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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 Develop an experimental active learning lecture series that incorporates GPT-4 capabilities and measure its effectiveness in student comprehension and retention
Devise 10 .
Devise 8 experiments to do in Fall 2023
First development sprint for 8 experiments
Fall 2023 - Make all course STEM courses and instructors at least GAI-aware.
Conduct initial set of 10 8 GAIPedagogy experiments across the STEM curriculum. Tracking of pedagogical experimental outcomes.
Oct 2023 workshop for Harvard STEM faculty.
Planning and invitations for GAISTEM GAISTEMP-1 conference.
Development sprint for API-interface tools
January 2024- Three day national Workshop on STEM GAI pedagogy.
Development sprint for GAI active learning and HW tools.
First draft of GAISTEM GAISTEMP experimental-results paper . from first 8 examples
Spring 2023 - General Education course offered.
Rollout of prototype active learning and HW modules in 4 intro STEM courses, with assessments.
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Fall 2024 - First offering of GAI-empowered courses across entire introductory Harvard STEM curriculum. Prepare for second national workshop.
January 2025 - Second national workshop on GAISTEMGAISTEMP-2.
Spring 2025 -
January 2025
Summer 2025
Fall term 2023
Instructional methodology viewpoint:
| Item | experiments | courses | lead | GPT aspect needed | Validation criteria |
|---|---|---|---|---|---|
Incorporation into lecture-format learning:
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| 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. |
| 15 a,b,c | 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. |
|
Course-based viewpoint
- Assess our assessments: run midterm and final exams of science courses through GPT-4 and grade the results. Compare to overall student performance.
- Enhance our assessments. Solicit constructive feedback on the exam questions we submit.
- Assess our homework: run homework assignments through GPT-4 and grade the results. Compare to overall student performance.
- Enhance our assignments. Solicit constructive feedback on the homework we submit.
- Ask (require?) students to use GPT-4 on selected assignments to get feedback and examples of how it can be used.
- Course-specific chat-bots- what training data?
- For large lecture classes- merge active learning with GPT
- For sections- aggregation of questions,
- For labs- try out data analysis methods and inference
- Customized training assembly of material - what do we need to start to capture?
- Khan academy like adaptive tutorials
- How does this shift the workload in our non-ladder teaching capacity? Especially sections and TFs and grading?
Student-centric viewpoint
- Learning how to craft a prompt that gets what you want
- GAI as a consultant
- GAI for self-assessment
- iterative refinements in GAI interactions
- Learning how to validate and verify results
- Honing critical thinking skills in the GAI context.
- Ethical, responsible, thoughtful use of powerful tools.
- Accommodating disabilities and ensuring equitable access.
IT-centric viewpoint
- 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?
- What is the best approach to licensing and token-purchasing?
- How do we throttle and regulate non-course abuse?
- How do we develop, curate, and support the use of this new toolkit?
- What are institutional roles, responsibilities, accountabilities, and authorities?
- What staffing is needed, at what levels of the organization?
- 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
GAISTEM core team, weekly meetings
C. Stubbs
L. McCarty
G. Kestin
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| stakeholder | ||
|---|---|---|
| physics | Matt Schwartz Louis Deslauriers | |
| statistics | Xiao-Li Meng Lucas Janson | |
| EPS | Brandon Meade | |
| MCB | Sean Eddy | |
| OEB | Michael Desai | |
| HEB | ? | |
| SCRB | ? | |
| CCB | ? | |
| Astronomy | Doug Finkbeiner | |
| SEAS | Martin Wattenberg | |
| Math | Cliff Taubes? | |
| College OUE | Amanda Claybaugh Anne Harrington | |
| Harvard College | Rakesh Khurana | |
| Bok Center | Adam Beaver | |
| HGSE | ||
| humanities div. | Robin Kelsey Jeffrey Schnapps | |
| social sci div. | ||
| VPAL | Bharat Anand | |
| HUIT | Klara Jelinkova |
Links
https://bokcenter.harvard.edu/artificial-intelligence Bok center AI page
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