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Article

Using Artificial Intelligence to Reshape Higher Education Such That It Is in Line with Sustainability: A Systematic Review of Pedagogical and Curricular Innovations

by
Rongkui Su
1,2,
Caiqi Liu
1,
Xiancheng Ma
1,
Runhua Chen
1,
Yiting Luo
1,3,* and
Yonghua Chen
1,*
1
College of Ecology and Environment, Central South University of Forestry and Technology, Changsha 410004, China
2
National Key Laboratory of Woody Oil Resources Utilization, Changsha 410004, China
3
Business School, Hunan First Normal University, Changsha 410114, China
*
Authors to whom correspondence should be addressed.
Sustainability 2026, 18(5), 2360; https://doi.org/10.3390/su18052360
Submission received: 23 December 2025 / Revised: 6 February 2026 / Accepted: 25 February 2026 / Published: 28 February 2026
(This article belongs to the Special Issue Sustainable Digital Transformation in Higher Education: Enhancing Sustainable Smart Education)
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Abstract

With the rapid advancement of artificial intelligence (AI), higher education is undergoing a significant transformation. AI offers unprecedented opportunities to enhance this field’s sustainability through pedagogical and curricular innovations, but there are significant challenges. Traditional teaching models often struggle to meet students’ personalized and diverse learning needs. In contrast, AI technologies offer new potential for enhancing education through intelligent data analytics, personalized recommendations, and automated management. Based on research sourced from the Web of Science Core Collection, we employed CiteSpace for visual analysis to map the current research landscape and emerging trends regarding AI in higher education. This review synthesizes these developments to provide a framework for stakeholders, contributing to the discourse on building a sustainable and intelligent higher education ecosystem that balances innovation and ethical governance.

1. Introduction

Higher education is facing significant challenges in its ongoing transformation [1,2]. Faced with an expanding student population, evolving learning needs, and the pressing demand for innovative talent, the traditional model of higher education is revealing inherent limitations. This situation positions higher education and digital transformation as two domains that are now inextricably and critically linked [3]. The accelerating pace of technological advancement compels higher-education institutions to equip students with the digital competencies essential for the modern workplace and society [4]. Under these conditions, the digital transformation of higher education has evolved from a discretionary option into a strategic imperative, necessitating innovative solutions to fundamentally reshape its educational ecosystem [5].
The rapid advancement of artificial intelligence (AI) provides a pivotal means of addressing these structural challenges. In education, AI is fundamentally disrupting the traditional, standardized paradigm by enabling the creation of highly personalized learning pathways [6]. Artificial intelligence tools are transforming conventional experimental methodologies, redefining established paradigms of scientific discovery, and generating novel opportunities to optimize learning processes [7]. Artificial Intelligence has evolved from a mere technological tool into a potentially transformative catalyst, prompting a significant re-evaluation of entrenched paradigms in higher education [8].
Through its innovative applications in intelligent teaching and learning, artificial intelligence is driving the transformation of instructional models in higher education, significantly enhancing their efficiency and effectiveness [9,10]. However, this transformative process is hampered by multifaceted challenges, including the digital divide [11], educational alienation [12], immature technologies, and concerns regarding personal information security [13]. Research and practice in AI-enabled higher education remain fragmented, with substantial discussions focused on isolated scenarios and lacking systematic attention to the synergistic transformation of teaching, learning, administration, and research [14].
In response, this review seeks to synthesize cross-disciplinary advancements and explore pathways toward a future intelligent education ecosystem that balances innovative dynamism with robust risk governance. By critically analyzing key research and representative case studies from 2016 to 2024, this analysis provides higher-education institutions, policymakers, and researchers with a framework that integrates both conceptual insight and practical utility. Specifically, it aims to answer three research questions: (1) What are the key research hotspots and evolutionary trends? (2) How does AI drive innovation in teaching and curriculum design? (3) What are the associated challenges?

