Crisis, AI and the Future of Higher Education

Transforming Education Systems Amid Global Crises: Lessons from the Pandemic Era

Yuanyuan Shi — Fri, 25 Jul 2025 00:00:00 +0000

The COVID-19 pandemic, as a typical global crisis event, has profoundly impacted the education sector. School closures and the shift to online teaching have led to major changes in educational models worldwide. This chapter explores the impact of global crises —specifically the COVID-19 pandemic—on education, as well as the transformations in teaching methods, curriculum settings, talent cultivation, and other aspects. Using a qualitative research approach, this study analysed the relevant literature on global crises and educational transformations. It also analysed the characteristics and deficiencies of current educational transformations, and put forward suggestions such as constructing a systematic theoretical system, paying attention to long-term impacts, and promoting sustainable development, aiming to provide theoretical references and practical ideas for the further development of education in the context of global crises. The findings showed that during COVID-19 pandemic, global educational reform exhibited trends such as the adoption of hybrid teaching models and a shift in educational philosophy. In particular, the importance of practical courses has become more prominent. Despite these advances, education systems continue to face significant challenges that require strategic responses. In the future, educational reform must continue to explore and adapt to changing social demands and global situations to cultivate innovative, practical, and well-rounded talents and promote sustainable educational development. Future research on the long-term impacts of global crises on education is of utmost importance for ensuring educational sustainability.

Comparative Study on Reading Habits and Supervisor Communication among Chinese Graduate Students Before and After COVID-19

Yuanyuan Shi, Yi Zou, Yan Hua, Hanlin Chen, Hongyi Shen — Fri, 25 Jul 2025 00:00:00 +0000

This study examines the impact of COVID-19 on postgraduate students’ reading habits and supervisor communication. An online survey was conducted among 2,298 students across different disciplines in mainland China. The results showed that after the COVID-19 outbreak, book reading remained unchanged for 75.6% of students, while 11% reported a decrease of over 30%. For paper reading, 76.7% of students reported no change, with 8.2% increasing their reading by over 60%. Supervisor communication frequency decreased significantly, with 48.7% of students maintaining the same communication level and the majority experiencing a reduction. These findings highlight the need for improved mentorship practices during pandemics and offer insights into supporting postgraduate students in disrupted learning environments.

Research Productivity and Delayed Graduation among Chinese Graduate Students During COVID-19: A Survey-based Study

Yuanyuan Shi — Fri, 25 Jul 2025 00:00:00 +0000

In January 2020, the COVID-19 outbreak began in mainland China with pandemic-like characteristics, rapidly spreading to numerous cities within a matter of weeks. As a result of the outbreak of COVID-19 in China, many Chinese students turned to online resources for their learning needs. This study delves into the ramifications of COVID-19 on the research productivity and degree completion of Chinese graduate students, aiming to offer insights and recommendations to help them adapt to university changes brought by the pandemic. This is a cross-sectional survey-based study. Therefore, a questionnaire survey was administered to 2,298 Chinese graduate students nationwide from June 1 to June 30, 2022, gathering their perceptions of COVID-19’s impact on their research productivity, graduation delays, research engagement time, and research output. In addition, the Post Hoc Pairwise Comparison Test was applied to determine the significant differences between groups. Here, the Cronbach’s alpha coefficient of the questionnaire was measured using SPSS Software and yielded a score of 0.908, indicating a high level of internal consistency in the measurement tool used in the study. The findings reveal that 64.6% of graduate students experienced a temporary decline in research productivity due to the pandemic, with male students, full-time learners, and those in engineering disciplines being more significantly affected. Additionally, 8.7% of students reported or anticipated graduation delays, particularly among doctoral students and those enrolled before 2018. While 34.1% maintained consistent research engagement time, 12.5%-34.1% saw a 10%-80% reduction, and research output fluctuations varied significantly by discipline, with medicine and engineering showing more pronounced impacts. In summary, the COVID-19 pandemic has had a significant impact on the research productivity of the majority of researchers, with negative effects being the norm. However, amidst this adversity, there have been instances of resilience, adaptability, and even unexpected positive outcomes among a subset of researchers. The study highlights the heterogeneous effects of the pandemic and recommends tailored support measures, including prioritised research resource allocation for hard-hit groups, enhanced online collaboration platforms, and targeted psychological and academic assistance, to mitigate disruptions and optimise graduate education during public health crises. Further research is recommended to address the limitations and to build upon the findings of this study.

