Generative AI Pedagogy

Instructional methodology viewpoint:

Item	experiments	course	GPT aspect
Lecture components: active learning methodology that leverages GAI	synthesis of student-provided questions, in real time open-response quizzes rather than multiple choice peer-instruction including GAI Sequential interaction on course prompts
short-cycle adaptive problem sets with real-time feedback
analysis and insights from lab data.
interactive student self-assessments.
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.	modulated-friction example.	15 a,b,c Math 22	numerical solution

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