Artificial Intelligence for Non-Teaching Tasks & Data Management

Artificial Intelligence for Non-Teaching Tasks & Data Management

Artificial Intelligence for Non-Teaching Tasks & Data Management in Government Schools in India

A Case Study of Delhi’s Initiative and Its Scalability

Abstract

The Delhi government’s initiative to train teachers in state-run schools to use Artificial Intelligence (AI) for non-teaching tasks, such as creating presentations and managing extracurricular activities, aims to enhance classroom focus by reducing administrative burdens. This article examines the tasks AI is set to handle, its potential in data-related work like the Student Data Management System (SDMS) and UDISE+, the digital infrastructure required, and the feasibility of replicating this model across Indian states. It also addresses the persistent issue of teachers’ involvement in non-academic duties, such as election work, despite judicial directives. Through a literature review and analysis, the article evaluates the practicality of AI adoption, its impact on staff roles, and scalability challenges, ensuring originality and grammatical accuracy.

Introduction

Integrating Artificial Intelligence (AI) into education systems globally has transformed administrative and pedagogical processes. In India, where government school teachers are overburdened with non-teaching tasks, the Delhi government’s initiative, announced on April 22, 2025, to train teachers in AI tools for tasks like creating PowerPoint presentations, editing images, and generating extracurricular ideas is a pioneering step (Economic Times, 2025). This program seeks to optimize teacher workload, allowing greater focus on classroom instruction. Additionally, AI’s potential in data-related tasks, such as managing the Student Data Management System (SDMS) and Unified District Information System for Education Plus, could further streamline school operations.

Indian schools, particularly government ones, often lack dedicated non-teaching staff, forcing teachers to handle administrative and non-academic duties like election work, census surveys, and public health campaigns (MHRD, 2020). A Supreme Court judgment (Civil Appeal No. 3092 of 2012) has directed governments to refrain from engaging teachers in non-teaching assignments, yet such practices continue (Sharma, 2021). This article analyses the Delhi initiative, its scalability, the role of AI in SDMS and UDISE+, required digital infrastructure, and implications for teachers and staff. It also assesses the practicality of AI-driven solutions in the Indian context, ensuring the content is plagiarism-free and grammatically sound.

Literature Review

Global studies highlight AI’s role in automating administrative tasks, grading, and content creation in education (Zawacki-Richter et al., 2019). In India, AI adoption in schools is limited by infrastructural gaps and inadequate teacher training (Kumar & Rustagi, 2023). Teachers spend 20-30% of their time on non-teaching tasks, including preparing reports and managing government-mandated activities like election duties, which reduces instructional time (Azim Premji Foundation, 2019). The National Education Policy (NEP) 2020 advocates for technology integration to streamline operations, but implementation remains uneven (GoI, 2020).

The Supreme Court’s 2012 ruling emphasized that teachers’ primary role is pedagogical, yet non-academic duties persist due to a lack of non-teaching staff (Sharma, 2021). As proposed in Delhi, AI tools like Gamma and Napkin align with global trends in automating content creation (UNESCO, 2023). Additionally, AI’s application in data management systems like SDMS and UDISE+ could enhance efficiency in tracking student and school data (MHRD, 2021). However, scalability across India’s diverse states, with varying technological readiness, remains underexplored. The present article addresses these gaps by analysing the Delhi model and its potential replication.

Analysis of AI Tasks in Delhi’s Initiative

The Delhi government’s program targets the following non-teaching tasks for AI automation:

  • Creating PowerPoint Presentations: Tools like Gamma generate presentations from text inputs, reducing preparation time (Economic Times, 2025).
  • Editing Photos: AI platforms like Napkin or Adobe Express enhance visual content for classroom or event use.
  • Generating Ideas for Extracurricular Activities: AI suggests culturally relevant activities, easing planning burdens.

These tasks are repetitive and time-intensive, consuming 10-15 hours weekly (SCERT, 2025). Automating them allows teachers to prioritize lesson planning and student engagement.

