Md Danish
L.LB Student, MANUU Law School,
Maulana Azad National Urdu University,
Hyderabad, (A Central University),
Email: md.danish4922@gmail.com
ResearchIDs: rid155799
Orcid ID: 0009-0009-5902-5863
Rushda Shakeel
L.LB Student, MANUU Law School,
Maulana Azad National Urdu University,
Hyderabad, (A Central University),
Email: rushdashakeel247@gmail.com
Orcid ID: 0009-0003-9559-6539
Jaza Abbas
B.A.L.L.B Student, MANUU Law School,
Maulana Azad National Urdu University,
Hyderabad, (A Central University),
Email: jazaaabbas64@gmail.com
Orcid ID: 0009-0003-8308-4069
Abstract
A revolution in higher education after the advent of Artificial Intelligence (AI) that has revolutionized the way academics are being taught, administered, assessed, researched, and the decision-making process in academic institutions. AI-powered tools like adaptive learning platforms, predictive analytics, automated grading systems, virtual assistants, online proctoring tools, and generative AI tools are becoming commonplace and are helping universities become more efficient and personalize learning experiences. This has also sparked a number of legal, ethical and regulatory issues surrounding data privacy, algorithmic bias, academic integrity, intellectual property rights, surveillance, and institutional accountability, however, the rapid expansion of AI use in higher education has created a new array of legal, ethical and regulatory concerns regarding data privacy, algorithmic bias, academic honesty, intellectual property rights, surveillance, and institutional responsibility. Many jurisdictions’ existing educational laws don’t sufficiently meet the challenges of today and tomorrow.
This study explores the regulatory landscape of AI in higher education by comparing the policies in India and Singapore, with a particular focus on the critical aspects of the policy. The policy vision of India’s digital transformation agenda, underpinned by the National Education Policy (NEP) 2020, the regulations of the UGC and other AI-specific initiatives like the SWAYAM and DIKSHA platforms, shows high expectations but also indicates considerable gaps in the regulations in addressing the specific legal concerns of AI. Contrastingly, Singapore has a more integrated and streamlined governance structure with Smart Nation Singapore, Ministry of Education Singapore and a strong governance structure around data protection via the Personal Data Protection Commission Singapore. This paper Doctrinal and comparative research methodologies are used in the study, which involves the study of the statutory framework, educational policies, institutional guidelines and secondary academic literature. It discusses important legal and ethical issues faced with AI in higher education such as privacy concerns, algorithmic discrimination, misuse of generative AI, intellectual property rights and liability issues.
In this paper, the author has tried to argue that India has been modernizing the technology but its approach in regulatory policy is still somewhat fragmented and reactionary as compared to the approach adopted by Singapore which is proactive in its approach towards regulating technology. It ends by suggesting a future-proof regulatory framework for India by enacting specific laws on Artificial Intelligence, setting higher ethical guidelines, institutional development, and building of inclusivity in the digital infrastructure. The research also adds to the growing body of scholarship on AI governance by highlighting the importance of finding a balance between technological innovation and constitutional principles, academic freedom, and students’ rights in an AI-driven world. Higher education organizations are leveraging AI to enhance the learning experience for students while creating new obstacles for those seeking admission.
Keywords: Artificial Intelligence, Higher Education, NEP 2020, Singapore, Digital Governance, Algorithmic Bias, Educational Regulation. Tasks that normally would need.
I. Introduction:
1.1 Evolution of Artificial Intelligence in Higher Education
In recent years, artificial intelligence has made significant strides in the field of higher education.AI has developed in the higher education space in a few years. The incorporation of Artificial Intelligence (AI) in higher education has initiated a change in the structure of the academic administration, teaching, student evaluation systems and system of governance in academic institutions. Traditionally, universities primarily used the traditional pedagogical methods, which mainly included the classroom-based teaching methods, manual administration system and human decision-making process. But the Fourth Industrial Revolution brought about the rapid evolution of digital technologies,[1] which has transformed the education landscape, and introduced AI as a more and more important factor in HEIs around the globe. Artificial Intelligence, which is also known as AI, is a type of computational system that can carry out tasks that normally would need to be assigned to a human being using intelligence, such as problem-solving, language processing,[2] predictive analyses, automated decision making and personalised recommendations. AI technologies are being integrated into higher education across various applications, such as adaptive learning systems, automated assessment, plagiarism detection, student behavior analytics, chatbots for student support, virtual tutoring platforms, decision support for admissions and institutional performance monitoring. AI technologies are being introduced in universities around the world to enhance efficiency, ease administration and create a personalized learning environment. For example, universities in Singapore have started to set up smart learning environments that leverage predictive analytics tools to track student performance and enhance the efficiency of the learning institutions. In the same way, the Indian universities are slowly grappling with AI solutions via digital platforms like the SWAYAM, DIKSHA,[3] virtual learning platforms, digital academic repositories and other initiatives of University Grants Commission. During the COVID-19 pandemic, the digital transformation of higher education was further fueled by the pandemic. The COVID-19 lockdowns and restrictions have compelled universities around the world to move to online platforms,[4] further heightening the reliance on artificial intelligence (AI) powered learning tools. Due to the COVID-19 lockdowns and restrictions, universities worldwide are having to rely on learning tools that have an AI component, even more. This quick shift brought forth both benefits and challenges of the implementation of AI. AI’s capabilities in facilitating access and flexibility within education have brought up issues concerning data security, algorithmic fairness, educational inequalities, and institutional accountability.[5] With the advent of generative AI, like ChatGPT and the like, from the OpenAI service, and others, this has further thrown into disarray higher education systems, and their ideas about academic integrity, originality, authorship, and assessment standards. With their introduction, universities now have to deal with complex ethical issues around ethical use of AI-generated content, plagiarism detection, and responsible use of technology.
Table: Global Growth of AI in Higher Education
| Year | Major AI Development in Education | Impact |
| 2015 | Rise of adaptive learning platforms | Personalized learning |
| 2020 | COVID-19 online education boom | Massive digital shift |
| 2022 | Launch of ChatGPT | Academic integrity concerns |
| 2023–26 | AI governance debates | Need for regulation |
1.2 Regulatory Vacuum and Emerging Legal Concerns
The following are issues of general regulatory interest and some emerging legal concerns: While AI tools have rapidly taken to the higher education “floor”, the rules and regulations haven’t kept up. Currently, there is no legislation that is specific to AI and academic institutions in most countries. Current education statutes are written for traditional and non-technology infused education systems, and are not necessarily suitable for algorithmic education. Data privacy is one of the most pressing issues that could arise in the legal realm. Students’ data on AI systems can range from academic records to attendance details, from behavioral patterns to biometric data, from online learning activities to much more. If not properly protected by law, this much information could be a violation of students’ privacy rights. The Digital Personal Data Protection Act, 2023 is an important step in India but it is silent about the AI-based technologies in education.[6] On the other hand, Singapore has established a reasonably robust regulatory regime under the Personal Data Protection Commission Singapore (PDPC) of the Personal Data Protection Act.[7] Data sets feed into AI systems can capture the pattern of social inequalities, resulting in inequities in the admission process, allocation of scholarships, assessment and monitoring of students.[8] This bias can disproportionately impact communities traditionally disenfranchised, and can jeopardize the ideals of equality and fairness.
