| Journal of Information and Communications Technology:
Algorithms, Systems and Applications
Received: 07 January 2026; Revised: 21 February 2026; Accepted: 14 March 2026; Published Online: 19 March 2026.
J. Inf. Commun. Technol. Algorithms Syst. Appl., 2026, 2(1), 26305 | Volume 2 Issue 1 (March 2026) | DOI: https://doi.org/10.64189/ict.26304
© The Author(s) 2026
This article is licensed under Creative Commons Attribution NonCommercial 4.0 International (CC-BY-NC 4.0)
The Digital Divide: Challenges in Artificial
Intelligence Adoption across Higher Education
Institutions
Shubham Kishor Kadam,
*
Gaurav Mishra, Pankajkumar Anawade, Chhitij Raj, Deepak Sharma and Anurag
Luharia
Datta Meghe Institute of Higher Education and Research, Wardha, 442001, Maharashtra, India
*Email: kadamshubham1195@gmail.com (Shubham Kadam)
Abstract
Higher education is being increasingly transformed by Artificial Intelligence (AI), particularly through the
emergence of Large Language Models (LLMs) that enable personalized learning, support research activities, and
automate various administrative processes. However, the adoption of AI in higher education institutions is
uneven, contributing to a widening digital divide between resource-rich Tier-1 urban universities and under-
resourced Tier-3 rural institutions. This paper examines the structural, technological, pedagogical, and policy-
related barriers that influence AI adoption across diverse institutional contexts. Using a qualitative comparative
framework supported by global and Indian case studies, the study analyzes infrastructural constraints, faculty
preparedness, digital literacy gaps, ethical considerations, and disparities in funding. Case studies from India
and Rwanda illustrate both the grassroots challenges faced by developing regions and emerging policy-driven
models of AI integration in the Global South. The findings indicate that infrastructural capacity, institutional
readiness, faculty AI literacy, and sustained publicprivate collaboration are key factors enabling equitable AI
ramework that emphasizes inclusive infrastructure
development, curriculum reform, ethical governance, adoption of open-source technologies, and sustainable
funding mechanisms. By integrating global policy perspectives with institutional-level analysis, this study offers
a systematic roadmap for reducing digital inequality and promoting inclusive AI-enabled higher education
systems.
Keywords: Digital divide; Artificial intelligence; Higher educational institutions; Faculty development; Inclusive
education.
1. Introduction
Artificial Intelligence (AI) in higher education institutions represents a transformative shift in how education is
delivered, managed, and conceptualized.
[1,2]
Technologies such as Large Language Models (LLMs) have the
potential to significantly enhance personalized learning, democratize research access, and automate several
institutional processes.
[3]
From AI-powered chatbots that assist students with academic queries to advanced
adaptive learning systems that tailor educational content to individual learning needs, AI is reshaping the
landscape of higher education.
[4]
However, this transformation does not occur uniformly. Well-funded, urban Tier-1 institutions, such as many
Indian Institutes of Technology (IITs), often have greater access to advanced digital infrastructure than Tier-2
and Tier-3 colleges do, particularly those located in rural or underserved regions. This digital divide reflects
underlying systemic inequalities related to infrastructure availability, digital literacy, faculty preparedness, and
institutional policy support. While leading institutions are better positioned to drive AI-enabled innovation,
many institutions in less developed contexts continue to struggle with limited internet bandwidth, outdated
technological infrastructure, and insufficient training opportunities for faculty. If these disparities remain
unaddressed, they risk reinforcing existing educational inequalities and excluding large groups of students from
the potential benefits of AI-enabled learning.
The digital divide is both a global and a local issue. While many western institutions are rapidly advancing
through institutional AI strategies and partnerships with technology industries, institutions in low- and middle-
income countries (LMICs) continue to face significant infrastructural and resource constraints.
[5]
In countries
such as India, disparities in access to digital technologies and artificial intelligence in education are further
shaped by socioeconomic inequalities and regional differences.
[6]
Addressing these challenges requires not only
improved technological infrastructure but also the ethical and sustainable integration of AI across all levels of
education, along with strategies aimed at bridging the digital access gap.
