This paper explores the design principles underlying AI-powered conversational support systems aimed at fostering student thriving in educational environments. As artificial intelligence increasingly integrates into learning ecosystems, chatbots and virtual assistants are emerging as accessible tools to promote engagement, motivation, and personalized guidance. Drawing on interdisciplinary literature from educational psychology, human-computer interaction, and learning sciences, this study identifies key principles for designing effective AI conversational agents, including empathy simulation, adaptive feedback, goal-oriented dialogue, and transparency in AI behavior. Case examples illustrate how these principles can be operationalized to support academic persistence, emotional well-being, and skill development. The paper concludes with recommendations for future research and implementation strategies to ensure AI tools align with diverse student needs and promote inclusive, ethical learning experiences.

AI-powered learning, conversational agents, student engagement

H. Li, R. Zhang, Y. C. Lee, R. E. Kraut, and D. C. Mohr, “Systematic review and meta-analysis of AI-based conversational agents for promoting mental health and well-being,” NPJ Digital Medicine, Vol. 6, 236, Dec 2023.

X. Lin et al., “Scope, characteristics, behavior change techniques, and quality of conversational agents for mental health and well-being: systematic assessment of apps,” Journal of Medical Internet Research, vol. 25, e45984, July 2023.

A. Graham, M. A. Powell, and J. Truscott, “Facilitating student well-being: relationships do matter,” Educational Research, vol. 58, pp. 366-383, Oct 2016.

S. Nelekar, A. Abdulrahman, M. Gupta, and D. Richards, “Effectiveness of embodied conversational agents for managing academic stress at an Indian University (ARU) during COVID‐19,” British Journal of Educational Technology, vol. 53, pp. 491-511, May 2022.

S. Jeong et al., “Deploying a robotic positive psychology coach to improve college students’ psychological well-being,” User Modeling and User-Adapted Interaction, vol. 33, pp. 571-615. April 2023.

H. Wang, S. Tang, and C. U. Lei, “AI conversational agent design for supporting learning and well-being of university students.” (information needed here)

N. E. Usta, “Designing for student well-being,” (Doctoral dissertation, Massachusetts Institute of Technology). June 2021.

K. H. Ly, A. M. Ly, and G. Andersson, “A fully automated conversational agent for promoting mental well-being: A pilot RCT using mixed methods. Internet interventions,” vol. 10, pp. 39-46, Dec 2017.

B. Inkster, S. Sarda, and V. Subramanian, “An empathy-driven, conversational artificial intelligence agent (Wysa) for digital mental well-being: real-world data evaluation mixed-methods study,” JMIR mHealth and uHealth, vol. 6, e12106, Nov 2018.

J. Narain, T. Quach, M. Davey, H. W. Park, C. Breazeal, and R. Picard, “Promoting wellbeing with Sunny, a chatbot that facilitates positive messages within social groups,” In Extended abstracts of the 2020 CHI conference on human factors in computing systems, pp. 1-8, April 2020.

M. J. O. Osorio, C. Zepeda, and J. L. Carballido, “MyUBot: towards an artificial intelligence agent system chat-bot for well-being and mental health,” In AAI4H@ ECAI, pp. 13-17, 2020.

S. Gabrielli, S. Rizzi, S. Carbone, and V. Donisi, “A chatbot-based coaching intervention for adolescents to promote life skills: pilot study,” JMIR human factors, vol. 7, e16762, Feb 2020.

S. Greer, D. Ramo, Y. J. Chang, M. Fu, J. Moskowitz, and J. Haritatos, “Use of the chatbot “vivibot” to deliver positive psychology skills and promote well-being among young people after cancer treatment: randomized controlled feasibility trial,” JMIR mHealth and uHealth, vol. 7, e15018, Oct 2019.

A. Ghandeharioun, D. McDuff, M. Czerwinski, and K. Rowan, “Emma: an emotion-aware wellbeing chatbot,” In 2019 8th International Conference on Affective Computing and Intelligent Interaction (ACII), pp. 1-7, IEEE, Sept 2019.