Another big challenge is academic integrity as a result of the advent of generative AI tools. The use of AI-generated content for assignments, research papers, and exams poses a significant challenge to institutions to uphold standards of originality and academic integrity. Institutions find it difficult to keep up with the standards of originality and academic honesty as students use AI-generated content for assignments, research papers and examinations.
1.6 Research Methodology
The paper uses the research approach of a doctrinal and comparative method. The doctrinal approach means that constitutional provisions, education policies, and statutory frameworks, government reports, institutional guidelines and international legal instruments are analyzed. Comparative approach is used to compare and contrast the organizational issues of AI in higher education between India and Singapore. Secondary sources like journal articles, policy reports, books, reports of international organisations and digital governance studies are used in the study.[9]
II. Theoretical Foundations of AI Regulation in Higher Education
2.1 Understanding Artificial Intelligence in Academic Institutions
The fundamental knowledge of the role of AI in academic institutions. Basic awareness of the application of AI in universities. AI has become one of the most impactful technologies of modern-day higher education systems. In general, AI is the term given to all types of computer systems that can carry out tasks normally associated with human intelligence: learning, reasoning, prediction, language processing, decision making and problem solving. The use of AI in higher education institutions demonstrates the ongoing digitalization of education and the increasing automation of systems for improving institutions’ efficiency and offering individual learning results. AI in Higher Education Institutions: It is being applied in various academic and administrative areas of Higher Education Institutions. AI systems are getting more common in universities, enabling adaptive learning to tailor curriculum to student needs and learning styles.[10] AI adaptive learning systems are more prevalent in universities today, allowing for curriculum to be adapted to individual student performance and learning patterns. AI tutoring platforms, AI grading systems, AI predictive tools, AI plagiarism and cheating detection tools, and AI student support chatbots are all becoming a part of the modern-day academic environment. The digital initiatives like SWAYAM by the University Grants Commission, virtual learning infrastructures have promoted India’s institutions to implement technologically enriched learning systems. In the same vein, Singapore’s institutions are all highly digitized and have access to a suite of policies and support from the Ministry of Education Singapore. In the field of college admissions, AI has likewise been revolutionizing the application screening process by evaluating applicant information and forecasting compatibility with the educational institution. Learning management systems are more likely to gather behavioral information to assess student engagement, attendance and student learning. Higher education institutions also employ AI in their security, resource management, research support, and institutional planning. Generative AI tools like ChatGPT, Google Gemini, and others have greatly broadened the scope of AI’s importance to academic research and writing. These systems are now used by students to create assignments, to do preliminary research, for coding assistance, and language editing. In addition, AI is also being integrated into the curriculum design process and into research methodologies, particularly by faculty members. But as AI systems become more prevalent, questions loom about their transparency, accountability, academic honesty, and legality come to the fore. Automated systems in the field of educational decision-making can decrease the human oversight and can lead to potential error in algorithms.
2.2 Algorithmic Governance and Educational Administration
Algorithmic governance is the term used for the use of an automated computational system to make, shape or inform human authorities’ decisions, which were previously made, informed or shaped by human authorities. Algorithmic governance is transforming governance in higher education, devoting some decision-making tasks to the systems of technology.[11] Traditionally, educational governance is a process of human discretion where the needs of students are considered, faculty recruited, academic performance measured, discipline is administered and students are admitted into the school. Increasingly, however, AI systems play a role in these functions, via predictive analytics, automated screening tools, and evaluation systems for performance. For example, universities can employ algorithmic systems to alert students who are at risk of failing their classes, to automatically select students to receive scholarships, to suggest which classes to take, track student attendance, and identify possible cheating. This type of systems can be considered efficient, cost saving, and decision-making based on data. Algorithmic governance comes with a number of significant risks, however. An automated system could be based on a process of making decisions that is not transparent to the individual who is affected, and is known as a “black-box algorithm.” This lack of transparency could be a violation of ideas of procedural justice and due process.[12] Algorithmic decision-making in educational administration can lead to discriminatory results if the training data used to develop the algorithms shows social inequalities by race, gender, caste, disability, and/or socio-economic status. This is a very important issue in India because of significant inequalities in access to education.[13]
2.3 Regulatory Theory and Digital Governance
Research on digital governance and regulatory theory. Research on Digital Governance and regulatory theory. Regulatory theory is a useful tool to analyse the role of governments in technology innovation. In many cases traditional regulation has been developed behind the back of technological progress, leading to a situation in which technology develops quicker than the legal regime. This is a challenge that is found in higher education, for example, with the use of artificial intelligence. Governments might find it challenging to effectively enforce the command-and-control regulatory approach in situations where AI applications are rapidly changing. This in turn leads many Governments to have more flexible regulatory frameworks, including co-regulation, self-regulation and principle-based governance. Digital governance is a term used to describe the institutional frameworks used by government for the regulation of digital technologies, data systems and online platforms. Digital governance in higher education encompasses the management of educational platforms, data processing of students, cybersecurity requirements, digital accessibility and accountability of AI. The Indian regulatory regime is fragmented with different bodies like Ministry of Education India, University Grants Commission, All India Council for Technical Education being responsible for different aspects of the education sector.
NEP 2020 has made a call for the adoption of technology; however, it doesn’t have a specific AI regulatory framework.[14] Some of the digital governance models Singapore has adopted are more centralized, like Smart Nation Singapore, Infocomm Media Development Authority and Personal Data Protection Commission Singapore.[15] These institutions offer a greater clarity of governance around digital innovation. There is a need to balance innovation and public protection by governments.
Good digital governance in HE should have the following elements:
- transparency obligations
- privacy safeguards
- institutional accountability
- cybersecurity standards
- anti-discrimination protections
- grievance redress mechanisms
If AI governance of education is not regulated, it could result in violations of students’ rights to education.AI-governance of education, if not regulated, can lead to violations of students’ rights to education.