2. Research Methodology

2.1. Data Source

The literature cited in this study was sourced from the Web of Science (WOS) Core Collection. The retrieval criteria applied to the WOS Core Collection database were as follows: TS (Topic Search) = (“AI Education” & “Higher Education” & “Machine Learning” & “Personalized learning” & “Educational reform”) AND PY = (2016-01-01 to 2024-12-31). This time frame fully covers the entire cycle from the early exploration phase to the current boom in generative AI, thereby ensuring the comprehensiveness of the research perspective. Document type was limited to research articles, and the language was restricted to English. Through a systematic screening process based on titles, abstracts, and keywords and in accordance with predetermined exclusion criteria, a final corpus of 814 English-language publications was identified.

2.2. Statistical Methods

In this study, we employed a mixed-methods approach to address the proposed research questions. First, a bibliometric analysis using CiteSpace was conducted on the collected corpus to map the intellectual landscape and identify trends. This process included keyword co-occurrence analysis and burst detection. Subsequently, a systematic thematic analysis of the literature was performed to delve into the mechanisms of innovation and the associated challenges, synthesizing findings from key empirical studies and theoretical discussions.

3. CiteSpace-Based Analysis of Research Status and Hot Topics in the Field of AI in Higher Education

3.1. Annual Distribution of the Number of Published Documents

Figure 1 illustrates the volume of research on AI-enabled higher education collected for this analysis. Under the specified retrieval criteria and following relevance screening, 814 pertinent English publications from the Web of Science (WOS) Core Collection were identified. The annual publication count demonstrates a pronounced upward trajectory, surging from 8 articles in 2016 to 370 in 2024, corresponding to a growth exceeding 400%. This marked acceleration, particularly evident since 2023, is likely due to breakthroughs in generative AI technologies, such as ChatGPT, and their widespread impact on global educational practices. This trend signals the field’s evolution from a phase of incremental growth into a period of rapid expansion. This exponential growth trajectory not only indicates a surge of attention within academia, but also empirically supports the core premise of this study: the integration of AI into higher education has evolved from a peripheral topic into an urgent and unignorable mainstream research field, providing quantitative evidence for the necessity of this review.

3.2. Journal Analysis

Among the 814 English publications retrieved, the corresponding research findings have been published across 353 academic journals. The data indicate that the top ten journals by publication volume (Table 1) account for 29.91% of the total literature, demonstrating a relatively concentrated distribution of research output. Among these top ten journals, six are ranked in JCR (Journal Citation Reports) Q1, and there are three in Q2. This distribution profile signifies that these periodicals maintain high academic standards overall and play a pivotal role in the scholarly communication and exchange within this field.

3.3. Keyword Analysis

In CiteSpace, the time span was set from 2016 to 2024. Keyword was selected as the node type, Cosine was chosen for link strength, and the scope was set to within slices. The selection criteria were specified as a g-index with a k-value of 18. Visualization was then performed to generate a knowledge map of keyword co-occurrence (Figure 2). The analysis identified “Artificial Intelligence” as the central node; it exhibits strong linkages with “Machine Learning” and “Higher Education,” together constituting the foundational triad of this research domain. Notably, “Generative AI” emerges as a distinct, densely connected node, reflecting its recent rise as a core driver of development. Furthermore, “Medical Education” forms a separate cluster, underscoring the early and in-depth application of AI within this highly standardized and practice-oriented professional field [15]. Artificial Intelligence (AI) is the discipline focused on creating systems capable of simulating human intelligence, including functions such as learning, reasoning, problem-solving, perception, and natural-language comprehension [16]. In the current landscape, higher education must cultivate adaptability and resilience to equip students for the job market transformations driven by artificial intelligence (AI), machine learning, and big data. Generative AI represents a frontier branch of artificial intelligence [17,18]. Its core function lies in enabling machines to learn patterns from data [19] and create novel, original content (e.g., text, images, code, audio, and video). Such technologies are typically powered by large language models (LLMs) [20], exemplified by generative AI systems like ChatGPT and DeepSeek. In higher education, the primary impact of AI lies in its capacity to enable personalized learning, thereby allowing more-targeted instruction. Furthermore, AI is evolving from a supportive tool into a “transformative agent,” fundamentally restructuring practices in teaching, research, and institutional administration [21].
The network structure explicitly delineates the inherent logical architecture of the research domain. The robust correlation between “generative AI” and “personalized learning” is non-random; it directly proves that the integration of the contemporary technological frontier (generative AI) into the core educational paradigm (personalization) serves as the pivotal thread driving the advancement of this field.
By setting the Burstness parameter γ [0, 1] to 0.8, we identified the top 10 burst terms in the AI-enabled higher education domain (Table 2). The keyword exhibiting the highest burst strength is “Big Data” (Strength: 4.28), indicating its predominant role as a core driver. It forms, together with AI in education and learning analytics, an essential technological triad that supports the implementation of smart education.

