AI evidence extraction

The impact of generative artificial intelligence on academic development of Chinese students in humanities and social sciences

Lei Fan, Fangxue Liu · arXiv (Cornell University) · 2026

AI-generated evidence extraction, verified across multiple analytical personas. Not a substitute for the peer-reviewed original.

9/10

Relevance

0/4

Quality (LMQS)

Evidence

Citations

This is an AI analysis. Read the peer-reviewed original at the publisher: https://doi.org/10.48550/arxiv.2606.24104

Methodology & findings

Study design

Large-scale survey of humanities and social sciences (HSS) students in China examining four dimensions: patterns of GenAI use, effects on learning processes and academic performance, challenges associated with use, and preferred approaches to curricular integration..

Sample

> 1000

Main result

The study found that "more than half perceived enhanced learning motivation, independent thinking and creativity, although a substantial minority reported little change or even decline." Additionally, "a notably larger majority reported academic performance gains, although these gains may partly reflect limitations in conventional assessment practices." The research also identified "variations in perceived learning and performance improvements among students with differing durations of GenAI experience, along with observable disciplinary differences and modest gender differences."

Reports effect sizes.

Research paradigm

Mixed methods (positivist survey with interpretivist elements)

Author conclusions

The authors conclude that "Grounded in student perspectives, this study offers evidence-based recommendations for the responsible and pedagogically meaningful integration of GenAI." They also note that "Students favored partial or optional curricular integration supported by practice-oriented training, and widely recognized GenAI's significance for their future professional development."

Risk of bias

Self-reported survey data susceptible to response bias; No control group for comparison; No longitudinal design to establish causality; Potential selection bias if survey participation was voluntary; Self-selection bias among students choosing to use GenAI; No information on survey response rate; Cross-sectional design limits causal inference; Selection bias (self-report survey data from HSS students); Potential self-selection bias in survey respondents; Recall bias (retrospective reporting of GenAI use patterns); Social desirability bias (students may overstate learning gains); Self-report bias (survey-based perception of learning outcomes); Selection bias (unclear if sampling was random or representative); Recall bias (students recalling past experiences with GenAI); Assessment validity concerns (acknowledged that performance gains may reflect assessment limitations rather than actual learning)

Open questions raised

The abstract notes that "systematic evidence on the educational impacts of GenAI on HSS students remains limited," which the current study addresses. Future research could examine longitudinal effects, causal mechanisms, and comparative effectiveness against traditional teaching methods.
The study explicitly addresses that "systematic evidence on the educational impacts of GenAI on HSS students remains limited" and aims to fill this gap through examining four dimensions: patterns of use, effects on learning processes and academic performance, challenges associated with GenAI use, and preferred approaches to curricular integration.
The paper addresses a stated gap: "systematic evidence on the educational impacts of GenAI on HSS students remains limited." Future directions include implementing evidence-based approaches to GenAI integration in curricula.

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