Cigarette smoking poses a major threat to public health, contributing to more than 6 million annual preventable deaths worldwide (Reitsma et al., 2017). With the recent advances of technology, a range of digital interventions for smoking cessation have been developed (for a review see Graham et al., 2016). Chatbots – for example - can serve as highly accessible and cost-efficient tools with the potential to facilitate personalized interactions. Despite the advantages of small barriers for smokers to adopt and continuous availability, however, digital interventions often struggle with low engagement rate and high attrition (Saul et al., 2016; Strecher et al., 2008; Richardson et al., 2013). Low engagement is problematic for digital interventions as greater engagement has proved to be a significant predictor of improved outcomes for general health behaviour change and smoking cessation specifically (Bricker et al., 2013; Buller et al., 2014; Cobb & Poirier, 2014; Pike et al., 2007), indicating that engagement plays a crucial role in increasing intervention effectiveness. Therefore, in light of these observations, identifying features that promote engagement with digital interventions is a research priority. Therapeutic relationship is another important factor in longitudinal smoking cessation interventions. therapeutic alliance has demonstrated a positive and consistent relationship with addiction treatment outcomes (Connors et al., 1997; Goldberg et al., 2013; Martin et al., 2000). In digital interventions, a primary challenge is whether a therapeutic alliance, considered a central component of successful intervention, can develop when the process is computerized and human therapists may not be involved (Cook & Doyle, 2004; D’Alfonso et al., 2020). Motivational Interviewing (MI) can be effective in promoting engagement and therapeutic alliance. MI is a client-centred, directive, yet non-confrontational counselling approach to enhancing motivation for change. (Miller & Rollnick, 2013). It is an effective counselling strategy in addictive behaviours (Hettema et al., 2005; Lundahl et al., 2013; Romano & Peters, 2015). It has found increasing prominence as an approach to address engagement difficulties in health promotion and smoking cessation specifically (Carroll et al., 2006; Medley & Powell, 2010). MI-consistent behaviours are also found to predict a stronger therapeutic alliance (Boardman et al., 2006; Torres et al., 2019). MI is a versatile approach. It has been used as additive to other interventions, as a prelude to another treatment where it serves as a preparatory role, and as a stand-alone intervention. A meta-analysis concluded that the effect of MI is most pronounced when it works as a pre-treatment prelude (Lundahl et al., 2010; Burke et al., 2003). These observations suggest the advantages of employing MI as early as possible in an intervention. However, to the best of our knowledge, only one study investigated the effectiveness of integrating MI into a pre-treatment intake session in a clinical program (Carroll et al., 2006), and no research has explored MI in a chatbot setting. Based on the established importance yet lack of engagement and therapeutic alliance in digital smoking cessation interventions, and the effectiveness of MI in addressing such challenges, the aim of this study is to evaluate the effectiveness of integrating MI into chatbot-conducted pre-treatment assessment-feedback sessions in (1) building a therapeutic alliance and (2) enhancing engagement. A primary challenge for a chatbot to integrate MI is to express empathy, a core spirit manifested by reflective listening. Unlike a human counsellor, it is difficult for a chatbot to deeply interpret client’s input. Perceived empathy has been proved as a strong predictor of positive treatment outcome (e.g., Kim et al., 2004; Olson, 1995). Therefore, this study also aims to answer the question that whether a chatbot using reflective listening can indeed be perceived as empathetic. References Boardman, T., Catley, D., Grobe, J. E., Little, T. D., & Ahluwalia, J. S. (2006). 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