Policy analysis of artificial intelligence in social science research at higher education institutions: problems and possibilities

Abstract

The rapidly growing landscape of artificial intelligence and its integration in higher educational institutions of India from 2022 compelled us to cautiously analyse the changing dynamics of social science education per se. Most higher education institutions in India now allow students and researchers to use artificial intelligence. India is known as a talent superpower and according to the Stanford AI Presentation Index, it ranks first in skill penetration related to artificial intelligence. In the 2025–26 budget, India committed ₹500 crore to establish Centres of Excellence in education, marking a strategic investment to elevate academic innovation and infrastructure. Additionally, India’s National Education Policy 2020 (Ministry of Human Resource Development, 2020) focuses on promoting AI and technological innovation. Given the intense push to promote technology and artificial intelligence in education, this research paper aims to analyse and evaluate the different policies impacting the usage of artificial intelligence in social science research at higher education institutions in India. The method is based on evaluating the textual content of documents by using human coding. The limitation of this research is that it is based on scanning textual documents and is limited to understanding the usage of AI in teaching and learning for the social science stream. In this process, it aims to deploy critical technology studies with the digital ethics framework. As a result of analysing the policy texts, we find that AI’s invisibility is the major issue. Algorithmic bias, trained on historical data reflecting caste, class, religion, region, race and gender disparities, and plagiarism via generative tools erodes authors’ dignity. Many of the detection tools are neither precise nor dependable in detecting AI usage and misuse. As educators, it is of urgent necessity to prioritise pedagogy that values human creativity, integrity and judgment. Similarly, too much dependence of Gen-Z learners on AI erodes critical thinking. This violates NEP 2020’s vision of balancing innovation with ethical safeguards. Present mechanisms to regulate and monitor AI usage in higher education and in the country are fragmented and indirect. With AI deployment occurring at such a massive scale, establishing a robust India-centric regulatory framework to govern its use is no longer optional but essential.

Introduction

The introduction of Artificial Intelligence into the pedagogy of Indian social science in the higher educational institutions marks a paradigmatic shift from conventional sole reliance on human intelligence and labour. In our study of these policy documents, one thing is clear: it is a constantly changing terrain with immense potential to democratise the education system, but it also needs utmost awareness and caution. There is no escape from the intrusion and intervention of AI in social science higher education. What we need to do is the synchronisation of human intelligence with AI. We realise that successful AI integration in social science needs apt policies and guidelines, but also critical AI literacy on the part of all stakeholders, like teachers, students and research scholars. The translator tools and digital humanities programs have expanded the reach of social science both in terms of its targeted students and interdisciplinary collaborations. The conventional social science teaching-learning process in Indian universities was limited to fixed curricula and methods. Innovation and multidisciplinary alliances were rare. However, Ministry of Human Resource Development, Government of India (2020) has been a game-changer in this direction. It emphasises multidisciplinary learning and flexibility in designing a course with multiple paper options for students. For the social science discipline, these experiments offer immense opportunities but also call for critical thinking and caution. The social science subjects engage with diverse and complex issues in Indian society. This requires utmost sensitivity towards the plurality of language, caste, class, religion and region. Similarly, it has to acknowledge the urban-rural divide, digital divide, gender divide and infrastructural challenges. If data is the new gold in an AI-driven society, then India is a goldmine. India offers a diverse set of data to train an algorithm. However, India needs to protect its citizens from abuse and exploitation. Indian citizens are not only consumers of AI services but are becoming a resource and product in the process. AI offers new pedagogical tools to analyse and translate historical texts, to simulate political systems and writing tools. However, the usage of AI in India raises ethical questions popularly known as ‘black box’, which leads to numerous limitations (Holmes et al., 2019; Williamson, 2021). Various researches point at limitations of the use of AI tools. The researches raises questions on regarding agency of the author that results in transparency problem (Dhir and Verma 2024; Marda, 2018), lack of knowledge and ethical concerns (Buragohain et al., 2026; Dadhich and Bansal, 2025; Mondal 2023) data protection and sovereignty (Dhir and Verma, 2024; Joshi, 2024); bias in algorithms (Biju and Gaythri, 2025a, b; Gupta, 2025; Pandey, 2025; Vijayaraghavan et al., 2025); deficit in dealing with cultural vastness, subjective values, experiences (Irani et al., 2010) and need for robust policy (Chatterjee, 2020; David et al., 2026; Marda, 2018). Despite several studies already undertaken, this paper drifts by particularly focusing on challenges for social science stakeholders in the higher education field. As a result, this study undertakes a textual analysis of policy papers and reports on AI made by the Government of India for social science education. The parameters for analysing these documents have been shortlisted using a human coding technique, which raises ethical concerns, inclusiveness, awareness and training, sustainability and scope for human critical thinking, which form the bedrock for social science education. The aim is to assess the practice of AI-driven social science pedagogy in India through the lens of critical technology studies and the Digital ethical framework, which can help propose necessary policy recommendations to make it more equitable, accessible, inclusive, accountable and ethical. The limitations of the study are that it is a text-based study with no field-based studies.

