Author: Shivani Singh, Amity University Patna
To The Point
The rise of Artificial Intelligence (AI) has initiated a profound transformation in the education sector. AI-driven personalized learning platforms promise to revolutionize teaching by tailoring instruction to individual learners. Adaptive technologies adjust pace, recommend resources, and provide real-time feedback, making the process more efficient and student-centric.
However, the integration of AI into education is not merely a technological advancement; it is a constitutional, ethical, and legal challenge. At its heart lies the question of whether AI-driven learning can be reconciled with principles of equity, fairness, and justice. Without appropriate safeguards, personalized learning could reinforce pre-existing inequalities, infringe upon privacy, and compromise due process.
Therefore, this article examines whether personalized learning in the age of AI can achieve inclusivity without
undermining constitutional guarantees. It argues that law and policy must evolve simultaneously with technology to ensure AI is an instrument of empowerment rather than exclusion.
Use of Legal Jargon
A legal-constitutional framework is essential for analyzing AI in education. Several doctrines and concepts become relevant:
1. Right to Education (jus education is):
Recognized under Article 21-A of the Indian Constitution.
It is widely acknowledged around the world through Article 26 of the UDHR and Article 13 of the ICESCR. Judicial interpretations in the cases of Mohini Jain and Unnikrishnan have expanded this concept to encompass not just physical access, but also access to quality, affordable, and equitable education.
2. Substantive Equality (lex aequitatis):
Derived from Article 14, it ensures outcomes are just, not merely procedures.
Relevant for AI, since algorithms may appear neutral but can produce discriminatory outcomes (e.g., UK A- Level grading fiasco).
3. Habeas Data (informational privacy):
Concept from Latin American constitutional law, now embedded in Indian jurisprudence through Puttaswamy v. Union of India (2017). Students’ learning data, biometrics, or behavioral analytics fall within the ambit of protected personal Information.
4. Due Process (audi alteram partem):
Enshrined in Article 21 jurisprudence in India. When AI assigns grades or makes admission decisions, students must have access to reasons, appeals, and Review mechanisms.
5. Algorithmic Accountability:
A developing legal doctrine requiring transparency, bias audits, and explainability of AI systems. Comparable to constitutional norms of administrative accountability.
6. Digital Divide Doctrine:
Based on equal protection under Article 14 (India) and the 14th Amendment (U.S.). Courts may interpret failure to provide equitable access to AI education as a form of discrimination akin to Segregation (Brown v. Board of Education).
The Proof
Empirical Evidence
UNESCO’s AI in Education Report (2022) shows adaptive learning increases comprehension rates by 30–40%. OECD’s 2021 study finds AI enhances teacher productivity by automating administrative tasks.
Failures and Concerns
UK A-Level Algorithm (2020): Nearly 40% of grades were downgraded, disproportionately impacting Disadvantaged students.
Data Breaches: Ed-tech companies such as Chegg and BYJU’s have been accused of mishandling student Data, exposing minors to surveillance capitalism.
Inequitable Access
Only 24% of rural Indian households have internet access (NSSO, 2022).
Linguistic minorities face barriers as most AI tools prioritize English or dominant languages, undermining Cultural rights under Articles 29–30.
Policy Developments
The National Digital Education Architecture (NDEAR) introduced by India in 2021 underscores the role of digital integration in education but offers limited provisions for safeguarding privacy. On the other hand, the European Union’s Digital Education Action Plan (2021–2027) explicitly prioritizes equity, inclusivity, and the protection of personal data.
Abstract
This article investigates the intersection of AI, education, and law by focusing on personalized learning. While AI promises efficiency and inclusivity, its misuse risks exacerbating inequality. Drawing on case law from India (Mohini Jain, Unnikrishnan, Puttaswamy), the U.K. (A-Level Algorithm case), the U.S. (Brown v. Board, Students For Fair Admissions), and the EU (Digital Rights Ireland), the paper argues that AI must be governed by a Rights-based framework.
Personalized learning is not just a technological choice but a constitutional obligation under the right to Education. Governments must provide equitable access, safeguard privacy, and ensure algorithmic Accountability. Courts, through evolving jurisprudence, remain pivotal in preventing AI from becoming an Instrument of exclusion.
Case Laws
Mohini Jain v. State of Karnataka (1992) – India
Facts: The case arose when private medical colleges in Karnataka charged exorbitant “capitation fees.” A Student challenged this exclusionary practice.
Held: The Supreme Court ruled that the Right to Education flows from Article 21. Denying education based on Economic capacity violated equality.
Analysis: In the context of AI, this case implies that access to AI-based personalized education cannot be Limited to individuals who have the means for internet access and devices. If AI becomes integral to modern pedagogy, Governments must treat it as part of the right to education.
