Tonight I watched another AI Lightning Talk from the QM Communications YouTube playlist. Dr. Bryan Malone presented at the QM Connect Conference on “AI for Course Data Analytics Development.” They have a four-month process for developing courses. Malone wanted to develop a 300-level Big Data Course using AI (Chat GPT paid subscription) and Subject Matter Expert (SME). They trained the ChatBot to be an SME. They told the AI “I want you to be a course developer and I want to be an SME in this subject” and then prompted the ChatBox to optimize. The output request included outcomes, introduction, session objectives, state the level of Bloom’s, reading assignments, videos, deliverables, reflection pieces on the content, and workload hours. The goal was then to have the SME refine by having a “conversation” with the ChatBot. Malone and team provide the course description, and the AI produces a topic title and bullet points. The team then aligned with learning outcomes and content. Malone noted that AI doesn’t replace the SME. The next step is to validate whether it is QM align. Malone also warned about using proprietary content with the AI. The next speaker was Dr. Jess Kahlow. They shared a link to a chapter on Rubrics as part of a book on AI course design. Kahlow spoke about the importance of rubrics for consistency, fairness, and helping instructors organize feedback. Step 1 of the process is to select the assignment you wan to make a rubric for. Step 2 is to specify the criteria or ratings. Kahlow explained that ChatGPT is good at describing criteria if you tell it the number of levels, for example. Step 3 is creating a prompt for ChatGPT. Step 4 is to review the rubric produced and revise. Kahlow has used this strategy from physics to English. Kahlow ended by sharing how this approach can be used and improved for a variety of courses and designers. Both Malone and Kahlow had structured step-by-step processes for using AI to create course modules and rubrics that I am interested in trying.
