First course: Approaching AIs
General objectives: AI is a very attractive topic, the present course aim at showing that it is possible to engage students' interests providing them with basic knowledge to stimulate and feed their critical thinking.

The course will provide teachers with the basic mathematical prerequisites:

• Elementary linear algebra, the use of vector and matrices.
• Elementary knowledge and experience of programming (e.g. python, java/java script).

At the end of the course, teachers are expected to be able to:

• illustrate and explore the notion of modelling: the perceptron as a possible model
  of a neuron, and the concept of neural network,
• explain the algorithms and the notion of training of a neuronal network and test it,
• implement very simple neural networks,
• illustrate the basic logic of AI algorithms and more importantly explain the use of prompts and more relevant exploring typical bugs and failures of AI which may boost critical thinking in students.

Second course: Robotics through ARDUINO
In recent years the necessity of improving microelectronics led to lower the costs of microcontrollers and this made possible the development of open-source projects like ARDUINO architecture which attracted people and eased the possibility to use microelectronics and robotics. This is a great opportunity for all branches in Education.

The course will provide teachers with the fundamental prerequisites:
Some basic abilities of working with electronics circuits with resistor, capacitors, diodes and transistors, use of open-source simulators.
The basic structure of a ARDUINO microcontrollers. The installation of the required software to allow teacher's device (Windows, Linux-PC, OSX) to program ARDUINO.

At the end of the course teachers are expected to be able to:

• design, construct and explore simple electronic circuits which include for instance resistors, capacitances, diodes, LED and transistors,
• design, construct and explore simple circuits controlled by the microcontroller ARDUINO,
• use ARDUINO and its sensors to make measurements,
• use ARDUINO to control simple robotic mechanisms.

 

Syllabus description
The courses are organised in 3 modules, all units are of 2 hours unless specified

Module 0 (Common tools) (8 contact hours)
SARA courses are mainly online and, coming to the pandemic experience, we set up a software ecosystems through which, as researches we do research and teaching.
This module will provide the basic information and tools to allow any teacher to organise
her own work though open-source software that can be used on every personal platform (Linux, OSX, Windows) and online.

Unit 1. Use of Latex (https://www.latex-project.org) for typesetting on Overleaf (https://www.overleaf.com). Installation of Latex on personal devices.

Unit 2. Introduction to Python on Jupiter (https://jupyter.org) Colab (https://colab.google). Installation of Python and JupyterNotebook on personal devices.

Unit 3 Circuit simulator https://www.falstad.com/circuit/, microcontroller simulator https://wokwi.com/

During classes it will be shown that online platforms Overleaf, Colab and JupyterHub have included an help tool which take advantage of AI.

 

Module 1 (Approaching AIs) (12 contact hours)

Unit 1 In the first part we would like to promote a general discussion on a crucial questions which are at heart of the AI era: what is a mathematical model? What is intelligence? In what sense we can say that a mathematical model can present a
form of intelligence?

Unit 2 Intelligence as the ability to classify objects: machine learning and methods to automatise the classification.

Unit 3 The perceptron: a mathematical model of a neurone.  Its features, its ability to make classifications except: the XOR gate. The necessity of considering networks of perceptrons.

Unit 4 Is it true that learning can be seen as an algorithm?
From best fit to backpropgation method: how a mathematical model can be trained.
The method of the steepest descent.  

Unit 5 (4 hours) How to program simple neural networks using Python. Is intelligence an emergent phenomenon?

Unit 6 (4 hours) Use free AI to make experiments posing questions to check in what sense the AI models can be useful in teaching activities.

Unit 7 (3 hours) Student's project: each participant will be asked to present an
activity with neural networks and AI relevant for her teaching subjects.

  

Module 2 (Robotics through ARDUINO) (10 contact hours)

Unit 1 How to organise lab activities to show the students the fact that behind technology physics and mathematics are at work and play a fundamental role. Analysis of circuits with resistors, capacitors and LED diode assembled using breadboard and also simulated. If possible transistors there will be also a short introduction to transistors.

Unit 2 Presentation of ARDUINO project. Structure of ARDUINO microcontroller. Use of the free simulator and introduction to ARDUINO programming and IDE installation on personal devices.

Unit 3 (3 hours) Project building trough examples:
How construct simple electronic circuits.
How to control a LED circuit through ARDUINO.
How to use ARDUINO to make a measure with and without sensors.
How to use ARDUINO to control motors and sensors.

Unit 4 (3 hours)
Student's project: each participant will be asked to design an ARDUINO project
that can be tested using a simulator