Shodh Sari-An International Multidisciplinary Journal

The rapid advancement of Artificial Intelligence (AI) has brought significant transformations across all sectors, including education. Artificial Intelligence has become one of the most effective tools to develop teaching learning practices in higher education institutions worldwide (Eager & Brunton, 2023). As the demand towards personalised, flexible and technology enhanced learning spaces grows, AI has taken centre stage in the modern-day pedagogical practices (Alam & Mohanty, 2023). The integration of AI in higher education supports adaptive learning, automated assessments, predictive analytics, virtual tutoring systems, and high-tech student services. Application of AI in teaching learning process of higher education is increasing to enhance the quality of instruction, personalized learning process and enhancement of academic decision making (UNESCO, 2021). The adoption of AI-based features such as smart tutoring systems, personalized learning environments, automatic grading systems, predictive analytics, and virtual learning assistants has revolutionized the traditional pedagogical design and allowed creating a more efficient and inclusive learning system (Holmes et al., 2019; Chan, 2023; Kavitha et. al., 2025). Globally, universities are adopting AI to assist learners in their individualized learning journeys, reduce administrative burden, and improve student engagement. According to the he World Economic Forum (2020) suggests that AI technologies in education will contribute considerably to Sustainable Development Goal 4 (Quality Education) because it has the potential to increase the access and learning outcomes. Similarly, McKinsey and Company (2022) reported that AI can be used to improve the performance of students through the provision of real-time feedback, learning gaps identification, and the provision of differentiated learning. In India, the National Education Policy (NEP) 2020 focuses on digital transformation and proposes to the higher education institutions to implement emerging technologies like AI to support innovation, quality improvement, and global competitiveness. Nevertheless, the level of AI implementation in Indian higher education is not homogeneous, as a significant number of institutions do not have an appropriate digital infrastructure, skilled faculty, and a detailed plan of action (NITI Aayog, 2021). It has been demonstrated that even though urban universities move towards the implementation of AI, semi-urban and rural institutions continue to face technological, financial, and pedagogical issues (KPMG, 2021). Furthermore, the efficacy of AI tools in specialized disciplines is evidenced by their ability to revolutionize the learning and teaching of the English language, offering innovative ways to personalize linguistic acquisition (Rani, 2024).

Moreover, the implementation of AI also brings up a new issue of data privacy, transparency of algorithms, ethical issues, and the potential deskilling of educators. Faculty perceptions and the willingness of the institution are also vital in the implementation of AI (Luckin et al., 2016; Verboom et. al., 2025). In this way, it is now critical to comprehend the existing situation with the application of AI, its effects on the quality of teaching-learning and existing challenges and opportunities in higher education.

Objectives of the Study

1. To analyse the integration of AI in teaching learning practices in higher education.

2. To examine the impact of AI on quality education and student learning outcomes in higher education.

3. To identify the challenges faced by higher educational institutions in implementing AI.

4. To explore the potential opportunities and future implications of AI for higher education.

Methodology 

Research Design

The research design is descriptive, analytical in nature and is based solely on secondary data to explore the integration of Artificial Intelligence (AI) in teaching and learning practices in higher education. The methodology includes reviewing of the available literature, international reports, policy documents, and institutional data to understand how AI can be integrated to improve the quality of education.

Data Source

The study identifies itself on secondary sources applicable to AI in higher education. Which include:

• Peer-reviewed academic journals on educational technology and higher education.

• Reports from global organizations (e.g., UNESCO, OECD, WEF) focusing on AI and education.

• Indian educational bodies such as UGC, AICTE, and Ministry of Education reports. 

• Publications about AI trends in the industry (e.g., Deloitte, ASSOCHAM, McKinsey, KPMG).

• EdTech platforms and case studies (e.g., Coursera, SWAYAM)

• Surveys and analytical papers related to teaching learning innovation.

Data Analysis and Interpretation

The methodology introduced in this paper is qualitative content analysis and comparative thematic analysis in order to interpret the collected data. The interpretation relies on the synthesis of information on research articles on AI-enabled learning tools and student performance, documents on Indian higher education digitalization, Data on global AI adoption, Challenges such as training gaps, infrastructure, and data privacy and opportunities for AI-driven quality enhancement. The analysis points out the benefits of AI in improving engagement, facilitating personalised learning, reducing teacher workload, and improving general quality of education, as well as identifies major challenges and opportunities of the higher educational facilities in the future.

