Therapeutic Potential of Social Chatbots in Alleviating Loneliness and Social Anxiety: Quasi-Experimental Mixed Methods Study

Abstract

Background: Artificial intelligence (AI) social chatbots represent a major advancement in merging technology with mental health, offering benefits through natural and emotional communication. Unlike task-oriented chatbots, social chatbots build relationships and provide social support, which can positively impact mental health outcomes like loneliness and social anxiety. However, the specific effects and mechanisms through which these chatbots influence mental health remain underexplored. Objective: This study explores the mental health potential of AI social chatbots, focusing on their impact on loneliness and social anxiety among university students. The study seeks to (i) assess the impact of engaging with an AI social chatbot in South Korea, "Luda Lee," on these mental health outcomes over a 4-week period and (ii) analyze user experiences to identify perceived strengths and weaknesses, as well as the applicability of social chatbots in therapeutic contexts. Methods: A single-group pre-post study was conducted with university students who interacted with the chatbot for 4 weeks. Measures included loneliness, social anxiety, and mood-related symptoms such as depression, assessed at baseline, week 2, and week 4. Quantitative measures were analyzed using analysis of variance and stepwise linear regression to identify the factors affecting change. Thematic analysis was used to analyze user experiences and assess the perceived benefits and challenges of chatbots. Results: A total of 176 participants (88 males, average age=22.6 (SD 2.92)) took part in the study. Baseline measures indicated slightly elevated levels of loneliness (UCLA Loneliness Scale, mean 27.97, SD (11.07)) and social anxiety (LiebowitzSocial Anxiety Scale, mean 25.3, SD (14.19)) compared to typical university students. Significant reductions were observed as loneliness decreasing by week 2 (t175=2.55, P =.02) and social anxiety decreasing by week 4 (t175=2.67, P =.01). Stepwise linear regression identified baseline loneliness (beta=0.78, 95% CI 0.67 to 0.89), self-disclosure (beta=-0.65, 95% CI -1.07 to -0.23) and resilience (beta=0.07, 95% CI 0.01 to 0.13) as significant predictors of week 4loneliness (R2=0.64). Baseline social anxiety (beta=0.92, 95% CI 0.81 to 1.03) significantly predicted week 4 anxiety (R2=0.65). These findings indicate higher baseline loneliness, lower self-disclosure to the chatbot, and higher resilience significantly predicted higher loneliness at week 4. Additionally, higher baseline social anxiety significantly predicted higher social anxiety at week 4. Qualitative analysis highlighted the chatbot's empathy and support as features for reliability, though issues such as inconsistent responses and excessive enthusiasm occasionally disrupted user immersion.