[7,8]
This paper examines these challenges across both global and local contexts, including infrastructure limitations,
policy gaps, and pedagogical considerations. It identifies several barriers that hinder the adoption of artificial
intelligence in higher education institutions and provides targeted recommendations to support more equitable
digital transformation. The study also highlights the importance of faculty development in artificial intelligence
to ensure effective implementation in teaching and learning practices. Ultimately, the paper envisions a future
in which technology supports equity and mobility for all learners, enabling them to move across institutions
and geographic boundaries in pursuit of their educational and workforce aspirations.
1.1 Objectives
• To investigate the structural, technological, and socioeconomic barriers impeding AI (LLM) adoption across
diverse HEIs.
• To recommend sustainable, inclusive frameworks for integrating AI tools in higher education without
reinforcing digital inequality.
1.2 Understanding the digital divide: a global and local perspective
By linking global disparities in AI readiness with local institutional challenges, the multilayered nature of the
digital divide in higher education is illustrated in Fig. 1. It highlights how variations in economic development,
infrastructure availability, digital literacy, and policy support interact to influence AI adoption across HEIs. The
figure provides a conceptual framework for understanding how global-level inequalities translate into
institutional-level barriers, particularly affecting Tier-3 and resource-constrained institutions.
Fig. 1: Understanding the digital divide: a global and local perspective.
1.3 Operational definitions
Table 1 presents the operational definitions of key concepts used in this study, including the digital divide, AI
adoption, AI literacy, and institutional tiers in higher education. These definitions outline the core dimensions,
measurable indicators, and institutional implications that help structure the analysis of AI integration across
different categories of Higher Educational Institutions (HEIs).
Table 1: Operational definitions of key concepts used in this study, including the digital divide, AI adoption, AI literacy,
and institutional tiers in higher education.
Concept
Detailed Definition
Core Dimensions
Measurable
Indicators
Institutional Implications
Digital
Divide
The structured and measurable
disparity among Higher
Educational Institutions (HEIs) in
terms of access to digital
infrastructure, technological
resources, computational power,
internet connectivity, digital skills,
and institutional AI preparedness.
It reflects systemic inequality in
technological readiness that affects
equitable AI integration.
1. Infrastructure
Gap
2. Connectivity Gap
3. Computational
Capacity
4. Digital Skill Gap
5. Policy Readiness
- Bandwidth speed
and reliability
- Number of
functional
computer labs
- GPU/Cloud access
availability
- Percentage of
digitally trained
faculty
- AI-related
institutional
policies
Institutions with higher
digital maturity
demonstrate greater
innovation capability, while
digitally deprived
institutions experience
restricted academic
competitiveness and
reduced student exposure
to AI-based learning tools.
AI
Adoption
The systematic institutional
integration of Artificial Intelligence
toolssuch as Large Language
Models (LLMs), predictive
analytics, adaptive learning
systems, intelligent tutoring,
plagiarism detection tools, and AI-
enabled administrative platforms
into academic, research, and
governance processes.
1. Pedagogical
Integration
2. Research
Utilization
3. Administrative
Automation
4. Policy
Integration
- AI-enabled course
modules
- Use of AI in
grading and
evaluation
- AI-supported
research tools
- AI governance
guidelines
Higher adoption levels
correlate with improved
efficiency, personalized
learning, and data-driven
decision-making. Limited
adoption results in
institutional stagnation and
competitive disadvantage.
AI
Literacy
The cognitive, technical, and ethical
competency of faculty and students
to understand AI systems, critically
evaluate AI outputs, responsibly
use generative tools, and recognize
associated ethical risks such as
bias, data privacy, and academic
integrity concerns.
1. Technical
Understanding
2. Critical
Evaluation Skills
3. Ethical
Awareness
4. Responsible Use
Practices
- Participation in AI
training programs
- AI literacy
modules in
curriculum
- Ethical AI
awareness
workshops
- Ability to interpret
AI-generated
outputs critically
Institutions with higher AI
literacy demonstrate safer,
more meaningful AI
deployment, while low
literacy increases misuse,
dependency, and academic
integrity risks.
Tier-1
HEIs
Highly funded, urban, research-
intensive institutions characterized
by advanced digital ecosystems,
strong industry collaborations, high
faculty expertise, and institutional
AI strategies. Typically, nationally
or internationally ranked
universities.
1. Advanced
Infrastructure
2. Strong Industry
Linkages
3. Research
Orientation
4. Dedicated IT
Support
- High research
funding
- Presence of AI
labs/centers
- High-speed
campus-wide
connectivity
- Dedicated AI
faculty positions
These institutions serve as
innovation leaders and
early adopters of AI
technologies, influencing
policy and pedagogical
trends nationwide.
