Methodology Article | | Peer-Reviewed

An Exploration of Pedagogical Reform in Higher Education in the Age of Artificial Intelligence

Received: 20 June 2025     Accepted: 26 June 2025     Published: 4 July 2025
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Abstract

This article delves into validity of skills development hypothesis in promoting inclusive growth in the context of a lower-middle income economy confronted with several macroeconomic problems and labour market challenges. The analytical outline is guided by the theory of inclusive growth (IG) and existing body of empirical literature on skills development. As debated in the literature, there is no single solution to foster inclusive growth. Each country approaches the inclusive growth challenge differently, based on its resource endowments and unique socio-economic circumstances. There is a major literature gap using skills development models to promote inclusive growth. Thus, industry and country specific studies contribute to ongoing theoretical debate on inclusive growth on one hand and enrich empirical evidence on interlinks between skills development and IG on the other. Here, we present a case study of a skills development project (2017-2024), designed to promote inclusive growth through skills development of the tourism industry. Its target beneficiaries include both formal and informal sector business owners and employees of tourism sector micro, small and medium scale enterprises (MSMEs) operating in economically backward regions. The study utilizes data from both primary and secondary sources and employs mixed methods approach for detailed analysis. The paper provides several interesting findings on key themes of inclusive growth. First, the evidence on skills development of employees in the tourism value chain reveals sharing of growth benefits by low-income earners and the unemployed including females and disabled persons. Second, the findings on employer capacity building and skills development demonstrate inclusive growth benefits through entrepreneurship development, incremental income, and new product development. Third, the findings on gender and disability inclusion demonstrate power of skills development in promoting inclusive growth through MSME development. Finally, the overall findings enable policy makers in developing countries to promote inclusive growth through skills development of MSMEs operating in the tourism sector.

Published in Education Journal (Volume 14, Issue 4)
DOI 10.11648/j.edu.20251404.12
Page(s) 177-181
Creative Commons

This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.

