Chatbots Mitigate Help-Seeking Avoidance

This research explores the transformative role of large language model (LLM) chatbots in fostering help-seeking behaviors among introverted and underrepresented students in educational settings. Despite the importance of help-seeking for academic success, certain groups exhibit avoidance behaviors due to self-presentation concerns and stereotype threats. By integrating LLM chatbots as non-judgmental, anonymous teaching assistants in an introductory information systems course, the study demonstrates how technology can mitigate these behaviors and promote inclusivity. The chatbots provided real-time, unbiased support, significantly enhancing the students' willingness to engage in help-seeking behaviors without fear of judgment. This practical application of chatbots not only supports academic achievement but also offers broader societal implications for equity in education. The findings suggest that LLM chatbots can effectively bridge gaps in educational resources, making them a valuable tool in addressing systemic barriers in academic environments.