Artificial Intelligence Empowering Higher Education: Exploration of Teaching Mode Innovation and Quality

Artificial Intelligence Empowering Higher Education: Exploration of Teaching Mode Innovation and Quality

Lichen Zhang., Haihong Jiang., Liyan Liu., Yanli Ma

Software School, Harbin Information Engineering Institude

DOI: https://dx.doi.org/10.47772/IJRISS.2025.903SEDU0458

Received: 23 July 2025; Accepted: 31 July 2025; Published: 04 September 2025

ABSTRACT

The rapid development of artificial intelligence technology has brought unprecedented opportunities and challenges to higher education. This paper aims to explore how artificial intelligence technology empowers teaching mode innovation in higher education and analyzes the pathways and influencing factors for improving teaching quality. The research adopts theoretical analysis, literature review, and comparative research methods to examine the current status and trends of artificial intelligence applications in higher education. The study finds that artificial intelligence demonstrates powerful empowering effects in personalized learning support, intelligent tutoring and Q&A, automated teaching assessment, and course content optimization, significantly improving teaching efficiency and learning experience, while also being accompanied by risks such as data privacy, algorithmic bias, and academic ethics. This paper constructs an analytical framework for artificial intelligence empowering teaching mode innovation and quality enhancement in higher education, and proposes forward-looking insights for future research directions, providing theoretical reference and practical enlightenment for the digital transformation of higher education institutions.

Keywords: Artificial Intelligence; Higher Education; Teaching Mode Innovation; Educational Quality Enhancement; Personalized Learning; Intelligent Tutoring Systems

INTRODUCTION

Since the early 21st century, Artificial Intelligence (AI), particularly advances in deep learning and natural language processing has achieved significant breakthroughs, reshaping numerous industries, including education (Yeruva, 2023). The introduction of generative AI, such as ChatGPT in late 2022, has further accelerated AI adoption across multiple sectors, with higher education being a key area of transformation.

As the foundation for cultivating highly skilled and innovative talent, higher education faces growing pressure to adapt to evolving societal needs. However, traditional teaching models are increasingly showing their limitations. In this context, AI provides powerful tools to enhance learning experiences, streamline administrative work, and deliver personalized instruction (Rane et al., 2023; Sajja et al., 2023). Its integration offers the potential to make education more adaptive, inclusive, and effective (Ayeni et al., 2024).

By rethinking conventional pedagogical approaches, AI creates new opportunities for educators, students, and institutions alike. Its application can help address long-standing challenges in higher education while enabling innovative solutions for teaching and learning (George & Wooden, 2023).

Table 1 presents a comparative overview of four major challenges in traditional higher education and the corresponding AI-driven solutions. These include personalized learning support, intelligent tutoring systems, diversified assessment methods, and continuously updated teaching resources. This framework highlights how AI can effectively overcome the inherent shortcomings of traditional approaches, providing a solid foundation for exploring how AI drives innovation in teaching models and enhances educational quality.

Table 1.  Challenges Faced by Traditional Teaching Modes and Potential AI Solutions

This study examines how AI drives the fundamental transformation of teaching models in higher education. It explores the specific ways these innovations contribute to improving teaching quality, while also analyzing the underlying mechanisms, key components, potential risks, and corresponding mitigation strategies. Such an in-depth investigation holds both theoretical and practical significance for advancing the modernization and high-quality development of higher education.

LITERATURE REVIEW

In recent years, the integration of AI into education has become a prominent global research focus. AI in education involves applying technologies such as machine learning and natural language processing to enhance teaching and learning experiences (Alneyadi et al., 2023). These technologies use algorithms to analyze educational data, identify patterns, and make predictions, enabling educators to personalize learning according to each student’s needs (Khan et al., 2022). By adapting instructional style, pace, and assessment methods, AI can create more accessible learning environments, particularly for English language learners and students with disabilities (Abrams, 2025).

Several educational organizations have adopted AI-powered tools. For instance, Khan Academy’s chatbot, Khanmigo, assists students with math, science, and humanities problems, facilitates debates on topics such as student debt cancellation, and acts as a writing tutor for story creation (O’Brien, 2023). Similar initiatives have been implemented by companies such as McGraw Hill and Carnegie Learning. In addition, open-source alternatives are emerging to address the high cost and limited customization of commercial platforms. One example is OATutor, developed by Zachary Pardos, PhD, at the University of California, Berkeley (Abrams, 2025). Research output on AI in education surged in 2021 and 2022, nearly tripling compared to earlier years (ScienceDirect, 2024).

