If you’ve been following the development of the APA’s Introductory Psychology Initiative (IPI), you have probably heard the term “backward course design.” Backward course design prioritizes the knowledge, skills, and abilities that we want our students to take away from our course. We begin with student learning objectivesopens in new window, kindly provided by the IPI, then design assessments to see if the objectives are met, and finally identify educational experiences needed to succeed in the assessments1. Even if you’re familiar with this approach, redesigning your course takes precious time. That’s where AI comes to the rescue.
AI can save instructors massive amounts of time. AI enhances brainstorming, as it suggests approaches we might not otherwise have considered. If we already have elements of design we like, AI can help us reword, refine, and format our designs in fancy tables to produce a polished finished product.
If the mention of AI brings up images of student cheating, you might be overlooking opportunities to make your life easier. According to a recent TopHat survey, only 12% of instructors were using AI regularly. I want to change that. Most of us don’t have time to become AI experts, but we can take advantage of the tips that experts share. One such expert is YouTuber Jeff Su, who offers guidance on prompt engineering. Prompt engineering helps us talk to the machine in ways that produce the best outcomes. You already know how to do this. If you are searching for articles in Google Scholar, you know that using quotation marks and Boolean search terms will focus your search. Instead of thousands of articles to sift through, you retrieve a smaller set of relevant pieces. Prompt engineering accomplishes the same thing. It limits the otherwise almost limitless possible answers provided by AI.
Su identifies six components of prompt engineering in descending order of importance: task, context, exemplars, persona, format, and tone. You don’t need to use all six in every interaction with AI. The task simply tells the machine what you want to accomplish, such as “give me three assessments that discourage copying and pasting from AI that will demonstrate student understanding of classical conditioning.” Context provides focus, such as “I am teaching an online, asynchronous introductory psychology class to mostly first-year non-psychology majors at a large public university.” This prompt will return different options than if you tell the machine you are teaching a graduate seminar in learning theory at an Ivy League school. If you already have examples of learning objectives, assessments, or class activities you like, you can upload them to the AI as exemplars. “Give me an assessment for classical conditioning that is similar to the one on operant conditioning that I attached.” Playing with persona is fun. I asked AI to make the instructions for a class demonstration of air puff classical conditioning sound like they were coming from Professor Deadpool. I was concerned that I’d have to edit out a lot of profanity, but the AI was well-behaved. Format is important for those of you who must provide supervisors or review committees with formal course maps. After you identify your SLOs, assessments, and educational experiences, you can tell the AI to “produce a table with three columns and the headings SLOs, assessments, and educational experiences. Put the SLO for classical conditioning in the first column, the assessments identified above in the second column, and the educational activities identified above in the third column.” Bingo. You have a nice table to copy and paste (after reviewing for errors, of course). Finally, you can specify a tone that is formal, witty, or whatever suits your style. AI learns your style, so this becomes easier and more effective over time.
If this sounds exciting, I recommend starting small. Experiment. Compare notes with others. Engage your students in the process and see what they can generate. It looks like AI is the future, and one that we’re all in together. The more we experiment and share, the more useful and friendly AI will be.
Laura Freberg is a professor of psychology at California Polytechnic State University, San Luis Obispo, specializing in behavioral neuroscience and introductory psychology. She received her PhD from UCLA after conducting research with Bob Rescorla at Yale University. Freberg is the author or co-author of textbooks on introductory psychology, behavioral neuroscience, applied behavioral neuroscience, and research methods in psychological science. She served as the psychology consultant to the New York Times in Education program. Freberg was the 2018–2019 President of the Western Psychological Association (WPA) and received the organization’s lifetime achievement award in 2024.
