Issues register

EdTech Assessment Toolkit

Personalised Learning Application (UK)

Prediction model

Personalised learning technologies abound across all facets of education. Often, these technologies don’t receive the attention they deserve, especially in the context of K-12 education. An example of such a platform is Century Tech in the UK. Century’s platform is crafted to offer personalised and adaptive learning experiences for students while supporting teachers in the classroom. The platform utilises artificial intelligence and data analytics to evaluate individual student strengths and weaknesses, monitor progress, and suggest personalised learning pathways. It can pinpoint areas where students require additional support or challenge and provide targeted content and exercises to meet those needs. Century’s AI-driven platform also seeks to lessen the teacher workload by automating certain administrative tasks, offering real-time feedback to teachers, and producing data-driven insights to guide instructional decisions. Moreover, the platform presents a broad spectrum of subjects and courses in line with various curricula, purporting its suitability for both K-12 and higher education settings (Century, 2022).

While personalised learning systems might appear promising, their adoption carries a plethora of implications. Personalised learning hinges on the principle of optimisation. Typically, the software strives to tailor content to enhance learning outcomes based on historical student data (Bulger, 2016). Thus, despite professing a focus on individual student needs, the software essentially categorises students based on past data. Additional concerns encompass the commercialisation of student data (Roberts-Mahoney, Means, & Garrison, 2016) and numerous ethical considerations surrounding student privacy and autonomy (Regan & Jesse, 2019).

Snapshot (July 2023)

System task/function: Personalise the learning content of students
Model: Prediction Model
Deployment: AI steered personalised learning
Location of application: Worldwide, (specific example from UK)
Rationale for introduction: Personalise learning for individual students without adding more workload to teachers
Vendor: Various, e.g., Century Tech
Pricing: Not disclosed
Data and computation: School level or school districts
Inequalities/harms: Potential discrimination based on historic data and decreasing equity (Bulger, 2016), monetisation of student data (Roberts-Mahoney et al., 2016) and ethical implications for student privacy and autonomy (Regan & Jesse, 2019).
Status: Active
Authority/regulation: School and district level
Unintended consequences: The use of machine learning in personalised learning has the potential to lock student’s in a permanent present (Smithers, 2023)
Sanction/redress: N/A

References/further info

Bulger, M. (2016). Personalized Learning: The Conversations We’re Not Having. Retrieved from

Century. (2022). 10× national average grade improvement*. Retrieved from
Regan, P. M., & Jesse, J. (2019). Ethical challenges of edtech, big data and personalized learning: twenty-first century student sorting and tracking. Ethics and Information Technology, 21(3), 167-179. doi:10.1007/s10676-018-9492-2

Roberts-Mahoney, H., Means, A. J., & Garrison, M. J. (2016). Netflixing human capital development: personalized learning technology and the corporatization of K-12 education. Journal of Education Policy, 31(4), 405-420. doi:10.1080/02680939.2015.1132774

Smithers, L. (2023). Predictive analytics and the creation of the permanent present. Learning, Media and Technology, 48(1), 109-121. doi:10.1080/17439884.2022.2036757