Tier-3
HEIs
Resource-constrained institutions,
often located in rural or semiurban
regions, characterized by limited
funding, outdated infrastructure,
inadequate digital support systems,
and minimal AI integration within
curriculum and administration.
1. Infrastructure
Constraints
2. Limited
Connectivity
3. Restricted
Budget Allocation
4. Low Digital
Readiness
- Limited computer-
to-student ratio
- Unstable internet
access
- Absence of AI-
specific courses
- Limited FDP
participation
These institutions face
structural disadvantages
that restrict AI
experimentation,
potentially widening socio-
educational inequalities
without targeted policy
intervention.
Source: Developed by the authors on the basis of an integrative review of the literature on digital divide theory, AI
adoption in higher education, AI literacy frameworks, and institutional differentiation models, drawing on studies by
Ghobadi & Ghobadi
[7]
, Scherer et al.
[10]
, Reed, Gannon & Dongarra
[15]
, Southworth et al.
[19]
, Roy & Swargiary
[18]
, Gu &
Ericson
[27]
, Kamalakar & Kamala
[17]
, and other relevant references cited in this study.
2. Methodology
2.1 Explaining the digital divide in education
The educational digital divide is a continuous gap among regions, institutions, and socioeconomic strata in the
field of access to digital technologies, infrastructure, and digital literacy. This gap can increasingly be seen in
the usage of AI tools (LLMs) within higher educational institutions. While some institutions may have digital
infrastructure, faculty expertise or funds to integrate AI, others in the hinterland or poorer regions are limited
by outdated systems, unreliable connectivity and few skilled field professionals. This leads to an inequitable
learning environment that stifles innovation and exacerbates school segregation.
[7,8]
2.2 AI access inequality around the world between HEIs
A significant correlation is observed globally between AI access inequalities and greater concerns about
economic advancements and digital maturity. Affluent countries have well-funded digital ecosystems, strong
research infrastructure, and access to state-of-the-art tools and training that support HEIs in those countries.
Such institutions are well positioned to lead AI adoption and expand user access since they can effectively
integrate LLMs into research, personalized learning, and administration. On the other hand, institutions in low-
and middle-income countries face significant structural disadvantages. Many institutions lack the financial
capacity to procure licenses or access cloud computing infrastructure, or their area of operation has unreliable
internet connectivity. Such international disparity not only limits innovation in education but also hinders global
cooperation, therefore strengthening structural differences between the Global North and South.
[9]
2.3 Regional and socioeconomic barriers to adoption
Within a country such as India, the digital divide occurs at regional and socioeconomic levels. The significant
advantage of urban HEIs (which can be considered Tier-1 institutions) is that they have better access to digital
resources, governmental grants, and connections with technological companies. Conversely, Tier-3 institutions,
primarily located in semi-urban or rural areas, face financial constraints, limited staffing and infrastructural
limitations. The socioeconomic situation (dissimilarities in income, the level of digital literacy among the faculty
and students, language barriers, etc.) also limits the use of AI tools. Furthermore, even when technological
infrastructure is available, a lack of required training and resistance to change among faculty members usually
undermine the effective use of technology.
[10]
Fig. 2: Methodology framework illustrating the research design, data sources, case selection criteria, and study
limitations used in the qualitative comparative analysis.
2.4 Case studies from developing and developed nations
Developed countries such as the United States and the United Kingdom have played a leading role in supporting
AI adoption in universities to personalize curricula, make administrative processes less time-consuming, and
enhance the production of research. As an example, AI is incorporated into MIT and Stanford universities to help
with grading in real time, learning analytics, and AI-based tutoring. Universities in less developed countries,
such as Kenya or India, struggle with the digital infrastructural basics. An example would be a rural Indian HEI
that would not have access to electricity and good broadband on a regular basis; therefore, using cloud-based
LLMs would not be a viable option. However, some developing countries are demonstrating measurable
progress.
[11,12]
3. Infrastructure, connectivity, and capacity gaps in HEIs
3.1 Technology infrastructure in tiered institutions
One of the main constituents of the digital divide is the difference in technology infrastructure between Tier-1
and Tier-3 HEIs. Such Tier-1 organizations are often provided with the latest computing infrastructure, state-
of-the-art laboratories, cloud-based infrastructure and AI-driven platforms. These facilities enable
experimentation, learning, and research on AI and LLMs. Conversely, Tier-3 HEIs, which are frequently located
in resource-limited areas, face problems with an aging computer infrastructure, low availability of digital
resources, and the absence of a scalable IT design. This infrastructure deficit has serious repercussions for the
capacity of lower-level institutions to access the latest technologies, resulting in limited exposure and
inadequately prepared graduates.
