The Incorporation of AI-Driven Study Support Tools to Promote Academic Self-Efficacy and Reduce Procrastination Behaviors in STEM Students

Abstract

The current research examines how artificial intelligence (AI) devices that facilitate study aided academic self-efficacy and procrastination behaviors among students in science, technology, engineering, and mathematics (STEM).  In a quasi-experimental design, 182 students enrolled in undergraduate programs from a large public university in Pakistan were assigned at random to either the treatment group (n = 91) who used AI devices for study, or control group (n = 91) using traditional techniques of study. The treatment programs lasted twelve weeks. Academic self-efficacy and procrastination were assessed by validated scales pre- and post-test. Results showed that students in the treatment group gained in self-efficacy significantly (from 3.64 to 4.32) whereas procrastination decreased significantly (from 2.73 to 2.18). Effect sizes were moderate to large (η² = 0.079 self-assess, η² = 0.064 procrastination). Control students exhibited slight changes in the variables. Mediation analyses indicated that self-efficacy mediated significantly the relation of the use of the AI devices with diminution in procrastination, showing significant indirect effect (β = -0.231, p < .001) with moderate effect size (η² = 0.15). AI learning tools can facilitate the creation of a more confident, motivated self-regulated workforce in STEM initiatives, thus lessening educational disparities and enhancing national innovation capabilities through better educational outcomes for students.