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Christopher Stubbs

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Spring 2025 - 

January 2025

Summer 2025

Fall term 2023

Instructional methodology viewpoint:  

ItemexperimentscoursesleadGPT aspect neededValidation criteria

Incorporation into lecture-format 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






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
  12. How does this shift the workload in our non-ladder teaching capacity? Especially sections and TFs and grading? 


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. 
  7. Ethical, responsible, thoughtful use of powerful tools. 
  8. Accommodating disabilities and ensuring equitable access. 

IT-centric viewpoint

  1. How do we integrate these learning tools with existing platforms? Examples include Canvas, grade sheets, Sharepoint, Jupiter notebooks, data repositories, assignments, work-uploading tools, etc? 
  2. What is the best approach to licensing and token-purchasing?
  3. How do we throttle and regulate non-course abuse? 
  4. How do we develop, curate, and support the use of this new toolkit? 
  5. What are institutional roles, responsibilities, accountabilities, and authorities?
  6. What staffing is needed, at what levels of the organization?
  7. Who pays for what? 

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, weekly meetings

C. Stubbs
L. McCarty
G. Kestin

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stakeholder

physicsMatt Schwartz
Louis Deslauriers
statisticsXiao-Li Meng
Lucas Janson
EPSBrandon Meade
MCBSean Eddy
OEBMichael Desai
HEB?
SCRB?
CCB?
AstronomyDoug Finkbeiner
SEAS

Martin Wattenberg
Scot Martin
Jim Waldo
Rebecca Neeson


MathCliff Taubes? 
College OUEAmanda Claybaugh
Anne Harrington
Harvard CollegeRakesh Khurana
Bok CenterAdam Beaver
HGSE

humanities div. Robin Kelsey
Jeffrey Schnapps
social sci div. 

VPALBharat Anand
HUITKlara Jelinkova


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

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