3.4. From Analytic Clusters to Thematic Focus: Structuring the Review

The keyword co-occurrence network (Figure 2) and burst-term detection results (Table 2) reveal not isolated, scattered terms but logically coherent thematic clusters that delineate the core of current academic discourse. To advance the transition from mapping the research landscape to deepening cognitive understanding, the subsequent thematic sections of this review are structured around the identified core clusters, as follows: (1) The cluster centered on personalized learning, generative artificial intelligence (AI), and ChatGPT underscores the prevailing research trend—leveraging emerging generative technologies to develop adaptive, student-centered learning models. (2) The cluster anchored in keywords including learning analytics, smart education, and data mining clarifies that data-driven strategies constitute the fundamental logic underpinning contemporary educational innovation. (3) The cluster associated with core concepts such as higher education, management, and systems demonstrates that the strong correlation between core AI terms and educational governance concepts indicates a critical research trajectory for institutional transformation.

4. AI-Driven Reshaping and Innovation of Teaching Models in Higher Education

4.1. Artificial Intelligence Facilitates Personalized Learning

Personalized learning, a significant educational trend, refers to a student-centered philosophy that accounts for individual differences in learning-related starting points, goals, pace, and pathways [22,23]. Within this framework, students assume the role of active participants in the learning process. Empowered to select content, set goals, adjust their pace, and demonstrate mastery, they develop key competencies through increased agency and ownership [24,25]. Building on student profiles, AI systems can deliver diverse and adaptive learning resources.
Artificial intelligence is the pivotal enabler that bridges the gap between the theoretical framework of personalized learning and its scalable implementation [26]. Using AI-driven algorithms, educational systems can provide personalized learning pathways that respond to the specific needs of individual students [27]. In conventional instructional settings, educators often struggle to monitor student progress comprehensively. By continuously collecting and analyzing large-scale learning process data, AI instead facilitates real-time, individualized tracking of learning states [28]. By employing machine learning and data-mining techniques, AI builds comprehensive, dynamic “learner profiles” that pinpoint knowledge gaps, learning styles, progress patterns, and potential risks (see Figure 3). The dynamic learner profile illustrates how AI transforms vague learner characteristics into analyzable data dimensions, which serve as the technical cornerstone for shifting from empirical teaching to precision diagnosis. This shift transforms the educational paradigm from one based on experiential intuition to a data-driven, scientifically precise diagnostic approach, effectively operationalizing the principle of “teaching students according to their aptitude” [29].
Furthermore, technologies within the AI domain, particularly natural language processing (NLP) and generative AI, have significantly enriched the dimensions of personalized interaction [30,31]. Students can pose questions to an AI tutor using natural language at any time and receive immediate, heuristic responses [32]. More importantly, generative AI can create personalized learning materials on demand [33], such as a vivid analogy for a student struggling with an abstract concept or an exploratory project proposal for an advanced learner. This intelligent interaction significantly aids student comprehension [34]. In summary, from the construction of dynamic learner profiles to intelligent interaction and feedback, AI-enabled personalized learning constitutes a systematic endeavor underpinned by the integration of multiple technologies, as illustrated in Figure 4. It clarifies that “personalized learning” is not a standalone technical application but a systematic project that requires the collaboration of multiple technologies, helping to facilitate an understanding of the complexities involved in its implementation.
However, this data-driven learner profiling raises profound ethical concerns. On one hand, continuous data tracking touches the boundaries of student privacy and carries risks of information misuse and security breaches. On the other hand, reducing complex and multidimensional learners into a series of analyzable data points entails the danger of “data simplification,” potentially overlooking key humanistic dimensions—such as emotion, sociality, and creativity—that cannot be quantified. Therefore, while leveraging AI for precise educational diagnosis, it is essential to remain vigilant against its potential to erode student agency and narrow the richness of the educational process.

