Name: Rebecca Storey 

School of Psychological and Brain Sciences – Ph.D. Dissertation Defense Meeting

Date: Thursday, November 12th, 2026

Time: 4:00 pm - 6:00 pm 

Teams link: Join here 

Dissertation Chair/Advisor: 

Phillip L. Ackerman, Ph.D. (Georgia Institute of Technology)

Dissertation Committee Members: 

Chris Wiese, Ph.D. (Georgia Institute of Technology)

Sashank Varma, Ph.D. (Georgia Institute of Technology)

Richard Catrambone, Ph.D. (Georgia Institute of Technology)

Chris Dede, Ph.D. (Harvard University)

Title: Creative Problem-Solving with Generative AI: An Examination of Aptitude-Treatment Interactions

Abstract: Organizations urge their employees to use generative artificial intelligence (AI) despite uncertainty about how it interacts with individual differences to influence job performance. A key individual difference linked to performance is cognitive ability; however, generative AI creates the potential for hybrid intelligence—human cognition augmented by AI—that may alter ability-performance relationships. To explore this possibility, I adopted an aptitude-treatment interaction (ATI) approach to test whether generative AI acts as a treatment that modifies the influence of cognitive and motivational aptitudes on creative problem-solving (CPS) performance. A sample of 111 undergraduate students participated in a three-part study that consisted of: 1) a survey measuring motivational traits and readiness to leverage generative AI; 2) cognitive ability tests; and 3) working on CPS tasks without and then with access to ChatGPT. For each task, participants read a scenario and prepared a short PowerPoint presentation of their solution. The results revealed three findings. First, solutions developed in the experimental condition (Internet plus ChatGPT) were rated higher than those in the control condition (Internet-use only), d = .72. Pilot data suggest the CPS tasks are similar in difficulty; however, because the tasks differed across conditions, these ratings cannot support claims that performance improved. Second, while none of the motivational aptitudes predicted performance in either condition, cognitive ability positively predicted performance in the control condition, B = .25, but not in the experimental condition, B = .07. Instead, generative AI readiness predicted performance in the experimental condition, B = .21, though the relationship was small. To better understand what differentiated high from low performance in the experimental condition, I reviewed screen recordings of the top and bottom performers to see how they used ChatGPT. High performers distinguished themselves by correctly anticipating that ChatGPT could adequately complete the task on its own. Those participants typed the full prompt into ChatGPT and then reallocated cognitive resources away from on-task activities (e.g., information gathering, ideation, reasoning) and put them toward self-regulatory activities that ChatGPT could not replace (e.g., managing time, modifying output, designing the presentation, ensuring it aligned with task expectations). This strategy is likely not optimal across all situations. Rather than viewing performance as a two-way aptitude-treatment interaction, performance may be better described as a three-way interaction that includes task complexity. That said, findings from the present study suggest that for relatively straightforward tasks requiring only general knowledge, generative AI may act as an equalizer, disproportionately supporting lower-ability individuals.