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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. 

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. 

Develop an experimental active learning lecture series that incorporates GPT-4 capabilities and measure its effectiveness in student comprehension and retention

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 paperDevelopment sprint for GAI active learning and HW tools. 

Spring 2023 - General Education course offered. Rollout of prototype active learning and HW modules in 4 courses, with assessments. 

Summer 2024 - Extension of prototype active learning and HW modules to all of intro STEM curriculum, with assessments. 

Fall 2024 - First offering of GAI-empowered courses across entire introductory STEM curriculum 

Spring 20252025 

Summer 2025

Fall term 2023

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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?

Specific examples : 

Modulated Friction example

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Gen ed 1188 course management

GAISTEM  core team, weekly meetings

C. Stubbs
L. McCarty
G. Kestin

GAISTEM interest stakeholder group, monthly meetings

Links

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

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