Work-related learning in the AI era: the influence of formal and informal learning on employee attitudes toward artificial intelligence
Purpose Limited research has examined how employees’ broader participation in work-related learning relates to their attitudes toward artificial intelligence (AI). This study aims to examine how formal and informal work-related learning predicts positive and negative attitudes toward AI and whether these relationships differ across traditional, remote and hybrid work settings. Design/methodology/approach A quantitative, survey-based design was used to collect data from 252 full-time employees in the USA working in traditional, remote and hybrid settings. Relationships were analyzed using partial least squares structural equation modeling, with multigroup analyses used to assess differences across work settings. Findings Formal work-related learning positively predicted both positive and negative attitudes toward AI. Informal work-related learning positively predicted positive attitudes toward AI, but was not significantly related to negative attitudes toward the technology. These relationships did not differ significantly across traditional, remote and hybrid work settings. Practical implications The findings demonstrate that employees’ broader engagement in work-related learning influences their perceptions of AI. Formal learning influences both favorable and unfavorable attitudes, which highlights the importance of developing employee capabilities while addressing concerns surrounding AI. Informal learning influences positive attitudes and may complement structured development through everyday workplace learning experiences. Originality/value This study extends organizational learning research into the context of workplace AI by examining whether employees’ broader engagement in formal and informal learning predicts both positive and negative attitudes toward AI. The inclusion of work setting further establishes whether these relationships differ across contemporary work environments.
Authors
- Joshua Ray
- Kelsey Metz (ORCID: https://orcid.org/0000-0002-5656-8717)
- Lisa Blair-Cox
Institutions
- Lincoln Memorial University (US)
Publication Details
- Journal
- SAM Advanced Management Journal
- Published
- 2026-09-21
- DOI
- https://doi.org/10.1108/samamj-02-2026-0018
- Primary Topic
- Ethics and Social Impacts of AI
- Type
- article
- Field-Weighted Citation Impact
- 0.00