Copyright

Copyright © The Author(s), 2025. Published by Science Publishing Group

Keywords

Higher Education, AI, Pedagogical Reform

1. Introduction
The rapid development of artificial intelligence (AI) technologies is profoundly reshaping traditional educational models. According to the Education Informatization Development Plan released by the Ministry of Education in 2023, the penetration rate of AI in the education sector has reached 67%, with an expected annual growth rate exceeding 20% over the next five years. Core AI technologies such as deep learning and natural language processing are enabling personalized and intelligent educational solutions, gradually transforming the long-standing ideal of individualized instruction into reality. AI is not only revolutionizing the way knowledge is delivered but is also restructuring various dimensions of the educational ecosystem ().
In the context of the AI era, higher education institutions face a dual scenario. On one hand, intelligent technologies offer new possibilities to overcome time and space constraints, optimize resource allocation, and enhance teaching efficiency. For example, the intelligent upgrading of MOOCs platforms has facilitated the broad sharing of high-quality educational resources. On the other hand, traditional teaching models are under increasing pressure, with urgent needs to redefine the roles of instructors, reshape evaluation systems, and realign talent cultivation objectives. Surveys indicate that 78% of university faculty believe their teaching roles need to be redefined in the AI environment ().
This study aims to systematically explore new models of AI-empowered higher education. By analyzing cases of intelligent teaching practices, it seeks to construct a theoretical framework that can serve as a reference for educational reform in universities. Theoretically, the study contributes to the development of a more complete intelligent education theory system; practically, it provides guidance for higher education institutions to navigate technological transformation and to cultivate new types of talent suited to the AI era .
2. Related Work
The rapidly evolving field of artificial intelligence (AI) has garnered significant attention from researchers and higher education practitioners, resulting in numerous studies exploring the impact of AI on higher education.
Jiali et al. (), based on a synthesis of 46 studies, demonstrate that AI enables precision teaching through adaptive systems and predictive analytics, while simultaneously raising substantial ethical concerns regarding algorithmic bias, data privacy, and teacher autonomy—underscoring the urgent need for robust governance frameworks.
Al-Zahrani and Alasmari () examined stakeholder attitudes toward the application of AI in higher education in Saudi Arabia. Their findings reveal generally positive perceptions but also emphasize the importance of addressing ethical, social, and educational challenges to ensure responsible and effective implementation of AI in teaching.
Sajja et al. proposed the Artificial Intelligence-Enabled Intelligent Assistant (AIIA) framework, an AI-based assistant designed to support personalized and adaptive learning in higher education. Through interactive services such as natural language processing, the AIIA aims to reduce students’ cognitive load and enhance their engagement with learning.
Katsamakas et al. () adopted a systems-thinking approach to construct a causal loop diagram model, revealing the dynamic feedback mechanisms involved in the AI-driven transformation of higher education institutions. Their work offers strategic insights into improving teaching, research, and administration, while also addressing academic integrity and evolving labor market demands.
Chan et al. () explored the perceptions of students and faculty regarding the use of generative AI in higher education. While acknowledging AI’s potential to support instruction, the study concludes that human teachers remain irreplaceable due to their capacity for critical thinking, emotional intelligence, and social interaction—highlighting the need for an integrated model combining AI and human-led teaching.
Arslan et al. () investigated the potential of applying generative AI to personalized educational assessments, aiming to increase learner engagement and accessibility. At the same time, the authors pointed out critical challenges related to core assessment principles such as validity, reliability, and fairness.
3. Methodology
This study follows a qualitative conceptual research design, combining interpretive document analysis and illustrative case synthesis. The main data sources include national policy documents (e.g., China's Smart Education Plan), institutional reports from leading universities (e.g., Tsinghua University, East China Normal University), and peer-reviewed academic literature. Rather than conducting empirical fieldwork, the study draws on publicly available data and existing secondary research to develop a framework for understanding and guiding AI-driven reform in higher education.
4. Core Pathways of Pedagogical Reform in Higher Education in the Era of Artificial Intelligence
4.1. Innovation in Intelligent Teaching Models
AI-based personalized learning recommendation systems have become a critical breakthrough in pedagogical reform. For instance, Tsinghua University’s Zhixue platform constructs personalized knowledge graphs by analyzing students’ learning behavior data, enabling precise delivery of learning content and resulting in a 40% improvement in learning efficiency.
Meanwhile, the deployment of intelligent teaching assistants (AI-TAs) is growing rapidly (). Shanghai Jiao Tong University’s AI-powered Q&A chatbot currently addresses approximately 65% of common student inquiries, significantly reducing instructors’ workload. International studies have also shown that classroom AI tools can save teachers up to 5 hours per week and lead to a 47% improvement in the quality of student responses after receiving AI-generated feedback.
In experimental and practical instruction, VR/AR technologies are demonstrating profound advantages. Meta-analyses in anatomy education show that VR contributes to significant knowledge gains, while AR currently has a more limited impact on long-term retention. In procedural medical training, students trained through VR complete tasks an average of 20% faster and perform 38% more steps correctly compared to those trained through conventional methods.
Combining immersive tools with traditional teaching creates a powerful blended learning model. For instance, a VR-based simulation trial in emergency medical training resulted in significantly higher accuracy in skill performance and greater learner satisfaction compared to conventional training approaches.
Taken together, these developments demonstrate that intelligent teaching models—ranging from AI-TAs and personalized recommendation systems to immersive VR/AR experiences—are reshaping higher education by improving instructional efficiency, enhancing learning outcomes, and increasing student engagement.
4.2. Data-Driven Instructional Management and Assessment
The application of learning analytics has made curriculum design more scientific and evidence-based. Zhejiang University, for example, reconstructed its core computer science curriculum using learning analytics data, leading to a 15-percentage-point increase in course pass rates. Learning analytics allows educators to track students’ progress, identify patterns of engagement, and adapt instructional strategies accordingly.
AI-assisted teaching quality monitoring systems are increasingly integrated into classroom environments (). Fudan University’s SmartEye for Teaching system analyzes indicators such as student–teacher interaction frequency, attention span fluctuations, and participation rates to provide actionable insights for instructional improvement. Similarly, Peking University has piloted a classroom behavior recognition system that uses computer vision to monitor attentiveness and engagement levels, helping instructors optimize their teaching pace and methods.
