In the early 1960s, Richard D. Smallwood introduced the idea of personalized learning (PL) in his dissertation on "teachable machines" (Laak & Aru, 2024). He imagined these machines would cater and adapt to individual learning speeds, use repetition until mastery, provide immediate feedback and record student performance (Laak & Aru, 2024). Smallwood's concept laid the groundword for AI-personalized learning systems.
Today, PL can provide an alternative to the one-size-fits-all and more traditional way of teaching and learning. Personalized learning is a student-centred approach "that addresses individual learning strengths, skills, and interests, and allows flexibility in the learning mode, process, time and space, where students can take ownership of their learning" (Cheung, Wang, Kwok & Poulová, 2023). This approach empowers students by giving them greater control over their educational journeys, enabling them to learn at their own pace and in ways that suit their unique preferences and needs.
Furthermore, technological advances have moved the capabilities of PL forward in new and exciting ways. AI, for instance, now plays a crucial role in tailoring PL and educational experiences. AI can analyze vast amounts of data to identify students' strengths and weaknesses, customize learning materials and provide real-time feedback (Claned, 2024).