Insights from Learning Machine Operators on Integrating Peer Support and Technology to Minimize Language Barriers and Improve Self-Efficacy

Abstract

Cost cutting measures have led to the elimination of apprenticeships and a focus on single language instruction, which has hindered migrant self-efficacy. A low-cost solution integrates peer support, Google Translate, and ChatGPT to support employee development. A four phased quasi-experimental approach examined 115 questionnaire responses from 28 non-native English speaking machine operators to determine how such an approach facilitates and constrains self-efficacy while exploring potential refinements for future innovation adoption. A thematic analysis uncovered ten major themes indicating that the approach facilitates self-efficacy by boosting problem-solving and providing rapid feedback while constraining self-efficacy via linguistic limitations. Refinements include only using ChatGPT while providing learners with extensive in-depth training to maximize its potential. Thus, the approach provides a relative advantage over alternatives because it is affordable, simple, sustainable, and effective while supporting inclusive training for a growing portion of the population. Shortcomings include the small sample, lack of extensive linguistic representation, and the novelty effect.