This page contains some evolving thoughts about use and abuse of AI as part of learning and teaching. Thoughts are random initially (Sept. 2023).

Generative AI and the KASH model of learning

Learning can be considered in terms of gaining or improving new knowledge, attitudes, skills and habits (KASH). (This is only one of many models for “domains of learning” but it is useful for this discussion.) Knowledge is the “stuff we know“, skills are “capabilities or things we can do“, attitudes include “awareness of self, situation, others and motivation“, and habits are “things you do regularly or repeatedly“.

Technology has been making knowledge increasingly available to all:

However, it takes practice and feedback to improve skills, and it takes a certain maturity within the discipline to gain attitudes that are perceptive, creative and productive. Generative AI is beginning to make certain “knowledge” increasingly available. HOWEVER, a human mind requires effort to become good at something that could be considered a skill, and attitudes are nuanced and complicated – they take time to develop.

Education has been shifting from a focus on imparting knowledge to fostering development of skills and attitudes, but that shift is still in progress. Maybe the emergence of generative AI will help us all accelerate that shift towards
– teaching as providing opportunities for students to practice skills,
– instead of teaching as delivering facts and figures.

That will require that teachers adjust their abilities from being expert presenters to becoming expert facilitators, assessors, and deliverers of timely and effective feedback. There is still room for selective delivery of knowledge – especially in the form of story-telling, but “teaching” no longer has to include the repetitive delivery of content that is obtainable elsewhere and “on demand”.

Notes and pointers

1. Sources of information, workshops and policies at UBC – needs links.
2. What is generative AI? sources:
   1. Wikipedia
      1. on generative AI
      2. on ChatGPT
   2. At UBC:
      1. More generic information about generative AI.
      2. ChatGPT Q&A at UBC’s Academic Integrity website. 9 FAQs addressed.
      3. UBC Library’s 6-page section about generative AI and ChatGPT.
      4. Starting point for CTLT’s several pages about generative ai.
      5. For more, try putting “generative ai” site=ubc.ca into your google search engine.
   3. Beyond UBC
      1. CBC  Radio FrontBurner episode: ChatGPT in university: useful tool or cheating hack? link with audio plus Transcript.
      2. Magazine piece about ChatGPT in Toronto Life, by journalist and writing instructor at U. of Toronto.
      3. IEEE is active in this area – for example “How ChatGPT Could Revolutionize Academia“.
      4. Likely to be more to come.
   4. Notes to be added from three CTLT 1-hr workshops in Sept & Oct 2023. See links to resources on that page, especially CTLT’s new GenAI resources home page.
      
   5. How ChatGPT Could Revolutionize Academia ; The AI chatbot could enhance learning, but also creates some challenges. An expert opinion piece in the IEEE Spectrum magazine, February 2023.
   6. Discussed in EOS, (McGovern, 2021): “Artificial intelligence (AI), machine learning (ML), and data science provide flexible, scalable, and interpretable approaches to harness the growing volume of available data that can help us improve the understanding and prediction of a wide variety of geoscience phenomena, including natural hazards, climate change, and severe weather events.“
   7. Also in EOS, Realizing Machine Learning’s Promise in Geoscience Remote Sensing.
3. Implications
   1. The end product alone, of a learning activity (eg – code written) may not be sufficient for assessment. The process, sequence of steps, thinking involved, etc. may be more important for assessment. Perhaps a flow chart, or pseudo code, etc. Something a text-based AI can not produce. Weekly interviews are used in an Engineering Physics project lab course (see next item).
   2. Generative AI in education at UBC: “A Learning Renaissance“, written for alumni in the UBC Magazine. Good read – 5 minutes only!
      1. “Tools are changing at a pace that is almost impossible to keep up with”
      2. It isn’t enough to become more tech-literate – students have to become more tech-critical.
      3. Instead of worrying about students using AI for homework … “He traded graded homework for weekly interviews, ensuring that students can communicate what kind of code they developed and how deeply they understand its function.”.
      4. “As intellectual tasks are increasingly offloaded to machines, today’s students are preparing to be tomorrow’s workers, learning less about the technical minutiae of their profession and more about how to oversee the machines that will be performing those tasks for them. “