[13,14]
3.2 Limitations on internet access and computational power
The cornerstones of effective AI integration are the presence of reliable internet connectivity and computational
capacity. Most Tier-3 HEIs lack stability in their broadband, data bandwidth, and server capacity; therefore, they
are unable to implement cloud-based AI solutions. Although institutions with the most funding in urban centers
can potentially have access to high-performance computing (HPC) clusters or artificial intelligence cloud
providers such as Azure or AWS, underfunded institutions may lack awareness and access due to a lack of
constrains administrative functions such as automated grading, plagiarism detection, and data analysis.
Additionally, lacking GPUs or greater processors, these institutions may lack the capacity to develop and deploy
advanced LLMs, and they will be at a long-term disadvantage.
[15]
3.3 Staffing, IT support, and digital readiness
Other significant constraints relate to human capital and institutional readiness. Tier-1 institutions typically
have dedicated IT staff, AI experts, and capabilities to support and drive digital transformation initiatives.
Regular training programs and workshops keep the faculty up to date about the new tools. Tier-3 institutions,
on the contrary, typically have IT personnel who are generalists and do not have expert knowledge of AI. A lack
of systematic digital literacy courses among faculty members and students further exacerbates this deficiency.
Consequently, the lack of well-trained staff limits effective utilization even when the infrastructure is upgraded.
The fact that no specialized change management units exist to facilitate change also hampers the rate of
digitalization and hobbles the ability of the institution to be innovative.
4. Case study: contrasting AI adoption in Tier-1 and Tier-3 HEIs in India with a global perspective
(Rwanda model)
4.1 Case study: Rwanda’s smart education and Microsoft AI partnership-a Global South success model
Rwanda, through its Smart Education Master Plan, has emerged as a leader in AI and digital inclusion among
developing nations. The government collaborated with Microsoft, the Mastercard Foundation, and Smart Africa
to digitally transform HEIs.
Key highlights:
• Deployment of AI Labs and digital classrooms in public universities.
• Focus on teacher training, student AI literacy, and multilingual tools.
•
• AI-driven platforms are being used for adaptive learning, administration, and research analytics.
Significance:
Rwanda's success proves that even low-income countries can overcome traditional barriers through policy
alignment, global partnerships, and localized implementation, making it a model for AI adoption in the Global
South. This case provides an alternative avenue for the integration of AI, which is supported by coherent policy,
publicprivate partnerships and intentional capacity building. Unlike the Indian HEI case, where AI
implementation is limited to a narrow set of discrete applications in the teaching-learning situation, in Rwanda,
AI implementation extends beyond teaching and learning to include administrative and research functions. The
report revealed that investment in infrastructure and technology-based teaching platforms, including advanced
AI platforms that support language capabilities, remains very limited in low-income settings. On the conceptual
level, it contributes to the debate regarding how decisions, policies and adherence to adopt AI are shaped not
only economically but also by congruence at the governance level. This offers a scalable model that can be scaled
to propagate the extensive use of AI in the Global South.
5. The role of faculty, pedagogy, and curriculum integration
5.1 Faculty perceptions and resistance
Large language models or other tools of AI can be implemented in HEIs under the influence of faculty members.
However, the degree of interest and adoption depends considerably on what happens institutionally. In Tier-1
HEIs, the faculty can be exposed to technological innovations in advance and have an increased propensity to
experiment with AI-driven solutions in teaching and research. Conversely, faculty in Tier-3 institutions can be
sceptical or reluctant to adopt AI due to unfamiliarity or may perceive AI as a threat to academic autonomy or
professional relevance. There is also resistance to workload implications, insufficient training, and a lack of
institutional support for AI integration. To the extent that perceptions remain unchanged, they can hinder the
process of digital transformation, with or without the existence of an appropriate infrastructure.