4.2. Intelligent Upgrading of Classroom Teaching

The intelligent transformation of classroom teaching, empowered by AI, is currently demonstrated primarily through the application of AI teaching assistants [35]. Advances in large language models (LLMs) have driven the widespread deployment of AI teaching assistants across diverse academic disciplines [36]. These intelligent systems are reshaping pedagogical practices and enhancing learning experiences [37]. By efficiently managing time-consuming routine tasks, they alleviate instructors’ workloads, allowing educators to devote greater attention to creative and student-centered instructional activities.
In a controlled study conducted by Tracy Kaitlyn and colleagues, 52 participants were divided into a control group (who received standard instruction) and an experimental group (whose teaching was supplemented with an AI teaching assistant) and instructed to watch a virtual-reality module on bubble sort algorithms [38]. The empirical results demonstrated that the AI-powered teaching assistant significantly improved student engagement and deepened conceptual understanding [39]. Complementary research evaluating virtual teaching assistants conducted by Professor Sajja Ramteja at the University of Iowa confirmed that VTAs effectively increase student participation, reduce faculty workload, and contribute to developing more inclusive and interactive learning environments [40].
While AI-driven personalized learning demonstrates tremendous potential in enhancing the precision and interactivity of education, its large-scale implementation is still hindered by multiple challenges, including risks related to data privacy and security, algorithmic biases that may undermine equity, a widening digital divide exacerbated by over-reliance on technology, high implementation costs, and teachers’ difficulty adapting to their evolving roles. Collectively, these factors impede the progress from conceptualization to full-scale deployment. From the perspective of constructivist learning, an excessively precise and rigid personalized pathway may deprive learners of critical opportunities to independently explore, experiment through trial and error, and construct their own knowledge frameworks—an outcome that runs counter to the essence of deep learning.

5. AI-Augmented Intelligent Course Development and Instructional Design

Unlike traditional models reliant on instructors’ personal experience and static syllabi, AI-driven intelligent curriculum design constitutes a data-driven [41], dynamically iterative, and highly personalized closed-loop process [42], transforming course design from a static framework into an adaptive dynamic architecture capable of meeting diverse learner-group needs [43].

5.1. Data-Driven Precision Requires Analysis in Curriculum Development

The design of higher-education curricula is increasingly shifting from traditional, generalized disciplinary frameworks to models that are directly informed by specific research needs [44]. This transformation is enabled by technologies like natural-language processing and text mining [45], which together constitute an intelligent “academic radar system.” This system performs real-time monitoring and analysis of global knowledge production, with its core function residing in integrated analysis of multi-source, heterogeneous data. By continuously tracking and analyzing leading global journals, research grants, and scientific reports, AI generates a “dynamic knowledge map” that tracks the evolution of research trends and innovation trajectories. Using topic-modeling algorithms, it can identify emerging research frontiers, offering forward-looking guidance for curriculum development [46,47].
Furthermore, AI constructs a “disciplinary knowledge graph” that comprehensively represents the domain’s knowledge structure and evolutionary trajectory [48]. By comparing the skill requirements revealed through this graph with existing course content, AI can precisely diagnose “generational gaps” and “curricular deficiencies” within the program architecture [49]. This workflow delivers data-informed decision support for creating and revising courses, enabling curriculum design to transition from experience-based to data-driven and from reactive to agile-forward approaches [50].
The flowchart in Figure 5 not only delineates the procedural steps but also depicts a novel paradigm for curriculum development. It reconstructs the conventionally linear and static design process into a dynamic iterative system with data as the core of feedback, vividly embodying the logical shift of AI-driven curriculum design from predetermination to generative design.

