Generative AI design aimed at supporting learner growth and well-being
An emancipatory vision for designing (generative) AI for learner flourishing
Computers and SocietyArtificial Intelligence
Summary
Generative AI tools are growing popular and could help people learn more efficiently, but they also risk making learners overly dependent and isolated. The authors argue that simply focusing on user preferences isn’t enough, since bigger systems like economics and human habits influence how people use technology. They suggest a new approach to designing educational AI that supports learners’ overall growth and well-being while considering these wider systems. This vision includes new design ideas and methods but still needs more work before it can be fully applied.
generative AIlearner flourishinghuman-centered designvalue-sensitive designeducational technologyagentic featuressystemic factorsuser dependencyindividualism
Authors
Luis P. Prieto, Yannis Dimitriadis
Abstract
The hype around generative AI seems to promise unprecedented productivity (and learning) gains. However, these technologies' increasing agentic features seem to push learners towards individualism (or individual isolation), over-reliance, and dependence on them. Human-centered design approaches (e.g., value-sensitive design) assume that, by unearthing human needs, preferences, and values, technology researchers/designers may avoid such dangers, which are driven by wider systemic factors like economic incentives or inherent human limitations (e.g., our tendency to seek, in the moment, the easiest path of action). Yet, so far these efforts seem insufficient to guide our design of educational technology that avoids the aforementioned dependency and isolation dangers, while finding widespread adoption. This paper presents an alternative, more emancipatory vision for future educational AI technology, oriented towards learner flourishing while considering the wider complex systems they inhabit, including tentative design principles and an overall design methodology. Yet, many open questions remain before this vision can be realized.