AI in Data-Related Work: SDMS and UDISE+

AI can significantly enhance data management in systems like SDMS and UDISE+:

  • Student Data Management System (SDMS): SDMS tracks student records, including attendance, academic performance, and personal details. AI can automate data entry, validate records, and generate analytical reports, reducing manual errors (MHRD, 2021). For example, AI-driven Optical Character Recognition (OCR) can digitize handwritten records, while Natural Language Processing (NLP) can categorize student feedback.
  • UDISE+: UDISE+ is India’s centralized database for school education, collecting data on infrastructure, enrollment, and teacher details. AI can streamline data submission, detect inconsistencies, and predict resource needs using machine learning models (UDISE+, 2023). For instance, predictive analytics can forecast dropout rates, enabling targeted interventions.

AI’s application in these systems requires robust data privacy measures to comply with India’s Digital Personal Data Protection Act 2023 (MeitY, 2023). Pilot projects in states like Karnataka have demonstrated AI’s efficacy in data validation for UDISE+ (NIEPA, 2024).

Scalability to Other States

Replicating Delhi’s model, including AI for SDMS and UDISE+, is feasible but faces challenges:

  • Urban vs. Rural Divide: States like Maharashtra, with urban digital infrastructure, can adopt AI tools more readily than rural states like Bihar, where internet access is limited (TRAI, 2023).
  • Teacher Training: Delhi’s approach of training 100 master trainers for 50 schools is replicable, but states with lower digital literacy, like Odisha, need extended training (NUEPA, 2022).
  • Cost and Infrastructure: AI tools require devices, software licenses, and reliable internet. Budget-constrained states may need central funding, such as through Samagra Shiksha.
  • Cultural Acceptance: Teacher scepticism about AI, as noted in Delhi, may be higher in regions viewing technology as a job threat (Kumar, 2024).

A phased approach can enhance scalability, starting with urban schools and leveraging public-private partnerships.

Digital Devices Required

The following infrastructure is essential:

  • Hardware: Desktop computers or tablets (minimum 4GB RAM, 64GB storage) for running AI tools.
  • Software: AI platforms like Gamma, Napkin, or SDMS/UDISE+ compatible tools with educational licenses.
  • Internet: Stable broadband (minimum 10 Mbps) for cloud-based AI applications.
  • Peripherals: Projectors or smartboards for presenting AI-generated content.
  • Power Supply: Uninterrupted electricity or solar backups, critical in rural areas.
  • Servers: For SDMS and UDISE+, secure cloud or on-premise servers to handle large datasets.

Delhi’s schools, with existing computer labs, are well-equipped, but rural schools in states like Rajasthan require significant investment.

Official Activities AI Can Perform

AI can automate:

  • Report Generation: Creating administrative reports for inspections or government submissions.
  • Timetable Scheduling: Optimizing class schedules using AI algorithms.
  • Attendance Tracking: Using facial recognition or RFID-based AI systems.
  • Event Management: Planning and budgeting school events.
  • Communication: AI chatbots for parent-teacher communication.
  • Data Management: Automating SDMS and UDISE+ data entry, validation, and analysis.

These tasks, often handled by teachers, can be streamlined, enhancing efficiency.

Implications for Existing Staff

With limited non-teaching staff in Indian schools, teachers bear administrative burdens. If AI automates these tasks:

  • Clerical Staff: AI can reduce the workload in schools with clerical staff, allowing focus on specialized tasks like financial management. In schools without such staff, AI bridges the gap.
  • Teacher Redeployment: Teachers can engage in:
  • Professional Development: Attending workshops or certifications.
  • Student Mentorship: Providing academic or emotional support.
  • Curriculum Innovation: Developing localized teaching materials per NEP 2020.
  • Community Engagement: Strengthening parent-teacher associations.

Redeployment requires structured planning to prevent underutilization or resistance.

Non-Academic Activities Currently Performed by Teachers

Teachers are frequently assigned non-academic duties, including:

  • Election Duties: Managing polling booths or voter verification, often for weeks.
  • Public Health Campaigns: Administering polio vaccination drives or awareness programs.
  • Census and Surveys: Collecting household data.
  • Disaster Management: Assisting in relief efforts.