5.2. The Transformation of Higher-Education Instructional Design Driven by Al

In higher education, the core objective of instructional design is to create a learning environment that fosters students’ critical thinking and guides them in autonomous knowledge exploration [51,52]. Within this context, artificial intelligence serves not only as a tool for efficiency but also as a strategic partner that deepens learning engagement and fosters higher-order thinking [53]. This collaborative role drives a fundamental pedagogical shift from a traditional, instructor-centric “lecture-based model” to a more interactive and adaptive “tutorial model” [54]. AI supports educators by enhancing their capacity to design curricula that are both coherent in structure and aligned with the forefront of a given field [55]. By utilizing AI-powered literature analysis tools [56], instructors can swiftly grasp the intellectual landscape and emerging frontiers of their discipline, thereby ensuring the contemporary relevance and academic rigor of their course content. Furthermore, AI can model knowledge systems to assist in optimizing logical sequencing and integration between course modules, thereby laying a solid foundation for developing students’ systematic thinking skills.
In supporting students’ deep learning, AI enables personalized attention within large-scale instructional scenarios [57]. By analyzing learning behavior data, AI systems can promptly identify students experiencing comprehension difficulties or insufficient engagement, thereby facilitating targeted instructor intervention. Furthermore, utilizing generative AI-powered simulated dialog tools [58], such as virtual conversations with historical figures or classical theorists, can create immersive academic scenarios that significantly stimulate students’ interest [59]. A study by Jiyoung Seo and Kim Sunah explored a novel human–AI collaborative paradigm: After having 182 undergraduate students independently construct knowledge frameworks, they compared their results with those generated by ChatGPT-3.5 to investigate their cognitive processes. Questionnaire feedback revealed positive outcomes, with students not only recognizing the activity’s value but also developing a strong interest in applying AI for language-learning purposes [60].
In the assessment phase, AI-assisted evaluation systems can provide students with feedback on aspects such as argumentation logic and structural coherence in their writing [61,62], enabling instructors to shift their focus from fundamental corrections to guiding core academic values like scholarly insight and innovativeness [63]. Such a feedback model not only significantly enhances assessment efficiency but also creates a continuous, iterative, and self-improving learning loop for students (Table 3).
Despite the broad prospects of AI-driven intelligent curriculum design, its core logical chain is still hindered by notable challenges: From the perspective of data sources, the relied-upon datasets (such as papers published in top journals) may carry systematic biases, leading to distortions in the constructed knowledge graphs and the demand analysis itself. This further causes problems pertaining to design and implementation; namely, dynamic and high-standard curriculum systems present severe tests of teachers’ development capabilities and schools’ governance models. In the teaching process, this design mode may invisibly encourage students to over-rely on AI tools, thereby inhibiting the development of their in-depth thinking and independent exploration abilities.