Dynamic feedback mechanisms powered by big data are also continuously evolving. Central China Normal University has developed an academic early warning system that leverages multidimensional data—such as attendance, assessment scores, online platform activity, and learning behavior patterns—to identify at-risk students early and provide targeted support. The system has been credited with increasing course retention rates by up to 12% and improving intervention efficiency by 30%.
Beyond individual institutions, national-level platforms are emerging. The “Smart Education of China” initiative integrates data from multiple universities to inform evidence-based policy decisions, enabling real-time tracking of learning outcomes and resource allocation across institutions. According to the Ministry of Education, over 300 universities have now adopted at least one form of AI-driven analytics to support curriculum optimization and student performance monitoring.
These data-driven approaches are transforming higher education by enabling personalized learning paths, improving teaching effectiveness, and enhancing institutional decision-making with greater precision and timeliness.
4.3. AI-Enabled Transformation of the Teacher’s Role
In the AI era, teachers are expected to shift from traditional knowledge transmitters to facilitators of learning, mentors of inquiry, and co-creators of knowledge (). This transformation emphasizes not only the redesign of instructional methods but also a fundamental change in teachers’ professional identity and pedagogical mindset. Surveys conducted by UNESCO and the OECD indicate that instructors who integrate AI tools into their teaching practices spend approximately 30% less time on administrative and lesson planning tasks, while allocating up to 50% more time to personalized student mentoring and formative feedback.
In the realm of research, AI-powered tools are significantly enhancing faculty productivity. Applications such as Academic Express, Semantic Scholar, and Dimensions use natural language processing to assist scholars in rapidly discovering relevant literature, identifying research trends, and synthesizing insights from massive volumes of publications. For instance, studies show that AI-assisted literature reviews can reduce time spent by up to 40% while increasing accuracy in source selection and citation mapping.
Teacher training and continuous professional development are critical for enabling this transition. Enhancing AI literacy among educators has become a top priority across institutions. Beijing Normal University was among the first to launch an AI competency development program for teachers, covering essential areas such as basic programming, educational data mining, AI ethics, and classroom applications. As of 2024, over 2,000 educators have completed the program, and similar initiatives are now being adopted in institutions like East China Normal University and South China University of Technology.
Moreover, interdisciplinary teaching models are gaining traction. Faculty members from education, computer science, and psychology departments are increasingly collaborating to develop cross-listed courses that integrate AI concepts with pedagogy. These efforts are helping educators not only use AI tools effectively but also critically examine their implications on learning, equity, and academic integrity.
Overall, the redefinition of the teacher’s role in the AI age goes beyond tool adoption—it requires institutional support, cross-disciplinary collaboration, and a sustained investment in developing a new generation of digitally fluent educators.
4.4. Ethical Considerations and Risk Mitigation
Data privacy protection remains the foremost ethical concern in the application of AI in education (). With the increasing reliance on learning analytics, biometric monitoring, and cloud-based AI services, large volumes of sensitive student data—including academic records, behavioral logs, and facial recognition outputs—are being collected and processed. To address these concerns, Nanjing University has implemented a robust “data anonymization” mechanism that ensures personal information is stripped of identifiable markers before use in algorithm training and educational analytics, thereby effectively safeguarding the privacy of both faculty and students.
Algorithmic bias is another pressing issue that has received growing attention. Research has shown that some intelligent grading systems and automated proctoring tools demonstrate implicit biases against students with non-standard accents, regional dialects, or neurodiverse behavior patterns. For instance, certain speech recognition models underperform when assessing students from specific linguistic backgrounds, leading to inequitable evaluation outcomes. These findings underscore the need for developers and educational institutions to audit algorithms regularly, incorporate diverse training data, and promote transparency in AI decision-making processes.
In the broader context of human-AI collaboration, it is essential to clearly define the scope and limits of technology use. While AI can enhance efficiency, there is a growing consensus that it should not replace human judgment in areas such as moral reasoning, emotional support, and value-based education. East China Normal University’s AI+HI (Artificial Intelligence + Human Intelligence) model exemplifies this balanced approach by promoting the integration of intelligent tools with human-centric pedagogy. The model advocates for AI to serve as a cognitive enhancer, assisting teachers in diagnosing learning needs and providing adaptive feedback, while preserving the teacher’s role in fostering critical thinking, empathy, and ethical reflection.
Additionally, the development of institutional AI ethics frameworks is gaining momentum. Universities in China, the EU, and North America are beginning to adopt ethical review boards for educational AI projects, guided by principles such as fairness, accountability, transparency, and human oversight. These initiatives aim to build trust among stakeholders and ensure that AI integration in education upholds the fundamental values of inclusivity, equity, and student well-being.
5. Discussion
Human-AI collaboration is expected to become the dominant instructional model, while the construction of smart campuses will integrate various intelligent technologies to create seamless learning environments. AI will also enhance the lifelong learning system, enabling full life-cycle coverage of educational resources. By 2030, it is projected that over 90% of university courses will incorporate some form of AI-assisted teaching.
Besides, universities should develop comprehensive AI education strategies. First, they should establish dedicated research centers for intelligent education. Second, investment in faculty AI competency training should be increased. Third, ethical norms governing AI applications in education must be refined. Finally, universities should build robust university–industry collaboration mechanisms to promote a virtuous cycle between technological innovation and practical application. Only through a multifaceted approach can the deep integration of AI and higher education be realized, ultimately cultivating innovative talents equipped for the intelligent era.
6. Conclusions
AI technologies are driving higher education toward greater personalization, intelligence, and data-driven approaches. This transformation goes beyond the modernization of teaching methods, fundamentally reshaping educational philosophies and talent cultivation models. Future education will place greater emphasis on meeting students' individual developmental needs and will prioritize competency-based learning over rote memorization.
Abbreviations

