STLinATL

This presentation will encourage attendees to interrogate current assessment practices, especially in the context of Generative AI’s increasing role in the classroom. With these tools capable of mimicking students’ work with remarkable accuracy, many educators are scrambling to “AI-proof” their assessments—often with mixed success. We will explore data on how students are currently using Generative AI tools in schools, providing a foundation for informed discussion and reflection. Participants will examine widely used summative assessment methods and consider what we truly learn—or believe we learn—from these tools when they are used in traditional formats.

Through hands-on activities, attendees will gain a deeper understanding of the biases inherent in Generative AI tools and those designed to detect AI-generated work, offering critical insights into their limitations and implications for assessment. Discussions will also explore how to refine the use of familiar tools like rubrics and portfolios to more effectively measure authentic student growth and learning over time.

We will introduce alternative assessment strategies, including formative, collaborative, reflective, and metacognitive approaches, and discuss how these methods can promote deeper engagement and learning. By the end of the session, attendees will leave with a practical awareness of these challenges, actionable strategies for navigating them, and a detailed template to guide the process of rethinking and updating their assessment practices for a rapidly evolving educational landscape.