Tag: grading

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A Degrading Experiment – Preliminary Results

In my last post, I pondered whether eliminating coercive elements of assessment changed student behavior. Would students complete assignments that had no direct effect on their grades?

So far, it looks like the answer is “no.” I’m teaching my usual autumn term course on economic development and environmental change. In previous years, I assigned a graded evaluation for each of the five games that students played in class. This time I assigned three evaluations. The first two were ungraded, but I provided students with feedback that they could apply to the third, graded evaluation, which is due next week.

Failure is not an option

In a class of twenty-five students, five completed the first evaluation. No students completed the second evaluation.

These numbers lead me to suspect that many of my students do what they do to avoid punishment, not because of intrinsic motivation or interest.

Perhaps I should investigate assessment optionality . . .

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5 Strategies for AI-Resistant Assignments

I recently got to attend the University of Central Florida’s Teaching and Learning with AI Conference in Orlando, and it’s one I highly recommend for anyone interested in practical ideas on how to think about, resist, and use Generative AI in higher education. The presentations are all 25 minutes and the presenters make the most of their short time. I want to share some ideas from one of those presenters, Ashley Evans, professor and Program Chair of Software Development and Cloud Computing at Valencia College. While her field is obviously far from political science, her strategies for transforming assignments from AI-vulnerable to AI-resistant are easily adapted to our field, and worth considering. I want to note that these are my recollections and takes on Evans’ presentation; all ideas here are hers, not mine (excepting the adaptations to polisci). Her website is https://ashley-evans.vercel.app/ and she has told me she’s happy to talk AI with other faculty; contact her by email.

Evans’ stated goals for creating AI resistant assignments include:

  • Reduce student ability to get high marks from solely AI-generated submissions
  • Eliminate the ability of AI to complete the task in place of the student
  • Challenge students to think critically about AI generated output, if used.
  • Relieve instructors from the role of AI-detectives.

All of these seem like worthy goals to me. Most importantly in my mind, she’s interested in taking the weight off of faculty of trying to root out unauthorized AI use, not by banning it, but by revising assignments so that AI is genuinely not useful on the assignment, and by establishing grading criteria that focuses on completion of tasks that AI cannot do well. This is a tough balancing act, but one that I think faculty need to wrestle with as they figure out how much they want to incorporate AI into their classes.

With that, here are her five strategies:

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