Framework Design Approach

Research Literature

The Tulna framework is informed by foundational learning theories such as constructivism and cognitive load theory, alongside best practices in digital teaching and learning design. It incorporates insights from global EdTech evaluation models, including the TPACK framework, which guided the structuring of Tulna across three core dimensions: content, pedagogy, and technology & design. For EdTech Tulna Standards for AI-Powered Solutions, this base is carried into AI-mediated learning: the socio-constructivist foundation extends into dialogic learning theory (Vygotsky; Mercer & Littleton) to ground the new Dialogic Pedagogy cluster, and cognitive load theory now also informs the AI-interaction criteria addressing the interpretive burden generative output places on novice learners. A review of existing instruments distinguished policy frameworks, model-level audit instruments such as the transparency index for AI in education, and institutional maturity models; the gap analysis found that international frameworks are not directly transferable to the Indian context, and that none offers observable, front-end criteria for AI-specific behaviours applicable without access to underlying models. Tulna is also periodically refined based on field insights to ensure continued relevance and contextual fit.

Educational Policies

Tulna is closely aligned with India’s national educational priorities. The framework draws on key government policy documents, including the National Strategy for Artificial Intelligence (#AIforAll) (NITI Aayog, 2018), the National Education Policy (2020), the National Curriculum Framework (NCF, 2023), and the Digital Personal Data Protection Act (DPDP Act, 2023), to stay grounded in the country’s evolving pedagogical and regulatory goals. Additionally, the PRAGYATA guidelines on digital education were reviewed to promote safe, balanced, and inclusive digital learning practices. The Try And Measure Aptitude And Natural Abilities (TAMANNA) instrument was also studied to embed grade-appropriate learning expectations and assessment benchmarks into the evaluation process.

Stakeholder Interviews and Product Landscaping

The Tulna framework is grounded in detailed benchmarking of real-world EdTech products, examining how research-backed features, such as personalisation, adaptivity, and learner engagement, are implemented in practice. This systematic product landscaping helps balance research objectives with real-world product feasibility and practical insights. To complement this, Tulna followed a co-design approach involving researchers, non-governmental organisations, government stakeholders, and EdTech product companies. Inputs from these diverse groups were critical in refining and contextualising the evaluation criteria to reflect local needs and policy priorities. By combining empirical product analysis with stakeholder perspectives, the framework remains both research-aligned and responsive to the evolving EdTech landscape.