Analyzing the Impact of COVID-19 on Graduate Students’ Learning and Research Output: A Structural Equation Modeling Approach

Lei Gong, Zhanjie Cai, Jia Wang, Yuanyuan Shi — Fri, 25 Jul 2025 00:00:00 +0000

The COVID-19 epidemic has impacted graduate students’ learning and research to varying degrees. To quantitatively analyze this impact, this study focuses on six dimensions: “research communication”, “research data acquisition”, “publication and cultivation process”, “financial support and research topics”, “internship and practice”, and “graduation and job-hunting”. An online questionnaire was distributed to graduate students nationwide in China, yielding 2,586 responses with 2,552 valid samples. Using structural equation modeling (SEM), the study found significant correlations between “research communication” and three key dimensions: “research data acquisition” (correlation coefficient = 0.477), “publication and cultivation process” (0.358), and “financial support and research topics” (0.517). Additionally, “research data acquisition” significantly influenced “publication and cultivation process” (0.662), while strong associations were identified between “research data acquisition” and “financial support”, “publication and cultivation process” and “financial support”, as well as “publication and cultivation process” and “practice and job-hunting”. These findings provide critical insights for refining educational policies and graduate training strategies to mitigate pandemic-induced challenges in academic research and career development.

Graduate Research in a Pandemic: The Role of Gender and Academic Discipline in Shaping Chinese Students’ Experiences

Yuanyuan Shi — Fri, 25 Jul 2025 00:00:00 +0000

Over the past three years, the COVID-19 pandemic has profoundly disrupted graduate research activities in China, prompting a critical need to understand its differential impacts across gender and academic disciplines. Existing studies have primarily focused on undergraduate education or general research productivity, leaving a gap in research on graduate students’ experiences, particularly regarding how gender and discipline shape pandemic-related challenges. This study addresses this gap by surveying 2,298 master’s and doctoral students from mainland Chinese universities (44.4% male, 55.6% female) using a 5-point Likert scale questionnaire. Data were analysed via independent samples t-tests, one-way ANOVA, and Pearson correlation analysis to examine disparities in research exchange, data acquisition, publication processes, funding applications, and internship/career development. Key findings revealed significant gender differences: Female students reported greater challenges in internship/career development (M=3.44, SD=1.11) and problem-solving efficiency, while male students faced more severe disruptions in data acquisition (M=3.12, SD=1.13) and laboratory access (p<0.05). Discipline-wise, engineering and medicine students experienced higher impacts on research exchange and data access, whereas humanities students reported greater effects on publication timelines (F=3.07, p<0.05). Correlation analysis showed strong associations between data acquisition and publication processes (r=0.528, p<0.01), underscoring their interdependency. This study highlights the need for targeted support measures, such as gender-sensitive funding policies and discipline-specific research infrastructure adjustments. By integrating quantitative evidence on pandemic impacts, the findings inform higher education policies to enhance graduate students’ resilience in crisis contexts.

Artificial Intelligence in China’s Higher Education: Opportunities, Challenges, and Strategic Pathways for High-Quality Development

Caixia Zhang — Fri, 25 Jul 2025 00:00:00 +0000

The Chinese government documents, like “The report of the 20th Party Congress” puts education, science and technology talents in a separate chapter, and puts forward new requirements for the high-quality development of education, and education, science and technology, and talents are the basic and strategic support for the comprehensive construction of a modern socialist country. Under the wave of digitalisation, artificial intelligence has been deeply integrated into higher education and has become a key force for innovation. However, the development faces many challenges: the imbalance in resource allocation exacerbates the regional smart education gap, the contradiction between technology iteration and insufficient digital literacy training for teachers and students is highlighted, while ethical and legal issues such as data privacy and algorithmic bias are becoming increasingly serious due to institutional lag. In this regard, it is necessary to solve the problem from three aspects: to strengthen the national strategic coordination and promote the balanced sharing of resources; to improve the training system for teachers and students to enhance digital literacy and thinking skills; and to formulate application specifications and establish a whole-process regulatory mechanism to ensure the high-quality development of higher education. Based on the background of the development of higher education in China mentioned above, this study aims to explore the connotation, dilemmas faced, and optimisation path of AI-enabled high-quality development of China’s higher education, to provide strategic suggestions for AI-enabled high-quality development of China’s higher education.

The Path of Cultivating Digital Literacy of College Students in the Era of Artificial Intelligence

Lei Wan — Fri, 25 Jul 2025 00:00:00 +0000

In the context of the era of deep integration of artificial intelligence into social development, digital literacy has become a key competency for college students, which has far-reaching significance in accelerating the construction of digital China, promoting personal growth and lifelong learning, and promoting the modernisation of ideological and political education. Based on the literature research method, this study collects and analyzes existing literature and finds that the current enhancement of college students' digital literacy faces many dilemmas, which mainly include college students' weak self-knowledge of digital literacy cultivation, educators' insufficient digital proficiency, uneven distribution of educational resources, and students' misbehavior in the field of digital morality and ethics. In view of this, this study aims to explore practical solutions to these problems and help college students adapt to the needs and challenges of the AI era. To achieve this goal, the article proposes a variety of solution strategies, including building knowledge systems, constructing a high-quality faculty, creating an intelligent teaching environment, and playing a leading role in the ideological and theoretical courses. Through these practical solutions, it is expected to comprehensively improve the digital literacy of college students and provide strong support for individual development and social progress.