6. AI-Transformed Advancement of Scientific Management and Decision-Making in Education

The enabling role of AI extends beyond the front-end of teaching and curriculum to profoundly permeate the back-end of educational management and decision-making, ensuring the coordination and efficiency of the entire educational ecosystem [64]. At the level of educational management and decision-making, AI significantly enhances the intelligence of “institutional research.” While traditional analyses often rely on static, isolated data, AI technologies—particularly machine learning and predictive analytics models [65,66]—can integrate and process massive datasets from admissions, career services, research, and other departments in real time, revealing dynamic trends and deep correlations that are difficult to detect manually [67]. From the perspective of the sociotechnical system, the introduction of AI is not merely an upgrade of management tools but also a profound transformation of organizational decision-making logic. The growing authority of data-driven decision-making is likely to reshape the internal power relations and trust foundations of institutions. In disciplinary and program planning, AI drives the optimization of program structures and the development of emerging disciplines by analyzing the relationships between industries, employment, and curricula, thereby precisely aligning with regional economic needs.
Beyond educational management, AI’s impact is particularly profound with respect to research administration, where it can serve as a powerful, multi-faceted research support tool [68]. AI-powered tools help researchers quickly grasp cutting-edge developments, identify research gaps, and refine project design [69]. They can process many types of experimental data and complex social network information, accelerating the scientific discovery process. For research administrators, AI enables systematic analysis of institutional output, collaboration networks, and scholarly impact, helping to identify strong research teams and potential areas for growth [70]. This provides an evidence-based foundation for strategic resource allocation and interdisciplinary collaboration, thereby enhancing an institution’s overall research vitality and innovative capacity.

7. Comprehensive Discussion of Challenges

The comprehensive intelligent transformation of teaching, curriculum, and administration is not a straightforward process; it conceals numerous risks and challenges at both technological and ethical levels. Empowering higher education with artificial intelligence is a profound change that touches the core of education itself [71]. The essence of education lies in its humanistic attributes, emphasizing emotional communication and value formation, while AI’s technological logic is grounded in data and algorithms. Overreliance on data metrics may lead to the loss of education’s “human touch” [72,73], thereby eroding the cultivation of students’ creativity, critical thinking [74], and humanistic care—the most precious elements of education. At the ethical level, the challenges extend far beyond personal information security, encompassing the more profound systemic harm algorithmic bias inflicts on educational equity. If the data used to train AI models contain historical biases, the resulting recommendations, predictions, and evaluation outcomes will perpetuate and potentially amplify these inequities [75,76]. The integration of AI into higher education is far from a linear process of technological dividend realization. As many scholars have cautioned, we must guard against a tendency toward “technological solutionism”—the simplistic assumption that technology alone can automatically resolve complex social and educational challenges. Current practices often reframe educational issues as engineering problems awaiting technological optimization, potentially overlooking the deeper political, economic, and cultural contexts in which they are embedded.
The traditional higher-education system has established a stable structure and management model [77]. However, the advancement of AI-driven personalized learning necessitates the development of more flexible educational infrastructure. This transformation is driving a shift in the teacher’s role from knowledge transmitter to learning facilitator while simultaneously requiring students to develop new competencies to navigate intelligent learning environments [78]. Nevertheless, the empirical foundation for such systemic change remains weak. Current research on AI education is largely confined to short-term pilot studies, lacking long-term evidence regarding its impact on critical thinking and other higher-order skills.
The practical implementation of this transformation is clouded by uncertainties regarding cost-effectiveness [79]. Universities must make substantial ongoing investments in AI infrastructure, platform acquisition, and curriculum development. However, the manifestation of educational outcomes is inherently a long-term process and delayed [80]. When massive-scale resource consumption fails to yield quantifiable immediate returns, the sustainability of such initiatives becomes questionable. Consequently, many AI education projects struggle to move beyond the pilot stage; they cannot achieve the crucial transition from isolated tool-based applications to comprehensive educational paradigm innovation [81].

