Key points are not available for this paper at this time.
Recent advancements in Generative AI (GenAI) and Large Language Models (LLMs) have reshaped the potential of AI in education. This study introduces an AI-Mediated Communication (AI-MC) framework designed to enhance mentor-mentee interactions through adaptive AI-driven conversation structuring, content recommendations, and linguistic feedback. A mixed-methods study was conducted using Self-Determination Theory (SDT) and Design Thinking, combining quantitative user surveys (N = 33) and qualitative interviews. Key findings show that the “Suggesting Content using Conversational Context” feature significantly enhanced intrinsic motivation (r = .389, p = .025) and perceived competence (r = .458, p = .007), supporting users’ confidence and autonomy in communication. The platform effectively supported competence and autonomy, although relatedness was rated moderately, reflecting the limitations of text-only interaction. Qualitative data reinforced that AI-MC features facilitated clearer communication and more informed mentor selection. These findings underscore the potential of AI-MC as an adaptive communication tool for mentoring, with applications that could extend to intelligent tutoring, professional coaching, and online learning platforms. Future research should aim to integrate real mentors, utilize multimodal AI communication, and offer long-term relationship support to enhance both ecological validity and relational depth further.
Wang et al. (Mon,) studied this question.