Access to the primary labor market for people with cognitive impairments is hampered by barriers, notably the lack of workplace information in Easy Language (EL). Producing such texts is time- and cost-intensive and requires specialized translators. The project STARK-LS (Strengthening participation in the primary labor market through AI-generated Easy Language) addresses this gap by implementing an AI-translation tool to translate workplace materials into EL and embedding the approach in internships for people with cognitive impairments. An interdisciplinary project team conducts mixed-methods evaluations by testing the EL translations for applicability, comprehensibility, and acceptance using lab-based eye-tracking and questionnaire studies, qualitative interviews with interns with cognitive impairments and experts for EL, and a quantitative online survey with company representatives. The findings will lead to process models and best-practice recommendations for companies and rehabilitation agencies. The project advances scientific understanding of the perceived usefulness and potential barriers of EL in organizational contexts, while critically evaluating AI’s influence on the diffusion of high-quality EL texts in companies. Funded by the German Federal Ministry of Labour and Social Affairs, the project aims to scale high-quality accessible communication and promote sustainable inclusion in the German labor market.

An appropriate level of human-robot trust is widely acknowledged as a crucial factor for successful human-robot interaction (HRI). Previous research has addressed anthropomorphic design but mostly neglected linguistic framing as a means to influence trust by altering the perceived human-likeness of a robot. Similarly, time-dependant patterns of trust and distrust during an HRI have rarely been investigated, hindering the development of a coherent theoretical framework on framing effects, their formation conditions, and their relation to trust and distrust evolution in the HRI context. A previous online study suggested that linguistic framing modulates inexperienced factory workers’ trust in a collaborative robot at the workplace. This article presents a follow-up study with a sample of students and an realistic HRI setting. Despite using similar framing stimuli, the study failed to replicate significant framing effects on trust, suggesting their context-sensitivity and dependence on individual characteristics of the recipients of the framing. Additionally, a significant increase in trust and decrease in distrust during the HRI highlights the need for time-dependant considerations. The results call for a more fine-grained investigation of linguistic framing effects as well as of trust and distrust evolution in order to develop a comprehensive theoretical framework applicable to the HRI context.