Transforming Collaborative Work: The Future of Adaptive Models in Human-Artificial Intelligence Interaction
Year: 2025
Type: article
Study design: Observational Study
Abstract: This nonexperimental survey-based online quantitative study was conducted to explore how an independent variable, human tasks, affects the dependent variable, AL Models percentage of tasks within the job role influenced by artificial intelligence (AI). The Technology Acceptance Model (TAM) theoretical framework is used to understand and predict dependent variable Al Models, which refers to the degree to which Artificial Intelligence AI will transform daily tasks. The study addresses the existing research gap by exploring areas that have not been sufficiently investigated and understood to improve human tasks and effectively fill a knowledge gap regarding the dynamics of human tasks that AI influences. AI Models in this study are impacted by how human tasks are integrated, representing AI's influence on the job. Different AI Models that include machine learning algorithms, deep learning, or rule-based systems may vary and be influenced by Human Tasks. In the online survey, participants were chosen from nearly every industry that has used AI systems to help with task automation and workload distribution, which makes them perfect subjects for assessing how beneficial and effective people think of Artificial Intelligence in practical situations.
Author: Arslan Isaac
Language: English
Subfield: Information Systems and Management
Field: Decision Sciences
Domain: Social Sciences
Open Access status: diamond
OpenAlex ID: W4409506306