Ensuring quality and standards in AI-EdTech Part 2

Group Submission Type: Formal Panel Session

Proposal

This panel explores the urgent need for robust standards in the EdTech space, particularly in light of the rapid expansion of AI-enabled digital technologies both inside schools and at home. It brings together global efforts to define, compare, and adapt quality benchmarks for educational technology (EdTech). The panel introduces new methodologies and benchmarks utilised by different stakeholders, and discusses them in light of existing evaluation frameworks, showcase how evidence-informed EdTech Standards streamline state procurement for population-scale adoption.
Drawing on the CIES 2026 theme of "Re-examining Education and Peace in a Divided World", the panel argues that stronger, shared standards are key to ensuring EdTech contributes meaningfully to equitable and transformative learning - rather than entrenching digital divides. With examples from research institutions, standards initiatives, and implementation contexts, the panel will examine how to assess what works, for whom, and under what conditions, especially when evaluating the promise of EdTech and AI-driven solutions.
The first presentation introduces EdTech Tulna, India’s pioneering public-good framework that equips governments with research-based rubrics to evaluate the pedagogical soundness, usability, and safety of EdTech tools. Developed through a collaboration of leading technical institutes and civil society partners, EdTech Tulna demonstrates how quality standards can be embedded into state procurement and scaled across diverse contexts, including for emerging AI-integrated solutions.
The second presentation turns to the challenge of agreeing and embedding standards in AI-EdTech, where competing terminologies and fragmented interests make universal agreement difficult. It presents a layered approach to standards—core, application-specific, and contextual—that balances global benchmarks with local adaptation. It shows how top-down and bottom-up strategies csan converge to create practical, widely adoptable standards for AI-driven learning technologies.
The third presentation advances an International Impact Benchmarking framework, proposing a multidimensional model to evaluate EdTech’s broader contributions beyond test scores alone. This framework offers policymakers, funders, and school leaders a transparent set of aspirational indicators that enable comparability across contexts while accounting for social, ethical, and ecological dimensions of education.

This is the second in a series of two linked panels focused on ensuring quality and standards in AI-EdTech. In the first panel, we showcased examples of testing and measuring quality from across the product development cycle, and in this second panel we build on this to see how such examples can inform and be guided by international quality standards in AI-EdTech.

Sub Unit

• Information and Communication Technologies for Development (ICT4D) SIG

Chair

• Gouri Gupta, Central Square Foundation

Individual Presentations

• Defining quality in EdTech: India’s EdTech Tulna framework for equitable and evidence-based adoption - Bhanu Potta, Central Square Foundation; Gouri Gupta, Central Square Foundation

• Agreeing and embedding quality standards in AI-EdTech - Usman Khawar, Fab Inc; Claudia Ramly, FabInc

• International impact benchmarking - Natalia Kucirkova, University of Stavanger