A tool to explore socio-technical issues and examples from around the globe plus expand multi-stakeholder knowledge sharing
How to use the issues register
Explore the register to learn about the socio-technical issues connected to computing types of models applied to education contexts
Select a technology which you would like to find more information about
Utilise the snapshot template to start gathering key details about its use around the world
Background
Artificial intelligence registers are websites that describe the features of AI systems used in geographical areas or organisations (Cath & Jansen, 2021). Registers can potentially support democratic participation in government use of AI by providing mechanisms for transparency, feedback, and building ‘trustworthy AI’ (Haataja et al., 2020). However, existing AI registers are seen as problematic. Issues include: registers with limited information; seen as a form of ethics ‘washing’ that is untrustworthy; and introduced with little input from those who either use or are affected by AI (Cath & Jansen, 2021). This tool aims to communicate a range of issues, models, and examples which can inform collective learning and experimentation specific to the education domain. Toolkit users can learn how socio-technical issues arise from the integration of specific computing models across a range of educational contexts.
Key to types of models
Algorithm matching models introduce new forms of allocation, such as matching scarce resources with system needs. Such models inform decision-making about allocating school seats with students, or casual teachers with school vacancies. Overflowing issues relate to student and parent/guardian preferences, school funding, and employment (in)security.
Automated scoring models introduce new forms of scoring based on applying computing techniques, such as natural language processing (NLP) and machine learning. Usage of such models aims to enhance timely student feedback and reduce teachers’ marking time. Overflowing issues relate to assessment regimes, impact upon professional practice, plus school infrastructure.
Big-tech models introduce new forms of infrastructure based on cloud/platform/software/device operations as a service. These models inform decision-making for school IT governance and classroom practices.Overflowing issues relate to corporate monopolisation, school/teacher/student autonomy, and sustainability.
Biometric models introduce new forms of student, teacher, and school-based monitoring. Such models inform decision-making directed toward efficiency, security, and/or duty of care. Overflowing issues relate to privacy, consent, surveillance, plus broader wellbeing and pedagogical purposes.
Prediction models introduce new forms of estimation based on particular forms of data selection. For example, such a model can estimate student grades based on historic school data and individual performance data. Overflowing issues relate to discrimination, bias, and fairness.
Value-added models introduce new forms of teacher and school measurement based on comparing actual and expected student achievement. These models inform decision-making for dismissal, promotion, and funding. Overflowing issues relate to transparency and public scrutiny.
