Differentiation of teaching and assessment is highly beneficial for equitably addressing needs of diverse students, e.g. gifted or high-achieving students. However, overworked and time-poor teachers face various practical difficulties in implementing large-scale differentiation.  

Over the last several years, I developed and differentiated a curriculum teaching artificial intelligence to school students. I delivered adapted versions of this curriculum in 6 quite different extra-curricular or curricular settings, to students aged 9 to 19, face-to-face or online-only. More importantly, I differentiated teaching to the needs, characteristics and interests of diverse students within each delivered program. The majority of students were gifted/high-achieving. I had to prepare differentiation in limited time, after-hours to my unrelated full-time job. While for some programs I was able to learn about students and prepare differentiation before teaching, in some short programs I had to differentiate “on-the-fly” during delivery because I was not able to learn about students beforehand.  

   My main insight is that successful differentiation is made easier by:  

1) a well-defined but adaptable curriculum,  

2) diversity and flexibility of learning resources,  

3) giving students choices that do not make helping and assessment overwhelming,  

4) interactive online learning systems with real-time teacher dashboards,  

5) software automating student data analysis.  

School Level: Primary & Secondary

Level of Expertise: Novice & Intermediate

Role/s of the Audience: Teacher &  Academic/researcher