2.4 Ethical Dimensions of AI in Education
The use of AI in higher education also brings up significant ethical issues due to the integration of the technology. Ethical concerns with the use of AI in higher education are not just limited to compliance, but extend to the broader implications of integrating artificial intelligence into the educational landscape. The institutions need to uphold integrity, fairness and transparency in governance and human dignity in teaching and learning in order to ensure ethical governance. The main ethical issue raised is with regards to privacy. Big quantities of personal information about students, such as their academic results, conduct, location data, and biometric details are constantly gathered by AI systems. Over supervision can set up situations that are counter-productive to student independence. Another ethical issue is the presence of algorithmic bias. AI systems that are trained using inaccurate data set could be discriminatory towards socially and economically disadvantaged students, minority students, and students with disabilities. Given the ability of generative AI systems to generate essays, coding assignments, summarisation of research, and examination answer sheets, academic integrity has become even more complicated. Whether they are academic misconduct or innovation is a decision that universities will have to make. Another potential reason for concerns about intellectual property is if AI-generated content is used in academic research. The authorship and originality of questions, copyright ownership and research ethics are still unclear. Another moral conundrum is the reliance on AI systems by humans. Over-automation could make students less critical thinkers, increase teachers’ dependence, and also lessen the human interaction in educational environment. For instance, UNESCO has called for ethical principles in AI, including principles of transparency, fairness, inclusivity, and human-centred governance, which extends to ethical issues.[16] Ethical issues come in too, with global organizations like UNESCO stressing the importance of adhering to ethical principles in AI, such as transparency, fairness, inclusivity, and human-centred governance.
III. India’s Regulatory Landscape: NEP 2020 and AI-Driven Higher Education
3.1 Digital Transformation under Ministry of Education India
The Higher Education sector is one area in India that has been rapidly moving towards digitization, with the National Education Policy (NEP) 2020 acknowledging technology as a key enabler to enhance access, equity, quality and governance efficiency. The policy clearly supports the use of new technologies like Artificial Intelligence, Machine Learning, Blockchain and Virtual Learning Systems in schools. One of the crucial recommendations made as a part of the NEP 2020 was the National Educational Technology Forum (NETF) which was to be an independent platform for the exchange of ideas,[17] best practices and policy recommendations on the technological intervention in education. To boost digital infrastructure, online education and eliminate geographical barriers in Higher Education, the Ministry of Education India has implemented a number of initiatives. These efforts have been hastened by the Covid-19 pandemic, which has driven the universities to quickly scale up their efforts in implementing online learning delivery, virtual classrooms, digital exams and remote academic administration. This change increased the opportunities for lots of students, while also revealing inequities in internet access, digital literacy and the technology infrastructure.
Table: Major Indian Digital Education Initiatives
| Initiative | Year | Regulatory Body | Purpose |
| NEP | 2020 | Ministry of Education | Digital reforms |
| SWAYAM | 2017 | MoE | Online courses |
| DIKSHA | 2017 | MoE | Digital learning |
| ABC | 2021 | UGC | Credit transfer |
| AICTE AI Curriculum | Various | AICTE | AI skill development |
3.2 Role of University Grants Commission in AI Regulation
The University Grants Commission (UGC) is the major regulatory body which ensures standards in Higher Education Institutions in India.[18] Strategic partnerships with policy makers and various other stakeholders have created a greater opportunity for UGC to promote digital learning reforms and technology facilitated learning. The strategic engagements with policy makers and other stakeholders have helped increase the spate of digital learning reforms and technology-supported learning. It has released the guidelines for online education, blended learning, virtual classroom and on-line examination to guarantee academic delivery.[19] An important project that UGC has been working on is the Academic Bank of Credits (ABC) which is a system for digitizing and storing students’ academic credit, and allows them to move it between institutions. UGC has also encouraged setting up of digital repositories, development of e-content, online faculty training and virtual laboratories.[20] Yet, UGC’s regulations are still not much focused on any legal issues surrounding the use of AI. The issue of algorithmic transparency, automatic grading systems, using AI in admissions decisions, profiling and accountability for harmful technology use by institutions remain unclear. As generative AI tools have emerged, it has become even more complex for academic regulation, and UGC has yet to develop a common national definition of what can and can’t be produced using AI in the academic sphere. The lack of regulation leaves universities with doubts about the legal landscape for using AI tools that are not explicitly regulated.
3.3 Role of All India Council for Technical Education and Emerging AI Policies
All India Council for Technical Education (AICTE) has emerged as one of the most proactive institutes to promote the field of education on Artificial Intelligence in India, especially technical and professional education institutes.[21] AI is a key factor in the global economy and is increasingly becoming a major factor in the future of work, with a lot of countries recognizing its potential benefits, AICTE established an exclusive field of study called Artificial Intelligence and promoted engineering institutes to incorporate AI courses in their curriculum. AICTE has introduced various initiatives to support and encourage innovation and skill development in the field of technology, such as curriculum modernization programs, faculty development programs, start-up cells and tie ups with private technology institutes. The programs are designed to equip students with the skills needed for the future of work in AI fields. AICTE has, however, concentrated on the developmental aspect of the field as opposed to regulatory. It fosters AI literacy and technological innovation but has not introduced legally binding ethical and/or legal principles for AI use in institutional administration. Algorithmic discrimination, surveillance technologies, admissions systems that use algorithms, and automated systems for discipline are not well regulated.
3.4 SWAYAM, DIKSHA and Digital Learning Platforms
The SWAYAM, DIKSHA and Digital Learning Platforms are part of this category. This category involves SWAYAM, DIKSHA and Digital Learning Platforms. Despite having a long way to go, India has created a vast digital learning ecosystem with several platforms supported by the government for accessibility and wider opportunities in learning. One of the most important Massive Open Online Course (MOOC) platforms in India, SWAYAM (Study Webs of Active Learning for Young Aspiring Minds) is a free platform for exploring courses in higher education from top institutions.[22] DIKSHA is a National Digital Learning Platform that serves as educational content, teacher training and learning materials. Some other significant projects are National Digital Library, e-PG Pathshala, Virtual Labs and the National Academic Depository. These platforms are India’s efforts towards the democratization of education using technology. They have built access to students spread out, while decreasing reliance on real space. There are concerns, however, about these platforms and issues of data privacy, Cyber security, algorithmic recommendation systems, accessibility for disabled students, and linguistic inclusivity.[23]
3.5 Regulatory Gaps and Implementation Challenges
There are many regulatory gaps and implementation challenges that remain. However, the Indian regulatory framework is fragmented and underdeveloped when it comes to regulation of artificial intelligence. While India has an ambitious digital transformation agenda, the regulatory framework is not consolidated and well- developed in the context of the regulation of AI. A major deficiency is the lack of specific laws governing the use of AI in HEIs. There is limited guidance on educational AI governance in existing laws or legal frameworks, including the Information Technology Act, 2000, and the Digital Personal Data Protection Act, 2023.[24] The other challenge is the digital divide in India. Rural students, students from economically weaker sections, students belonging to the marginalised communities and PWD students are less likely to have access to the digital infrastructure, devices, internet etc. Access to digital infrastructure, devices, internet etc. is less in rural students, economically weaker sections, marginalised communities and PWD students. This results in unequal access to learning opportunities through AI. Preparation of institutions is also poor. In many universities trained faculty, cybersecurity systems, technical expertise and administrative capacity is not in place to effectively regulate AI technologies. Further, governance is complicated by regulatory fragmentation, with policy responsibility being shared between the Ministry of Education India, University Grants Commission, All India Council for Technical Education and the state authorities, with often overlapping policies, and governance issues emerging on the enforcement of these policies. Furthermore, India is not equipped with any clear accountability procedures for the harms arising from algorithmic decision making such as unfair admissions outcome, privacy violations and academic errors made by algorithms.[25]
IV. Singapore’s Digital Education Governance Model
4.1 Smart Nation Vision and Educational Innovation
Refresh the concept of a ‘Smart Nation’ Vision and Educational Innovation, The Singapore Plan to become a Smart Nation is closely tied with its digital transformation in education.[26] It aims to leverage the use of AI, big data, automation, cybersecurity systems, and smart governance capabilities in different industry verticals, such as the education sector, to make Singapore a global leader in digital economy. Education was identified as a strategic sector as with advancement of technology, a digitally skilled workforce that can adapt to future economic changes is needed. The government emphasized on the development of digital infrastructure, the availability of high-speed internet, cloud-based education systems, and AI-based learning models under this initiative. Schools and universities were invited to use digital pedagogical tools to support personalized learning experiences and enhance the efficiency of the school-institution. AI-powered student support systems and digital content repositories, smart classrooms and online learning systems grew in prevalence. Singapore also launched the National Digital Literacy Programme, in order to imbibe digital skills as early as possible in education.[27] At the higher education level, Universities started to incorporate AI, robotics, coding and data science into their curriculum. It is in keeping with Singapore’s long-term education-policy approach of preparing students for the jobs and technologies available in the labour market. The COVID-19 pandemic also showed how prepared Singapore is to be digital. The existing technological systems at these universities proved to be helpful to move to on-line learning with minimal disruptions. This was in contrast to many developing countries that faced difficulties transitioning to the digital world.
