Idea 17 for 2025: Reflect on the Feedback that You Receive

As a long-standing educator, I am very accustomed to giving feedback, although I admit that I have limited insights into how constructive the feedback that I give is to the learners that I give it to. I try to be transparent with my assessments, by being a staunch user of rubrics, and writing  specific comments to point out strengths and needs of improvement. Indeed, I find that the personalization of feedback provides helps me to clarify my own evaluation process, check for any potential biases, reinforce fairness, and consistency.

While I am grateful for the feedback that I receive from teaching or facilitating, it is not as varied or specific as I would like. It is also received after-the-fact, when the course or session has been long completed; the possibility of improvement is then only possible for the next iteration of the course or learning event. Certainly, I do receive what I collectively describe as “kind words” in an email throughout a semester or ongoing, from a client that has engaged me on a larger project; Although these are informal and do not get “tracked” by institutions or administrations, to me, they can confirm what’s going well or help me glean what can and should be changed.

Against this context, I wanted to share a brief comparison of the two categories of comments – feedback – on my recent piece on Anxiety over AI?: One Way to Cope is By Building Your Uniquely Human Skills.

One category, which I would call less optimistic or skeptical, suggested that workers should benefit from the increased productivity and reduced workloads of greater AI integration into the workplace. Commentators in this group suggested that this could include work at same or greater pay and shorter work weeks and/or other benefits. They argued that companies would have to be compelled to do this, as they would have to bear at least some of the costs of this new employer/employee arrangement. At the same time, these commentators were skeptical that human skill building would be sufficient or even desirable against an onslaught of rapid AI workplace integration and disruption.

The other category was what I would call both historical and pragmatic, eager to consider AI as a Big Data revolution, part of a series of industrial revolutions that can be linked to the advent of cars, computers, and the Internet in the past. Here, the emphasis was on increased productivity and effectiveness as well as new discoveries and insights, such as in healthcare, that could lead to new innovations. There was also a sense of the process of progress, rather than AI as a discontinuous, unforeseen disruption.

To my mind, the first category of commentators speak to the familiar reluctance to learn something new or more accurately, forced to learn something new, as they took my call to build human skills from creativity to cross-cultural competence. Moreover, the second category of commentators argue of inevitability of technological advancement that AI is ushering, with the destruction caused as necessary in the longer-term.

Taken together, they have encouraged me to keep thinking about the tension between revolutionary disruptive technological change and its variegated human impacts and indeed the shifting terrain of what constitutes “human,” which increasingly will incorporate nonbiological elements.

The word "Feedback" crafted in wooden lettering against a brown background. Photo by Ann H (from pexels.com)