📄 social_science

First-Year Chinese College Students’ Perceived Support, School Adjustment, and Mental Health: A Perspective from the Social Support Theory and Conservation of Resources Theory

This study of 1,170 first-year Chinese college students reveals that perceived teacher, peer, and AI support positively influence mental health both directly and indirectly through school adjustment, highlighting the potential for human-machine collaborative support systems to enhance student well-being.

Original authors: zhiyun zhang, Linlin Cheng, Lianlian Ji, Chenshi Deng, Wenyin Xiong

Published 2026-08-13

📖 6 min read🧠 Deep dive

CC BY 4.0

Original authors: zhiyun zhang, Linlin Cheng, Lianlian Ji, Chenshi Deng, Wenyin Xiong

Original paper licensed under CC BY 4.0 (https://creativecommons.org/licenses/by/4.0/). ✨ This is an AI-generated explanation of the paper below. It is not written or endorsed by the authors. For technical accuracy, refer to the original paper. Read full disclaimer

The New Kid on the Block: A Guide to the University Jungle

Imagine you just moved to a brand-new city where you don't know anyone, the streets are confusing, and the rules are totally different. That's what starting college feels like for a first-year student. They are stepping out of the familiar safety of their family home and high school bubble into a massive, complex environment. In the world of psychology, scientists call this "school adjustment." It's the process of learning how to navigate the new academic demands, make friends, and figure out how to live your life without falling apart.

But here's the tricky part: when you move to a new city, you lose your old support system (your old friends and teachers) before you've built a new one. This is where two big ideas come in to help us understand what happens next. The first is Social Support Theory, which basically says that having people around you who care, listen, and help makes life's stress much easier to handle. Think of it like a safety net; the more ropes in the net, the less scary it is to jump. The second idea is Conservation of Resources Theory, which suggests that we all have a limited amount of "energy" or "resources" to deal with problems. When we are stressed, we are losing resources. If we can find new ones—like a helpful teacher, a cool friend, or even a smart computer program—we can stop losing energy and start building it back up.

Why does this matter? Because if a student can't adjust to this new life, their mental health can take a hit. They might feel anxious, lonely, or depressed. But if they have the right support, they can thrive. This paper asks a very modern question: In a world where we all talk to AI chatbots, can a robot be part of that safety net? Can a computer program help a new student feel less alone and more ready for college?

The Study: Humans, Friends, and Robots in the College Jungle

This study, conducted by researchers from universities in China, decided to take a closer look at first-year college students to see how different types of support affect their ability to adjust and their mental health. They gathered data from 1,170 students (mostly around 19 years old) and asked them to fill out surveys about three specific things: how much they felt supported by their teachers, how much they felt supported by their peers (other students), and how much they felt supported by AI (like chatbots or smart learning tools).

The researchers wanted to see if these three sources of support acted like a "human-machine team" to help students survive their first year. They didn't just ask if support made students happy; they looked at the whole chain of events. They wondered: Does support help you adjust to school first, and then does that adjustment make you mentally healthier? Or does support just make you happy directly?

What They Found: The Power of the Trio

The results were clear and positive. The study found that all three types of support were like superpowers for first-year students.

Teachers, Peers, and AI all helped students adjust. Whether it was a teacher offering guidance, a friend sharing a laugh, or an AI chatbot answering a question at 2 AM, each of these helped students feel like they belonged and could handle their new school life.
All three types of support improved mental health. Students who felt supported by teachers, friends, or AI reported feeling better mentally—less anxious and more confident.
Adjustment is the secret bridge. The study discovered something really important: feeling supported didn't just magically fix mental health on its own. Instead, the support helped students adjust to school better first. Once they felt like they were fitting in and handling their classes and social life, then their mental health improved. It's like having a map (support) helps you find your way (adjustment), and once you aren't lost, you stop feeling panicked (mental health).

The Numbers Behind the Magic

The researchers used a fancy statistical method called "structural equation modelling" to map out these relationships. Here is what the numbers told them:

Teacher Support was the strongest helper. It had a strong connection to helping students adjust (a score of 0.411) and a solid connection to mental health (0.269).
Peer Support and AI Support were also very important. They both helped students adjust with a score of 0.231.
When it came to mental health, Peer Support had a score of 0.173, and AI Support had a score of 0.181.
The study showed that these three types of support together explained about 27.6% of why some students adjusted better than others.
When you add school adjustment into the mix, they explained 28.1% of the differences in mental health.

The study also used a method called "bootstrapping" (which is like running a simulation thousands of times to be sure) to check if the "bridge" theory was true. They ran 5,000 resamples and found that the path from support to mental health through school adjustment was definitely real. The confidence intervals (the range where the true answer likely sits) did not include zero, meaning the effect was statistically significant.

What This Means for the Future

The authors suggest that we shouldn't just rely on humans anymore. In the past, we thought only teachers and friends could be our safety net. This paper suggests that AI is now a valid part of that team. It's not about replacing teachers or friends, but adding a new layer of support. AI is great because it's always available, doesn't judge, and can answer questions instantly.

However, the paper is careful to say that AI isn't a magic wand that solves everything. The data showed that teachers and peers still had a slightly stronger impact on mental health than AI did. This suggests that while AI is a fantastic tool for quick help and emotional relief, it can't fully replace the deep, human connection of a caring teacher or a best friend.

The researchers conclude that universities should build "human-machine collaborative support systems." Imagine a school where you can talk to a counselor, hang out with a study group, and chat with an AI bot that helps you organize your schedule or calm your nerves. By combining all three, schools can create a safety net that catches students no matter how they prefer to get help.

