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Our 2 year goal, by the end of AY 24-25, is to produce GAI-enabled course structure across the introductory STEM curriculum.
The tools should be agnostic to the textbook(s) used in the course, and should be implementable with a minimal time investment in courses nationwide.
Initial backend is likely to be ChatGPT, currently at version 4, but we should  strive to build an architecture that can evolve and adapt to new and better and different tools. We need to have an overall architecture philosophy evolve with our development spirals.  

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ItemexperimentscoursesleadGPT aspect neededValidation criteria

A: Incorporation of GAI into lecture-format STEM learning: 

  1. Develop experimental active learning lecture modules that incorporate GAI capabilities, and devise methods to measure their effectiveness in student comprehension and retention. 
  2. Develop first-generation tools 
  1. synthesis of student-provided questions, in real time
  2. open-response quizzes rather than multiple choice
  3. peer-instruction including GAI
  4. Sequential interaction on course prompts






B: Develop and Exploit short-cycle adaptive problem sets with real-time feedback




C: Assist with analysis and gain insights from lab data. 
  1. import data collected 










D: interactive student self-assessments. 




E: In-class group consultation (peer instruction) with ChatGPT participation




F: capturing and submitting GAI-enabled student work for GAI evaluation by course staff




G: automated evaluation of understanding of material, by evaluating answers to questions we provide. 




H: Ascertain subject-level mastery needed to exploit natural-langauge-driven code development.
         Try out a non-analytic problem and assess the results. 
  1. modulated-friction example. Modulated Friction example

15 a,b,c
Math 22


numerical solution code
I: incorporate into HW and assessments the ability to perform calculations, as pioneered by Khan Academy


arithmetic capability
J: incorporate course-specific training inputs and give that high weighting


custom training inputs
K: Automation grading and assessments of student competence. 


sequential prompts run open loop, no adjustment
L: dynamic tutoring


sequential prompts with iterative adjustment 
M: GAI assisted generation and refinement of course instructional and assessment materials- HW, exams, quizzes, etc. 
  1. request a critique of exam questions
  2. request answer key to HW and exam questions




N: Course management- facilitating course selection
  1. upload Canvas course materials and any ppt from previous year, including student CUE evaluation narratives and restrict responses to that material. 


Need to restrict dissemination of uploaded CUE score materials. 
O: Course management- 




respondentemail notes
Carlos Arguelescarguelles@fas.harvard.eduHola Chris:

Thanks for sending this email. I am very excited about this new technology. In fact, with Louis Deslauriers, we used some of it in my Physics 15B last semester.
I am currently writing a proposal for the Cottrell Scholars, where this will be featured, and I intend to use them in my incoming classes.
I will look at the links in your email. Have a wonderful summer, cheers,

Carlos

























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Who? From? email
Christopher StubbsFAS Sci Divstubbs@g.harvard.edu 
Logan McCartyFAS Sci Divmccarty@fas.harvard.edu 
Greg KestinFAS Sci Divkestin@fas.harvard.edu 
Erin CollinsFAS Sci Diverin_collins@fas.harvard.edu
Jefferson BursonHUITjefferson_burson@harvard.edu
Ventz PetkovHUITventz_petkov@harvard.edu
David LaPorteHUITdavid_laporte@harvard.edu
Colin MurtaughHUITcolin_murtaugh@harvard.edu. 
Rebecca NessonSEASnesson@g.harvard.edu
Eske PedersenFAS Scienceeskepedersen@fas.harvard.edu




Departmental 

GAISTEM stakeholder group, quarterly meetings

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