DPDP Act Compliance for Schools and EdTech Platforms in India: Children’s Data Protection and Student Privacy

Posted On - 12 February, 2026 • By - Aniket Ghosh

Introduction: Education as a High-Trust Data Environment

Education has always been built on trust. Schools and universities are responsible not only for academic growth but also for protecting the identity and future prospects of their students. In today’s digital environment, that trust increasingly depends on how institutions and EdTech platforms collect, use and manage student data.

From learning management systems and online classrooms to AI tools, proctoring software and biometric attendance, education has become deeply data-driven. Much of this data relates to children and young adults, who receive heightened protection under Indian law. With the introduction of the Digital Personal Data Protection Act, 2023 (DPDP Act) and the DPDP Rules, 2025, schools, universities and EdTech platforms must reassess how they handle consent, profiling, monitoring and data sharing to ensure DPDP compliance.

Applicability of the DPDP Act to Education and EdTech

A. Entities Within Scope

The DPDP Act applies to any entity processing digital personal data, including:

  • Schools (public and private)
  • Universities and higher-education institutions
  • Coaching centres and test-prep providers
  • EdTech platforms and marketplaces
  • Online tutoring and MOOC providers
  • Learning management and assessment platforms
  • Examination boards and accreditation bodies
  • Campus service providers (transport, hostels, ID systems)

Both Indian and foreign EdTech platforms offering services to students in India fall squarely within scope.

B. Who Is the Data Fiduciary?

In most educational settings, schools and universities act as Data Fiduciaries because they decide why and how student data is collected and used. EdTech platforms typically function as Data Processors when they provide tools strictly under the institution’s instructions. However, an EdTech company becomes an independent Data Fiduciary if it determines its own purposes of processing such as developing learning analytics models, monetising student data, training AI systems, or offering direct-to-student services. Large platforms handling children’s data at scale may also be classified as Significant Data Fiduciaries (SDFs), which brings additional compliance and governance obligations under the DPDP Act.

Student and Learning Data: What Is Being Processed?

A. Categories of Data

Educational institutions and platforms process:

  • Student identity and contact information
  • Academic records, grades and assessments
  • Attendance and behavioural data
  • Audio-visual data from online classes
  • Proctoring data (screen, webcam, keystrokes)
  • Biometric data (fingerprint, facial recognition)
  • Learning analytics and performance predictions

Even derived insights like risk of dropout, aptitude scores, behavioural flags—can constitute personal data if linked to an identifiable student.

B. Why Education Data Is High-Risk

Education data affects:

  • Academic progression and opportunities
  • Psychological well-being
  • Long-term employability and reputation

Errors, misuse or over-profiling can have lasting consequences, particularly for minors. This elevates enforcement sensitivity under the DPDP Act’s harm-based framework.

Children’s Data: The Core Compliance Challenge

A. Who Is a “Child” Under the DPDP Act?

Under the DPDP Act, any individual below the age of 18 is considered a child. This broad definition significantly affects K-12 schools, coaching centres, EdTech platforms used by minors, online assessments and gamified learning apps. Any platform or institution handling data of students under 18 must follow stricter compliance requirements.

Processing a child’s personal data requires verifiable parental consent. However, simple methods such as generic checkboxes, standard admission forms or basic “I agree” clicks on apps are not sufficient. Consent must be informed, specific to the purpose, and capable of being withdrawn. Schools and platforms must also ensure that access to education is not unfairly conditioned on agreeing to excessive or unnecessary data collection.

C. Prohibition on Harmful Profiling

The DPDP framework restricts behavioural tracking, targeted advertising and manipulative design practices when directed at children. Learning analytics that label students as “low potential” or predict behavioural issues without proper safeguards may be treated as harmful profiling. Institutions and EdTech platforms must therefore use analytics carefully and ensure that children are not unfairly categorised or disadvantaged.

Many core educational activities, such as attendance tracking, grading and issuing certificates, are essential to the functioning of an institution. Relying on consent for these activities can be weak because if consent is withdrawn, the institution may not be able to perform its basic duties. In such cases, data processing is better justified on the basis of contractual necessity (through enrolment) or statutory and regulatory obligations. However, this justification cannot be extended to cover commercial analytics, EdTech product development or marketing activities.

B. Transparency and Student Notice

The DPDP Rules require institutions and platforms to provide clear notice about what data is collected, why it is processed, who it is shared with, how long it is retained and how grievances can be raised. Privacy policies and student handbooks should not rely on vague or technical language. Instead, they must offer simple, clear and accessible explanations so that students and parents understand how personal data is being used.