This study didn't prove that AI will solve all mental health issues forever, nor did it claim that robots are better than humans. Instead, it suggests that in our modern world, the best way to help new students is to use everything we have: the wisdom of teachers, the empathy of friends, and the accessibility of AI, all working together to help students find their footing in the big, scary, exciting world of college.

Technical Summary: First-Year Chinese College Students' Perceived Support, School Adjustment, and Mental Health

Problem Statement
The transition to university life presents significant challenges for first-year students, who must navigate new academic demands, social environments, and behavioral expectations. This period is often characterized by a reduction in access to original support systems (family, secondary school peers) before new networks are fully established, potentially exacerbating maladjustment and mental health issues. While existing research acknowledges the roles of teacher and peer support, there is a gap in understanding how Artificial Intelligence (AI) support functions within this ecosystem. Specifically, few studies have integrated perceived teacher support, peer support, and AI support into a unified framework to examine their collective impact on school adjustment and mental health, particularly through the lens of Social Support Theory (SST) and Conservation of Resources (COR) Theory.

Methodology

Participants: The study utilized a cross-sectional design with a sample of 1,170 first-year undergraduate students from seven universities in Eastern China. The sample had a mean age of 18.99 years (SD = 0.910), with a gender distribution of 37.35% male and 62.65% female.
Procedure: Data were collected via an online survey on the "Wenjuanxing" platform between April 29 and May 25, 2026. Snowball sampling was employed, facilitated by academic advisors.
Instruments: All constructs were measured using 5-point Likert scales:
- Perceived Teacher Support: 8 items (emotional and academic) from Patrick et al.
- Perceived Peer Support: 8 items (emotional and academic) from Patrick et al.
- Perceived AI Support: 5 items from Ning et al., assessing AI's role in alleviating negative emotions.
- School Adjustment: 13 items (reduced to 11 for model fit) from Fang et al., measuring life satisfaction and adaptation.
- Mental Health: 7 items from Wu et al.
Analysis: Data were analyzed using SPSS 26 and AMOS 26. The study employed Exploratory Factor Analysis (EFA), Confirmatory Factor Analysis (CFA), and Structural Equation Modeling (SEM) to test the hypothesized model. Reliability was confirmed via Cronbach's alpha (0.905–0.940) and Composite Reliability (0.915–0.940). Common method bias was assessed using Harman's single-factor test. Mediation effects were tested using the bootstrapping method with 5,000 resamples.

Key Results
The structural equation modeling results supported all proposed hypotheses:

Direct Effects on School Adjustment: Perceived teacher support ( $\beta$ = 0.411, $p$ < 0.001), peer support ( $\beta$ = 0.231, $p$ < 0.001), and AI support ( $\beta$ = 0.231, $p$ < 0.001) all significantly and positively predicted school adjustment. Together, these variables explained 27.6% of the variance in school adjustment.
Direct Effects on Mental Health: All three support types directly predicted mental health: teacher support ( $\beta$ = 0.269), peer support ( $\beta$ = 0.173), and AI support ( $\beta$ = 0.181), all at $p$ < 0.001.
Adjustment to Mental Health: School adjustment positively predicted mental health ( $\beta$ = 0.235, $p$ < 0.001).
Mediation: School adjustment served as a significant mediator. The indirect effects of teacher support ( $\beta$ = 0.097), peer support ( $\beta$ = 0.054), and AI support ( $\beta$ = 0.054) on mental health were all statistically significant ( $p$ < 0.001), with 95% confidence intervals excluding zero. The full model explained 28.1% of the variance in mental health.

Key Contributions

Theoretical Extension: The study extends the applicability of Social Support Theory into the digital era by incorporating AI as a valid "virtual partner" within the support framework. It integrates the Conservation of Resources (COR) theory to explain how AI functions as a resource that helps students protect existing resources and acquire new ones, thereby broadening the SST's explanatory scope to include human-machine interactions.
Unified Framework: It is among the first studies to simultaneously analyze teacher, peer, and AI support within a single analytical model, demonstrating that these distinct sources form a cohesive "human-machine collaborative support system."
Targeted Population: The research specifically addresses first-year students during the critical transition period, elucidating the specific pathways (via school adjustment) through which support resources influence mental health in this vulnerable demographic.

Significance and Practical Implications
The paper posits that the findings provide empirical evidence for the development of "human-machine collaborative support systems" in higher education.

System Integration: Universities are encouraged to integrate AI learning and mental health tools as supplementary resources to traditional counseling and peer support, leveraging AI's accessibility and low psychological burden to address gaps in immediate response and after-hours support.
Human-Centric Focus: While AI support showed positive effects, the study notes that teacher and peer support remained more significant predictors. Consequently, the authors recommend strengthening the core roles of human support. This includes enhancing teacher training to proactively identify adaptation difficulties and facilitating peer relationship building through group counseling.
Mechanism Understanding: The results clarify that support resources improve mental health not only directly but also by enhancing a student's ability to adjust to the school environment. This suggests that interventions should target both the provision of support and the facilitation of school adjustment.

Limitations
The authors acknowledge that the study is limited by its cross-sectional design, which precludes causal inferences regarding the temporal relationships between support, adjustment, and mental health. Additionally, the focus on first-year students limits the generalizability of the findings to other educational stages. Future research is suggested to employ longitudinal designs and cross-stage comparisons to validate these dynamics over time and across different student populations.