Findings

Objective 1: To analyse the integration of AI in teaching learning practices in higher education.

International and national reports (UNESCO, 2021; Deloitte, 2022) on secondary data reveal that adaptive learning systems, automated assessment systems, AI chatbots, plagiarism detection tools, and predictive analytics are all AI tools that are becoming more popular within institutions of higher learning worldwide. In India, NEP 2020 focuses on the incorporation of digital, as well as AI-based tools in higher education. Surveys conducted by AICTE (2023) and EdTech industry analyses indicate that colleges are increasingly integrating AI-based learning management systems and virtual assistants to help instructors in classrooms. Nevertheless, there is still an uneven adoption with metropolitan universities exhibiting greater AI utilisation than semi-urban and rural institutions.

The increasing use of artificial intelligence (AI) in higher education is witnessed in various surveys conducted globally among the students, faculty, and other academic leaders. In Indian higher education, the rate of adoption of AI rose at a rapid pace, with the adoption rates going up by 68 percent in 2023 compared to 10 percent in 2018, meaning that AI-based solutions are rapidly being adopted by institutions (Narayanan and Niveditha, 2025). UNESCO and the OECD recognize Artificial Intelligence as one of the catalysts towards fulfilling Sustainable Development Goal 4, which aims at quality education. According to a report prepared by Ernst & Young -Parthenon in collaboration with FICCI, entitled as Future-Ready Campuses: Unlocking the Power of AI in Higher Education, more than 56% of higher education institutions in India have already integrated AI-related policies, with almost 40% utilizing AI-enabled tutoring platforms and chatbots. The report is a survey of 30 well-known Indian HEIs and examines the introduction of AI in academic and administrative processes, usage trends, governance readiness, curriculum development, and faculty development. Similarly, findings from the Cengage report (2025) indicate that 77% of educators are conversant with AI tools, and more than 72% acknowledge the role of AI in enhancing the student learning outcomes due to customized lesson planning. The increasing application of AI by educators in order to address the needs of different learners is an indication of a gradual transformation into technological-based learner-centered education. According to the Digital Education Council Global AI Student Survey (2024), 86% of students reported using AI in their schoolwork, with ChatGPT being the most utilized tool; students, however, identified a lack of AI literacy and a lack of institutional support of AI. Similarly, the Chegg Global Student Survey (2025) discovered that 80% of students across the globe have already used generative AI where accuracy was noted as a key issue and high interest in AI tools that are purpose-built to serve educational purposes. In line with this trend, the HEPI/Kortext Student Generative AI Survey (2025) found that 92% of students are using AI tools, which have been increasing significantly in 2025 as compared to 2024, primarily to save time and enhance the quality of academic work. From an institutional perspective, the Ellucian AI in Higher Education Survey (2024) indicated that faculty and administrators across more than 330 institutions in the United States and Canada plan to expand AI usage, while also raising concerns about bias, data privacy, and the potential impact of AI on critical thinking. In line with this, the Elon/AAC&U Survey of Higher Education Leaders (2024) showed that 89% of academic leaders reported student use of AI and 59% observed an increase in academic dishonesty. Faculty-focused evidence further highlights mixed readiness, as the Digital Education Council Global AI Faculty Survey (2025) found that 61% of faculty members have used AI in teaching, although 88% reported minimal use and expressed concerns about students’ ability to critically evaluate AI-generated outputs. 

Fig 1: Conceptual Representation of AI Integration in Higher Education

Application of AI in teaching learning is advancing in the world, although the adoption rate of AI in teaching learning depends on the digital capacity and readiness of the faculty of the institutions. Although AI tools are improving the delivery of instruction and student interaction, colleges that have weaker infrastructure continue to be left behind and need more robust digital ecosystems to ensure complete integration of AI to pedagogy.

Objective 2: To examine the impact of AI on quality education and student learning outcomes in higher education.