India’s educational sector’s tryst with artificial intelligence: understanding through policies

India’s education sector is a powerhouse in terms of numbers. As per the Economic Survey of (2024–25), India’s school education system is serving 24.8 crore students across 14.72 lakh schools with 98 lakh teachers. The same report says that school drop-out has steadily declined, and at the secondary level, it stands at 14.1%. The same period saw Higher educational institutions increase from 51,534 (2014–15) to 58,643(2022–23) (Press Information Bureau [PIB], 2025, 31 Jan). To tap the potential of the youth and not be left out in the growing landscape of Artificial Intelligence, the government has unveiled the essential requirements for integrating Artificial Intelligence in the education system. At the same time, innovations like peer teaching to attain financial literacy were stressed (PIB, 2025). To give a concrete structure for its vision of AI, the government had unveiled various documents for the educational sector: Ministry of Education, India (2021), Ministry of Human Resource Development, Government of India (2020), NITI Aayog report (2018), NCERT (2023), NITI Aayog report (2026). This has been integrated with the Viksit Bharat (2047) policy document. These reports shall be analysed on the parameters of Ethics, Inclusivity, Awareness, Sustainability, and critical thinking. The rationale behind choosing these principles is rooted in social science as a discipline, which tends to make policy accessible ethically. As a result, technology supposed to be neutral, tends to show the inherent hegemonic power based on inequality.

Inclusivity

The advent of NITI Aayog’s report (2018) on a strategy for the AI education sector was identified to strategise the implementation of AI. In this, the focus is on ‘personalised and adaptive training’ with stress on ‘teacher support and resource mobilisation by adopting new AI and skill enhancement through inclusion of machine learning curriculum’. Special centres for AI were planned, and models were supposed to be deciphered to understand student drop-out. The broad mandate did talk about inclusivity, but it was just a passing reference. As the government stressed building new AI centres. Consequently, the Ministry of Human Resource Development, Government of India (2020) document talks about teaching Students social ethics, constitutional values like empathy, and teamwork, but ignores the regulation of AI Ethics (National Education Policy Ministry of Human Resource Development, Government of India, 2020, p. 4; 5; 7; 20). It clearly mentions that “the new education policy must provide for all the students…” irrespective of the background they belong to (NEP, p. 4). With the coming of AI, that goal will be served disproportionately as those who will have access and skills to AI will excel more, something policy itself recognizes (NEP, p. 3). NEP also talks about inclusivity of all the students, “education for all” vision, but the problem is that this document saw this more in terms of AI as a neutral technology, without understanding that technology is influenced by power dynamics in society. This has resulted in algorithmic biases (Biju and Gaythri, 2025a; Vijayaraghavan et al., 2025). Simultaneously, the NCF-SE 2023 framework that came out had directed AI as a compulsory subject from class 3 onwards with the Central Board of Secondary Education (CBSE) and National Council for Education, Research and Training (NCERT) unveiling textbooks and implementing it from March 2026. Since Education is a state subject, NDEAR also gives flexibility to states to adopt it. The trends show that a lot of debates have begun on this compulsory order. Recent discussions are already stating that there are multiple problems with respect to ‘Lack of infrastructure’ whereby computers are there, but they lack software and other know-how to implement an AI curriculum. This is further ascertained by statistics, whereby the percentage of schools having computers increased from 38.5% in 2019–20 to 57.2% in 2023–24, but at the same time, access to the internet increased only from 22.3% to 53.9%, showing a huge digital divide (PIB, 2025). ‘Lack of preparedness of teachers and the mindset is AI necessary’ is another concern which has been flagged (Madalla, 2026, 10 March) As per UDISE + report (2024–25), there are more than 1.01 crore teachers who have to be trained, and a lack of consistent internet access for all, despite a surge in internet users to 82.2% for (age 14–16), still poses a challenge. Though the Government has online portals under NDAER like DIKSHA (Digital Infrastructure for Knowledge Sharing), which is the biggest and principal repository for AI-related teaching-learning resources and continues to add AI-driven features such as video keyword search and adaptive reading tools even for the visually disabled. CIET-NCERT also ran a dedicated “Leveraging AI for Transforming School Education” training series (English: 19–23 January 2026; Hindi: 26–30 January 2026), live-streamed on the NCERT YouTube channel, aimed at building AI literacy among educators and familiarising them with governance frameworks for responsible AI use but these trainings were forced upon thus unable to eliminate the bias towards why AI was necessary. Along with the infrastructure Gap, the GOI was giving more impetus to building infrastructure rather than focusing on how it has to be equitable and accessible, which has led to algorithmic biases and ethical concerns (Biju and Gaythri, 2025a, 2025b; Gupta, 2025; Pandey, 2025; Vijayaraghavan et al., 2025). Even in the NITI Aayog report (2026), major stress is on building infrastructure and integrating the technology in the curriculum and at the same time have flagged concerns for lack of ‘digital equity’ in the burgeoning scenario of AI. Ministry of Electronics and Information Technology (2025) did stress upon ‘people first approach’ by following the principle of ‘Fairness, Equity and Non-discrimination’, but these only remain thrust areas as the actual working of these documents speaks something else. In social sciences, we study human behaviour, societies and communities and their various intersections with politics, economy, society, and the environment. Social sciences, therefore, always identify the systems that are responsible for marginalisation, exploitation and exclusion, and recommend how these systems can be reformed. The problem with AI is that it is trained on the data that is given to it and works according to various algorithms. When AI is used without any regulatory and ethical measures, it can also percolate societal biases, and both students and teachers can be the victims of it, thereby pushing inclusivity to the brink.

