Content Analysis of College Student Perceptions: Mental Health-Related Support from Generative AI Versus Faculty Mentors

Jimin Lee; Si Wang; Alena G Esposito

doi:10.29333/ajqr/16815

Jimin Lee ¹ ^* , Si Wang ², Alena G Esposito ²

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AM J QUALITATIVE RES, Volume 9, Issue 4, pp. 70-100

https://doi.org/10.29333/ajqr/16815

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Abstract

As generative AI tools have become more widely adopted in higher education, understanding how students perceive these resources, especially compared to traditional human mentorship, is critical. This qualitative study applied the Perceptual Bias Activation (PBA) framework to explore how undergraduates interpret mentorship responses identified as either human- or AI- generated in the context of managing mental health challenges and coursework. Using Inductive Content Analysis (ICA) of students’ written explanations, we found that judgments varied. Once students categorized a response as human or AI, their perceptions were filtered through biases influencing interpretations of tone, language, specificity, and empathy. Participants who believed a response was human-authored emphasized authenticity, warmth, and contextual detail, whereas those perceiving it as AI-generated highlighted generic phrasing, impersonal tone, and limited contextualization. Misattributions, where human responses were labeled as AI and vice versa, showed that initial biases, rather than intrinsic message qualities, guided interpretations. While AI responses seen as “human” could be viewed as somewhat caring or helpful, accurate recognition of AI often diminished perceived empathy and usefulness. These findings demonstrate that perceptual biases shape how students engage with both human and AI mentorship, potentially influencing long-term behavior and preferences. Future research should explore strategies to mitigate these biases, investigate diverse mentorship scenarios and student populations, and develop evidence-based guidelines for responsibly integrating generative AI tools into educational mentorship and support systems.

Keywords: Inductive Content Analysis, Higher Education, Mentorship, Cognitive Psychology, Perceptual Bias, Generative AI, Human-AI Interaction

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Citation

Lee, J., Wang, S., & Esposito, A. G. (2025). Content Analysis of College Student Perceptions: Mental Health-Related Support from Generative AI Versus Faculty Mentors. American Journal of Qualitative Research, 9(4), 70-100. https://doi.org/10.29333/ajqr/16815

Lee, J., Wang, S., and Esposito, A. G. (2025). Content Analysis of College Student Perceptions: Mental Health-Related Support from Generative AI Versus Faculty Mentors. American Journal of Qualitative Research, 9(4), pp. 70-100. https://doi.org/10.29333/ajqr/16815

Lee J, Wang S, Esposito AG. Content Analysis of College Student Perceptions: Mental Health-Related Support from Generative AI Versus Faculty Mentors. AM J QUALITATIVE RES. 2025;9(4), 70-100. https://doi.org/10.29333/ajqr/16815

Lee, Jimin, Si Wang, and Alena G Esposito. "Content Analysis of College Student Perceptions: Mental Health-Related Support from Generative AI Versus Faculty Mentors". American Journal of Qualitative Research 2025 9 no. 4 (2025): 70-100. https://doi.org/10.29333/ajqr/16815

Lee, Jimin et al. "Content Analysis of College Student Perceptions: Mental Health-Related Support from Generative AI Versus Faculty Mentors". American Journal of Qualitative Research, vol. 9, no. 4, 2025, pp. 70-100. https://doi.org/10.29333/ajqr/16815

Lee J, Wang S, Esposito AG. Content Analysis of College Student Perceptions: Mental Health-Related Support from Generative AI Versus Faculty Mentors. AM J QUALITATIVE RES. 2025;9(4):70-100. https://doi.org/10.29333/ajqr/16815