Unnikrishnan J.P. v. State of Andhra Pradesh (1993) – India
Facts: Revisited capitation fee practices and defined the scope of educational rights.
Ruling: Receiving an education until the age of 14 is a basic right. Beyond that, it is a directive principle subject to Economic capacity.
Analysis: If AI becomes necessary for effective education, states must ensure it is universally available at Least at the school level. Courts may impose positive obligations on governments to bridge the digital divide.
Justice K.S. Puttaswamy v. Union of India (2017) – India
Facts: Concerned the Aadhaar scheme and recognition of the right to privacy.
Ruling: Privacy is fundamental to personal freedom and dignity, safeguarded by Article 21. Data collection should be carried out only when it is lawful, necessary, and proportionate to the purpose for which it is intended.
Analysis: AI in education involves vast data collection (attendance tracking, keystroke analysis, performance Metrics). Without explicit consent and safeguards, this violates privacy Personalized learning platforms are required to adhere to the proportionality criteria established in Puttaswamy.
UK A-Level Algorithm Case (2020) – United Kingdom
Facts: Exams were canceled during COVID-19. An algorithm determined grades, which disproportionately Downgraded students from disadvantaged schools.
Outcome: Large protests led the government to stop using the system.
Analysis: This situation shows how risky it is when AI is not clear or transparent. Courts could treat such outcomes as breaches of Substantive equality and due process, requiring explainability and appeal mechanisms in AI grading.
Brown v. Board of Education (1954) – United States
Facts: Challenged racial segregation in public schools.
Held: Segregation violates the Equal Protection Clause. “Separate but equal” is inherently unequal.
Analysis: Today’s digital divide mirrors segregation. Students without access to AI-powered personalized.
Learning are effectively excluded from modern education. Courts could extend Brown’s reasoning to
Mandate equal technological access.
Students for Fair Admissions v. Harvard (2023) – United States
Facts: Supreme Court struck down affirmative action in admissions.
Held: Admissions must be race-neutral.
Analysis: If AI admissions systems are trained on biased data, they may inadvertently replicate discriminatory Patterns. This case underlines the need for bias audits and transparent datasets in AI-driven education Systems.
Digital Rights Ireland Ltd. V. Minister for Communications (2014) – EU
Facts: Challenged EU Data Retention Directive requiring telecoms to store user data.
Held: CJEU struck it down as violating proportionality and privacy.
Analysis: AI education platforms retaining excessive student data (e.g., browsing history, personal identifiers) Would be unconstitutional under this principle. Courts could apply data minimization obligations to ed-tech Firms.
Additional Doctrinal & Legal Points
1. Right Against Discrimination (Article 15, India):
AI tools applied in admissions, assessments, or student tracking must uphold fairness and remain free from bias related to caste, gender, religion, or disability.
If algorithmic profiling reinforces stereotypes, it could violate Article 15.
2. Reasonable Restrictions Doctrine:
Borrowed from Article 19 jurisprudence, it suggests that AI interventions in education must balance innovation with restrictions necessary to protect fundamental rights like privacy and equality.
3. Proportionality Test (Puttaswamy, 2017):
Courts may apply proportionality in evaluating ed-tech surveillance (e.g., webcam proctoring, keystroke monitoring). The state or private actors must prove that surveillance is necessary and the least restrictive means to achieve educational goals.
4. Parens Patriae Role of State:
Since children are a vulnerable class, the state has a constitutional duty to act as guardian, ensuring that AI-based tools are child-safe, developmentally appropriate, and free from exploitative commercial practices.
5. Right to Language & Culture (Articles 29–30, India):
AI platforms must provide multilingual support to prevent marginalization of linguistic and cultural minorities. Courts could extend cultural rights to include digital pedagogy in mother tongues.
Technological & Governance Dimensions
1. AI Literacy as a Fundamental Skill:
Just as digital literacy became essential, AI literacy (understanding algorithms, data rights, and digital citizenship) could be recognized as part of the Right to Education.
2. Public vs. Private Ed-Tech Models:
Over-reliance on private AI platforms risks “privatization of constitutional rights.” State-backed AI infrastructure (like NDEAR) must prioritize accessibility and prevent monopolization.
3. Cross-border Data Flows:
Many AI tools in Indian classrooms are owned by foreign companies, raising questions about data sovereignty. Laws must prevent educational data being commodified abroad without safeguards.
4. Child Rights Framework (UNCRC):
Under Articles 16 (privacy) and 28–29 (education), AI must respect the best interests of the child. Excessive screen-time, data-tracking, or behavioral manipulation could be challenged as violating international child rights obligations.