8. Conclusions and Prospects

The integration of artificial intelligence into higher education has become a prominent and, likely, enduring development [82]. This study explores the integration of artificial intelligence (AI) and higher education via three focused research questions and arrives at the following key findings. First, bibliometric analysis indicates that this domain is undergoing a phase of explosive growth, with research hotspots evolving rapidly from foundational concepts such as big data and learning analytics to cutting-edge application-oriented topics, including generative AI and task analysis. The sharp surge in the volume of publications since 2023 underscores the transformative implications of large language models (LLMs) for the field. Second, further in-depth analysis elaborates on concrete AI-driven innovations, ranging from the construction of dynamic learner profiles to enable personalized instruction to the deployment of AI teaching assistants in order to augment classroom teaching and the utilization of data analytics to facilitate agile curriculum development. Existing empirical evidence demonstrates that these innovations have yielded measurable gains in boosting student engagement, enhancing teaching efficiency, and improving diagnostic precision. Nevertheless, this study also cautions that such innovations are inextricably associated with systemic challenges, encompassing algorithmic biases that jeopardize educational equity, the “datafication” risk that undermines the humanistic essence of education, ambiguities surrounding the evolving role of teachers, problems upholding academic integrity, and issues relating to sustainable cost-effectiveness. In conclusion, the integration of AI into higher education is not a linear, benefit-driven process but rather a sophisticated dynamic in which opportunities and challenges are inherently interdependent and mutually constitutive. Looking ahead, the deep integration of AI into higher education will evolve in multiple dimensions [83]. Instructors will transition from knowledge transmitters to learning designers and developmental guides. Simultaneously, the educational ecosystem [84] will become more open, transcending traditional disciplinary boundaries and spatiotemporal constraints. The ultimate objective of AI-enabled higher education is to construct a new educational ecology characterized by human–machine collaboration [85] and mutual reinforcement between teaching and learning. Achieving this goal requires striking a balance between technological innovation and pedagogical principles to promote high-quality development in higher education [86]. Future research should place greater emphasis on evaluating the long-term effects of AI applications in education and exploring how technology can more effectively serve the mission of talent development [87]. The long-term educational impacts of AI will be verified through longitudinal tracking and causal research, and a comprehensive evaluation framework integrating environmental, economic, and social dimensions will be developed so as to guide higher education to achieve an inclusive and sustainable transformation in the intelligent era.
This study systematically delineates the enabling pathways regarding artificial intelligence in higher education, not only providing a theoretical foundation and practical framework for building a more adaptive, equitable, and efficient future educational ecosystem but also injecting intelligent momentum into the long-term sustainable development of the education system through talent cultivation, resource optimization, and model innovation.

Author Contributions

Conceptualization, R.S. and C.L.; methodology, Y.L. and C.L.; formal analysis, Y.L. and R.S.; data curation, R.S. and C.L.; writing—original draft, R.S. and C.L.; writing—review and editing, Y.L., Y.C., X.M. and R.C.; project administration, R.S. and Y.L.; funding acquisition, R.S. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the science and technology innovation Program of Hunan Province (2025RC3182), the Research Project on Teaching Reform in Ordinary Undergraduate Universities in Hunan Province (202401000120), and the Teaching Reform Research Project of Central South University of Forestry and Technology.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Data are contained within the article.

Acknowledgments

The authors thank all the participants who devoted their free time to participating in this study.

Conflicts of Interest

The authors declare no conflicts of interest.

