Three Immediately Implementable Measures for Contending with a World in Constant Beta.
Updated: May 26
In education, how do we position ourselves for the increasing amount of flux we’re encountering with AI? The good news is that experts are confident it’s manageable—as long as we are intentional. The EdSAFE Fellowship for Education Leaders met earlier today to focus upon efficacy topics within AI. The session was co-lead by EdSAFE Advisory Council members, Dr. Michelle Barrett of Edmentum and Cristina Heffernan of Assistments.
Dr. Barrett set the tone for the discussion by reminding us that efficacy in any sort of intervention is determined by the measurement of outcomes. The diversity of those outcomes are determined by the values of all involved with the manner in which the mission is executed. For the student, it’s academic growth leading to opportunities. For the education professionals, efficiency may be also defined by productivity gains that reduce time and thus costs for achieving the same results with incumbent systems and approaches. This has implications on all departments at all levels from kindergarten through post-secondary education: academics, college and career readiness, recruitment, operations, HR, and beyond.
As decision makers within the field and beyond contend with unanticipated change management challenges brought forth by the advent of ChatGPT and other Large Language Models, efforts to determine efficacy are going to require new benchmarks, bespoke to our local priorities. The conversation centred upon what Dr. Barrett distilled into a frame with three immediately implementable measures:
Develop a logic model and spend time considering and ranking potential outcomes
Know how you will measure those most valuable outcomes
Make sure you are benchmarking at an appropriate cadence to show improvement.
Heffernan added realism to the dialogue acknowledging that for in-service teachers and administrators, such research work to determine efficacy may feel excessive in the present given resource restraints pointing out that, “there is pilot- and test-exhaustion. She continued saying that at first, “You have to decide if you want to actually find efficacy.” The conversation turned to brainstorming needs to support teachers conducting research with emerging tool efficacy to bolster teaching practice more closely aligned with useful benchmarks.
With Heffernan’s commentary, reminding us all that it comes back to the logic model and setting boundaries to begin making incremental progress sooner than later. With the pace of change in the AI landscape, we need simple, scalable approaches to understanding the impact on the field and our practice.
Conversation contributions from the fellows were helpful. Be sure to follow the work of Elizabeth Laird, Sara Vispoel, Shannon Terry, Elena Sidorava, Clare Walsh, Veerappan Swaminathan, and Bryan Contreras, This group of education experts focussing upon AI provided the following resources to support your own local work in elevating efficacy to the top of your conversations as well:
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