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Christopher Stubbs

Christopher Stubbs

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Instructional methodology viewpoint:  

ItemexperimentscoursecoursesleadGPT aspect Tools neededValidation criteria
Lecture components: active learning methodology that leverages GAI
  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






short-cycle adaptive problem sets with real-time feedback




analysis and insights from lab data. 




interactive student self-assessments. 




In-class group consultation with ChatGPT




capturing and submitting work for evaluation by course staff




automated evaluation of understanding of material, by evaluating answers to questions we provide. cooperative learning in a group of 3-4 students plus GPT




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
include ability to perform calculations, as pioneered by Khan Academy




incorporate course-specific training inputs and give that high weighting


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


sequential prompts run open loop, no adjustment
dynamic tutoring


sequential prompts with iterative adjustment 

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. Advising and course selection
  12. Khan academy Khan academy like adaptive tutorials


Student-centric viewpoint

  1. Learning how to craft a prompt that gets what you want
  2. iterative refinements 
  3. validation tools
  4. critical thinking skills

Modulated Friction example

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