Multimodal data processing for affective perception in AI-supported developmental bibliotherapy: a mixed-methods feasibility study in university libraries.

Authors

Chenyang Zhou, Wei Kang

Published in

Frontiers in psychology. Volume 17. Pages 1927988. Epub Oct 06, 2026.

Abstract

Multimodal data processing is increasingly important for understanding human affective experience in naturalistic settings. In this study, "multimodal" refers to the integration of heterogeneous human-centered evidence streams rather than synchronized perceptual or physiological sensing. We evaluated a low-threshold university-library support model integrating developmental bibliotherapy salons with AI chatbot-supported emotional expression.
A convergent mixed-methods feasibility and acceptability design was conducted at a Chinese university. Of 180 baseline respondents, 45 voluntarily registered for a three-week intervention and 38 completed all sessions and post-intervention assessments; 68 non-participating students with complete baseline and follow-up assessments served as a descriptive comparison group. Evidence sources included psychological self-report data, participation records, AI-use feedback, participant evaluations, and semi-structured interviews with nine intervention participants. Quantitative indicators assessed recruitment, retention, acceptability, and exploratory SCL-90 change, while thematic analysis examined participants' affective experiences and perceived mechanisms.
All available intervention places were filled within two days, retention reached 84.4%, and 94.7% of completers reported moderate or considerable benefit. The intervention-group mean SCL-90 change was -0.12 points, with an approximate 95% confidence interval of [-0.23, -0.01]. Because allocation was voluntary and non-randomized, no causal group-by-time intervention effect is claimed. Qualitative findings suggested that bibliotherapy salons supported interpersonal resonance, cognitive reframing, and shared reflection, whereas the AI chatbot provided a private and non-judgmental channel for emotional disclosure.
AI-supported developmental bibliotherapy appears feasible and acceptable as a low-threshold university-library wellbeing service. The findings support feasibility, acceptability, and perceived mechanisms only; they do not establish treatment efficacy, clinical effectiveness, or a neuroscience mechanism. More broadly, the study illustrates how heterogeneous psychological, behavioral, and experiential evidence can be integrated in a naturalistic educational setting.

PMID:
42903122
Bibliographic data and abstract were imported from PubMed on 22 Oct 2026.

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