Generative AI–Social Media Coordinated Learning and University Students’ Psychological Well-Being: Dual Pathways and the Buffering Role of Perceived Support

Abstract

The integration of generative artificial intelligence (GAI) and social media platforms is reshaping how university students coordinate learning, collaborate with peers, and experience psychological outcomes. This study proposes Intelligent Media–Coordinated Learning Experience (IMCLE) to capture perceived coordination quality in intertwined GAI–social media learning. IMCLE is operationalized into four experiential cues—empowerment effectiveness (EE), collaboration facilitation (CF), feedback visibility (FV), and boundary safety (BS)—and examined through a dual-path framework linking collaborative gains and strain processes. Using two-wave survey data from Chinese university students, we applied an explanation–prediction–necessity strategy integrating PLS-SEM, artificial neural networks (ANN), and necessary condition analysis (NCA). Results show that all IMCLE cues positively predict collaborative learning (CL), which in turn enhances psychological well-being (PWB). IMCLE cues also positively predict psychological distress (PD), and PD constitutes a significant indirect pathway to PWB, suggesting that strain may co-occur with intensive engagement in highly coordinated learning. Perceived support (PSup) buffers distress formation by weakening the IMCLE→PD relationships and attenuates the PD–PWB association. ANN analyses corroborate the predictive salience of key IMCLE cues and indicate nonlinear importance patterns, while NCA provides threshold-oriented evidence on baseline conditions for high PWB. Overall, the findings clarify how coordination quality in GAI–social media learning relates to both gains and strain, and inform actionable improvements in feedback governance and support infrastructures to foster students’ digital well-being.