The Role Change of College Teachers in the Era of AI: Challenges and Solutions

Peijin Wang — Fri, 25 Jul 2025 00:00:00 +0000

Amid the wave of educational transformation driven by artificial intelligence technology, new models such as “human-machine co-teaching”, “dual-teacher teaching”, and “multi-teacher collaboration” are reshaping the current state of higher education, and the traditional role positioning of university teachers is facing challenges. This paper, through a literature analysis method, systematically analyzes the role predicament and transformation path of university teachers in the era of artificial intelligence. The research finds that the essence of the current role conflict among teachers is the contradiction caused by changes in role expectations, which has exacerbated the identity crisis of teachers. The role positioning of university teachers also needs to be reconstructed in this era background. In response to the challenges faced by university teachers during the role transformation process, corresponding coping strategies are proposed, providing an operational framework for the professional development of university teachers, assisting educational managers in designing adaptive training systems, and more importantly, providing a theoretical support point for higher education to adhere to the essence of nurturing people and achieve innovative development in the intelligent era. The role upgrade of university teachers can innovate the model of higher education from knowledge transmission to ability cultivation, laying a solid foundation for the development of compound teachers with both digital literacy and humanistic accomplishment.

Artificial Intelligence in Educational Evaluation: Values, Challenges and Optimization Strategies

Yuqing Zhang — Fri, 25 Jul 2025 00:00:00 +0000

This conceptual study explores the dual impacts of artificial intelligence on educational evaluation systems. Drawing on recent literature, it highlights benefits like improved monitoring and personalised feedback, while also addressing concerns such as algorithmic bias and privacy issues. The study aims to re-evaluate traditional educational paradigms and explore future education models, with the overarching goal of constructing an educational ecosystem attuned to the digital age. It seeks to bridge the gap between technological advancement and educational reform by examining the synergistic intersection of AI and educational evaluation. Adopting a literature research methodology, it synthesises multidisciplinary frameworks from educational philosophy, data ethics, and assessment theory without empirical validation. The research content points out that while artificial intelligence empowers educational evaluation with values such as data-enhanced outcome evaluation, intelligent process analysis, longitudinal value-added assessment, and multidimensional comprehensive evaluation, it currently brings risks, including ethical dilemmas, personal privacy violations, human-machine relationship imbalances, and algorithmic unfairness. To effectively mitigate these risks, this study proposes strategies including strengthening institutional supply to regulate educational evaluation practices, adhering to the logic of educational evaluation to promote the coupling of value rationality and instrumental rationality, and formulating ethical norms for data governance to establish an optimal educational evaluation paradigm. This research contributes to both theoretical advancement in intelligent education assessment and practical guidelines for policymakers navigating AI integration in educational systems.

Leveraging AI for Enhanced English Learning: Understanding Preferences and Perceptions of University Students in China

Yuanyuan Shi, Liying Lu, Qingling Zhang — Fri, 25 Jul 2025 00:00:00 +0000

In China, English is considered an important tool for economic prosperity, modernisation, and global integration, and proficiency in English is widely recognised as an effective mechanism for enhancing cross-cultural dialogue. As the integration of artificial intelligence in educational practices expands globally, its application in English language learning has become increasingly significant, especially within the Chinese context, where English proficiency is highly valued. This study investigates the preferences and perceptions of university students in China towards Artificial Intelligence (AI)-enhanced English as a Foreign Language (EFL) learning tools. Through a survey of 269 participants, the study aimed to explore the perception and usage patterns of these tools across different student populations and to identify areas where AI algorithms can be improved to better support language learning. The study employed descriptive statistics to summarise perceptions, usage intentions and usage purposes, etc., and k-means analysis to identify subgroups explaining usage status, scenarios of use, purposes of use, and possible suggestions for improvement, to further identify the different customer groups and patterns. The results of the study show that Chinese university students have a high level of awareness and use of AI tools. The study shows that Chinese university students currently prefer English-assisted learning tools that focus on translation applications and vocabulary learning. The most commonly preferred AI-assisted English learning tools among Chinese university students include translation software and multifunctional platforms such as MoMo Word and ChatGPT. In this study, a significant portion (60.1%) utilises AI with the goal of enhancing their English proficiency, demonstrating a strategic use of AI for skill development. The findings highlight the need for AI tools that are adaptable, interactive, and capable of integrating various learning scenarios. These findings can inform the development of more effective educational technologies that can meet the needs of Chinese university students and improve their English proficiency. As a cross-sectional study, the findings cannot rule out the possibility that high-achieving students simply use AI more (correlation), rather than AI usage directly leading to higher grades. Future research could address this limitation by using longitudinal studies or experimental designs that track students’ AI use and academic performance over time.