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Sustainability 18 02360 g001
Figure 1. The number of articles related to how AI is transforming higher education published in the 2016~2024 period (source: authors).
Figure 1. The number of articles related to how AI is transforming higher education published in the 2016~2024 period (source: authors).
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Sustainability 18 02360 g002
Figure 2. Co-presentation map of the keywords pertaining to research on AI’s transformation of higher education (source: authors).
Figure 2. Co-presentation map of the keywords pertaining to research on AI’s transformation of higher education (source: authors).
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Sustainability 18 02360 g003
Figure 3. Example of a “Learner Profile” (source: authors).
Figure 3. Example of a “Learner Profile” (source: authors).
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Figure 4. A concise image depicting how AI technology is transforming personalized learning (source: authors).
Figure 4. A concise image depicting how AI technology is transforming personalized learning (source: authors).
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Figure 5. Flowchart pertaining to course design transformed by AI technology (source: authors).
Figure 5. Flowchart pertaining to course design transformed by AI technology (source: authors).
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Table 1. Top 10 journals in terms of the number of articles on AI-enabled higher-education research.
Table 1. Top 10 journals in terms of the number of articles on AI-enabled higher-education research.
RankingJournal NameJCR Broad CategoriesPublication CountProportion (%)Impact Factor (2024)
1SustainabilityEnvironmental Sciences Q2809.833.9
2IEEE AccessComputer Science, Information Systems Q2445.413.6
3Applied Sciences-BaselEngineering, Multidisciplinary Q2242.952.2
4HeliyonMultidisciplinary Sciences Q1242.953.4
5International Journal Of Human–Computer InteractionComputer Science, Cybernetics Q1172.094.9
6Bmc Medical EducationEducation and Educational Research Q1141.723.2
7Medical TeacherEducation, Scientific Disciplines Q1111.354.28
8IEEE Transactions On Learning TechnologiesEducation and Educational Research Q1101.224.4
9Soft ComputingComputer Science, Artificial Intelligence Q3101.282.5
10Computers & EducationEducation and Educational Research Q191.118.9
Table 2. The top 10 burst terms in the field of AI-transformed higher education.
Table 2. The top 10 burst terms in the field of AI-transformed higher education.
KeywordsYearStrengthBeginEnd
Big Data20204.2820202023Sustainability 18 02360 i001
Generative AI20234.2520232024Sustainability 18 02360 i002
AI Education20203.8720212023Sustainability 18 02360 i003
Learning Analytics20213.3320212023Sustainability 18 02360 i004
Deep Learning20193.0620192022Sustainability 18 02360 i005
Machine Learning20192.9720192020Sustainability 18 02360 i006
Design20202.3520232024Sustainability 18 02360 i007
System20202.2720202021Sustainability 18 02360 i008
Challenges20171.7420222024Sustainability 18 02360 i009
Data Science20191.4720212022Sustainability 18 02360 i010
Legend: Red = the duration of the burst. Blue = the period from 2016 to 2024.
Table 3. AI transformation in higher-education instructional design: a simplified table.
Table 3. AI transformation in higher-education instructional design: a simplified table.
Empowerment AreaCore AI Applications
Course Content DevelopmentAnalyzing academic frontiers and generating teaching cases and simulated data to ensure there is cutting-edge and practical content.
Learning Activity DesignEnabling personalized attention in large classes, acting as a research assistant, and creating simulations to enhance inquiry-based learning.
Assessment and FeedbackProviding initial feedback on students’ papers and checking for compliance and freeing up instructors so that they may focus on guiding academic innovation.
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Su, R.; Liu, C.; Ma, X.; Chen, R.; Luo, Y.; Chen, Y. Using Artificial Intelligence to Reshape Higher Education Such That It Is in Line with Sustainability: A Systematic Review of Pedagogical and Curricular Innovations. Sustainability 2026, 18, 2360. https://doi.org/10.3390/su18052360

AMA Style

Su R, Liu C, Ma X, Chen R, Luo Y, Chen Y. Using Artificial Intelligence to Reshape Higher Education Such That It Is in Line with Sustainability: A Systematic Review of Pedagogical and Curricular Innovations. Sustainability. 2026; 18(5):2360. https://doi.org/10.3390/su18052360

Chicago/Turabian Style

Su, Rongkui, Caiqi Liu, Xiancheng Ma, Runhua Chen, Yiting Luo, and Yonghua Chen. 2026. "Using Artificial Intelligence to Reshape Higher Education Such That It Is in Line with Sustainability: A Systematic Review of Pedagogical and Curricular Innovations" Sustainability 18, no. 5: 2360. https://doi.org/10.3390/su18052360

APA Style

Su, R., Liu, C., Ma, X., Chen, R., Luo, Y., & Chen, Y. (2026). Using Artificial Intelligence to Reshape Higher Education Such That It Is in Line with Sustainability: A Systematic Review of Pedagogical and Curricular Innovations. Sustainability, 18(5), 2360. https://doi.org/10.3390/su18052360

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