Tools
Are you ready to integrate generative AI into your own teaching? Is your staff?
We have developed a tool to help educators (P-12 and higher education) and educational administrators evaluate how ready they are to integrate generative AI (like ChatGPT or BARD) into their teaching practice or organizations. Requiring less than 5 minutes total, your responses to the 12 items are used to determine your FAMILIARITY with generative AI (how much is known in general about AI and how it works) and either your or your staff’s RECEPTIVITY (the openness to using AI without anxiety).
Curious how the BARS-AT can help your organization?
We can work with your school to deploy a dedicated version of the tool to your faculty and your administrators. Once the data are collected, we can then show you:
How ready your faculty are to use generative AI (blue circles)
How ready your administrators believe that your faculty are (bluish triangles)
Then we can compare the average readiness for each group (faculty = red circle; administration = red triangle) in order to highlight any disparities and to help organizations focus on what their staff need.
In the example shown here, we can see that:
By comparing the vertical placement of the average RECEPTIVITY score for each, both groups appear open to using new technologies, like generative AI, for teaching and learning, with admins perhaps underestimating slightly how open the average faculty member is.
Although faculty are, for the most part, open and perhaps excited about beginning to implement generative AI into their practice, the average FAMILIARITY score (placement of the red “AVERAGE” marker for each on the X-axis) suggests that they are unsure about how to do so and what the capabilities and potential pitfalls of using the technology could be.
Based on these scores, the recommendation would be to increase faculty familiarity with generative AI, perhaps through workshops, the creation of professional learning communities, or by providing other professional development opportunities.
We could also segment these data by any number of different factors, such as years spent teaching, to see how those factors are influencing the overall readiness.