[16]
5.2 Digital pedagogy and the redesign of learning models
The implementation of AI in education requires the application of a paradigm shift in education pedagogy, i.e.,
the replacement of static curriculum delivery with interactive and student-centered learning. The ability to
generate content in real time, adaptive assessments, and intelligent tutoring are examples of possibilities that
LLMs are able to deliver and that have made educators reimagine classroom interaction. The success of digital
pedagogy does not solely involve the use of LLMs because they are no longer support systems but are part of
the learning platform. Nonetheless, most institutions, particularly Tier-3 colleges, are still working on legacy
models that are based on rote learning and summative evaluations. The design of new learning models that
embrace inquiry-centered learning and project-based assessment, as well as collaborative digital spaces, has
not yet been developed owing to institutional and curricular conservatism.
[17]
5.3 Upskilling educators for AI Tools
The introduction of LLMs into the educational process requires systematic upskilling programs. The faculty
should be trained not only in the technical use of AI tools but also in their pedagogical potential and ethical
implications. Tier-1 institutions typically provide internal training and peer-learning courses as well as MOOCs
in artificial intelligence-related fields. In contrast, Tier-3 institutions do not have steady upskilling opportunities
because of budget and staffing constraints. This gap is further enhanced by the lack of institutional policies
requiring faculty to be digitally literate. National-level programs, such as the AI-for-education programs of
Digital India, can help fill this gap, but only if they are designed to be localized and inclusive.
[18]
5.4 Curriculum gaps in AI literacy
In recent years, with increasing interest in AI, it has been surprising that very few undergraduate or
postgraduate-level academic curricula have modules designed entirely for artificial intelligence literacy,
especially in non-STEM subjects. The gap is more pronounced in the case of Tier-3 HEIs, where curricula are
often outdated and centrally regulated. When colleges offer AI courses, they are often theoretical and provide
little practical experience with LLMs or AI platforms. This produces a cohort of AI-unprepared college
graduates. A proactive approach would involve integrating AI literacy across disciplines and departments,
ranging from transdisciplinary modules to laboratory courses, along with discussions aimed at strengthening
ethical understanding in the context of AI. Policy frameworks that support innovation, flexibility, and industry
alignment in the development of new classes must accompany curricular reforms.
[19]
6. Ethical, legal, and policy challenges
6.1 Data privacy and bias in LLM tools
The architecture of constructed large language models is built on the basis of large-scale data scraping from the
internet, which has attracted some serious concerns, such as the problems of data privacy and bias in such
models. The tools that will be used by HEIs tend to work with highly sensitive information (research by students
or institutional records). Poor security or integration may result in the loss of data, unwanted surveillance or
violation of local privacy laws. Moreover, training data biases may result in LLMs reinforcing stereotypes based
on gender, race or location. This becomes a major challenge for inclusive education, particularly in multicultural
and multilingual learning environments. It is likely that such risks will increase in an organization that does not
have tailored data ethics policies, especially in certain lower-level tier-3 systems in which AI governance is
unstable.
[20]
6.2 IP and academic integrity concerns
The generative abilities of LLMs raise acute challenges related to Intellectual Property (IP), authorship and
scholarly honesty. Students also have the opportunity to compose assignments, write a code, or research paper
with the help of LLMs, and it becomes difficult to distinguish between the original work and the one that has
been done with the assistance of AI. Similarly, lecturers and publishers employing AI-generated content in their
lectures or publications may face challenges in determining authorship and originality. The current plagiarism
detection systems are extremely ineffective against paraphrased or AI-generated content, thereby creating a
loophole in academic integrity policy. The use of proprietary LLMs also concerns the matter of ownership in
regard to the results produced with the assistance of this type of tool. The absence of a specific piece of law
concerning IP will put institutes at risk of lawsuits involving ownership, licensing, and copyright
infringement.
[21]
6.3 Regulatory gaps and institutional risks
Unlike numerous states, India does not yet possess a proper regulatory system aimed at the implementation of
AI in education. The absence of national-level guidelines or accreditation-related standards on how AI is used
in HEIs makes the topic of acceptable conduct ambiguous. Institutions often interpret ethical boundaries
independently, and this causes a variation in the application of the rules, with the possibility of reputational
risk. HEIs at the Tier-1 level with legal and policy access may proceed to implement institutional ethics
committees or compliance processes. However, Tier-3 colleges may lack awareness of the legal implications
associated with deploying commercial AI platforms, thereby increasing their susceptibility to risks related to
data misuse, discrimination, and potential regulatory non-compliance in the context of AI.
