Skip to content

6. Put on your producer hat: considerations for FAIR video material

Learning Outcomes

  • Describe the advantages and challenges of video media in FAIR training
  • Understand active and responsible AI usage in creating FAIR video materials
  • Find video materials to use in FAIR training
  • Annotate video material to make it FAIR
  • Apply FAIR video materials to your training delivery system

6.1 Presentation

Here you can find the presentation for this session:

The full presentation can be downloaded as a pdf by clicking here.

6.2 Discussion topics

  1. Sample video critique - how FAIR is this?
  2. What is your experience with creating videos?
  3. AI discussion - video production
    • How might using AI tools affect learning?
    • What biases might this genAI output be subject to?
    • Where would you not want to use AI tools?
    • Where could AI tools help you in developing learning materials?

6.3 Exercise: Generate a caption or transcript to a sample video

You can download the sample video from the course website or record a 1-2 minute video on your smartphone or laptop to use.

Warning

Ensure you do not record anyone without their permission!

6.3.1 OpenAI Whisper tutorial

The following cheat sheet explains several ways of achieving a LLM-generated transcription. These are:

  • Recommended: Using the SciLifeLab serve instance of Whisper (recommended, easy)
  • Independent: Using an on-device GUI such as Whisper Transcription
  • Challenge mode: Installing your own local instance of Whisper

You can also download this cheat sheet as a pdf here

When you are finished transcription, inspect the transcript and answer the following:

  • Is it accurate?
  • Is it usable?
  • How does this help me achieve FAIR learning aims?

6.3.2 Adding FAIR video & transcript to your sample course

Following the basic guidelines for FAIR video material in the slides, add your video to your sample course.

Tip

If you created your own video, you can use your SciLifeLab NextCloud Folder to host it.

Citations

  1. Noble, S.U. (2018). Algorithms of oppression: how search engines reinforce racism (New York university press).
  2. Popenici, S. (2022). Artificial Intelligence and Learning Futures: Critical Narratives of Technology and Imagination in Higher Education 1st ed. (Routledge) https://doi.org/10.4324/9781003266563.
  3. Johnson, A. (2020). An Intersectional Physics Identity Framework for Studying Physics Settings. In Physics Education and Gender Cultural Studies of Science Education., A. J. Gonsalves and A. T. Danielsson, eds. (Springer International Publishing), pp. 53–80. https://doi.org/10.1007/978-3-030-41933-2_4.
  4. Dignum, V. (2024). Beyond the AI Hype: Balancing Innovation and Social Responsibility. Distinguished Lecturer seminar at the Institute for Experiential AI, January 31, 2024. Accessed via YouTube: https://www.youtube.com/watch?v=IsMY6NZZnWc