4.2 Role of Ministry of Education Singapore in AI Governance
The MES has a very active and centralized involvement in educational institutions’ regulation and digital reform. In India, unlike other countries, responsibilities are not shared. Singapore’s centralised system enables better policy coordination and quicker implementation as the policies are distributed by multiple regulatory bodies. The Ministry has implemented some changes in the academic systems to incorporate technology. The Student Learning Space (SLS), a national online learning platform offering digital resources, interactive learning and personalized learning opportunities to all students, is one of the most important initiatives.[28] The Ministry also keeps its curriculum frameworks up-to-date for the inclusion of digital skills, awareness of artificial intelligence and technological literacy. It is encouraged to create interdisciplinary courses with AI, legal, ethical and engineering and business studies. Another priority for faculty development should also be addressed. The Ministry provides training for teachers and academic leaders on the topic of digital pedagogy and integration of AI. In addition, the Ministry closely cooperates with national technology agencies to guarantee the educational innovation is coordinated with the digital governance strategies. This integrated governance approach reduces uncertainty of regulations and increases accountability. The centralised governance structure of education in Singapore allows for effective and efficient rollout of AI policies, with institutional oversight.
4.3 Data Protection under Personal Data Protection Commission Singapore
In this unit, students will learn about the role of data protection in personal data management. In this unit, students will learn about Data Protection in Personal Data Protection Commission Singapore. Singapore’s one of the best legal edge is the data governance framework. The Personal Data Protection Commission Singapore (PDPC) has been given the powers to regulate the collection, processing, storage and disclosure of personal information under the Personal Data Protection Act (PDPA).[29] AI systems have strong dependence on student data, thereby making this framework a critical one in higher education. Academic records, attendance details, behavioural analytics, biometric data, online learning records, and performance metrics are gathered by universities and used for running AI-based systems. The PDPA provides a range of legal protections including informed consent, purpose limitation, data security and prohibitions on data sharing. All institutions are responsible for making sure that the information of students is processed in a compliant manner. Moreover, Singapore has taken measures to reinforce the regulation of AI with the Infocomm Media Development Authority (IMDA) that has established the Model AI Governance Framework (MIF) with a focus on transparency, fairness, explainability, and accountability.[30] These legal protections diminish the risks of people being supervised in education, being profiled without consent, and have data misused. Singapore’s framework is more developed and efficient than India’s developing privacy law, the Digital Personal Data Protection Act.
4.4 Institutional Adoption of AI in Universities
Singaporean universities have been proactively incorporating A.I. into their education, management, studies, and campus governance. The National University of Singapore and Nanyang Technological University are international institutions, noted for their use of innovative digital technologies. The universities leverage adaptive learning platforms based on AI to tailor educational materials to students’ learning styles. Digital research tools and AI chatbots, automated grading systems, virtual tutors, predictive academic analytics and more are increasingly being leveraged across campuses. AI also powers administrative systems for admissions, resource allocation, the use of smart campus systems, and monitoring student engagement. AI research centers have been set up by Singapore universities, bringing together legal scholars, computer scientists and public policy experts, to engage in interdisciplinary research on AI and innovation. This provides for responsible technological development. The government also promotes lifelong learning opportunities, such as SkillsFuture Singapore, which provides ongoing reskilling opportunities in the realm of AI. The achievements highlight the significance of institutional preparedness for the effective utilization of AI.[31]
4.5 Regulatory Best Practices
There are a number of regulatory practices that Singapore has successfully implemented and can be learnt by India. To begin with, having a central government makes policies more consistent, and helps prevent any kind of bureaucratic duplication. There are clear accountability mechanisms with the Ministry of Education Singapore, Personal Data Protection Commission Singapore and Infocomm Media Development Authority. Second, equitable technological implementation across institutions can be enabled, with the aid of strong digital infrastructure. Thirdly, Singapore takes a proactive approach to training faculty and preparing the institution for major use of technology. Fourth, the country incorporates ethical principles and values of AI like fairness, transparency, explainability, and oversight by humans into governance structures. Fifth, continuous policy adaptation means that regulation will be adaptive and follow technological development. Last but not least, Singapore’s lifelong learning, research innovation and cross-disciplinary education in AI, helps to build a sustainable digital education ecosystem. Although the Singapore model cannot be blindly copied because of the size and diversity of India’s population, it offers valuable lessons in governance in terms of regulations in the creation of an efficient and legally sound AI governance framework in the Indian higher education sector.
