personal data are used or if the AI has access to sensitive information. Consent is also crucial; participants should be informed about the extent of AI involvement in the study. Additionally, there's the risk of bias—AI tools might not be equally effective across different languages, demographics, or content types, potentially leading to skewed results. Addressing these biases and ensuring fairness is essential.

4. How can AI influence the quality of written work?

AI can influence the quality of written work in various ways. It can improve grammar, spelling, and punctuation through automated corrections. AI tools like predictive text and content suggestions can aid in idea generation and coherence, potentially enhancing the flow and structure of writing. However, over-reliance on AI might stifle creativity or result in homogenization of writing style. The impact of AI largely depends on how it's integrated into the writing process and the writer's adaptability to these tools.

5. What are common metrics used to evaluate the effectiveness of AI in writing?

Common metrics for evaluating AI's effectiveness in writing include quantitative measures like error reduction rate, time spent on writing tasks, and improvements in readability scores. Qualitative metrics might involve assessing creativity, argument strength, or user satisfaction. Surveys and feedback from both writers and readers can also provide insights into the perceived impact of AI on the writing quality and the writing process itself.

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