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Introduction
Voice assistants (VAs) are software programs that use artificial intelligence (AI), natural language processing, speech recognition and large language models to provide information, perform various tasks, engage in conversations with users and generate responses based on text or speech inputs (Terzopoulos and Satratzemi, 2020). They offer flexible features, allowing users to switch the default voice interface by language, accent and gender or choose the preferred voice and gender while setting up the device (Bilal and Barfield, 2021a). These features have intrigued researchers’ interest in exploring voice-switching behavior as a new form of interaction behavior with VAs. Nonetheless, we still know very little about this behavior, especially from young adult users’ (i.e. college students) perspectives.
VAs have been implemented in many disciplines, including education, to enhance teaching and learning and provide students access to coursework, augment autonomous and personalized learning, and promote computational thinking (Al Shamsi et al., 2022). Students learn to use VAs through trial and error or other experiential experiences. Eliciting young adults “perceptions of VAs” information accuracy, intelligence, trust and usefulness. Eliciting the students’ perceptions will unveil their VAs’ literacy knowledge and skills and whether interventions are needed to enable them to use these devices effectively and efficiently.
Studies revealed that users assign VAs anthropomorphic attributes (Calahorra-Candao and Martín-de Hoyos, 2024), treat them as humans (Ki et al., 2020; Seymour and Van Kleek, 2021; Pitardi and Marriott, 2021), personify them by ascribing friendship (Lopatovska et al., 2019; Pradhan et al., 2019; Schweitzer et al., 2019; Wienrich et al., 2021), trust (Girouard-Hallam and Danovitch, 2022; Seymour and Van Kleek, 2021; Wienrich et al., 2021) and personality traits (Bilal and Barfield, 2021a; Lopatovska and Williams, 2018; Snyder et al., 2023). Users also prefer their VA’s voice to match their gender, language and accent (e.g. Bilal and Barfield, 2021b; Brahnam and De Angeli, 2021). In the case of chatbots and embodied voice interfaces (EVIs), users prefer the characteristics of the agents EVIs to match their race and ethnicity (e.g. Bilal and Barfield, 2021b; Bonfert et al., 2021; Liao and He, 2020).
With the growth of Generative AI (GenAI), such as ChatGPT and similar tools, VAs are increasing in sophistication by performing more conversational than transactional tasks...