Lei Fan and Fangxue Liu (2026) — Xi'an Jiaotong-Liverpool University, University of Liverpool. 📄 Full text (arXiv)
This large-scale survey of humanities and social sciences (HSS) students in China examines how generative-ai reshapes academic development across four dimensions: usage patterns, effects on learning processes and performance, challenges, and preferred curricular integration approaches. Over half of respondents reported enhanced learning motivation, independent thinking, and creativity, though a substantial minority saw little change or decline. A larger majority reported academic performance gains, though the authors caution these may partly reflect limitations in conventional assessment practices.
Key Findings
Usage patterns: HSS students use GenAI primarily for writing assistance, information synthesis, and idea generation — tasks that align closely with HSS learning outcomes expressed through written and interpretive forms. Variations emerged by discipline and duration of GenAI experience, with modest gender differences.
Learning processes: More than half of students perceived enhanced motivation, independent thinking, and creativity. However, a substantial minority reported little change or even decline, suggesting that personalized-learning approaches to GenAI integration may be necessary.
Academic performance: A notably larger majority reported academic gains, though these may partly reflect assessment practices ill-equipped to distinguish AI-assisted from independent work — a challenge related to academic-integrity.
Challenges: Limited accuracy and over-reliance emerged as the most pressing concerns. While an overwhelming majority valued ethical considerations, only slightly more than half were satisfied with privacy protections. Students favored partial or optional GenAI integration into curricula.
Implications
The study highlights the need for higher-ed institutions to develop nuanced policies that balance GenAI's potential benefits against risks of over-reliance and assessment validity. Disciplinary differences suggest that faculty-development programs should tailor guidance to specific fields rather than adopting one-size-fits-all approaches.
Related Pages
- generative-ai — Core concept page on generative AI in education
- higher-ed — AI applications and challenges in higher education
- student-experience — How students experience AI in their learning
- academic-integrity — Integrity challenges in AI-era education
- ai-literacy — Developing AI literacy among students
- over-reliance — Risk of over-reliance on AI tools
- faculty-development — Supporting faculty in AI integration
- personalized-learning — Personalized approaches to learning with AI
- cross-cultural-student-perceptions-genai-computing — Did Alice Do Wrong? Cross-Cultural Differences in Student Perceptions of Generat