Call for Applications: GAITAR @Scale (GenAi Student Learning Module) F24

Applications due: Tuesday, July 9, 2024 

Do you want to participate in a low effort, large scale teaching-as-research project investigating the impacts of online learning modules designed to cultivate students’ generative AI knowledge and skills?

Eligible instructors of record selected by the Eberly Center will receive $1,000 and get the opportunity to participate in a special interest group with colleagues.

The Eberly Center is looking for Fall 2024 instructors-of-record (IoRs) to participate in the first Generative Artificial Intelligence Teaching As Research (GAITAR) @Scale initiative. The theme of this iteration of GAITAR@Scale is enhancing student knowledge and skills related to generative AI (genAI) in their role as a learner.

The research question for this iteration of GAITAR @Scale is:

Does engagement with a set of targeted online modules increase students’ knowledge and skills related to genAI?

GAITAR@Scale participants WHO ARE SELECTED must commit to the following:

• Attend one of the 60-minute program launch meetings during the week of August 12 from 12:00-1:00 pm ET to onboard to the research study’s design, implementation protocol, and teaching materials. 
• Assign a pre-packaged set of asynchronous online modules in Canvas to your students in one course you teach in Fall Semester 2024. Modules include learning activities regarding generative AI knowledge and skills as well as pre- and post-assessments, The entire package will take approximately 90 minutes to complete (see additional study design details below).
• Attend a 90-minute meeting at the end of the fall semester to debrief your experience implementing the modules in your course and discuss the aggregate data and its implications. 

Study Design & Participation

Upon confirmation of participation, instructors will be randomly assigned to participate in one of two different experimental conditions.

1. Treatment condition: Instructors will ask students to complete the asynchronous modules, including pre- and post-assessments, during the first week of the fall semester.
2. Control condition: Instructors will ask students to complete pre- and post-assessments at a specific time early in the semester without accessing the module content. (Note: You and your students can access these modules after the study concludes in early September 2024).

Eligibility 

• All CMU instructors of record (faculty, staff, postdoc) for the F24 semester (full semester or mini 1) teaching courses on Pittsburgh or Silicon Valley campuses are eligible to apply. 
• Fall 2024 course must have an expected minimum enrollment of 15 students.
• Instructors from ALL disciplines are eligible.
• Prior experience with generative AI or educational research is NOT required.
• Generative AI is NOT required to be part of your existing course content.

Application and Submission

To apply, fill out this short Google Form, which includes the following questions:

1. What is your AndrewID?
2. What is your current position at CMU? (faculty, staff, postdoc)
3. What is the number (XX-YYY) of the course for which you are applying?
4. Are you the instructor-of-record for this course?
   1. Are there co-instructors? (if so, who? Andrew ID?)
   2. Do you have the ability to assign/require the online modules for your students to complete for this study?
   3. Please indicate ALL of the following for which your course is confirmed in the CMU schedule of classes (F24, Mini 1, Mini 2, none of the above)
   4. What day of the week is the first day of your course? 
      1. Monday, Tuesday, Wednesday, Thursday, Friday
   5. Within the first week of classes, could you assign your students to complete 90 minutes of additional asynchronous work without exceeding the expectations for student effort associated with course units? (yes/no/maybe)
5. From which campus(es) do students enroll in this course (PGH, SV, none of the above)? 
6. Are you teaching multiple sections of this course during Fall Semester 2024?
7. How many students do you expect to enroll in the course? If you teach multiple sections, please provide the total number of students. 
8. Who are your students? (undergraduate, graduate, mix of undergrad and grad)
9. What college(s) is this course offered in? Check all that apply.
10. Do you currently provide students in your course with instruction or support related to the use of genAI tools? (yes/no)

If you have questions about this application, email eberly-assist@andrew.cmu.edu.