This session will explore how artificial intelligence (AI) tools can be harnessed to support students who struggle with task initiation and completion. Drawing from a recent study of procrastination patterns among over 500 secondary school students in grades 7 and 10 from three independent schools, we will delve into the intricate relationships between self-regulation, gender, and age in the components of procrastination.
Attendees will gain insights into:
- The differentiated impact of procrastination on students based on age and gender and how AI can be tailored to address these variations.
- The alignment of Temporal Motivation Theory (TMT) with low procrastinators’ behaviors and how AI can reinforce these positive patterns.
- The connection between Expectancy-Value Theory (EVT) and high procrastinators’ experiences, exploring how AI can reshape these students’ approaches to academic tasks.
- Practical applications of AI tools in the classroom to support students’ self-regulation and time management skills.
- Strategies for implementing AI-assisted interventions that focus on improving students’ approaches to beginning and progressing through schoolwork rather than solely emphasizing outcome satisfaction.
This session will give attendees a unique perspective that builds on foundational theoretical frameworks by exploring connections to technology and innovative practices. Participants will leave with actionable insights on leveraging AI tools to create more effective, personalized interventions for students at different procrastination levels, ultimately fostering a more engaged and self-regulated learning environment.