V. Legal and Ethical Challenges in AI-Based Higher Education
AI is reshaping the educational landscape in higher education by revolutionizing academic processes, such as automated admissions, grading, attendance tracking, student counselling, research support, online learning, and institutional management. AIs tools are now used in universities to boost productivity, deliver more customized learning experiences and overall operational efficiency. But, even as technology advances at a rapid rate, there are also significant legal and ethical issues arising that current laws in education are inadequate to deal with. The increasing adoption of predictive analytics, automated decision-making systems, generative AI tools, biometric surveillance systems, algorithmic platforms and more present critical issues of privacy, fairness, accountability, IP and academic integrity. The concerns are especially relevant in a comparative context like India and Singapore where there is a growing tendency to adopt technology, but the ‘solution’ with regard to regulations varies in its volume and impact.[32]
5.1 Student Data Privacy and Surveillance Concerns
Large-scale data collection and processing of student data is one of the most significant legal issues of AI applications in higher education. Artificial intelligence systems operate on large amounts of data, necessitating the gathering of personal data at the University, including student attendance records, academic performance, online activity, behavioural patterns, and biometric information, as well as psychological data.[33] Continuous streams of student data can be collected to track academic conduct and institutional performance through learning management systems, on-line proctoring, facial recognition attendance and predictive analytics platforms. This was especially the case when online education has been expanded due to the COVID-19 pandemic, which has led to a greater level of educational surveillance. Webcam monitoring software, voice recognition systems, keystroke tracking software and automated proctoring software were all increasingly being utilized by universities in order to monitor for cheating during online exams.[34] These technologies emerged as a facilitation to the administration, but raised significant privacy concerns, sometimes leading to a rather oppressive atmosphere that impacted student autonomy and mental health. The Digital Personal Data Protection Act, 2023, has given India a broad definition of personal data and outlined its principles for the regulation of such data, but fails to give specific attention to educational surveillance or AI-based profiling systems.
Under the Personal Data Protection Act, Singapore offers more robust protection for the use of personal data, which is provided by the Personal Data Protection Commission Singapore. However, there are still issues of proportionality and transparency of data collection in education in Singapore. Lack of clarity over data storage, informed consent, algorithmic profiling and data-sharing practice also poses threats to the misuse of data, disclosure of it and cybersecurity breaches. To eliminate cheating in an online exam, various tools such as recognition systems and keystroke tracking, as well as automated proctoring systems, are used. These technologies were brought in for administrative needs, but they had serious privacy implications and sometimes created an environment of too much surveillance, impacting students’ independence and psychological health. In India, the Digital Personal Data Protection Act (DPDPA), 2023 offers a broad guideline for the personal data regulations, however it does not particularly cover the area of schooling surveillance or AI-based profiling systems.[35] The Personal Data Protection Act in Singapore offers more protections for personal data in Singapore. However, proportionality and transparency in data collection in education remains an issue in Singapore today as well. Lack of clarity on how data is stored, how informed consent is obtained, the lack of standards for algorithmic profiling and data sharing practices, and the risks of misuse, unauthorized disclosure, and cyber security breaches are potential risks.[36]
5.2 Algorithmic Bias and Discrimination Risks
While AI systems are supposed to be objective and neutral in their decision-making, they are often based on the biases that exist in the data that they are trained on. Algorithmic systems can be used to reproduce and/or amplify historical inequalities if they are built into the system. At the college level, AI systems can be applied in the screening process, in determining the allocation of scholarships, in the prediction of academic performance, in the recruitment of faculty, in the monitoring of discipline and in the assessment of risk. Such systems are prone to marginalizing students of a marginalised group if trained on biased data. Algorithmic discrimination can impact students from marginalised groups such as SC, ST, religious minorities, rural, economically weaker sections of society, and persons with disabilities in India disproportionately. For example, because of structural educational disadvantages, students from disadvantaged backgrounds can be “high-risk” students not so much because they are an individual student but because of their group. Another significant issue is language bias, which is prevalent in India as many AI tools are designed mainly for English-speaking students, leaving the students from different regional languages out of the picture.[37] While Singapore has relatively robust technological infrastructure and regulatory controls, issues of too much trust in predictive technologies that could potentially harm vulnerable groups still exist. Algorithmic bias can be considered a violation of the constitutional principles of equality, fairness and non-discrimination from a legal angle. However, a majority of educational regulations do not mention algorithmic fairness audit and anti-discrimination protections.[38]
5.3 Academic Integrity and Generative AI Misuse
Students are responsible for meeting the standards of academic integrity. Students must uphold the standards of academic integrity. The advent of generative AI has been a game changer for traditional academic systems, including the creation of OpenAI’s ChatGPT, Google’s Gemini and other large language models. With this, students can now use AI tools to create essays, generate assignments, solve coding problems, summarize their research and take exams. The use of these tools can improve the efficiency and access to learning, but they also raise significant issues of academic integrity. Academic integrity is becoming a challenge for universities as they struggle to separate AI assistance from academic misconduct. Academic integrity finds itself being challenged in the era of universities trying to differentiate between academic assistance and academic misconduct with the aid of AI.
Students can abuse AI in the following ways:
- ghost-writing assignments
- generating research papers
- producing fabricated citations
- Plagiarizing by copying and pasting from a different source
- Cheating on exams (with or without the intent to misrepresent the results)
Some of the traditional plagiarism detection tools are not effective against AI-generated work, thus posing a problem in the regulatory aspect for educational institutions. The faculty are also struggling to identify a suitable and acceptable use of AI in classrooms. Some institutions have banned the use of Bluetooth devices altogether and others have allowed limited use but with disclosure. Standardisation of policy have not been consistent as there is no uniformity. Generative AI calls into question the ideas of “author” and “originality,” “merit” and “academic assessment.
5.4 Intellectual Property Challenges
The emergence of Artificial Intelligence has raised many intellectual property issues in the academic environment. The existing copyright and patent laws were created to protect the work of human authors, and are not readily applicable to the work created by AI. AI tools have become a staple in creating various academic publications, including software code, datasets, creations, presentations, and research articles, for students, researchers, and faculty alike. There are some unanswered questions of law.[39] Who owns the AI generated Academic Content? Is it possible to have copyright for AI-generated research? Is there a right to students’ work that is generated by AI? Is it legal to use academic materials that have been copyrighted to train AI systems without obtaining permission? The issues are yet to be resolved in India and Singapore. The legal landscape in India, governed by the Copyright Act, primarily gives copyright protection to human authors and this leaves the law uncertain with regards to the protection of AI generated works. The question of ownership is also under discussion in Singapore with automated creation of works of art. In addition to technical challenges, patent law is also likely to encounter issues in the context of scientific discoveries and technological inventions enabled by AI. If there is no clarity in the law, universities could find themselves in conflict with others regarding ownership rights, business opportunities and academic authorship.[40]
5.5 Accountability and Regulatory Liability
All staff members are held accountable for their actions. Staff members are held accountable for their conduct. A highly complex legal dilemma in the realm of AI in higher education is liability in cases where technology is responsible for damage.[41] The challenges become very complex when AI systems make a faulty and discriminatory decision. For instance, when a good applicant is denied admission by an AI tool, who or what is at fault – the university, software creators, the private vendor, the administrative body or the regulators? Likewise, AI grading software that fails to provide accurate student grades or issues privacy violations of student information could lead to lawsuits for academic incorrectness and violations of standards. The black-box nature of a lot of AI systems also makes it hard to be accountable for the decisions made affecting the individual, as they may not be aware of how they were reached. At present, India doesn’t have a specific law to address issues of liability for educational damages caused by AI. While Singapore has relatively good regulatory oversight, there are uncertainties and grey areas in law when it comes to errors in automated decision making.[42] To overcome these difficulties there should be clear grievance redress systems in place, transparency requirements should be placed on universities, independent audits should be conducted, and human oversight systems should be put in place.
