TWO POSITIONS:
**A summary of the two roles is provided here, please download full job descriptions.
POSITION ONE: Senior AI Consultant: Quality Assurance of AI EdTech
The Role:
This is a technical leadership role in the initiative, taking ownership of managing the work on quality assurance of AI-products for education. This is key – as the cost of generating AI content and materials fall, the world needs high quality, tractable ways of transparently assessing quality. For AI-EdTech tools, this means incorporating AI-benchmarks into our existing evidence frameworks. These benchmarks don’t fully exist – so this means also spearheading a team that can create these, curating these, and explain them clearly to Governments, donors and implementing partners.
The ideal candidate will work with the initiative’s leadership – reporting to the CEO, and take ownership of the day-to-day management of this strand of work. They will be responsible for leading, planning, coordinating, and implementing the tasks within the project.
They will also effectively monitor and present project updates to relevant stakeholders, clients, or project team members.
This role involves knowing about cutting-edge AI technologies across the range of use cases – large language models, vision models, classifiers, voice-AI tools, thinking what they do and how they can be evaluated. It’s suited to someone who knows education well, can employ a MEL lens, but can think creatively about merging knowledge from various disciplines to develop the right solutions. This means having an interest (and ideally knowledge) of AI and software development for education, alongside knowledge of how MEL usually works.
POSITION TWO: Senior AI Consultant: Voice AI
The Role:
The role is to help manage and deliver a project on improving Voice-AI systems to improve the assessment of foundational literacy – by looking at automating EGRA (alongside other assessments). While there has been a lot of innovation in this space, it has been fragmented, and we want to pull this together into a coherent whole as a global public good. This means mapping out what currently exists in this space (building on our previous work); then coming up with a plan to improve the ease of voice-data collection for children for training models – but crucially in a way which protects data privacy and allows for others to train and improve models in the future.
The ideal candidate will work with the Director and take ownership of the day-to-day management of this important project. They will be responsible for leading, planning, coordinating, and implementing the tasks within the project. They will also effectively monitor and present project updates to relevant stakeholders, clients, or project team members. This role involves working on cutting-edge AI technologies, including the application of Voice AI in educational assessments, with a focus on foundational literacy and numeracy assessments in various languages – as such, it’s suited to someone with an interest (and ideally knowledge) of AI and software development for education.
AI-for-Education.org is a platform to help realise the potential of AI to equitably improve learning at scale in low- and middle-income countries. We are supporting the development of AI tools to be evidence based and technically sound; equitable and affordable to all; and be able to improve learning at scale. The initiative is seed funded by Bill & Melinda Gates Foundation and the Jacobs Foundation.
We believe that we are in the early stages of a new cycle of upgrading how we work in education, driven by AI. To help, we are building an open-source platform and community to provide knowledge, guidance and training; open-source code and tools and public good infrastructure. We are doing this with and through the education community through hosting events, collaborating with high-impact organisations and through sharing regularly and openly.
The initiative is about to enter its second year and is well positioned to be the global authority on AI for Education for LMIC’s. It will do this by defining and shaping the ecosystem around AI for Education – shaping a vision for what is needed and identifying emerging solutions for AI for Education that can improve FLN at scale. It will create the spaces to host the dialogue on AI for education, building a deep technical knowledge of the details of both AI and education to help larger organisations (such as UNICEF, UNESCO, the GPE and the World Bank) and governments and implementers to help navigate new products and ways of working.
We work in partnership with others – engaging with big technology firms and large multilaterals to shape their investments and programming alongside broking north-south partnerships to speed up technology and knowledge diffusion to LMIC’s and making sure they’re part of the conversation.
Our initiative is open, combining knowledge generation with advisory support, training and capacity building for Government partners and implementers to ensure the sector has the AI literacy required to navigate emerging technologies, and the knowledge and tools optimise the integration of AI into their work, by merging technology and pedagogy support.
Underpinning the eco-system shaping work is the continued work to shape the backbone of equitable AI we will focus on ensuring evidence standards and quality assurance processes are adapted to AI tools – this means creating and curating the benchmarks and evidence standards for tools – providing the systems to align the needs of those purchasing them (Ministries, implementers and parents) with those aiming to build sustainable business models.
Alongside this, there is a need to continue supporting open-access, transparent, training data to provide better foundations for products that are better suited to LMIC contexts and based on evidence of what works. This means building on the work on content curation – and voice AI training datasets, to allow those building products to access better materials.
Alongside this, we will manage small grants - ‘learning by doing’ investments in high-impact organisations, to continue to ensure that the work is built from the bottom up, testing and iterating on R&D investments that can become public goods.
