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. 
This effort will take place in stages, as follows:

Summer 2023 - establish structure for the process, secure resources, build team, delineate clear goals for the Fall 2023 term, define a subset of experiments for Fall 2023. Initial tool development for API-interface exploitation. 

Fall 2023 - Make all course GAI-aware. Conduct initial set of 10 GAIPedagogy experiments across the STEM curriculum. Tracking of pedagogical experiments. First draft of experimental-results paper. Oct 2023 workshop for Harvard STEM faculty.  

January 2024- Three day national Workshop on STEM GAI pedagogy. First draft of experimental-results paper

Spring 2023 - General Education course offered. 

Summer 2024 - 

Fall 2024

Spring 2025

Summer 2025

Fall term 2023

Instructional methodology viewpoint:  

ItemexperimentscoursesleadGPT aspect 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






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




Assist with analysis and gain 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. 




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 
Generation and refinement of course instructional and assessment materials- HW, exams, etc. 
  1. request a critique of exam questions
  2. request answer key to HW and exam questions




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


Student-centric viewpoint

  1. Learning how to craft a prompt that gets what you want
  2. GAI as a consultant
  3. GAI for self-assessment
  4. iterative refinements in GAI interactions
  5. Learning how to validate and verify results
  6. Honing critical thinking skills in the GAI context. 


Specific examples : 

Modulated Friction example


Spring term 2024

Gen Ed 1188 https://gened.fas.harvard.edu/classes/catching-tsunami-riding-gpt-wave

Gen ed 1188 course management

GAISTEM  core team

C. Stubbs
L. McCarty
G. Kestin

GAISTEM interest group

department

physicsMatt Schwartz
Louis Deslauriers
statisticsXiao-Li Meng
Lucas Janson
EPSBrandon Meade
MCBSean Eddy
OEB

HEB

SCRB

CCB

AstronomyDoug Finkbeiner
SEAS

Scot Martin
Jim Waldo
Rebecca Neeson


Math

College OUE

Bok CenterAdam Beaver
HGSE

humanities div. 

social sci div. 

VPAL




Links


https://bokcenter.harvard.edu/artificial-intelligence Bok center AI page

https://science.fas.harvard.edu/chatgpt divisional resource page