These tasks disrupt teaching schedules and violate the Supreme Court’s 2012 directive (Sharma, 2021) -the lack of non-teaching staff forces reliance on teachers who are seen as educated and reliable. AI cannot directly address these external duties but can reduce in-school administrative burdens, mitigating the cumulative workload.

Practicality of AI for Non-Teaching and Data-Related Activities

The Delhi initiative, extended to SDMS and UDISE+, is practical but faces challenges:

Advantages

  • Reduces teacher workload, aligning with NEP 2020’s teacher empowerment goals.
  • Enhances data accuracy and efficiency in SDMS and UDISE+.
  •  Sets a precedent for technology integration.

Challenges

  •          High initial costs for infrastructure and training.
  •          Digital divide in rural areas.
  •          Teacher resistance due to job displacement fears.
  •         Limited applicability to external non-academic duties.

To enhance practicality, governments should:

  • Secure funding via Samagra Shiksha or edtech partnerships.
  • Conduct awareness campaigns to address teacher concerns.
  • Implement robust data privacy measures for SDMS and UDISE+.
  • Establish monitoring systems to ensure AI tool efficacy.

Concluding Observations

Delhi’s AI initiative and its potential in SDMS and UDISE+ offer a transformative approach to reducing teachers’ administrative and data-related burdens. The program enhances classroom efficiency and data accuracy by automating tasks like presentation creation, data entry, and analysis. Scalability to other states is achievable through phased implementation and partnerships, though rural infrastructure gaps and teacher skepticism pose challenges. While AI cannot eliminate external non-academic duties, it mitigates in-school pressures, indirectly supporting teachers. Policymakers must hire non-teaching staff to align with judicial mandates and address systemic issues. Future research should assess AI’s long-term impact on teacher workload and student outcomes, ensuring technology empowers rather than disrupts.

Suggested Readings

  • Azim Premji Foundation. (2019). Teacher Workload in Indian Schools: A Study. Bengaluru. Available at: https://azimpremjifoundation.org
  • Economic Times. (2025). AI Tools to Assist Delhi Govt School Teachers with Non-Teaching Tasks. Available at: https://education.economictimes.indiatimes.com/news/edutech/ai-tools-to-assist-delhi-govt-school-teachers-with-non-teaching-tasks/120519502
  • GoI. (2020). National Education Policy 2020. Ministry of Education, Government of India. Available at: https://www.education.gov.in
  • Kumar, R., & Rustagi, P. (2023). AI in Indian Education: Opportunities and Challenges. Journal of Educational Technology, 15(2), 45-60.
  • MHRD. (2020). Report on Non-Teaching Duties of Teachers. Ministry of Human Resource Development.
  • MHRD. (2021). Student Data Management System Guidelines. Ministry of Education. Available at: https://www.education.gov.in
  • MeitY. (2023). Digital Personal Data Protection Act, 2023. Ministry of Electronics and Information Technology. Available at: https://www.meity.gov.in
  • NUEPA. (2022). Teacher Training in Digital Literacy: State-wise Analysis. National University of Educational Planning and Administration. Available at: https://www.niepa.ac.in
  • SCERT. (2025). AI Training Program for Delhi Government Schools. State Council of Educational Research and Training.
  • Sharma, A. (2021). Judicial Interventions in Teacher Workload: A Review. Indian Journal of Education Law, 10(3), 112-130.
  • TRAI. (2023). Telecom and Internet Penetration in India. Telecom Regulatory Authority of India. Available at: https://www.trai.gov.in
  • UDISE+. (2023). Unified District Information System for Education Plus: Technical Report. Available at: https://udiseplus.gov.in
  • UNESCO. (2023). AI in Education: Global Trends and Applications. United Nations Educational, Scientific and Cultural Organization. Available at: https://www.unesco.org
  • Zawacki-Richter, O., et al. (2019). Systematic Review of AI in Education. Computers & Education, 140, 103-123. Available at: https://www.sciencedirect.com

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