VI. Comparative Analysis: India and Singapore
The research that compares India and Singapore, indicates two different methods for controlling the use of AI in higher education. Both nations see the transformative role of AI in enhancing academic teaching and learning, administration, innovation in research, and learning outcomes. Their governance models, however, differ in terms of the creation of policies, institutional readiness, regulatory coordination, data protection and digital inclusion approaches. India is a big and diverse education system, which is gradually progressing towards digitalization, but still suffers from regulatory fragmentation and infrastructural inequality. Unlike Singapore, China has a more decentralised governance system, where policies are slower to be put into practice and technology is less closely monitored. The comparative analysis shows the inbuilt strengths and weaknesses of both the systems and some lessons that can be learnt by India in the context of its socio-economic condition.
6.1 Policy Framework Comparison
The National Education Policy (NEP) 2020 developed by the Ministry of Education India is the key policy framework to shape digital transformation in education.[43] The use of digital platforms, technological innovations, virtual education and the use of emerging technologies, including artificial intelligence is a strong focus of the policy. It also recommended formation of the National Educational Technology Forum for the promotion of innovations in educational technology. The policy, however, is more geared toward the adoption of technology and does not have robust measures on how to hold tech accountable, ensure the ethical safeguarding of AI, or regulate the system. Singapore has a more structured and centralized policy framework, with the Ministry of Education Singapore leading the way, and the Smart Nation Singapore initiative offering support. However, in Singapore, AI governance is part and parcel of broader national digital planning as opposed to a stand-alone initiative for educational technology reform.[44] Educational institutions are working in a policy framework that is coordinated, which is supported by technology, law, and management. India, therefore, has an innovation-oriented policy framework, which, however, lacks at least in its regulatory aspects, while Singapore has a more balanced policy framework between innovation and governance.
6.2 Institutional Readiness
The readiness of the institutions is crucial in assessing the success of the implementation of artificial intelligence (AI) technologies at universities. India is challenged by huge size of higher education institutions, regional variations, inadequate funding and different technological readiness of the institutions in the country. A relatively high level of AI infrastructure is available at elite institutes like Indian Institute of Technology, Indian Institute of Management and prestigious private universities. But, many public universities as well as rural institutions are facing challenges related to poor Internet connectivity, less technology resources, low level of faculty training and less cyber security system. There is a significant level of institutional readiness in Singapore. The universities of Singapore also have robust state funding, high-quality digital facilities, well-trained academic staff and research ecosystems, such as the National University of Singapore (NUS) and Nanyang Technological University (NTU). Singapore being a smaller geographical area and having a more centralized governance system, will make the implementation uniform, while in India it is a large area and thus will be difficult to implement.
6.3 Data Governance Mechanisms
One of the biggest disparities between India and Singapore is in the field of data governance. In the context of AI in higher education, data protection is critical, with privacy regulations and oversight playing a vital role in safeguarding student information. The Digital Personal Data Protection Act, 2023 was passed in India as one of the significant steps taken towards Privacy regulation.[45] But the law is still very general and does not explicitly mention educational AI systems, algorithmic profiling or automated decision making, or institutional surveillance practices. Student data is frequently being collected by universities, but there are no clear guidelines for retention, consent, transparency or accountability. Singapore has a more advanced data governance regime, under the umbrella of the Personal Data Protection Commission Singapore, established under the Personal Data Protection Act. The framework contains informed consent, purpose limitation, data security and breach reporting requirements. Further, Singapore has AI governance principles, which encourage responsibly using AI, explainability, transparency, and fairness. India’s is a reactive approach, while Singapore’s is a proactive digital regulation approach. One of the biggest disparities between India and Singapore is in the field of data governance. In the context of AI in higher education, data protection is critical, with privacy regulations and oversight playing a vital role in safeguarding student information. The Digital Personal Data Protection Act, 2023 was passed in India as one of the significant steps taken towards Privacy regulation. But the law is still very general and does not explicitly mention educational AI systems, algorithmic profiling or automated decision making, or institutional surveillance practices. Student data is frequently being collected by universities, but there are no clear guidelines for retention, consent, transparency or accountability. Singapore has a more advanced data governance regime, under the umbrella of the Personal Data Protection Commission Singapore, established under the Personal Data Protection Act. The framework contains informed consent, purpose limitation, data security and breach reporting requirements. Further, Singapore has AI governance principles, which encourage responsibly using AI, explainability, transparency, and fairness.[46] India’s is a reactive approach, while Singapore’s is a proactive digital regulation approach.
6.4 Accessibility and Digital Inclusion
One of the major issues in India’s AI transformation of education is accessibility. Even after a massive effort of digital initiatives like SWAYAM and DIKSHA,[47] millions of students are experiencing issues due to poor internet connectivity, lack of digital devices, language barriers, electricity problem and low digital literacy. Children from disadvantaged groups, such as rural communities, economically weaker sections, marginalized communities and children with disabilities are at risk of being excluded from digital learning opportunities. This is a matter of grave concern on the issues of equality and educational justice.[48] Compared to others, Singapore has relatively low accessibility issues because of its superior digital infrastructure, high Internet access rates and large-scale digital literacy initiatives. Most students have access to digital learning resources, provided for by government funding. Nonetheless, there are still technological dependency and vulnerable student population issues that Singapore has to contend with. The fundamental difference lies in the focus on expanding access and improving quality and ethical regulation, respectively, for India and Singapore.[49]
6.5 Lessons for Regulatory Reform in India
The comparison provides a few valuable insights for India’s future regulatory changes. First, India needs a dedicated legal framework that clearly addresses the artificial intelligence in higher education institutions, and not just on a policy-level. Secondly, there is a need for coordination between ministries at the national level (between Ministry of Education India, University Grants Commission, All India Council for Technical Education and state level) to minimise regulatory fragmentation. Last but not least, India can implement more robust privacy protections based on Singapore’s data governance approach. Fourth, there is a need for increased investment in the training of faculty, institutional preparedness and the security systems. Fifth, India needs to focus on the digitally excluded in order to avoid perpetuating educational disparity as a result of technological innovation. Lastly, India should apply ethical principles to AI in education, such as transparency, accountability, fairness, and human supervision to gain public trust in AI-based education systems.