Missing vision of ethics

The above section points to the impetus given towards AI to be included for all sections of society, but the implementation scenario takes a different turn. This is largely because ethical concern in designing and using AI is missing (Bhalla et al., 2024; Biju and Gayathri, 2025a; Dhir and Verma, 2024; Ranaware and Karale, 2026). Though the starting NITI Aayog (2018) report stressed making AI ethical, its implementation stands weak. It framed ethics strictly as a set of moral imperatives. The focus was on identifying the societal pitfalls of AI, like algorithmic bias, lack of data privacy, and the “black box” phenomenon. Ethics were viewed through a defensive lens, which was trying to ensure that technology does not inadvertently harm marginalised groups. But the entire focus was on making India aware of AI technology rather than seeing critical technology. This trend continued in the Ministry of Human Resource Development, Government of India (2020) document, where AI is recognised as any other technology like computer sciences, data sciences, etc. NEP thus misses out on how AI is going to impact the educational system as a whole; instead, it speaks only about what things need to be taught at the school and university levels. But we also cannot blame it completely, as AI was new during that time, and nobody had imagined the pace at which AI would start shaping our lives. Mittal et al. (2025) reviewed more than 3000 computer science curricula and identified that only 2.21 per cent of the curricula talk about AI ethics. This largely underscores the Digital ethical framework, which has constantly argued for responsible technology whereby the criteria for judging a good technology is not through its economic value but how this technology shall affect mankind. Unfortunately, various studies have shown that AI is based on biased data that leads to biased results. It supports a particular kind of data sets. Similarly, while the textbooks curated for students and facilitators under the NCF-SE include a module dedicated to ethics, its treatment remains largely surface-level, which is the result of the broader techno-optimist orientation of the policy stack rather than a mere pedagogical oversight. Since students at this introductory stage are primarily being acquainted with what AI technology is, ethics is taught as presented as a fact to be learned alongside other facts about the technology, rather than being developed as a critical lens through which the technology itself is interrogated. Ethics is thus treated as though it takes care of itself simply by being named as a module, when in a Critical Technology Studies reading, ethical engagement requires actively questioning who designed the system, whose interests it encodes, and what it obscures. This has resulted in a generation of AI-literate but not AI-critical students, capable of using the technology but not equipped to ask whether, or on whose terms, it should be used at all. The Ministry of Electronics and Information Technology (2025) formalised a lightweight ethics through the ‘do no harm principle’, which was seen through the inclusion of the principle of transparency with responsible innovation and trust as its guards. The NITI Aayog (2026) documents itself to identifying structural gaps rather than engaging with abstract principles of ‘ethics. The next section shall deal with analysing policy documents on awareness and adoption.

