AI-enabled sign language interpretation in E-learning: A structural modelling of the perspectives of African sign language interpreters
| dc.contributor.author | Adigun, Olufemi Timothy | |
| dc.date.accessioned | 2026-06-18T13:54:21Z | |
| dc.date.available | 2026-06-18T13:54:21Z | |
| dc.date.issued | 2026 | |
| dc.description | In this study, future perspective (FP) was used to signal both intention and behaviour of professional Sign Language Interpreters from selected Sub-Sahara African countries about AI-enabled sign language interpretation while PB, PR, and PT, and CI were shaped by the position of PU and PE of the TAM model. By implication, this quantitative study which examined the professional interpreters’ perspectives on AI-enabled SLI for e-learning extends the Technology Acceptance Model (TAM). Although studies have shown that the role of attitude and culture towards emerging technologies cannot be overlooked (Kao & Sapp, 2022; Emon & Khan, 2025; Brauner et al., 2024), but the mediating effects of cultural influence on the PB, PR, and PT regarding future perspectives of AI-enabled SLI in Africa are yet to be ascertained in existing studies. Thus, this current study bridges the identified research gap by structurally modelling African SLIs’ future perception (FP) of AI-enabled SLI through CI, PB, PR, and PT. It is believed that the outcome of this study will contribute significantly to academic discourses on issues that concerns professional interpreters’ perspectives on AI-enabled SLI for e-learning. Findings obtained in the study will positively re-orientate the perceptions of African SLIs and clarify the implications of cultural interference in the use of AI-enabled SLI for students who are Deaf or hard of hearing. Lastly, the findings of this study will critically shape policy issues and pedagogical practice that advance the adoption of AI for equitable learning opportunities in virtual learning environments across the African continent. | |
| dc.description.abstract | The development of artificial intelligence-enabled sign language interpretation (AIenabled SLI) is changing the dynamics in the sign language interpreting space and there have been various arguments for and against the use of AI-enabled SLI, especially for academic use. Leveraging on the importance of artificial intelligence for persons who are Deaf or hard of hearing, this study brought to forward the perspectives of professional sign language interpreters from Sub-Saharan Africa about AI-enabled SLI. Framed by the technology acceptance model, a total of 412 sign language interpreters from 12 African countries participated in the study and responded to the e-questionnaire. The survey responses were analysed using structural equation modelling. The findings revealed that perceived benefits (PB), perceived risks (PR) and perceived trust (PT) had a significant positive relationship with future perspectives (FP) about AI-enabled SLI. It was also found that while PB had a negative but direct relationship with cultural influence (CI), PT was found to be positively significant with CI. This study could not establish any significant mediative influence of CI in the relationship between PB, PR, PT and FP about AI-enabled SLI among African SLIs. Therefore, CI has not been found to have any influence on FP about AI-enabled SLI among African SLIs. Based on the findings, appropriate recommendations were made | |
| dc.identifier.uri | https://link.springer.com/article/10.1007/s10639-026-14031-y | |
| dc.identifier.uri | http://hdl.handle.net/11070/4256 | |
| dc.language.iso | en | |
| dc.publisher | Springer Nature | |
| dc.subject | Namibia | |
| dc.subject | University of Namibia | |
| dc.subject | Artificial intelligence-enabled sign language interpretation | |
| dc.subject | Students who are Deaf or hard of hearing | |
| dc.subject | E-learning | |
| dc.subject | Sign language interpreters | |
| dc.title | AI-enabled sign language interpretation in E-learning: A structural modelling of the perspectives of African sign language interpreters | |
| dc.type | Article |