VII. Towards a Future-Ready Regulatory Framework: Recommendations and Conclusion
The comparative study of India with Singapore shows that AI is no longer a future phenomenon in higher education, but it is already a part of the governance, administration, research innovation and learning system of institutions. The integration of AI technologies in universities for screening admission applications, predictive analytics, automated grading, personalised learning, research and administrative support is increasing. The technological advances offer great potential for enhancing educational outcomes, but also raise complex legal, ethical and regulatory issues.[50] While the existing policy reforms in India have demonstrated significant policy intent with the implementation of National Education Policy (NEP) 2020 and the various digital policy initiatives, they do not have a strong legal framework to address privacy, algorithmic discrimination, institutional accountability, and ethical challenges of governance. The experience of Singapore, however, illustrates the possibilities of technological progress and at the same time deepens the regulatory oversight, institutional readiness, and proactive governance mechanisms. Keeping this comparative study, there are some reformations required to develop a framework for the future for India.
7.1 Need for Dedicated AI Regulation in Higher Education
The growing need for specific AI regulation in Higher Education is also discussed. The need of specific AI regulation in HE is also mentioned. The most crucial issues in this study are the lack of a dedicated legal framework for regulating AI in HEIs and the lack of awareness among students and faculty regarding the potential impacts of AI in the education field.[51] Some existing laws, like the Information Technology Act, 2000, and the Digital Personal Data Protection Act, 2023, offer some guidance, but they are not specific to the AI-driven educational governance regime. Broad policies like NEP 2020 do provide some guidance on the need for digital transformation, despite not being specific to AI. There is limited guidance in existing laws such as Information Technology Act, 2000, the Digital Personal Data Protection Act, 2023 and in broad policies like NEP 2020, but these laws do not explicitly discuss the specific risks from AI-driven educational governance regime. Universities must have a clear framework on the use of AI for their admissions processes, automated grading systems, predictive assessments, student monitoring, research assessment and institutional decision-making. This law ought to define the legality of algorithmic transparency, human involvement, student consent, data protection, procedure to address grievances, and liability for technological harm. The Ministry of Education India, University Grants Commission, and All India Council for Technical Education (AICTE) should work together to provide a set of national guidelines that are binding for the use of AI in universities. India could also set up a dedicated regulatory body like Singapore’s coordinated governance to keep an eye on the implementation of AI.[52] A bespoke piece of legislation would limit the regulatory uncertainty and benefit universities by clarifying their compliance duties, while safeguarding students’ rights.
7.2 Strengthening Ethical Governance Standards
For this CSR, these standards are strengthened by the following: Legal solutions are not enough to solve the problems brought by AI. To make sure technological innovation does not detract from human rights, academic freedom and institutional fairness, ethical governance should be a major aspect of higher education policy. AI should be ethically regulated in the universities by imposing principles of the world that have been promoted by UNESCO and other world institutions. Fairness, transparency, explainability, accountability, privacy protection, non-discrimination and human centred governance should be among these principles.[53] AI systems need to be properly implemented with algorithmic impact assessments to assess for potential discrimination, privacy issues, and ethical concerns before they are put into practice. Independent audits should also be performed to make sure compliance. Pupils should be made aware if AI is being used for assessment of academic performance, behaviour, admissions or grading. Trust in the institutions can be enhanced by transparent disclosure mechanisms.[54] Good governance principles are crucial to maintain efficiency without compromising on academic integrity and human dignity.
7.3 Enhancing Institutional Capacity
The biggest challenge in India is the readiness of the institutions to effectively manage the technologies of AI. The majority of universities still experience low-quality digital infrastructure, low cyber security protection, lack of trained faculty members and lack of administration expertise. Institutions need to develop technical and regulatory capacity for effective governance of AI. Faculty need to be AI literate, understand the ethics of digital, to be aware of cyber security and to know the technology use and be responsible. Interdisciplinary committees of legal, technical, ethical, administrative, and student experts are recommended to be formed at universities to oversee the use of AI. Financial assistance is needed to upgrade the technological infrastructure and to create and apply a secure digital system in public institutions, especially in rural regions. There is also a need to encourage research institutions to establish interdisciplinary centers of AI governance, which focus on legal, ethical and policy issues related to EdTech. Even the most well-intentioned regulations can fail to work when not properly implemented in the absence of institutional preparedness.
7.4 Building Inclusive Digital Infrastructure
Digital divide is still one of the biggest challenges to accessing AI equitably in higher education in India. Although the use of digital platforms like SWAYAM and DIKSHA has helped to increase access to education, there are still millions of learners who are struggling with structural barriers. The majority of students in rural areas, economically weaker sections, marginalized communities and persons with disability may not be equipped with reliable internet connectivity, digital devices, access to electricity sources, technological skills etc. However, many students do not benefit from digital platforms, which tend to be in the English language, due to language barriers. Undressing these inequalities could exacerbate educational exclusion and not inclusion in the context of AI-driven learning. The government should invest more in the infrastructure of broadband in rural areas, provide digital devices for disadvantaged students with subsidies, create multi-linguistic educational materials, and make them accessible to students with special needs. A digital infrastructure that is inclusive is a key element to ensure that technological development is not used to further the division of people.[55]
7.5 Conclusion
States will have to navigate the Artificial Intelligence landscape to ensure it is regulated effectively, while maintaining the values of education, which will increasingly become crucial in determining the future of higher education governance. India is at a juncture where it can either enable unrestricted technological growth or create a regulatory regime that is rights-based and has mechanisms of accountability.[56] This comparative study highlights the lessons to be learned from Singapore’s model for centralized governance, data protection, institutional preparedness and ethical regulation. India, however, has to craft reforms which reflect its socio-economic realities, as well as its constitutional duty to equality and access. The debate about what the future of AI in higher education holds is not about innovation versus regulation.AI in higher education is not an “innovation vs. regulation” issue. Instead, a balanced approach is needed, one that allows for the advancement of technology while respecting privacy, fairness, access and academic honesty in education, through a sustainable educational transformation. The regulatory framework for India, therefore, needs to be robust, ethical, institutional and socially inclusive for being future ready. Artificial intelligence is only suitable to help empower education if it is used in a way that doesn’t create inequality or regulatory uncertainty.
[1] KLAUS SCHWAB, The Fourth Industrial Revolution 14–21 (Crown Business, New York 2017).
[2] ROSE LUCKIN ET AL., Intelligence Unleashed: An Argument for AI in Education 7–12 (Pearson Education, London 2016).
[3] Ministry of Education, Government of India, Annual Report 2022–23 47–53 (Ministry of Education, New Delhi 2023).
[4] UNESCO, COVID-19 and Higher Education: Today and Tomorrow (UNESCO, Paris 2020).
[5] SHOSHANA ZUBOFF, The Age of Surveillance Capitalism: The Fight for a Human Future at the New Frontier of Power 176–183 (PublicAffairs, New York 2019).