AI awareness and adoption

AI is widely promoted, and its use has been popularised greatly. The awareness level of it exists, but the adoption is something yet to be achieved widely. Tracing “awareness and adoption” is a connecting thread that has been diagnosed from 2018 to 2026. The NITI Aayog 2018 document was based on ‘AI for all’, thus making awareness a visible barrier. It had listed one of the five barriers to implementing AI. As a result, the paper proposed institutional solutions to prepare India for AI. It tried to promote best practices through different case studies of countries based on collecting and augmenting data. Ministry of Human Resource Development, Government of India (2020) brought it at the curriculum level by making AI an essential criterion for literacy for every student. As a result, courses were designed to make an AI-literate population. NCERT (2023) saw a definite path for awareness. It brought in the NEP literacy mandate into an actual module (CBSE 15-hour AI skill module and optional subjects from Class IX-XII). However, as per Critical Technology studies, it was limited to functional awareness (What is AI) rather than understanding the structural awareness of AI (what power dynamic work). The India AI mission made it a national economic infrastructure by outlining the opportunities AI shall give and creating a huge specialised infrastructure, the Yuv AI Initiative, Meta’s Srijan centre at IIT Jodhpur. Thus, the policy moves towards ’skilling’, which is an instrumental approach from the lens of critical technology studies. As it leaves the asymmetry of societies untouched in their robust push to massive supply-side investment. However, the NITI Aayog 2026 policy gives a jolt whereby awareness encounters power asymmetries of society. Unequal infrastructural accessibility makes the document bring ‘governance and equity’ with declining government school enrolment and an increase in secondary school dropouts. A study on faculty adoption of AI and its use by Roopa Shettigar (2026) argued that faculties are aware of it but are moderately adopting it due to ethical concerns, and a lack of institutional policy clarity over its use, but they accept that the technology is more efficient than doing without AI. Recent researchers have identified that skilling and making students and teachers aware of AI are still barriers (Mehta, 2026, 7 July; Maddala, 2026, 10 March) As per the students’ generative AI survey 2025 (Freeman, 2025), almost all students (92%) are using AI, up from 53 per cent in 2024. According to the EY-FICCI survey (Tomar, 2025, 8 Oct) of 30 top higher education institutions, almost 57 per cent of the institutions have some kind of AI policy or rule, while the other 40 per cent are in the process of making one. But these data are based on a very limited sample size. In India, many students don’t have digital access. How can they access this technology without that? Schools are in dire need of providing basic education, and the adoption of AI is far from those schools. Huge infrastructural investment will be required to adopt such technology. In the context of social sciences, AI awareness is somewhat vague. Students are preparing notes, writing essays and assignments, and then facing citation errors, which leads to a high similarity index. This categorically came out when UGC tracked many PhD’s having a high AI similarity index, due to which it had to come up with a guideline in 2026 stating that an AI similarity index of more than 20% shall amount to disciplinary action (Saikia, 2026, 04 February). The fact that these incidents occurred showed a clear defect in awareness, which was without ethical understanding, and the knowers saw AI as an easy tool to use for their own interest without ethics. Thus, we see that the Government of India’s policy documents have tried to raise awareness for AI by seeing it from being a barrier (2018) to being included in the curriculum (2020) to building up a concrete module (2023) to skilling youth (2025), but concerns for equity and governance (2026) still prevail.

Sustainability

All five documents point to the need for sustainable AI. However, applying CTS, one sees that these documents rarely pay scant regard to the environmental footprint which AI is generating, which is raising other concerns. The NITI Aayog (2018) document mentions the use of AI for having ’sustainable and inclusive growth’. It ignores the carbon footprints which technology leaves on earth. In the Ministry of Human Resource Development, Government of India (2020) document, sustainability comes under the mandatory environmental education curriculum, delinked with reflexive AI itself. NCF-SE again brings environmental education into the curriculum, which is a surface-level module with no connection with AI. They run parallel without showing any signs of convergence. India’s Ministry of Electronics and Information Technology (2025) gives a huge incentive to AI by showing it to be necessary for economic growth, so huge infrastructures are being pushed. Unfortunately, the mission draws a blank on having a road map to building energy-conscious AI. This has to be seen as global; ‘Green AI’ literature is becoming explicit. They are discussing energy-intensive infrastructures which AI requires, which can lead to energy crises. Regulatory frameworks underway have to integrate renewable energy sources to leave a smaller carbon footprint. Unfortunately, this mission is only focused on scaling infrastructure and skills without engaging with environmental sustainability. NITI Aayog (2026) raises concern over the sustainability of AI itself due to the huge demand. The question looms large whether the NDEAR infrastructure and online training are sufficient to reach everyone. The question looms large on program sustainability rather than on environmental stewardship. From the CTS standpoint, India’s policy documents are largely silent about green AI. They are being looked at from the standpoint of economic durability, environmental curriculum content and institutional continuation. The absence itself points to a techno-optimistic stack. From a social science perspective, in the near future, it would lead to more clashes in society due to a huge surge in energy demands.