Research conducted by McKinsey (2022), World Economic Forum (2021), and other peer-reviewed journals point to the observation that AI-based personalised learning is a highly effective way of improving learning outcomes. The automated feedback systems enable students to find learning gaps within a short period of time whereas intelligent tutoring systems facilitate differentiated instruction. Indian research reports (NITI Aayog, 2023) also indicate that learners who are exposed to AI-based learning systems have better clarity of concepts, better retention, and better academic performance. AI can also promote inclusive learning to serve the needs of learners with disabilities i.e. speech-to-text, language translation and adaptive content.

A global survey of students indicates that 85.4% perceive AI-based tools, such as ChatGPT, to be more effective for learning than conventional tutoring methods. In addition, AI enables real-time feedback and continuous assessment, allowing educators to customize instructional strategies more efficiently (EY-Parthenon & FICCI Report, 2025). The role that AI plays in the quality of education and student performance is becoming more apparent in the Indian context. The integration of AI technologies has supported advance personalized pathways in learning, refined the accuracy of assessment practices, and streamlined the administrative procedures. According to the Elon/AAC&U Survey of Higher Education Leaders (2024), 91% of academic leaders thought that AI had the potential to improve learning outcomes. In a report by the Internet and Mobile Association of India (IAMAI) and Grant Thornton Bharat (entitled Impact Study of EdTech in India: Driving Innovation and Creating Opportunities) it has been reported that 85% of students have linked a positive change in their learning outcomes to the EdTech platform. In addition, 86% of the learners underline the low cost of courses in EdTech and 87% of the learners underline their significance in providing job related and practical skills like coding and artificial intelligence, making learners workforce ready. The survey and report provided by Ellucian also indicate that students and educators perceive AI as an important tool to improve the quality of education. The positive correlation between institutional adoption and constructive perceptions of AI benefits is high because over 56% of institutions of higher learning already have AI-focused policies. Regarding the learning outcomes, AI-based platforms improve adaptive learning, enhance the feedback mechanism, and lessen the task of instructing students, allowing teachers to devote more time to mentoring and student assistance (EY-Parthenon & FICCI Report, 2025). The findings of the report by the ASSOCHAM (2025) indicate that the Faculty members in the sphere of higher education consider AI tools mainly valuable in enhancing the strength of assessment and feedback (64%), the learning experience (61%), and direct assistance to students (65%), which demonstrates that AI-powered assessment systems enhance the time-sensitiveness of the evaluation process and reduce the bias factor, as well as enhance the impartiality of the evaluation. Also, AI improves the engagement of the student by dynamically adapting to the needs of the individual student. It has also been used to automate the administrative processes, including admissions and record management, providing institutions with the opportunity to shift the resources to enhancing overall educational quality (EY-Parthenon & FICCI Report, 2025). Moreover, AI literacy is regarded as a key to the future employability, and 83% of students and 92% of educators emphasize its role. This highlights a very close connection between the implementation of AI in education and the acquisition of student’s employability and workforce-ready competences (Cengage Report, 2025). AI assists the institutions by Data-driven decision-making, Dropout prediction models, Curriculum optimisation and Student success analytics. These features are useful in raising the benchmarks of education standards. AI is positively correlated with the quality of education, enhancing personalization, advancements in feedback system, and data-oriented pedagogies. It directly relates to SDG-4 (Quality Education) since it simplifies the process of learning, makes it more efficient and student-centred.

Objective 3: To identify the challenges faced by higher educational institutions in implementing AI.

According to reports from KPMG (2021), NASPA (2024), and Indian HEI surveys, institutions face several barriers in the implementation of AI.

