Work

Impact of artificial intelligence on task performance and perceived task load: a pragmatic randomized experiment

Year: 2026

Type: article

Abstract: Introduction This study aimed to assess the impact of artificial intelligence (AI) assistance on immediate task performance and evaluate perceived task load and AI acceptance among dental interns in an educational setting. Methods A pragmatic experiment was conducted among 132 dental interns during the 2024–2025 academic year. Participants were randomly allocated to either an AI-assisted group ( n = 67) or a baseline knowledge control group ( n = 65) to complete a 15-question quiz based on pediatric orthodontic cases. Perceived task load was measured using the National Aeronautics and Space Administration Task Load Index. AI acceptance was assessed using the Technology Acceptance Model (TAM). Task performance (quiz scores), task load, and AI acceptance were analyzed using Wilcoxon rank-sum tests and an adjusted generalized regression model. Results The AI-assisted group achieved higher task performance scores (median, 13 vs. 11; p < 0.0001) and lower perceived task load scores (median, 21.7 vs. 41.7; p < 0.0001) than the control group. The AI-assisted group had 1.67 times higher odds of answering a question correctly compared to controls in the adjusted model. Responses to the TAM demonstrated high levels of perceived usefulness, perceived ease of use, and behavioral intention (Cronbach’s α = 0.92–0.95). Conclusion The AI-assisted group demonstrated improved immediate task performance and reduced perceived task load compared to the control group. This study serves as a preliminary step toward understanding how AI tools can support clinical learning and decision-making processes in educational settings.

Source: Frontiers in Education

DOI: 10.3389/feduc.2026.1754136

Authors: Ghalia Y. Bhadila, Dania Bahdila, Nujud O. Saber, Dana A. Alyafi

Institution: King Abdulaziz University

Language: English

Topic: Artificial Intelligence in Healthcare and Education

Subfield: Health Informatics

Field: Medicine

Domain: Health Sciences

SDGs: Quality education, Good health and well-being

Open Access status: gold

Estimated APC paid: $2,353