"Artificial Intelligence or Natural Stupidity? Deep Learning or Superficial Teaching?"
by Chan, Lap Ki; Pawlina, Wojciech (2020)
Abstract
Robots with artificial intelligence and sensorimotor skills are surpassing humans in the performance of many tasks and doing so at a surprising pace. Many analyses have thus been done to identify the human jobs that are at risk of being replaced by robots (Frey and Osborne, 2013; Chui et al., 2016; Berriman and Hawksworth, 2017; Makridakis, 2017). At least according to one analysis, jobs in the wholesale and retail trades are at the highest risk, while those in education are at the lowest (Berriman and Hawksworth, 2017). It appears that we anatomy educators are safe for now, or are we?
Key Passage
As time progresses, artificial intelligent systems should become better and more flexible by incorporating changes in the academic culture and learning preferences of new generations of students (Baker, 2016). Robots are developing their human touch too. Robots that can recognize emotional states are currently in development (Azuar et al., 2019; Yu and Tapus, 2019). Only time will tell what robots with deep learning ability will be capable of, and whether they will be artificially intelligent and we anatomists remain naturally stupid. It is too early to say that anatomy educators are safe from losing their jobs to robots. However, this is not all bad news. Chatbots, if well developed, can potentially relieve anatomy educators from the repetitive and mundane parts of their work. Collaboration, instead of competition, between AI and anatomy educators, may be a welcome result. (p.6)
Keywords
Artificial Intelligence, Machine Learning, Chatbots, Education, Academic Work, Worker ReplacementThemes
Employment, AI and Education, AI and Computerisation, AutomationLinks to Reference
- http://dx.doi.org/10.1002/ase.1936
- https://www.ncbi.nlm.nih.gov/pubmed/31837097
- https://doi.org/10.1002/ase.1936
- https://anatomypubs.onlinelibrary.wiley.com/doi/abs/10.1002/ase.1936
- https://anatomypubs.onlinelibrary.wiley.com/doi/pdf/10.1002/ase.1936
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