[22]
6.4 Balancing innovation with ethics
However, a balance between the possible power of AI innovation and morality needs to be sought, which should
be sought by HEIs. This requires a complicated course of action that includes policy creation, stakeholder
training, and continuous monitoring. To analyze new implementations and determine the risks, companies
should implement AI Ethics Boards or Digital Policy Committees. Among the above, transparent and open-
source LLMs can be favored over opaque commercial models as tools for promoting accountability. Faculty
members should be given training on the concepts of ethical use, informed consent, and AI literacy. Finally,
ethical frameworks may be considered in the approach to adoption, and this may assist HEIs in innovating in a
responsible way, meeting academic integrity, and protecting the rights of students and teachers.
[23]
7. Funding models and public‒private partnerships
7.1 Role of government funding in digital transformation
Government financing forms the basis of driving digital transformation and AI usage in higher educational
institutions (HEIs), especially in low- and middle-income areas. National Education Policy (NEP 2020),
Rashtriya Uchchatar Shiksha Abhiyan (RUSA), and Digital India are some of the initiatives in India that intend
to increase the infrastructure and encourage learning through the application of technology. Investment is
channeled to make smart classrooms, digital libraries, and virtual laboratories, and some of them can indirectly
support the implementation of AI. However, Tier-3 HEIs also tend to lack the ability to access or use these funds
efficiently. In most instances, bureaucratic delays, a lack of proposal-writing skills and misalignment with
funding requirements hinder these institutions from taking advantage of available grants. It is necessary to
develop a focused strategy with set funds dedicated to AI capacity building in underserved HEIs.
[24,25]
7.2 Industry collaborations and freemium models
The private sector has been a great contributor to AI integration within HEIs. Major technology corporations
such as Microsoft, Google, and Amazon have also introduced educational-related alliances to offer cloud credits,
AI toolkits, and training opportunities. Such partnerships, specifically within Tier-1 HEIs, assist in fast-tracking
AI-powered curriculum reform and the pedagogical modernization of faculty. For example, the AI-for-education
package offered by Microsoft and TensorFlow provided by Google is frequently used in data science curricula.
Another emerging trend is the widespread use of so-called freemium models or the distribution of simple AI
tools, such as ChatGPT, Grammarly, or Copilot, free of charge to students and educators, with paid extensions.
These models may create dependency on proprietary platforms while they increase access at early stages of
adoption. In the case of Tier-3 institutions, sustainability and vendor lock-in risks have to be dealt with
diligently.
[26]
7.3 NGO and international support models
International development agencies and Non-Governmental Organizations (NGOs) play key mediating roles in
bridging the gap in AI access. Organizations such as the Wadhwani Foundation, UNESCO and the British Council
have conducted pilots and initiatives to increase AI literacy and digital skills among under-resourced Indian
HEIs. Such efforts frequently revolve around the creation of the basics of digital skills, the notion of inclusivity
by means of tools of regional languages, and the bolstering of local digital learning ecosystems. Exchange of
knowledge, fellowships and open-source educational technologies are also facilitated through international
collaborations. Nevertheless, these kinds of interventions tend to be small-scale and short-term and accordingly
need institutional backup to produce long-term resonance and scale.
[27]
8. Strategies for bridging the digital divide
A strategic framework for bridging the digital divide in AI adoption across HEIs is presented in Fig. 3. It
integrates key intervention areas such as infrastructure development, faculty capacity building, policy support,
ethical governance, and collaborative partnerships. The figure demonstrates how coordinated action across
these dimensions can promote inclusive and sustainable AI integration, particularly for under-resourced
institutions.
Fig. 3: Strategies for bridging the digital divide.
8.1 Infrastructure development and shared services
The digital divide in HEIs can be reduced by ensuring more equitable distribution of infrastructure. Whereas
Tier-1 institutions enjoy world-class facilities, in the case of Tier-3 and rural HEIs, there is hardly any basic
digital infrastructure. The development of shared services is a successful technique that includes regional AI
resource centers, cloud computing hubs, and remote learning labs to serve a group of under-resourced
establishments. This mutual strategy curbs the redundancy of expenses and maximizes scarce resources. Public
infrastructure projects should also be accompanied by monitoring systems to ensure the maintenance,
upgrading, and pedagogical utilization of installed infrastructure. Hybrid classrooms, mobile labs, and
community internet kiosks are other scalable and cost-effective infrastructural interventions.
