The converging theme from recent research and industry reports is the contrast between the global explosion of Educational Technology (EdTech) and its lack of evidence base. The consensus from recent international gatherings is that to transform public education, EdTech needs to be driven by science and not hype.
Yet, despite the shift towards a systemic language, the collective challenge remains to ensure that EdTech evidence and evaluative rigour are equitable between the Global North and Global South.
The US and the UK government’s unprecedented investment in education has set in motion the research and development necessary to accelerate the research and development of specific EdTech solutions. Globally, major philanthropic commitments were secured through the Jacobs Foundation’s deployment of $30 million in leading EdTech venture funds and through the $15 million EdTech Co-Creation Hub for African startups. In addition, public-private cooperation in several European states (e.g. Sweden) partly compensates for resource constraints in public schools and positions teacher communities as evidence generators.
Learning scientists galvanize efforts to integrate research at all stages of EdTech design with EdTech network initiatives, such as GETN, aiming to enable improved global cross-sector partnerships towards shared impact goals (see figure below). All these efforts are driven by one mantra: EdTech can, and will, have a positive impact on children if its design, implementation and growth are driven by evidence. But there are two challenges with the Edtech Evidence movement and these are: Global North and Global South disparities.
Global North and Global South disparities
The first challenge is to ensure that the EdTech produced in the South is not merely adopting innovative projects developed in the North. With generative AI and advanced data infrastructures embedded in many apps and learning platforms, policy-makers and businesses can use evidence to make real-time decisions about which EdTech works best for which type of learners.
Such intelligent EdTech can incorporate generative AI in its design and train the models with data generated directly from and with the users. Yet, the economic and social benefits of AI remain geographically concentrated in the Global North. So that the new generation of EdTech is equitable, EdTech’s AI models need to be trained on genuinely diverse populations.
The second challenge relates to Global North-Global South disparities in standards of proof and evaluation of evidence. To measure evidence of which app or learning platform works, many Global North countries expect evidence from completed randomised controlled trials (RCTs). While an RCT represents the highest form of evidence in the ESSA Tiers of Evidence used in the US, it is not always the most suitable form of evidence for an EdTech tool, especially for highly personalised solutions that centre user interaction. While efficacy trials are necessary for some contexts, they may not uphold the evidence legitimacy operating in local policy arenas. Teachers’ views on usability and qualitative evidence on EdTech implementation generated by locally trained researchers are vital for EdTech to be embraced by local communities.
What can be done about these challenges?
First, research and innovation projects need to move beyond EdTech that targets traditional learning outcomes in the Global South and learning innovation in the Global North. Yes, well-designed EdTech can accelerate children’s maths, literacy or STEM skills and thus address educational inequalities. Many traditional skills, however, are being supplemented by AI, generating new learning paradigms. EdTech innovation in generative AI is progressing at lightning speed in the Global North and could soon monopolise the market if its development is not prioritised in the Global South.
Second, to understand what works, for whom and under what conditions, it is vital to focus not only on the type of evidence but also on the way the evidence is gathered. With the current very low number of research-based EdTech, policymakers must embrace methodological plurality in evaluating EdTech’s evidence claims. This implies the need to focus on methodological rigour, transparency and ethics of various types of evidence. So that evidence evaluations do not harm innovation, they must be followed with actionable recommendations that develop local research capacity and catalyse collective innovation.
Changing the evidence narrative
So that EdTech ‘does things better’ as well as ‘does better things,’ we need to change the culture that gives global EdTech prominence only to the North. Policy-makers and funders need to remember that the EdTech evidence imperative is introduced in an unequal system of EdTech innovation and, as such, could have an unequal impact on the EdTech ecosystem. Evidence narratives that assume golden standards of evidence are a Northern invention and should acknowledge their exclusivity, especially if fulfilling these standards requires large-scale, resource-intensive investments.
Listening to the South when it comes to EdTech will innovate not only the technology, but also the ways we measure its impact on communities. The Socio-Economic Impact Assessment Model from South Africa uses various types of relevant evidence to ensure that “the desired behavioural change should not in itself prove excessively onerous compared to the anticipated benefits. Thus, regulations that keep street traders from busy public places may mean they lose their livelihoods.” This imperative is relevant for all types of policy initiatives, including EdTech evidence.
Given that the Global South is on the lower end of the stick of evidence implementation and compliance costs, the global EdTech Evidence movement needs to account for the unintended consequences of hierarchical and exclusive definitions of ‘what works.’ Education has a long history of colonising tendencies, with assessment systems standardised for major languages, typical learners and the highly politicised nature of what counts as learning and wisdom in ‘advanced societies.’ With open AI and open data, the new generation of EdTech has a unique opportunity to generate new knowledge and redress educational injustices.
Natalia Kucirkova, Professor of Children’s Reading and Development, The Open University and University of Stavanger
The article originally appeared in the World Economic Forum.
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Source: The Print
