Fall term 2023
Instructional methodology viewpoint:
| Item | experiments | courses | lead | GPT aspect needed | Validation criteria |
|---|---|---|---|---|---|
| Lecture components: active learning methodology that leverages GAI |
| ||||
| 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. | |||||
| 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 |
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
Student-centric viewpoint
- Learning how to craft a prompt that gets what you want
- iterative refinements
- validation tools
- critical thinking skills
Modulated Friction example
Spring term 2024
Gen Ed 1188 https://gened.fas.harvard.edu/classes/catching-tsunami-riding-gpt-wave
Divisional GAI team
| department | ||
|---|---|---|
| physics | Matt Schwartz Louis Deslauriers | |
| statistics | Xiao-Li Meng Lucas Janson | |
| EPS | Brandon Meade | |
| MCB | ||
| OEB | ||
| HEB | ||
| SCRB | ||
| CCB | ||
| Astronomy | Doug Finkbeiner | |
| Math |
Links
https://bokcenter.harvard.edu/artificial-intelligence Bok center AI page
https://science.fas.harvard.edu/chatgpt divisional resource page