[6] The Digital Personal Data Protection Act, 2023, No. 22 of 2023 (India). Singapore has established a reasonably robust regulatory regime under the Personal Data Protection Commission Singapore (PDPC)
[7] The Personal Data Protection Act, 2012 (Singapore), No. 26 of 2012, s. 3.
[8] CATHY O’NEIL, Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy 65–83 (Crown Publishers, New York 2016).
[9] Personal Data Protection Commission Singapore, Model AI Governance Framework (2nd ed., PDPC, Singapore 2020) 12–19 [hereinafter MAIGF].
[10] UNESCO, Recommendation on the Ethics of Artificial Intelligence, adopted 23 November 2021, General Conference, 41st Session (UNESCO, Paris 2021) para. 4–20 [hereinafter UNESCO AI Recommendation 2021].
[11] KATE CRAWFORD, Atlas of AI: Power, Politics, and the Planetary Costs of Artificial Intelligence 212–219 (Yale University Press, New Haven 2021).
[12] FRANK PASQUALE, The Black Box Society: The Secret Algorithms that Control Money and Information 3–10 (Harvard University Press, Cambridge, Massachusetts 2015).
[13] VIRGINIA EUBANKS, Automating Inequality: How High-Tech Tools Profile, Police, and Punish the Poor 9–15 (St. Martin’s Press, New York 2018).
[14] Ministry of Education, Government of India, National Education Policy 2020 para. 23.4–23.7 (Ministry of Education, New Delhi 2020) [hereinafter NEP 2020].
[15] Government of Singapore, Smart Nation: The Way Forward (Prime Minister’s Office, Singapore 2018) 5–12.
[16] UNESCO, Recommendation on the Ethics of Artificial Intelligence, adopted 23 November 2021, General Conference, 41st Session (UNESCO, Paris 2021) para. 4–20 [hereinafter UNESCO AI Recommendation 2021].
[17] NEP 2020, supra note 4, para. 4.20–4.26.
[18] The University Grants Commission Act, 1956, No. 3 of 1956 (India), s. 12(a)–(g).
[19] University Grants Commission, Guidelines for Online Education (UGC, New Delhi 2020).
[20] University Grants Commission, UGC Regulations on Credit Framework for Online Learning Courses through SWAYAM, 2016, regs. 4–7.
[21] All India Council for Technical Education Act, 1987, No. 52 of 1987 (India), s. 10(1).
[22] Ministry of Education, Government of India, SWAYAM: Study Webs of Active Learning for Young Aspiring Minds — Platform Overview (Ministry of Education, New Delhi 2020).
[23] Ministry of Education, Government of India, National Digital Infrastructure for Teachers (DIKSHA): Programme Overview (Ministry of Education, New Delhi 2017).
[24] The Information Technology Act, 2000, No. 21 of 2000 (India).
[25] NITI Aayog, Responsible AI for All: Adopting the Framework — A Use Case Approach on Healthcare and Agriculture (NITI Aayog, New Delhi 2021) 5–11.
[26] Government of Singapore, Smart Nation and Digital Government Blueprint (Smart Nation and Digital Government Office, Singapore 2018) 18–22.
[27] Ministry of Education Singapore, Education Statistics Digest 2022 (Ministry of Education Singapore, Singapore 2022) 7–14.
[28] Ministry of Education Singapore, Personalised Digital Learning with Student Learning Space (Ministry of Education Singapore, Singapore 2021).
[29] The Personal Data Protection Act, 2012 (Singapore), No. 26 of 2012, ss. 13–22.
[30] MAIGF, supra note 15, at 25–34.
[31] SkillsFuture Singapore, SkillsFuture Programme Overview (SkillsFuture Singapore, Singapore 2022).
[32] Ministry of Education Singapore, Education Statistics Digest 2022 (Ministry of Education Singapore, Singapore 2022) 7–14.
[33] Justice K.S. Puttaswamy (Retd.) and Anr. v. Union of India and Ors., (2017) 10 SCC 1 (Chandrachud J.) (recognising informational privacy as a constitutional guarantee).
[34] ZUBOFF, supra note 7, at 176–199.
[35] The Digital Personal Data Protection Act, 2023, supra note 8, s. 4.
[36] The Personal Data Protection Act, 2012 (Singapore), supra note 9, ss. 11–17.
[37] SAFIYA UMOJA NOBLE, Algorithms of Oppression: How Search Engines Reinforce Racism 10–24 (New York University Press, New York 2018).
[38] The Constitution of India, Art. 14 (guaranteeing equality before law and equal protection of laws); Art. 15(1) (prohibiting discrimination on grounds of religion, race, caste, sex or place of birth).
[39] The Copyright Act, 1957, No. 14 of 1957 (India), s. 2(d) (defining ‘author’).
[40] World Intellectual Property Organization, WIPO Conversation on Intellectual Property and Artificial Intelligence, Third Session (WIPO, Geneva 2021), Doc. WIPO/IP/AI/3/GE/21/1.
[41] PASQUALE, supra note 11, at 140–156
[42] UNESCO AI Recommendation 2021, supra note 14, para. 56–62.
[43] NEP 2020, supra note 4, para. 23.4–23.7.
[44] MAIGF, supra note 15, at 8–15.
[45] The Personal Data Protection Act, 2012 (Singapore), supra note 9, ss. 13–25.
[46] Personal Data Protection Commission Singapore, Model AI Governance Framework (2nd ed., PDPC, Singapore 2020) 12–19 [hereinafter MAIGF].
[47] UNESCO, Global Education Monitoring Report 2023: Technology in Education — A Tool on Whose Terms? (UNESCO, Paris 2023) 45–62.
[48] OECD, Recommendation of the Council on Artificial Intelligence (OECD, Paris 2019) Principles 1.1–1.5 [hereinafter OECD AI Principles 2019].
[49] Infocomm Media Development Authority, Singapore’s National AI Strategy (IMDA, Singapore 2019) 10–14.
[50] Ministry of Education Singapore, Education Statistics Digest 2022 (Ministry of Education Singapore, Singapore 2022) 7–14.
[51] UNESCO AI Recommendation 2021, supra note 14, para. 7–15.
[52] The University Grants Commission Act, 1956, supra note 21, s. 12; All India Council for Technical Education Act, 1987, supra note 24, s. 10
[53] UNESCO, Artificial Intelligence and Education: Guidance for Policy-Makers (UNESCO, Paris 2021) 57–68.
[54] MAIGF, supra note 15, at 35–44; OECD AI Principles 2019, supra note 55, Principle 1.4.
[55] European Parliament and Council of the European Union, Artificial Intelligence Act, Regulation (EU) 2024/1689, OJ L, 12 July 2024, Arts. 9–13 (establishing obligations for high-risk AI systems in education and employment).
[56] Infocomm Media Development Authority, Singapore’s National AI Strategy (IMDA, Singapore 2019) 10–14.