Fig. 2: Impact of AI on Quality Education and Student Learning Outcomes

These are Limited digital infrastructure and lack of good internet connectivity, High prices of AI tools and maintenance, Insufficient professional staff and faculty, Concerns of data privacy, algorithmic bias and ethical use, resistance of technological change by older faculty, and institutional policy of AI governance. Higher AI systems are also expensive to develop and might not be affordable by all institutions. The primary issues with AI implementation in educational institutions are that it is expensive to implement AI (23%), infrastructure is insufficient (21%), faculties are untrained in AI (21%), it raises concerns about privacy (19%), and faculties are resistant (16%) (Narayanan and Niveditha, 2025). The technological constraints are not the only factors that deter the use of AI but also the human and institutional factors. Institutions are unable to implement AI without the proper training, administrative support, and ethics. These challenges can be addressed through strategic planning, capacity-building efforts, and good policy frameworks. Although AI could be strategically important, only 39% of institutions have detailed policies and funding is still more focused in particular areas and institutions (AI in higher education statistics: The complete 2025 report). According to UNESCO (2023), there is a widespread shortage of teachers with proper training to utilize AI and digital technology in their work, and there is a lack of institutions on the global stage that can help prepare teachers to teach using AI. EdTech platforms (Coursera, SWAYAM) report increasing enrolment in courses on AI but minimal faculty involvement, which means it has not trained many teachers. The Ellucian survey (2024) highlights that fear of data privacy and bias in AI models are increasing, with concerns rising from 36% in 2023 to 49% in 2024. Similarly, privacy and security worries have increased from 50% to 59% over the same period. In the report by Deloitte, 92% of Indian executives consider security vulnerabilities as the most significant challenge to the responsible use of AI, and privacy and regulatory considerations are also problematic. Most colleges and universities use decentralized data storage (department, single laboratories, alumni offices, cloud, personal devices) and this has expanded the attack surface and made it difficult to enforce the same level of consistency in implementing security measures. While many institutions have adopted AI policies, there is a lack of a comprehensive framework for sustainable AI integration. EY-Parthenon & FICCI Report states that 60% of Higher Education Institutions have listed AI as their strategic priority, which indicates an opportunity to develop more strategic planning.

Fig. 3: Challenges faced by Higher Education Institutions in implementing AI

Objective 4: To explore the potential opportunities and future implications of AI for higher education.

The future of AI in Indian higher education is promising, presenting numerous opportunities to enhance learning experiences and institutional efficiencies. The artificial intelligence can reshape higher education into more customised, creative and student-oriented. Global EdTech trends (WEF, 2023; OECD, 2022) states that AI will affect the future of higher education by offering completely personalised learning pathways, AI-based academic advising, virtual universities, smart administrative systems, and curriculum design powered by AI. The automation of administration can greatly relieve administrative workloads (up to 20% or more) so that faculty can spend time on mentoring and research. According to the Deloitte report, 67% of education leaders think that AI can automate such processes as admissions and record keeping. With 84% of students believe that AI skills will be important when seeking employment in the future, introducing AI literacy into the curriculum will become a necessity, and students will be ready to work in AI-driven environments (Cengage Report, 2025). NEP 2020 in India is concerned with the digital transformation and creates new opportunities of AI -based skill development, smart classrooms, and hybrid learning models. Artificial Intelligence can facilitate informed decision-making, enhance the capacities of research, and innovate the ways of teaching. These developments will be triggered by the creation of AI Centers of Excellence and a higher number of training programs (UGC, NEP 2020). Institutions adopting AI early are likely to provide competitive advantages in research, providing support to students, and academic innovation. AI has enormous opportunities of transforming the education system in a more flexible, effective, and competitive system across the world. The long-term implications would be a redefinition of the teacher roles, more equity in education, and the development of AI-based smart campuses. Current strategic investment will create more innovative and future-oriented institutions.

Conclusion

This paper concludes that Artificial Intelligence is becoming a revolutionary practice in higher education sector, which influences teaching learning activities significantly and enhances the quality of education. The global and Indian trends reflected the accelerated creation of AI technologies, including adaptive learning systems, intelligent tutoring, automated assessment, learning analytics and virtual labs. These technologies enhance the quality of instruction, lessen the workload of the faculty, and facilitate the individualised and student-centred learning resulting in improved engagement, understanding of concepts, and academic performance. At the institutional level, AI can be used to support data-driven planning, curriculum optimisation, and student retention, which can directly lead to the quality improvement and SDG-4 goals. However, the research also reveals the problems such as the lack of training of the faculty members, digital divide, high prices, and ethical concerns associated with the privacy of data. Regardless of these impediments, AI-based innovation, including smart classes, predictive analytics, virtual campuses and smart academic systems, have massive potential in the future of higher education. With proper implementation, AI can revolutionise higher education by making it more inclusive, adaptable, efficient, and learner-centred.