Three ideas of how to help students use AI that do not include copying and pasting my assignments directly into a chatbot

 In my last post I shared my AI policy (in short: red light) and that I included the following in my syllabus. 

To be clear this means using generative A.I. for assignments is not allowed, and there are other aspects of learning and studying a student could use these tools for. To help guide students towards useful and acceptable uses of this technology, each Weekly Overview will contain a recommended activity students could use this technology for, in addition to other information.  

I have to write these anyway, so here are the first three ideas I have. Again, this is for an introduction to statistics course.

Learning Outcomes Help

In past terms I have tried standards based grading to mixed results. However in doing that work I have a pretty good set of learning outcomes I want students to accomplish each term. They were initially taken from our current textbook, and over time I realized which ones I wanted to assess students on. This summer I spent time aligning my homework questions, activities, and assessment questions on these outcomes. 

A persistent issue with learning outcomes is that, unless used to generate students grade, many students don't know what they are, and what to do with them. The ideal would be that students refer to them regularly as they are learning the course material over the week, and as the week progresses they assess themselves as whether they know how to accomplish each objective. Unfortunately humans are generally task oriented, and in turn students believe doing well in their courses is a matter of accomplishing tasks, and not developing the understanding of course concepts. I get the sense students 'get it' in an abstract sense, that they are there to learn, but there is something tangible about a task that is not inherent to an outcome or an objective. To be clear I do not judge students for this, and I take pains to explain how the assignments (tasks) build on their understanding.

This is where I think AI can come in and help both myself and students, by having students complete the following task. 

This week I am going to suggest that you use AI like a test prep tutor. Our first assessment will contain questions you have not seen before, and to prepare for that assessment you should probably do the same, answer new questions. After you complete the assignments for the week, for each learning outcome this week, ask your generative AI of choice for three questions aligned to this outcome, one easy, one of medium difficulty, and one of a hard difficulty. Then try answering those questions to determine if you really can do the learning outcome. 

2A Describe how the selection of an appropriate graphical display depends on the number of variables in the data set, the data type, and the purpose of the graphical display. 

2B Construct graphical displays for categorical data (bar charts and comparative bar charts) and for numerical date (dotplots and comparative dotplots, histograms).

2C Describe the distribution of a numerical variable in terms of shape, center variability, gaps, and outliers. 

2D Describe similarities and differences in the data distributions of two or more groups. 

2F Describe trends over time based on a time series plot. 

2H Critically evaluate graphical displays that appear in newspaper, magazines, and advertisements. 

For example this would look like "For the following learning outcome, write an easy, medium, and difficult question. 2A Describe how the selection of an appropriate graphical display depends on the number of variables in the data set, the data type, and the purpose of the graphical display." After attempting these questions yourself you can ask for solutions, and then compare your solution. If these answers are radically different then you may want to review the prior assignments, your notes, and/or ask for further clarification from myself.

Do be critical of the questions that are posed. If any seem odd or strange, you are welcome to share them with me, and your thoughts on them and their solutions. 

Essentially they are using AI to generate assessment questions aligned to these outcomes. I am curious that if students do the above to see what kinds of questions they will get, whether they will ask for variations, and if they will be more focused on the outcome instead of individual questions. 

Textbook Reading Help

As mentioned in my previous post, I have students take handwritten (paper or digital) notes on the textbook, and have had them do this for about two years now. One persistent issue is that students will sometimes come to a phrase or term, and don't know what they mean. For the second week this is what I am thinking of asking students. 

This week we will be learning about a variety of computations with specific notation, terminology, and descriptions. As you complete the Note-Taking Assignments for Chapter 3, I want you to be on the look out for confusing terms and phrases. Circle these, or put a start next to them on your notes. After our Tuesday class, go back to your notes and enter these terms and phrases into your AI of choice, and ask it to explain what they mean. For example you could enter the following into a chatbot "What does it mean to be a measure of center? Can you use a baseball example to describe it?" 

 As you read these examples, refer back to your Note-Taking Assignments, in-class activities, and homework to see how well they match what we are learning. 

Correlation and Causation Help

In the third week I talk about correlation and causation within the context of linear regressions. This is usually the moment where students start seeing statistics that are beyond descriptions of groups or trends, and how 'messy' statistics can really be. Sometimes there isn't a clear statement you can draw from a two-dimensional numerical data set, and that frustrates students to no end. 

In short we generally can't determine if one variable causes and effect of another variable based solely on them being correlated. The website Spurious Correlations has myriad number of variables that are correlated, but do not have a causal relationship. To determine that we would need to perform an experiment, controlling for other variables. 

Anyway, that's a long way to saying that students struggle with this idea. Similar to the last suggestion, I am thinking of the following. 

This week we will be discussing the difference between correlation and causation, which can be a confusing concept for many students. After attending Tuesday's class, consider entering the following into your chatbot of preference; "Provide three examples of varying levels of causation given a correlation between two variables." You are welcome to specify a discipline (business, biology, etc.) or a personal interest for these examples.

Again, as you read these examples, refer back to your Note-Taking Assignments, in-class activities, and homework to see how well they match what we are learning. 

There is an aspect of culturally relevant curriculum that I struggle with in some of these suggestions; to integrate student interests within instruction. I struggle with this because a big part of college is being exposed to new ideas, and to stretch what 'interests' us. For students who will be more motivated by examples relevant to their interests, I can AI doing that work fairly well.

You can also see me asking students to be critical of AI, and you can guess as to why. Some of these systems produce inaccurate facts, and can engage in 'sycophantic modes' that I believe are incompatible with critical thought.

Thoughts? Suggestions? Accusations? Other ideas on how students could use AI for learning? Again, I am a fulsome 'no' on having students copying and pasting my assignments into AI. I'm giving one strike before an 'F'. At the same time I do see potential for AI, if it used the right way. Hopefully the above will have students try it in a way that doesn't take their learning away from them.