Artificial Intelligence in The Classroom: A Step Too Far?

Education Approach

This is called the Intelligent Classroom Behavior Management System and is using facial recognition technology system to scan and observe student’s behaviour in the classroom. 7 difference expressions are recognised such as angry, disappointed, happy, neutral, sad, scared and surprised (yet no bored classification!). The system scans the students every 30 seconds so no room for a quick sleep or messing around here.

A.I. Too Far?

Imagine being constantly watched in the classroom. The systems allows greater feedback and classroom awareness, but what about the impact on behaviour and creativity? This has the potential for enforcing expected behaviours and expressions, rather than allowing individuality. All to much like big brother for me, take a read of 1984 by George Orwell. But it will be interesting to see how surveillance and AI is viewed by students and societies across the world. Let’s hope student freedom to learn is the focus and not safety fears.

Resource

Techjuice. (2018). This school scans classrooms every 30 seconds through facial recognition technology.

 

Big Data & Machine Learning

Journal Club Article: Obermeyer, Z., & Emanuel, E. J. (2016). Predicting the future—big data, machine learning, and clinical medicineThe New England Journal of Medicine375(13), 1216.

Background

  • “Big data will transform medicine. It’s essential to remember, however, that data by themselves are useless. To be useful, data must be analyzed, interpreted, and acted on. Thus it is algorithms — not data sets — that will prove transformative.
  • Machine learning, conversely, approaches problems as a doctor progressing through residency might: by learning rules from data. Starting with patient-level observations, algorithms sift through vast numbers of variables, looking for combinations that reliably predict outcomes.
  • But where machine learning shines is in handling enormous numbers of predictors — sometimes, remarkably, more predictors than observations — and combining them in nonlinear and highly interactive ways.
  • Consider a chest radiograph. Some radiographic features might predict an important outcome, such as death. In a standard statistical model, we might use the radiograph’s interpretation — “normal,” “atelectasis,” “effusion” — as a variable. But instead, why not let the data speak for themselves?

Precautionary Aspects

  • Of course, letting the data speak for themselves can be problematic. Algorithms might “overfit” predictions to spurious correlations in the data.
  • Correlated predictors could produce unstable estimates.
  • Another key issue is the quantity and quality of input data. Machine learning algorithms are highly “data hungry,” often requiring millions of observations to reach acceptable performance levels.
  • In addition, biases in data collection can substantially affect both performance and generalizability.”

Changes to Healthcare

Obermeyer & Emanuel (2016) state that in the future the ability to transform data into knowledge will be disrupted in at least three key areas due to machine learning:

  1. Improved prognostic’s.
  2. Machine accuracy will displace much of the work of radiologists and anatomical pathologists through machine learning.
  3. Improved diagnostic accuracy.

Despite concerns that with robotics and artificial intelligence the ability of machine learning may actually improve healthcare delivery. “Machine learning will become an indispensable tool for clinicians seeking to truly understand their patients. As patients’ conditions and medical technologies become more complex, its role will continue to grow, and clinical medicine will be challenged to grow with it”.

Keywords: Big data; machine learning; disruption; analytics; artificial intelligence.

References

High, R. (2012). The era of cognitive systems: An inside look at IBM Watson and how it worksIBM Corporation, Redbooks.

IBM (2017). Cognitive Analytics and 6 Solutions: IBM Research – Haifa.

Marr, B. (2016). How Machine Learning, Big Data And AI Are Changing Healthcare Forever. Forbes.com.

Nursing Education Network (2017). Analytics & Big Data.

Nursing Education Network (2017). Educating Nurses for the Future of Healthcare.

Nursing Education Network (2017). Industry 4.0: The Future of Work.

Obermeyer, Z. & Emanuel, E. J. (2016). Predicting the Future — Big Data, Machine Learning, and Clinical Medicine. NEJM Catalyst.