[28]
8.2 National AI education policy initiatives
Government policy is a catalyst for systemic reform. Countries such as India aim to democratize access to
emerging technologies through national initiatives such as National Digital Education Architecture (NDEAR)
and AI-for-All programs under the Digital India framework. These frameworks demand curriculum change,
professional skills, and digital inclusion in schools and higher education. To ensure that HEIs deploy AI tools,
such as the resurgence of LLMs, in their standard academic and administrative cycles, policy interventions must
be targeted and context-specific. Policy guidelines should also require that AI be taught as part of the curriculum
across disciplines, not just the STEM subjects. State governments can augment these arrangements by providing
grants, policy toolkits and district-level AI innovation hubs. Policy application should also be local and sensitive
to regional settings.
[29]
8.3 Faculty development and community-of-practice models
Faculty play a central role in driving digital transformation. Faculty development programs should be provided
on a large scale with the aim of updating educators about AI applications and digital ethics, as well as the latest
innovative approaches. However, one-time workshops are not enough. Institutions should be able to create CoP
models that are collaborative, peer-driven networks that enable faculty to experiment, share, and reflect on AI-
supported teaching processes. These community-based groups facilitate the creation of long-term learning
opportunities, decrease the isolated nature of the institutions in rural areas, and encourage contextualized
innovations. Digital platforms such as SWAYAM, NPTEL, and FDP.ai may be incorporated into proper capacity-
building programs. Interchange of the faculty between Tier-1 and Tier-3 HEIs, either through grants or virtual
mentorship, will also fill the knowledge and skill gaps.
[30]
9. Future roadmap for inclusive AI in education
9.1 Recap of core challenges and takeaways
The implementation of AI, especially LLMs, at higher educational institutions presents immense opportunities
in terms of teaching, learning, research, and administration. Nonetheless, this potential is not fulfilled equally
because of a current digital gap. The significant implications that arose in the course of the study are
infrastructural gaps between Tier-1 and Tier-3 institutions, uneven internet access, insufficient computing
facilities, faculty preparedness and the curriculum. In addition, the questions of data privacy, algorithm bias,
intellectual property, and a lack of cohesive national regulations pose another problem in terms of AI adoption.
Whereas Tier-1 universities have adequate funding, alliances, and digital networks, Tier-3 colleges can be left
on the periphery of modern-day innovation. These disparities risk perpetuating existing social inequalities and
-first-century learning opportunities.
9.2 Vision for “AI for All” in HEIs
To create an inclusive and future-ready educational ecosystem, the concept of AI, which aims to make it
accessible to everyone, should be placed at the core of the policy and practice. This vision involves the
democratization of AI accessibility among all of the HEIs; the extent of democratization is not dependent on
size, location, or financial power. It does not see AI as a luxury that can be used only by elite institutions but
rather as a means of increasing equity in learning opportunities and academic excellence. Realizing this vision
requires the ethical design of AI, cross-cultural and multilingual inclusivity, and sustained collaboration
and literacy to understand AI and supports educators in becoming agents of innovation and inclusion.
9.3 Future research and policy recommendations
The existing study needs to be expanded with other studies to investigate localized approaches to AI adoption
in various institutional settings, especially in less represented regions and fields. The potential long-term effects
of AI-enabled pedagogy on student success, faculty involvement, and employability should also be studied.
International comparative studies can provide best practices in the entire world and problem-specific issues.
Some of the policy recommendations involve the development of a national framework on AI in higher
education, which advocates the use of open-source and multilingual AI systems and the creation of AI ethics and
innovation committees at the institutional level. Funding models should be long term.
10. Conclusion
CRediT Author Contribution Statement
Shubham Kadam: Conceptualization; Writing-original draft. Gaurav Mishra: Writing-review & editing.
Pankajkumar Anawade: Data curation. Chhitij Raj: Data curation. Deepak Sharma: Methodology. Anurag
Luharia: Supervision; Writing - review & editing. All authors have read and agreed to the published version of
the manuscript.
Funding Declaration
This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-
profit sectors.
Data Availability Statement
Data sharing is not applicable to this article, as no datasets were generated or analyzed during this study.
Conflict of Interest
There is no conflict of interest.
Artificial Intelligence (AI) Use Disclosure
The authors confirm that no artificial intelligence (AI)-assisted technologies were used in the writing of the
manuscript, and no images were generated or manipulated using AI. AI-based tools were used solely for
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