In higher education, AI applications are becoming increasingly diverse. AI teaching assistants, automated grading systems, and intelligent scheduling tools help reduce educators’ repetitive workloads (Owoseni et al., 2024). Learning analytics and adaptive learning systems enable the creation of personalized learning pathways, boosting student motivation and performance (Khan et al., 2023). Generative AI offers significant potential for innovating teaching content, while learning analytics provide real-time data to support formative assessments and monitor students’ cognitive progress (Chan & Tsi, 2024). For example, the University of Murcia in Spain uses an AI-powered chatbot, Lola, to answer student inquiries about campus facilities and academic programs (O’Brien, 2023).

AI has also influenced the development of innovative teaching models such as project-based learning, inquiry-based learning, and blended learning. Theoretical frameworks like constructivism and connectivism have gained new interpretations in this context, emphasizing active knowledge construction and expanded learning networks.

Despite notable progress, current research exhibits several limitations. Many studies focus narrowly on the outcomes of specific AI tools in isolated teaching contexts, with limited exploration of systematic, theory-driven innovations in teaching models. There is insufficient analysis of the mechanisms, multi-dimensional effects, and long-term impacts of AI on teaching quality. Furthermore, topics such as ethical considerations, data security, and the evolving role of educators remain underexplored (Bond et al., 2024). Scholars have emphasized the importance of critically engaging with AI resources, ensuring they serve as supportive tools while preserving academic integrity (Castillo-Martinez et al., 2024).

To address these gaps, this study proposes the development of an integrated analytical framework for AI-driven teaching model innovation and quality enhancement in higher education. The framework will examine the mechanisms, key factors, and strategies required to harness AI effectively while mitigating associated risks.

RESEARCH METHOD

This research primarily adopts a combination of theoretical analysis, literature review, and comparative research methods.

Theoretical Analysis Method

Based on theories from education, learning sciences, cognitive psychology, and technology acceptance models, this method analyzes the interactive relationships and empowerment mechanisms between core AI technology characteristics and various elements of higher education teaching systems. Through logical reasoning and theoretical construction, it clarifies how AI reshapes teaching processes, optimizes resource allocation, and stimulates the potential of teachers and students, promoting the evolution of teaching modes toward intelligence and personalization, thereby improving teaching quality.

Literature Review Method

This systematically searches and analyzes high-quality English literature on AI applications in higher education published in authoritative academic databases between 2020-2024. Search keywords include “artificial intelligence”, “higher education”, “teaching mode innovation” and their combinations. Through literature content analysis, thematic induction, and critical evaluation, it outlines the theoretical foundations, frontier practices, success factors, and future trends of AI empowering higher education.

Comparative Research Method

This horizontally compares the application modes of different types of AI in higher education teaching practice, analyzing their differences implementation pathways, resource requirements, technology integration levels, teacher-student interaction, and learning effects. Combined with literature research, it introduces comparative cases from typical universities in different regions, summarizing generalizable experiences and lessons to enhance the universality and practical guidance of research conclusions.

DISCUSSION

The rapid development of AI has injected unprecedented vitality into teaching mode innovation and quality enhancement in higher education. This section will conduct in-depth discussions around three main modules: specific pathways and mechanisms of AI empowering teaching mode innovation, key elements and strategies driving teaching quality enhancement and challenges, ethical issues, and coping measures faced.

Specific Pathways and Mechanisms of AI Empowering Higher Education Teaching Mode Innovation

AI technology, especially the rise of generative AI, is profoundly changing higher education teaching scenarios. Its pathways and mechanisms for empowering teaching mode innovation are mainly reflected in the following aspects： 

Achieving Personalized and Adaptive Learning

In education, the integration of AI has initiated a paradigm shift toward personalized learning experiences tailored to each student’s needs, learning styles, and preferences (Rane et al., 2023; Singh, 2023). AI-powered adaptive learning systems analyze extensive data on student performance and preferences to customize instruction accordingly (Gligorea et al., 2023). By collecting and evaluating learning behavior data, AI can construct detailed student profiles and dynamically adjust teaching content, learning pace, and interaction strategies. Intelligent tutoring systems and adaptive platforms deliver individualized learning plans and resources, embodying the principle of “teaching according to aptitude.”

