Intervention description {11a}
In the intervention group, the computer screen will be set up in a split-screen format. On the left side of the screen, the participant will receive instructions on the writing prompt, writing requirements, time requirements, grading feedback, and the grading rubric. The instructions will also highlight to the participant that they can use ChatGPT in any way they like to assist their writing, and there is no penalty in their writing score for how ChatGPT is used. The right side of the screen will display a blank ChatGPT interface where the participant can prompt questions and receive answers.
Explanation for the choice of comparators {6b}
In the control group, as in the intervention group, the computer screen will be set up in a split-screen format. On the left side of the screen, the participant will receive the same instructions on the writing prompt, writing requirements, time requirements, grading feedback, and the grading rubric. Additionally, the instructions will highlight to the participant that they can use a text editor in any way they like to assist their writing. On the right side, instead of ChatGPT, a basic text editor interface will be displayed. In summary, this comparator will keep the split-screen format consistent between the two groups and ensure that participants in the control group will complete the writing task with minimal support.
Criteria for discontinuing or modifying allocated interventions {11b}
This study is of minimal risk, and we do not anticipate needing to discontinue or modify the allocated interventions during the experiment. Participants can withdraw from the study at any time.
Strategies to improve adherence to interventions {11c}
Adherence to the interventions will be high because the procedures are straightforward and will be clearly explained in the step-by-step instructions on the computer screen. The participant will be alone in a noise-canceling room during the entire experiment. The participant can reach out to the experimenter through an intercom if they need any clarification.
Relevant concomitant care permitted or prohibited during the trial {11d}
Not applicable. This is not a clinical study.
Provisions for post-trial care {30}
Not applicable. This is a minimal-risk study.
Outcomes {12}
The study has two primary outcomes. First, we will measure participants’ writing performance scores on the analytical writing task. The task is adapted from the Analytical Writing section in the GRE, a worldwide standardized computer-based exam developed by the Educational Testing Service (ETS) [27]. Participants’ writing performance will be scored using the GRE 0–6 rubric and by an automatic essay-scoring platform called ScoreItNow!, which is powered by ETS’s e-rater engine [32, 33]. We chose to adapt from the GRE writing materials for two reasons. First, their writing task and grading rubrics were established writing materials designed to measure critical thinking and analytical writing skills and have been used in research as practice materials for writing (e.g. [34]). Second, OpenAI’s technical report shows that ChatGPT (GPT-4) can score 4 out of 6 (~ 54th percentile) on the GRE analytical writing task [31]. This gives us a benchmark for assessing the potential increase in writing performance when individuals collaborate with generative AI.
Second, we will measure participants’ cognitive effort during the writing process. Participants’ cognitive effort will be measured using a psychophysiological proxy—i.e., changes in pupil size [35, 36]. Pupil diameter and gaze data will be collected using the Tobii Pro Fusion eye tracker at a sampling rate of 120 Hz. During the preparation stage of the study, the room light will be adjusted so that the illuminance at the participants’ eyes is at a constant value of 320 LUX. Baseline pupil diameters will be recorded during a resting task in the experiment preparation stage that asks the participant to stare at a cross that will appear for 10 s each on the left, center, and right sections of the computer screen. Pupil diameters and gaze data will be recorded throughout the writing process.
The study has several secondary outcomes. First, to identify the neural substrates of cognitive effort during the writing process, we developed an additional psychophysiological proxy, changes in the cortical hemodynamic activity in the frontal lobe of the brain. Specifically, we will examine hemodynamic changes in oxyhemoglobin (HbO). Brain activity will be recorded throughout the writing process using the NIRSport 2 fNIRS device and the Aurora software with a predefined montage (Fig. 2). The montage consists of eight sources, eight detectors, and eight short-distance detectors. The 18 long-distance channels (source-detector distance of 30 mm) and eight short-distance channels (source-detector distance of 8 mm) are located over the prefrontal cortex (PFC) and supplementary motor area (SMA) (Fig. 2). The PFC is often involved in executive function (e.g., cognitive control, cognitive efforts, inhibition) [37, 38]. The SMA is associated with cognitive effort [39, 40]. The sampling rate of the fNIRS is 10.2 Hz. Available fNIRS cap sizes are 54 cm, 56 cm, and 58 cm. The cap size selected will always be rounded down to the nearest available size based on the participant’s head measurement. The cap is placed on the center of the participant’s head based on the Cz point from the 10–20 system.