Learning Analytics, AI and Automated Decision-Making

A. Rise of Predictive Education

EdTech platforms increasingly use AI to:

  • Personalise content
  • Predict exam performance
  • Identify “at-risk” students
  • Recommend interventions

Such systems rely on extensive data aggregation and inference.

B. DPDP Implications

While the DPDP Act does not ban automation, institutions must ensure:

  • Transparency about analytics use
  • Human oversight for consequential decisions
  • Mechanisms to challenge or review outcomes

Opaque models that affect grades, progression or opportunities raise fairness and accountability concerns.

Surveillance, Proctoring and Biometric Systems

A. Online Proctoring as High-Risk Processing

Remote examinations often involve continuous webcam monitoring, screen recording, audio capture and behavioural analysis. This type of surveillance is highly intrusive and therefore carries significant compliance risk under the DPDP Act. Institutions must be able to clearly justify why such monitoring is necessary, limit how long the data is retained, and provide transparent disclosures to students. Where possible, less intrusive alternatives should be considered, as excessive reliance on proctoring tools may be viewed as disproportionate surveillance.

B. Biometrics on Campus

The use of biometric systems such as fingerprints or facial recognition for attendance or access control increases data protection risk. Biometric data is sensitive and difficult to replace if compromised, making the harm potentially irreversible. There is also a risk of function creep, where data collected for one purpose is later used for another. Institutions should deploy biometric systems only where strictly necessary, ensure strong security safeguards, and regularly review their continued use.

Data Sharing: Parents, Vendors and Authorities

A. Parents and Guardians

Institutions often share data with parents. While generally legitimate, sharing must still be:

  • Purpose-limited
  • Appropriate to the student’s age
  • Sensitive to older students’ autonomy

B. Vendors and EdTech Partners

Schools and universities rely on multiple vendors. Under the DPDP Act:

  • Institutions remain accountable as fiduciaries
  • Vendor contracts must impose DPDP-aligned safeguards
  • Secondary use by vendors must be prohibited

C. Regulators and Examination Boards

Statutory disclosures to boards or authorities are permissible, but institutions must:

  • Limit disclosures to necessity
  • Secure transmission channels
  • Maintain records of sharing

Cross-Border Data Transfers in EdTech

A. Global Platforms and Cloud Infrastructure

Many EdTech platforms depend on overseas cloud providers, global analytics systems and international support teams. As a result, student data may be stored or processed outside India. Under the DPDP Act, cross-border data transfers are allowed only to jurisdictions notified by the Government of India. Institutions and platforms must therefore carefully assess whether their international data flows comply with these restrictions.

B. Institutional Risk

Schools and universities that adopt global EdTech tools must clearly understand where student data is stored and processed. They should evaluate whether cross-border transfers are legally permitted and consider localisation or restricted-access models if required. Blind reliance on international platforms without reviewing data flows can expose institutions to significant compliance risks under the DPDP framework.

Data Breaches and Student Harm

A. Mandatory Breach Notification

Under the DPDP Act and Rules, any breach involving student personal data must be reported to the Data Protection Board of India. Affected individuals must also be informed, and in the case of minors, their parents or guardians must be notified. Educational institutions and EdTech platforms cannot delay or avoid notification simply because the breach appears minor.

B. Long-Term Impact

Data breaches in the education sector can expose sensitive information such as minors’ identities, academic records and behavioural profiles. The impact can extend far beyond financial penalties, affecting students’ privacy, safety and future opportunities. For institutions, the reputational and ethical consequences of such breaches can be severe and long-lasting.

Penalties, Enforcement and Institutional Exposure

A. Monetary Penalties

The DPDP Act authorises penalties up to INR 250 crore per contravention, considering:

  • Involvement of children
  • Scale of processing
  • Mitigation measures

Children’s data violations sit at the highest end of enforcement risk.

B. Reputational and Regulatory Consequences

Beyond penalties, institutions face:

  • Loss of parental trust
  • Student attrition
  • Regulatory scrutiny
  • Litigation and public criticism

Compliance Roadmap for Education Institutions and EdTech Platforms

1. Data Mapping and Child-Risk Assessment: Identify all student data and prioritise children’s data safeguards.

3. Surveillance and Analytics Review: Assess necessity and proportionality of proctoring and AI tools.

4. Vendor and Platform Governance: Update contracts; restrict secondary use; audit compliance.

5. Governance and Culture: Embed data protection into academic ethics and institutional policy.

Conclusion: Educating Without Exploiting

The DPDP Act and Rules challenge the education sector to reaffirm its core values. Learning cannot become a pretext for unchecked surveillance, profiling or commercial exploitation of children’s data.

Institutions and EdTech platforms that embrace privacy-by-design, transparency and restraint will not only comply with the law, but strengthen the trust that education fundamentally depends on.