Fall term 2023
Instructional methodology
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viewpoint:
| Item | experiments | course | lead | GPT aspect | Tools needed | Validation criteria |
|---|---|---|---|---|---|---|
| Lecture components: active learning methodology in lecturesthat leverages GAI |
| |||||
| short-cycle adaptive problem sets with real-time feedback | ||||||
| analysis and insights from lab data. | ||||||
| interactive student self-assessments. |
For each participating course
| capturing and submitting work for evaluation by course staff | ||||||
| automated evaluation of understanding of material, by evaluating answers to questions we provide. | ||||||
| cooperative learning in a group of 3-4 students plus GPT | ||||||
| try out a non-analytic problem and assess the results. |
| 15 a,b,c | numerical solution | |||
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?
- Advising and course selection
- Khan academy
Student-centric viewpoint
Modulated Friction example
Spring term 2024
Gen Ed 1188 https://gened.fas.harvard.edu/classes/catching-tsunami-riding-gpt-wave
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