Design of the fNIRS montage
Third, we will measure participants’ subjective perceptions of the writing task by self-reported survey measures in the post-survey (Table 1). We will measure participants’ subjective perceptions of the two primary outcomes—that is, their self-perceived writing performance and self-perceived cognitive effort. Self-perceived writing performance will be measured with a one-item scale using the same grading rubric described in the instructions for their writing task and used in the scoring tool. Self-perceived cognitive effort will be measured using a one-item scale adapted from the National Aeronautics and Space Administration-task load index (NASA-TLX) [41, 42]. We will also measure participants’ subjective perceptions of several mental health and learning-related outcomes, including stress, challenge, and self-efficacy in writing. Self-perceived stress will be measured using a one-item scale adapted from the Primary Appraisal Secondary Appraisal scale (PASA) [43, 44]. Self-perceived challenge will be measured using a one-item sub-scale adapted from the Primary Appraisal Secondary Appraisal scale (PASA) [43, 44]. Self-efficacy in writing will be measured using a 16-item scale that measures three dimensions of writing self-efficacy: ideation, convention, and self-regulation [45]. Furthermore, we will measure participants’ situational interest in analytical writing using a four-item Likert scale adapted from the situational interest scale [46]. Additionally, we will measure participants’ behavioral intention to use ChatGPT in the future for essay writing tasks [47].
Participant timeline {13}
The time schedule is provided via the schematic diagram below (Fig. 3). The entire experiment will last for approximately 1–1.5 h for each participant.
Schedule of enrollment, interventions, and assessments of the study
Sample size {14}
To estimate the required sample size, we conducted a simulation analysis on the intervention effect on writing performance using ordinary least squares (OLS) regression. Recent empirical evidence suggests that the effect size of generative AI on writing tasks ranges around Cohen’s d = 0.4–0.5, such as [1, 48]. In our simulation analysis, the simulated data assumes normally distributed data, equal and standardized standard deviations between the two conditions, and an anticipated effect size of Cohen’s d = 0.45. In the end, our analysis indicated that recruiting a minimum of 160 participants would be necessary to achieve a statistical power greater than 0.8 under an alpha level of 0.05. The simulation was implemented in R, and the corresponding code is available at the Open Science Framework (OSF) via https://osf.io/9jgme/.
We opt to base our sample size estimation on writing performance, but not on the other primary outcome, cognitive effort, for two reasons. First, the effect of generative AI on performance outcomes has been studied [1, 48], but we did not find prior evidence on the effect size of generative AI on cognitive effort using physiological measures. Second, our physiological measure of cognitive effort may likely be powered once the sample size satisfies our behavioral measure of writing performance. Pupillometry studies on cognitive efforts, such as the N-back test, typically recruit 20–50 participants in short, repeated, within-subject trials (e.g., [49]). These studies provide a general estimation of participants needed. Although our study design (i.e., a between-subject RCT) differs from common pupillometry studies, cognitive effort is still a repeated outcome measure using time series pupil data throughout the entire writing process. Repeated outcome measures generally can enhance statistical power by taking into account within-subject variability [50].
Recruitment {15}
The recruitment will follow a convenience sampling strategy. To aim for a student population with diverse academic backgrounds, participants will be recruited broadly through social media platforms, email lists, and flyers at the research university where the experiment will be conducted. Given that the experiment will start during the summer, the research team can recruit summer school students as participants. Thus, the study sample will not be limited to the students presently at the university. The recruitment materials include a brief description of the study, the eligibility criteria for participation, and the compensation for participation. Individuals who are interested in participation can sign up on a calendar by selecting available time slots provided by the experimenters. Participants will receive 30 euros in compensation upon completion of the experiment. Participants who withdraw in the middle of the experiment will receive partial compensation, prorated based on the amount of time they spend in the experiment.