AI- scope for critical thinking

Critical thinking has the richest textual presence of the five values, but tracing it across documents shows the same pattern flagged before; it is strong as a general pedagogical goal but thin as an AI-specific one. The NITI Aayog (2018) talks about AI for all, with a focus on spreading AI. It aims to make the workforce ready with AI rather than seeing the technology as imbuing critical thinking. The Ministry of Human Resource Development, Government of India (2020) document speaks loudest in terms of developing critical thinking by designing a new curriculum based on evidence, discovery, analysis, and discussions. However, the actual design of NEP courses shows that AI falls under technical skills, and critical thinking is a cognitive virtue; there is no bridge to connect them. This same flaw is carried in NCERT (2023); the dedicated AI module remains at an “acquainting students with what AI is” level. So critical thinking gets taught as a general disposition (question sources, reason through problems) in one part of the curriculum, while AI gets taught as a functional skill (how to use tools) in another. India Ministry of Electronics and Information Technology (2025) gives impetus to skill-based learning, but Yuva AI is rarely the concept of critical thinking operative. The NITI Aayog 2026 document tries to bring critical thinking when teachers become ‘learning facilitators’ rather than being ‘Knowledge providers’, thus trying to inculcate the art of questioning AI technology. Verma and Kumar (2025) in an Economic Times article show many examples of how Indian universities are using AI in educating the students based on NEP’s core principle of “education for all” and “learner-centric education system” based on hi-tech laboratories rather than infusing critical thinking. In India, multiple courses of teacher training are running, but unfortunately, there is no distinction between ‘digital literacy’ and ‘critical AI literacy’. As a result, there appear to be grey areas in evaluating students using AI without compromising data breaches or eliminating algorithmic biases. The PIB (2026) report mentioned the data incorporating AI in pedagogy, but there is no measurement of its inclusion in social sciences. Technical knowledge and critical thinking are two parallel banks with no solid bridge connecting them (PIB, 2026, 3 March, p. 9)

Thus, we see that India’s AI-in-education policy stack has institutionalised vocabulary for AI adoption, but has not been able to provide inclusive, ethical, interrogative AI. It is at its nascent stage, and the environmental cost remains beyond the purview. This kind of techno-spatial understanding of AI will be harmful for the long-term sustainability of technology, which is a cause of concern for social science pedagogy. The next section shall deal with some concrete policy recommendations for any future AI policy to be framed.

Policy recommendations

Instead of conclusion

The documents under discussion have considered AI adoption as a matter of infrastructure, training and curriculum design, leaving critical questions on whose terms AI is getting embedded in the huge student population of India unanswered. Social science education, therefore, can provide a critical link to interrogating power, consent and structural inequality of the technology of AI, thereby eliminating the techno-optimistic framework. The study has its share of limitations. Firstly, this study is a theoretical exercise where policy documents are analysed. The deconstruction of the texts has been undertaken. However, there is a need for fieldwork to analyse the practical unfolding of these policies at higher education institutions. Secondly, we have relied on ‘public documents’, readily available in the public domain, to avoid ethical conflicts. There may be some private initiative-based policy documents developed by industry stakeholders that we are not aware of in the Indian context. Thirdly, the dynamic nature of AI and the government’s response to this ever-evolving field make our study a challenging one, as things are changing while this work is under publication process. Finally, the implementation of NEP 2020 only began in 2022 and our study focuses on these initial years. However, we recognise the significance of early detection and reflection on educational experiments for course correction, so that the vision for Viksit Bharat 2047 can be realised in its true spirit. Trust across different stakeholders, developers, regulators, implementers and consumers is the fundamental requirement for optimum usage of AI. The future of AI in India depends on how far it will be contextual. AI has to acknowledge and adjust to the particular needs and realities of Indian citizens. The aim has to be to protect and empower citizens over technology. There has to be a balance between responsible innovation and cautionary restraint. From a social science perspective, the authors envision that these guidelines underscore the significance of the involvement of social scientists along with technicians and scientists, to mitigate risks of bias, discrimination and exclusion. These steps can foster an informed usage of AI rather than its avoidance. Thus, making the working of AI a part of citizens’ understanding rather than an elitist tool of technicians, engineers and scientists. The involvement of social scientists will make the field of AI more democratic, wherein the voices of people, environmental concerns and responsibility of using resources with accountability will increase.

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