5. AI & Disability Rights:
Personalized learning holds immense potential for inclusive education (assistive technologies, speech recognition, text-to-speech). However, lack of accessibility design could exclude students with disabilities, violating the Rights of Persons with Disabilities Act, 2016 (India) and CRPD (2006).
Policy & Comparative Insights
1. Global Best Practices: EU AI Act (2024 draft): Classifies AI in education as “high-risk,” requiring strict audits, transparency, and human oversight.
US Children’s Online Privacy Protection Act (COPPA): May inspire India to develop child-specific AI data protections.
2. Community-Based Models: UNESCO suggests localizing AI tools with open-source, community-driven platforms to reduce dependency on profit-driven ed-tech giants.
3. Ethical Design Principles: Algorithms must be designed for inclusivity — for instance, datasets must include rural, non-English speaking, and marginalized communities to avoid systemic exclusion.
Conclusion
AI-driven personalized learning signifies a transformational change in the educational landscape. While it has the potential to Democratize access, it risks perpetuating inequality if unchecked. Case law across jurisdictions demonstrates a consistent judicial willingness to intervene when education becomes exclusionary.
Indian jurisprudence (Mohini Jain, Unnikrishnan, Puttaswamy) establishes education, dignity, and privacy as Constitutional guarantees.
U.S. cases (Brown, Students for Fair Admissions) highlight equality in access and the risks of bias in systemic Decision-making.
UK and EU cases (A-Level Algorithm, Digital Rights Ireland) reinforce due process, fairness, and data proportionality in technological governance.
To ensure AI fulfills its emancipatory promise, the following are imperative:
Universal Access: Governments must provide infrastructure, devices, and connectivity to prevent digital Segregation.
Algorithmic Transparency: AI systems must be auditable, explainable, and open to challenge.
Privacy Safeguards: Data minimization, consent, and proportionality must be strictly applied.
Ethical AI Development: Design guided by fairness, inclusivity, and human dignity rather than market Efficiency.
Judicial oversight should evolve further, with courts broadening their jurisprudence to address issues of algorithmic accountability.
Ultimately, personalized learning in the age of AI must align with the constitutional vision of equity and justice.
Innovation should not be pursued at the expense of fairness; rather, equity must be the foundation upon Which educational innovation thrives.
Human Oversight Principle: No AI decision in education (grading, admissions, behavioral analysis) should be final without human review.
Future Jurisprudence: Indian courts may soon need to articulate a doctrine of “Digital Educational Equity”, analogous to Brown v. Board, the goal is to guarantee equal and universal access to AI-powered education.
Long-term Constitutional Vision: Personalized learning through AI should not only meet today’s equity demands but also prepare a democratic, informed citizenry capable of engaging critically with AI itself.
FAQS
What does personalized learning mean in the age of AI?
Personalized learning uses AI to adapt content, pace, and teaching methods to individual student needs. AI Tools such as adaptive platforms and intelligent tutors make learning more flexible and student-centered.
How does AI enhance the learning experience?
AI can monitor student progress in real time, identify areas of strength and difficulty, recommend tailored Resources, and deliver instant feedback — creating a more engaging and effective learning journey.
Why is equity critical in AI-driven education?
Equity is essential to make sure AI benefits every learner, no matter their financial situation or access to Digital resources. If fairness is overlooked, AI could deepen the learning gaps that already exist in education.
Can AI replace teachers?
No. AI can assist in routine tasks and offer personalized support, but it cannot take the place of teachers. Educators are still crucial for providing guidance, emotional connection, and meaningful human interaction In the classroom.
What risks come with using AI in education?
Major concerns include data privacy, algorithmic bias, unequal access to digital tools, and over-dependence On automation, which may overlook the human side of learning.
How does AI encourage innovation in education?
AI enables innovations like adaptive curricula, gamified learning, predictive analytics for early intervention, And virtual tutors — all of which encourage creativity and lifelong learning.
What can schools do to ensure AI is used equitably?
Provide equal access to devices and reliable internet.
Use transparent and fair AI systems.
Train teachers in responsible AI use.
Regularly check AI tools for bias.
Will AI-powered personalized learning be affordable for all?
Affordability is a challenge, but open-source AI tools, government support, and collaborations with edtech Companies can make personalized AI learning more widely accessible.
References
Primary Sources
Constitutions and Statutes
Constitution of India, arts 14, 21, 21-A, 29–30
International Covenant on Economic, Social and Cultural Rights (adopted 16 December 1966, entered into force 3 January 1976) 993 UNTS 3, art 13 US Constitution, amend XIV
