School of Psychology – Ph.D. Dissertation Proposal Meeting

Date: Friday, November 14th, 2025

Time: 12:00 pm - 2:00 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: Workers are rapidly integrating generative artificial intelligence (genAI) into their workflow (Brachman et al., 2024; Dwivedi et al., 2023). Yet, despite growing use of this technology, it remains unclear whether genAI use enhances job performance — and, more importantly, for whom. Cognitive ability is the single strongest predictor of job performance (Gottfredson, 1997; Schmidt & Hunter, 1998). However, the widespread application of genAI creates the potential for hybrid intelligence (human cognition augmented by artificial intelligence; Akata et al., 2020), which may alter established cognitive ability-performance relationships. To address this possibility, the proposed dissertation adopts an aptitude-treatment interaction (ATI) approach (Snow, 1989) to test whether cognitive ability, among other performance predictors, functions as an aptitude that shapes the effectiveness of genAI use on open-ended problem-solving performance. The study will be a laboratory experiment that uses a sample of Georgia Tech undergraduates. Before the study, participants will complete an online survey with measures of creative problem-solving self-efficacy, Typical Intellectual Engagement (TIE), genAI-use frequency, attitudes toward genAI-assistance, and genAI self-efficacy. During the in-person laboratory session, participants will complete four cognitive ability tests and then work on creative problem-solving tasks with and without access to genAI. For each task, participants will read a scenario and prepare a short PowerPoint presentation of their solution. Results will determine whether genAI: 1) provides a uniform boost, such that individuals of all ability levels benefit equally from its assistance, 2) operates as an equalizer, disproportionately supporting lower-ability individuals and thereby narrowing performance gaps, or 3) amplifies existing differences such that higher-ability individuals accrue a cumulative advantage.