AI

Artificial Intelligence

VR/AR

Virtual Reality/Augmented Reality

AIIA

Artificial Intelligence-Enabled Intelligent Assistant

AI-TAs

Intelligent Teaching Assistants

AI+HI

Artificial Intelligence + Human Intelligence

Author Contributions
Jianming Li is the sole author. The author read and approved the final manuscript.
Funding
This work is partially supported by the Fundamental Research Funds for the Central Universities under grant Nos. 25CAFUC09010, 24CAFUC04015, and 24CAFUC03042. Educational and Talent Development Project of the Civil Aviation Administration of China MHJY2025016.
Conflicts of Interest
The authors declare no conflicts of interest.
References
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[4] Zouhaier S. The impact of artificial intelligence on higher education: An empirical study [J]. European Journal of Educational Sciences, 2023, 10(1): 17-33.
[5] Jafari F, Keykha A. Identifying the opportunities and challenges of artificial intelligence in higher education: a qualitative study [J]. Journal of Applied Research in Higher Education, 2024, 16(4): 1228-1245. October 2022).
[6] Jiali S, Dayo F, Jun G, et al. The Impact of Artificial Intelligence on Personalized Learning in Education: A Systematic Review [J]. Pakistan Journal of Life & Social Sciences, 2024, 22(2).
[7] Al-Zahrani A M, Alasmari T M. Exploring the impact of artificial intelligence on higher education: The dynamics of ethical, social, and educational implications [J]. Humanities and Social Sciences Communications, 2024, 11(1): 1-12.
[8] Sajja R, Sermet Y, Cikmaz M, et al. Artificial intelligence-enabled intelligent assistant for personalized and adaptive learning in higher education [J]. Information, 2024, 15(10): 596.
[9] Katsamakas E, Pavlov O V, Saklad R. Artificial intelligence and the transformation of higher education institutions: A systems approach [J]. Sustainability, 2024, 16(14): 6118.
[10] Chan C K Y, Tsi L H Y. Will generative AI replace teachers in higher education? A study of teacher and student perceptions [J]. Studies in Educational Evaluation, 2024, 83: 101395.
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    Jianming, L. (2025). An Exploration of Pedagogical Reform in Higher Education in the Age of Artificial Intelligence. Education Journal, 14(4), 177-181. https://doi.org/10.11648/j.edu.20251404.12

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    Jianming, L. An Exploration of Pedagogical Reform in Higher Education in the Age of Artificial Intelligence. Educ. J. 2025, 14(4), 177-181. doi: 10.11648/j.edu.20251404.12

