The Nexus of Human and Machine: Exploring Perception as a Spur in AI-Powered Employee Training

Abstract

More phased than before, organizations are relying on artificial intelligence (AI) in an increasing capacity to develop and/or train workers. However, we still do not know what leads to the successful performance of AI-based training platforms. This study examines the following dynamics: content quality, personalization, usability, skill development, employee motivation, and organizational support, which are likely to influence perceived platform effectiveness, with employee perception acting as a moderating variable. Data were collected from 300 participants across multiple companies and analyzed using Partial Least Squares Structural Equation Modeling. The results indicate that employees’ perceptions related to the value of training provided via an AI-enabled training platform are positively influenced by relevant information, engaging learning opportunities, perceived development of skills, and motivational factors. The perceived organizational support has a direct, large effect on platform impact, while perceived usability has a small effect on perceived platform impact. More specifically, the data indicate that employee perceptions associated with the platform influence the platform’s features and the effectiveness of the training. Overall, this study has implications for better design toward impactful AI-enabled training systems and contributes to the developing field of AI and organizational learning. It underlines the significance of user-centered approaches and organizational readiness for maximizing training effectiveness.

Received: 12 August 2025 | Revised: 27 November 2025 | Accepted: 9 February 2026

Conflicts of Interest

The authors declare that they have no conflicts of interest to this work.

Data Availability Statement

The data that support the findings of this study are openly available in Zenodo at https://doi.org/10.5281/zenodo.18435557.

Author Contribution Statement

Chanchal Molla: Conceptualization, Methodology, Formal analysis, Resources, Data curation, Writing – original draft, Writing – review & editing, Visualization. Khaled Islam: Conceptualization, Software, Formal analysis, Investigation, Data curation, Writing – original draft, Writing – review & editing, Visualization. Md. Razib Hossain: Conceptualization, Software, Formal analysis, Investigation, Data curation, Writing – original draft, Writing – review & editing, Visualization. Pronoy Kumar Paul: Conceptualization, Software, Validation, Formal analysis, Investigation, Data curation, Writing – original draft, Visualization. Md. Najibul Kabir: Conceptualization, Software, Validation, Formal analysis, Investigation, Resources, Data curation, Writing – original draft, Visualization. Abu Naser Mohammad Saif: Methodology, Investigation, Resources, Writing – original draft, Writing – review & editing, Supervision, Project administration. Rehnuma Mostafa: Methodology, Validation, Resources, Writing – review & editing.