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Christopher Stubbs

Christopher Stubbs

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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. 




D: interactive student self-assessments. 




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




F: capturing and submitting GAI-enabled work for 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




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

























Course-based viewpoint

  1. Assess our assessments: run midterm and final exams of science courses through GPT-4 and grade the results. Compare to overall student performance. 
  2. Enhance our assessments. Solicit constructive feedback on the exam questions we submit. 
  3. Assess our homework: run homework assignments through GPT-4 and grade the results. Compare to overall student performance. 
  4. Enhance our assignments. Solicit constructive feedback on the homework we submit. 
  5. Ask (require?) students to use GPT-4 on selected assignments to get feedback and examples of how it can be used. 
  6. Course-specific chat-bots- what training data? 
  7. For large lecture classes- merge active learning with GPT
  8. For sections- aggregation of questions, 
  9. For labs- try out data analysis methods and inference
  10. Customized training assembly of material - what do we need to start to capture?
  11. Khan academy like adaptive tutorials
  12. How does this shift the workload in our non-ladder teaching capacity? Especially sections and TFs and grading? 

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