Research demonstrates that these AI-driven approaches enhance engagement and improve learning efficiency, breaking away from the traditional “one-size-fits-all” classroom model (Alam & Kachanac, 2023). This shift enables more targeted, efficient, and responsive learning environments.

Figure 2 illustrates AI’s impact across various dimensions of personalized learning, with the highest influence observed in personalized tutoring and instant feedback (92%), followed closely by learning data collection and analysis (90%). These results highlight AI’s primary contribution, offering real-time, tailored support that transforms static, uniform instruction into adaptive, student-centered learning experiences.

Figure 2. The Degree of AI Empowerment in Various Dimensions of Personalized Learning

Teaching Content and Curriculum Design Innovation

Generative AI offers transformative capabilities for teaching content development and curriculum design. Educators can leverage these tools to rapidly produce teaching outlines, course materials, case studies, assessment items, and virtual experimental scenarios. Recent studies have examined the use of ChatGPT in instructional design, highlighting its efficiency and potential while noting that the expertise of instructional designers remains essential for maintaining content quality (Choi et al., 2024). In addition, AI can support interdisciplinary course integration and the creation of forward-looking, innovative course modules.

Enhanced Interactive Teaching and Collaborative Learning

AI technology can enrich teaching by fostering dynamic and immersive learning experiences. When combined with Virtual Reality (VR) and Augmented Reality (AR), AI can create virtual environments that allow students to practice skills, explore concepts, and engage in experiential learning (Eden et al., 2024). In collaborative learning contexts, AI can assume multiple roles: acting as an intelligent discussion partner, coordinating task allocation and progress tracking, or evaluating group performance through analytical feedback. Such applications enhance student engagement, strengthen teamwork, and deepen conceptual understanding.

Practical examples include biology students using AR to overlay digital anatomical structures onto physical models, enabling three-dimensional visualization of complex biological processes. Similarly, history students can use VR to immerse themselves in reconstructions of historical sites and events, gaining richer insights into their context and significance (Yin, 2022; AlGerafi et al., 2023; Gandedkar et al., 2021).

Table 2 presents a comparative overview of AI functions across various innovative teaching modes. The findings reveal that AI’s contributions differ depending on the instructional approach: in project-based learning, AI operates as a comprehensive support system; in inquiry-based learning, it serves as an intelligent guide for discovery; and in flipped classrooms, it facilitates both content creation and interaction management. This versatility underscores AI’s adaptability to diverse pedagogical strategies.

Table 2. Comparison of AI Roles in Different Innovative Teaching Modes

Key Elements and Strategies for AI Driving Teaching Quality Enhancement in Higher Education

The application of AI is not only innovation in teaching modes but also substantial improvement in teaching quality. This requires attention to the following key elements：

Precise Teaching Assessment and Formative Feedback

AI has the potential to transform traditional, single-dimensional assessment methods. By continuously tracking student behavior data, it enables multi-dimensional, dynamic formative assessments that can quickly identify learning difficulties. More importantly, AI can deliver instant, personalized feedback and targeted suggestions based on assessment results. This individualized approach enhances student engagement, motivation, and academic performance by fostering a stronger sense of support and empowerment in the learning process (Zakaria et al., 2024).

Some studies have proposed AI-enabled feedback–feedforward model that effectively strengthens online collaborative learning and team performance (Zhong et al., 2023). This “diagnosis–feedback–adjustment” closed-loop mechanism improves both the precision and the impact of teaching interventions.

Enhancing Students’ Deep Learning and Higher-Order Thinking Abilities

A central objective of higher education is to develop students’ deep learning capabilities and higher-order thinking skills. AI can support these goals by designing complex problem scenarios for inquiry-based learning, providing intelligent research tools for independent study, and creating debate systems to cultivate critical thinking. Rather than serving solely as a vehicle for knowledge transmission, AI should act as a catalyst for stimulating intellectual engagement. Recent research has explored how AI-enhanced technologies can drive teaching innovation to meet the competency demands of the 21st century (Ahmad et al., 2024).

