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

• URL: http://arxiv.org/abs/2606.24104v1
• Date: Tue, 23 Jun 2026 03:35:30 GMT
• Title: The impact of generative artificial intelligence on academic development of Chinese students in humanities and social sciences
• Abstract summary: This study focuses on four dimensions: patterns of use, effects on learning processes and academic performance, challenges associated with GenAI use, and preferred approaches to curricular integration.<n>We found that more than half perceived enhanced learning motivation, independent thinking and creativity, although a substantial minority reported little change or even decline.<n>While an overwhelming majority valued the importance of ethical considerations, only slightly more than half were satisfied with privacy protection.
• Score: 1.8625021794520016
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Generative artificial intelligence(GenAI) is reshaping learning in higher education, with particularly pronounced implications for the humanities and social sciences(HSS), where learning outcomes are commonly expressed through written and interpretive forms that align closely with GenAI's capabilities. Yet, systematic evidence on the educational impacts of GenAI on HSS students remains limited. Addressing this gap, this study draws on a large-scale survey of HSS students in China to examine its role in academic development. Guided by relevant learning theories, this study focuses on four dimensions: patterns of use, effects on learning processes and academic performance, challenges associated with GenAI use, and preferred approaches to curricular integration. We found that more than half perceived enhanced learning motivation, independent thinking and creativity, although a substantial minority reported little change or even decline. Comparatively, a notably larger majority reported academic performance gains, although these gains may partly reflect limitations in conventional assessment practices. The study identifies variations in perceived learning and performance improvements among students with differing durations of GenAI experience, along with observable disciplinary differences and modest gender differences. While an overwhelming majority valued the importance of ethical considerations, only slightly more than half were satisfied with privacy protection. Limited accuracy and overreliance emerged as the most pressing concerns reported by students. Students favored partial or optional curricular integration supported by practice-oriented training, and widely recognized GenAI's significance for their future professional development. Grounded in student perspectives, this study offers evidence-based recommendations for the responsible and pedagogically meaningful integration of GenAI