Integrating Artificial Intelligence into Learning: An Examination of User Intention, Behavior and Satisfaction

Yuanyuan Shi — Fri, 25 Jul 2025 00:00:00 +0000

The rapid advancement of artificial intelligence (AI) has significantly transformed the educational landscape, offering new opportunities to enhance learning experiences. This study explores the relationships among university students’ attitudes toward AI learning tools (AIUI), their usage behaviours (AIUB), and their user satisfaction (AIUS) within the Chinese context. A quantitative research design was employed, involving a cross-sectional survey of 263 valid responses. A convenience sampling method was used to select the study sample. The survey was conducted via a social media platform and an online survey tool. Data were analysed using Partial Least Squares Structural Equation Modelling (PLS-SEM) with Smart PLS 4.0 software. The findings reveal that AIUI positively influences both AIUB and AIUS. However, AIUB shows a non-significant direct effect on AIUS, indicating that user satisfaction is more closely related to initial attitudes than usage frequency. These findings underscore the importance of fostering positive attitudes toward AI learning tools and provide valuable insights for educators and developers aiming to improve educational outcomes through AI integration. One significant limitation of this study is the potential for geographical bias, as all participants were from universities in Jiangsu Province, China. Therefore, future research should include a more diverse sample, encompassing students from different regions, educational levels, and cultural backgrounds to gain a more comprehensive understanding of attitudes and behaviours related to AI learning tools.

The Impact of Artificial Intelligence on College Students’ Learning Strategies, Information Literacy and English Proficiency: A Study in China

Yuanyuan Shi — Fri, 25 Jul 2025 00:00:00 +0000

This empirical study examines the transformative impact of Artificial Intelligence (AI) on college students’ English language learning, focusing on the interactions between learning strategies, information literacy and English proficiency. This study collected survey data from 256 college students in Jiangsu Province, China. It used structural equation modelling to examine the direct and indirect effects of learning strategies on English proficiency. The study pinpoints the AI tools frequently utilised by students to facilitate English learning, such as Baidu Translate, Youdao Dictionary, Baicizhan, Shanbay, Bubei, Maimemo, ChatGPT, Liulishuo, Itest, BBC Learning English, Zhixue, Qtyy, Hellotalk, and Callannie, and explores how these tools are incorporated into the students’ learning process. The results of the study show that AI greatly improves students’ English proficiency by improving learning strategies and information literacy. By leveraging AI technologies and optimising learning methods, educational stakeholders can create more effective and inclusive learning environments. This study provides educators and curriculum designers with some insights and practical guidance on how to integrate AI into language learning programs to improve students’ English performance.

Navigating the Future of Higher Education: AI Integration and Global Crisis Resilience

Yuanyuan Shi — Fri, 25 Jul 2025 00:00:00 +0000

The global higher education landscape has undergone profound disruptions due to crises such as the COVID-19 pandemic, while simultaneously being reshaped by the rapid advancement of artificial intelligence (AI). This chapter synthesizes key findings on the transformative impact of global crises and artificial intelligence (AI) on higher education, employing a literature review method to critically examine research limitations and future directions. Empirical evidence from the literature reveals how the COVID-19 pandemic disrupted graduate education through declining academic engagement and research productivity, with notable gender and disciplinary disparities. Concurrently, AI has reshaped pedagogy through personalized learning and data-driven governance, yet raises ethical concerns regarding algorithmic bias, data privacy, and digital wellbeing. Despite these insights, the research faces limitations in geographic generalizability (primarily China-focused samples), short-term outcome measurement, and insufficient empirical validation of ethical AI frameworks. The chapter proposes three critical future research trajectories: (1) interdisciplinary studies integrating cognitive science and AI ethics to design wellbeing-centric tools, (2) global equity investigations addressing the AI accessibility divide, and (3) policy-research partnerships to develop standardised ethical guidelines. These directions aim to balance technological innovation with equitable, human-centred education systems in an era of disruption. Our study is framed within the theoretical perspectives of crisis resilience and technological disruption, aiming to provide valuable insights for diverse audiences, including researchers, policymakers, and educators.