A group of students and a lecturer working on a robotic arm during a class

AI for teaching

AI presents opportunities to streamline and assist with certain aspects of teaching practice. Some examples from the literature and the wider sector include: 

  • Generating additional examples to illustrate points for teaching
  • Providing multiple explanations of the same idea/ concept
  • Analysing main themes from student feedback from such classroom assessment techniques as the Muddiest point or One minute paper.

The examples above are based on Mollick and Mollick (2023). More guidance on the theoretical rationale for each of those uses as well as practical tips on how to work with LLM to generate those can be found in the paper. A short video with some ideas produced by the two authors can also be found in this video. 

Practical AI for Instructors and Students Part 4: AI for Teachers

In a video, Wharton Interactive's Ethan Mollick and Lilach Mollick explore AI's role in simplifying teaching. They demonstrate using prompts for personalized examples, explanations, and tests, and creating a sound syllabus.

Further ideas include:

  • Developing questions to low stake tests – some guidance on this can be found in this case study.
  • Refining marking rubrics – an example of this is discussed in the reflective essays case study. The case study website contains a downloadable resources box where you can see what prompts were used to generate and refine rubrics (under prompt Engineering for rubrics file)
  • Helping to generate drafts of lesson plans
  • Helping refine feedback – here it is important to ensure that the assessment is done by you and AI simply helps you refine the feedback. An important consideration needs to be given whether this should be disclosed to the students.
  • Generating automated feedback – an example of this is the Lambda feedback project where students’ answers are analysed and feedback automatically generated based on students’ past mistakes.