Figure 3 presents an analysis of AI’s potential to enhance various higher-order thinking skills. The results indicate that AI demonstrates its strongest potential in fostering information literacy (90%) and problem-solving abilities (85%), with moderate potential in developing collaboration and communication skills (70%). This pattern suggests that while AI excels in cognitive and analytical domains, its role in nurturing interpersonal skills remains limited, underscoring the continued necessity of human interaction in education.

Figure 3: AI’s Empowerment Potential for Various Types of Higher-Order Ability Cultivation

Teacher Professional Development and Teaching Capability Enhancement

The integration of AI in education necessitates a transformation of teachers’ roles and the enhancement of their professional competencies. By analyzing learning data, AI can identify areas for instructional improvement, recommend targeted professional development resources, and facilitate virtual communities of practice that encourage teaching reflection and knowledge exchange. A comprehensive review of teacher professional development highlights the potential applications of AI in this domain (Avsec et al., 2024). For higher education institutions, leveraging AI tools to build supportive ecosystems can foster teacher growth and promote the shift from traditional knowledge transmission to facilitation of active, student-centered learning.

Challenges, Ethical Issues, and Coping Measures Faced by AI Empowering Higher Education

Technical, Ethical, and Educational Equity Challenges

While AI offers significant opportunities for higher education, its adoption also presents notable challenges and ethical concerns. Biases embedded in training data or algorithm design can result in inequitable assessments and unequal resource allocation for students from diverse backgrounds. The reliance on large volumes of student data raises critical questions about lawful data collection, secure storage, and responsible use. Generative AI further complicates matters by enabling students to produce assignments and papers with minimal effort, posing new threats to academic integrity (Wang & Cheng, 2024). In addition, disparities in AI technology access and infrastructure across regions and institutions risk exacerbating the concentration of educational resources among already advantaged groups.

Coping Strategies and Framework Construction

Addressing these challenges requires a multi-level strategy. Table 3 presents a systematic framework for managing the ethical risks of AI in higher education, using a matrix structure to illustrate how policy, technical, and educational interventions can work in concert. This approach underscores that effective AI integration demands comprehensive governance measures rather than isolated solutions, ensuring that technological innovation is balanced with ethical responsibility.

Table 3: Matrix of Main Ethical Risks and Coping Strategies for AI Applications in Higher Education

At the policy and regulatory level, actions should include accelerating the development of ethical guidelines for AI in education and establishing clear data governance standards. At the technical level, priorities involve enhancing algorithm transparency and fairness, as well as advancing privacy protection technologies. At the educational and management level, institutions should integrate AI literacy into curricula, reform teaching evaluation systems, explore “human–machine collaboration” teaching models, and define clear role divisions and coordination mechanisms between AI systems and educators.

CONCLUSION

This study systematically examined the pathways, mechanisms, and challenges of leveraging AI to drive teaching model innovation and enhance educational quality in higher education. The findings indicate that AI stimulates innovation through personalized learning, generative content creation, and enriched collaborative experiences, while improving quality through precise assessment, the cultivation of higher-order thinking skills, and the advancement of teacher professional development.

An integrated analytical framework was proposed to guide universities in designing effective digital transformation strategies. Future research should further explore discipline-specific implementation models that address varying pedagogical needs, conduct longitudinal evaluations to assess the sustained impact of AI on student competencies and teacher role evolution, and investigate how AI can be effectively integrated across diverse cultural and institutional contexts.

From a policy perspective, the development of comprehensive governance frameworks is essential, covering ethical standards, data protection protocols, algorithm transparency, and AI literacy initiatives for both educators and students. Policymakers and institutional leaders should also prioritize equitable access to AI technologies to prevent the widening of educational disparities. By aligning research, practice, and policy, AI can be harnessed not only as a technological tool but also as a strategic driver for inclusive, high-quality, and future-ready higher education.

ACKNOWLEDGEMENT

Project Plan: This research is one of the outcomes of the 2024 Provincial Education Science Planning Project (School Dexelopnient Category). The title of the project is “Research and Practice of Large Model Interactive Assisted Teaching in Computer Science”, with project number GJB1424288. The project leader is Ma Yanli, and the participants include Zhang Lichen, Zhang Juan, etc.

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