AI detection and writing toolsfor academic work

1. Introduction

The rapid advancement of artificial intelligence has fundamentally transformed the landscape of academic research. Furthermore, the integration of machine learning methodologies facilitates unprecedented analytical capabilities. Our lab measured the effect across three cohorts between 2021 and 2023.

It is imperative to acknowledge the multifaceted implications of these emergent technologies. The results surprised us — the second cohort behaved nothing like the first. This paradigm shift necessitates a comprehensive reevaluation of established frameworks.

2. Method

We recruited 214 undergraduate students across three cohorts and collected their written assignments over two semesters. Each submission was anonymised before analysis, and two raters coded every essay independently. Moreover, the proposed framework leverages state-of-the-art techniques to ensure robust and reliable outcomes.

3. Results

Scores in the second cohort rose by a third between the first and the last assignment, while the other two cohorts stayed close to their starting point. We return to this difference in the discussion, where we compare the three groups' writing habits week by week.

Table 2 lists the mean score of every assignment by cohort, with the number of essays behind each value. Missing essays were left out rather than imputed, so the later columns rest on slightly fewer submissions than the first.