Related papers

• Is Solving Better Than Evaluating GenAI Solutions? [45.714140455691485]
  This study compares evaluating GenAI-generated solutions with traditional problem solving in a junior-level algorithms course.<n>We found no statistically significant differences between groups in midterm scores, final exam scores, overall course grades, or exam problems structurally aligned with the homework interventions.<n>Students received significantly higher homework scores when evaluating GenAI-generated solutions, but this localized advantage did not translate into downstream gains.
  arXiv  Detail & Related papers  (2026-07-30T02:08:29Z)
• STEM Faculty Perspectives on Generative AI in Higher Education [2.63946599983876]
  Generative artificial intelligence (GenAI) tools are increasingly present in higher education.<n>While some faculty have embraced GenAI for pedagogical purposes, others approach these tools with caution.<n>We present findings from a focus group study with 29 STEM faculty members at a large public university in the United States.
  arXiv  Detail & Related papers  (2026-03-04T12:46:26Z)
• Generative AI Practices, Literacy, and Divides: An Empirical Analysis in the Italian Context [32.495879271249414]
  This study presents the first comprehensive empirical mapping of GenAI adoption, usage patterns, and literacy in Italy.<n>Our findings reveal widespread adoption for both work and personal use, including sensitive tasks like emotional support and medical advice.<n>We identify a significant gender divide where women are half as likely to adopt GenAI and use it less frequently than men.
  arXiv  Detail & Related papers  (2025-12-03T11:01:28Z)
• Socratic Mind: Impact of a Novel GenAI-Powered Assessment Tool on Student Learning and Higher-Order Thinking [2.192176712066146]
  This study examines the impact of Socratic Mind, a Generative Artificial Intelligence (GenAI) powered formative assessment tool on learning outcomes.<n>Students who engaged with the GenAI tool experienced significant gains in their quiz scores compared to those who did not.<n>Our findings highlight the promise of AI-mediated dialogue in fostering deeper engagement and higher-order cognitive skills.
  arXiv  Detail & Related papers  (2025-09-18T03:08:24Z)
• Understanding Student Attitudes and Acceptability of GenAI Tools in Higher Ed: Scale Development and Evaluation [22.35484281040714]
  This study introduces a validated survey instrument designed to assess students' perceptions of generative AI (GenAI)<n>The instrument includes six thematic domains: institutional understanding, fairness and trust, academic and career influence, societal concerns, and GenAI use in writing and coursework.<n>Students from multilingual households perceived greater clarity in institutional policy, while first-generation students reported a stronger belief in GenAI's impact on future careers.
  arXiv  Detail & Related papers  (2025-08-03T21:22:34Z)
• Synergizing Self-Regulation and Artificial-Intelligence Literacy Towards Future Human-AI Integrative Learning [92.34299949916134]
  Self-regulated learning (SRL) and Artificial-Intelligence (AI) literacy are becoming key competencies for successful human-AI interactive learning.<n>This study analyzed data from 1,704 Chinese undergraduates using clustering methods to uncover four learner groups.
  arXiv  Detail & Related papers  (2025-03-31T13:41:21Z)
• Early Adoption of Generative Artificial Intelligence in Computing Education: Emergent Student Use Cases and Perspectives in 2023 [38.83649319653387]
  There is limited prior research on computing students' use and perceptions of GenAI. We surveyed all computer science majors in a small engineering-focused R1 university. We discuss the impact of our findings on the emerging conversation around GenAI and education.
  arXiv  Detail & Related papers  (2024-11-17T20:17:47Z)
• Hey GPT, Can You be More Racist? Analysis from Crowdsourced Attempts to Elicit Biased Content from Generative AI [41.96102438774773]
  This work presents the findings from a university-level competition, which challenged participants to design prompts for eliciting biased outputs from GenAI tools. We quantitatively and qualitatively analyze the competition submissions and identify a diverse set of biases in GenAI and strategies employed by participants to induce bias in GenAI.
  arXiv  Detail & Related papers  (2024-10-20T18:44:45Z)
• A Meta-analysis of College Students' Intention to Use Generative Artificial Intelligence [5.13644976086965]
  This study conducted a meta-analysis of 27 empirical studies under an integrated theoretical framework. Main variables are strongly correlated with students' behavioural intention to use GenAI. Gender, notably, only moderated attitudes on students' behavioural intention to use GenAI.
  arXiv  Detail & Related papers  (2024-08-25T15:46:57Z)
• A machine learning approach to predict university enrolment choices through students' high school background in Italy [42.57210316104905]
  This paper explores the influence of Italian high school students' proficiency in mathematics and the Italian language on their university enrolment choices. We investigate potential gender differences in response to similar previous educational choices and achievements. The findings shed light on the complex interplay of academic proficiency, gender, and high school background in shaping students' choices regarding university education.
  arXiv  Detail & Related papers  (2024-02-29T10:05:37Z)
• Bringing Generative AI to Adaptive Learning in Education [58.690250000579496]
  We shed light on the intersectional studies of generative AI and adaptive learning. We argue that this union will contribute significantly to the development of the next-stage learning format in education.
  arXiv  Detail & Related papers  (2024-02-02T23:54:51Z)
• Students' Voices on Generative AI: Perceptions, Benefits, and Challenges in Higher Education [2.0711789781518752]
  This study explores university students' perceptions of generative AI (GenAI) technologies, such as ChatGPT, in higher education. Students recognized the potential for personalized learning support, writing and brainstorming assistance, and research and analysis capabilities. Concerns about accuracy, privacy, ethical issues, and the impact on personal development, career prospects, and societal values were also expressed.
  arXiv  Detail & Related papers  (2023-04-29T15:53:38Z)
• Personalized Education in the AI Era: What to Expect Next? [76.37000521334585]
  The objective of personalized learning is to design an effective knowledge acquisition track that matches the learner's strengths and bypasses her weaknesses to meet her desired goal. In recent years, the boost of artificial intelligence (AI) and machine learning (ML) has unfolded novel perspectives to enhance personalized education.
  arXiv  Detail & Related papers  (2021-01-19T12:23:32Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.