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    AMA Style

    Jianming L. An Exploration of Pedagogical Reform in Higher Education in the Age of Artificial Intelligence. Educ J. 2025;14(4):177-181. doi: 10.11648/j.edu.20251404.12

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  • @article{10.11648/j.edu.20251404.12,
      author = {Li Jianming},
      title = {An Exploration of Pedagogical Reform in Higher Education in the Age of Artificial Intelligence
    },
      journal = {Education Journal},
      volume = {14},
      number = {4},
      pages = {177-181},
      doi = {10.11648/j.edu.20251404.12},
      url = {https://doi.org/10.11648/j.edu.20251404.12},
      eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.edu.20251404.12},
      abstract = {This article delves into validity of skills development hypothesis in promoting inclusive growth in the context of a lower-middle income economy confronted with several macroeconomic problems and labour market challenges. The analytical outline is guided by the theory of inclusive growth (IG) and existing body of empirical literature on skills development. As debated in the literature, there is no single solution to foster inclusive growth. Each country approaches the inclusive growth challenge differently, based on its resource endowments and unique socio-economic circumstances. There is a major literature gap using skills development models to promote inclusive growth. Thus, industry and country specific studies contribute to ongoing theoretical debate on inclusive growth on one hand and enrich empirical evidence on interlinks between skills development and IG on the other. Here, we present a case study of a skills development project (2017-2024), designed to promote inclusive growth through skills development of the tourism industry. Its target beneficiaries include both formal and informal sector business owners and employees of tourism sector micro, small and medium scale enterprises (MSMEs) operating in economically backward regions. The study utilizes data from both primary and secondary sources and employs mixed methods approach for detailed analysis. The paper provides several interesting findings on key themes of inclusive growth. First, the evidence on skills development of employees in the tourism value chain reveals sharing of growth benefits by low-income earners and the unemployed including females and disabled persons. Second, the findings on employer capacity building and skills development demonstrate inclusive growth benefits through entrepreneurship development, incremental income, and new product development. Third, the findings on gender and disability inclusion demonstrate power of skills development in promoting inclusive growth through MSME development. Finally, the overall findings enable policy makers in developing countries to promote inclusive growth through skills development of MSMEs operating in the tourism sector.},
     year = {2025}
    }
    

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    AB  - This article delves into validity of skills development hypothesis in promoting inclusive growth in the context of a lower-middle income economy confronted with several macroeconomic problems and labour market challenges. The analytical outline is guided by the theory of inclusive growth (IG) and existing body of empirical literature on skills development. As debated in the literature, there is no single solution to foster inclusive growth. Each country approaches the inclusive growth challenge differently, based on its resource endowments and unique socio-economic circumstances. There is a major literature gap using skills development models to promote inclusive growth. Thus, industry and country specific studies contribute to ongoing theoretical debate on inclusive growth on one hand and enrich empirical evidence on interlinks between skills development and IG on the other. Here, we present a case study of a skills development project (2017-2024), designed to promote inclusive growth through skills development of the tourism industry. Its target beneficiaries include both formal and informal sector business owners and employees of tourism sector micro, small and medium scale enterprises (MSMEs) operating in economically backward regions. The study utilizes data from both primary and secondary sources and employs mixed methods approach for detailed analysis. The paper provides several interesting findings on key themes of inclusive growth. First, the evidence on skills development of employees in the tourism value chain reveals sharing of growth benefits by low-income earners and the unemployed including females and disabled persons. Second, the findings on employer capacity building and skills development demonstrate inclusive growth benefits through entrepreneurship development, incremental income, and new product development. Third, the findings on gender and disability inclusion demonstrate power of skills development in promoting inclusive growth through MSME development. Finally, the overall findings enable policy makers in developing countries to promote inclusive growth through skills development of MSMEs operating in the tourism sector.
    VL  - 14
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Author Information
  • School of Computer Science, Civil Aviation Flight University of China, Guanghan, China

    Biography: Jianming Li is a lecturer at School of Computer Science, Civil Aviation Flight University of China. He completed his PhD in University of Chinese Academy of Sciences in 2021. His research interest includes artificial intelligence and higher education.

    Research Fields: Jianming Li: Artificial intelligence, higher education, computer vision, neural architecture search, database application.