“Lēš byiḥčīš?” (“Why don’t you speak?”) was uttered by a participant during fieldwork while attempting to interact with a conversational agent. This study investigates communication frustration in human–AI interaction and its relationship with technological linguistic inequality in low-resource language varieties. It asks how speakers of underrepresented varieties experience conversational AI systems that are primarily trained in dominant languages or standardized varieties. The analysis focuses on interactions involving speakers of Palestinian Arabic, a variety that remains largely underrepresented in digital language infrastructures.
The study is based on an observational experiment involving 150 speakers aged 20–30 living in Israel. Participants were observed while interacting with conversational AI through smartphones, computers, and domestic smart devices. Tasks consisted of simple information requests such as asking for the nearest bus stop or pharmacy. Prior to the experiment, participants were asked whether they typically interacted with conversational agents in Arabic and to evaluate their overall experience. Initial perceptions were largely positive.
During the experiment, participants were instructed to formulate their requests in their local dialect. The outcomes diverged sharply from initial expectations. Responses rarely appeared in the same variety. Instead, conversational agents frequently replied in other languages—Hebrew, due to geolocation—or in unrelated Arabic varieties such as Lebanese or Saudi Arabic, or hybrid forms. When requests were repeated or elaborated, interactions frequently failed: in 23% of cases the service stopped responding, while in 18% it returned incorrect information associated with locations outside the local context. These interactional breakdowns generated frustration among participants.
Results highlight technological linguistic inequality, reflecting limited representation of Palestinian Arabic in the speech resources that underpin conversational AI systems. The study examines the structural causes of these failures and proposes strategies to improve the performance of conversational AI in low-resource varieties, with implications for technology developers and emerging linguistic markets.