This document discusses the challenges and opportunities that machine learning and artificial intelligence present for design and technology (D&T) education. It notes that students' lives will increasingly involve AI and machine learning, which enables new types of automated jobs. The document argues that D&T education should focus on developing students' data agency, curiosity, creativity, sense-making skills, and design skills that are difficult to automate like social-emotional intelligence. It asserts that when machines perform routine tasks, they can free up people to focus on more imaginative work.

1. Computational thinking and
making in the age of
machine learning
and its challenges to Design and Technology education
Dr. Henriikka Vartiainen
Professor Matti Tedre
University of Eastern Finland

2. Design
skills of designing,
making and
evaluating
prototypes and
products
Understanding
the technology
-infused world

3. D&T education also provides a rich
context for connecting
computational thinking (CT)
with digital and material making
(e.g. Kafai, Fields, & Searle, 2014; Blikstein, 2016; Resnick, 2017; Fields, Shaw, & Kafai 2018).

4. ● Computational thinking was first
introduced by Seymour Papert (1980)
● He elaborated the constructivist ideas of
Jean Piaget into a learning theory he
labeled constructionism
● The Logo programming language was
the first attempt in education to show
that the computer is a powerful tool
for design and construction
(Papert, 1980; Enkenberg, 1993; Blikstein, 2013; Tedre &
Denning, 2016)

5. In the next wave, Paperts visions were brought to the material world, for example, through
Mindstorm kits and the Scratch programming language (Resnick, 2017)

6. Developments such
as laser cutters, 3D
modelling, and
microcontrollers
gave new tools for
making
(Blikstein, 2013; P)

7. Design-oriented learning process for
computational/material thinking and making
(Vartiainen, Tedre, Salonen & Valtonen)

8. However,
the next big wave
in D&T education is on it's way

9. AI and machine learning

10. Today’s
students' lives will be
heavily influenced by
AI and machine learning.

11. ML enables
new classes of
jobs to be automated
by learning from
examples

12. How does that
feature in
D&T education?

13. Machine Learning in everyday life
● Machine learning: identifying patterns in massive datasets
● Usually relies on user-generated data
● Works the better the more training data it gets
● By combining data from millions of users ML systems can predict what people
want to see, do, or experience next

14. User-generated data opens new ways to affect people’s everyday lives ,
social interactions, and sway elections

15. Hybrid technology and
transformation
of material world
● Nowadays, sensors are everywhere
■ Walls, doors, piping, tables, carpets, cars, clothes, ...
● And they can sense everything measurable
■ Noise, air pollution, heart beat, calories burned, ...

16. Sensing technologies can
● Help us to monitor our
well-being
● Automate many of the
routine tasks of daily life
● Privacy?

17. From coding to
data-driven design

18. Growing understanding and design skills
(Adapted from
Ceschin &
Gaziulusoy, 2019)

19. What does all this mean
for D&T education?

20. When machines perform predictable,
repeatable and routine knowledge
work tasks, they simultaneously
release people to focus on
imaginative and creative tasks
Mitchel Resnick

21. ● Data agency
● Curiosity, creativity and
imagination
● Sense making and breaking
● Social and emotional
intelligence
● Design skills and mindset
● Computational thinking
● And other skills that are
difficult to automate?
How can we support our students to develop their
(e.g Denning, & Tedre, 2019; Resnick, 2017; Kafai, 2016; Pendleton-Jullian, & Brown, 2018; Valtonen et al., 2019: Mariescu-Istodor, & Jormanainen, 2019).)

22. Thank You!
Dr. Henriikka Vartiainen
University of Eastern Finland
School of Applied Educational Science and Teacher Education
henriikka.vartiainen@uef.fi
Professor Matti Tedre
University of Eastern Finland
School of Computing
matti.tedre@uef.fi

23. References
Blikstein, P. & Worsley, M. (2016). Children are not hackers: Building a culture of powerful ideas, deep learning, and equity in the Maker Movement.
In K. Peppler, E. Halverson, & Y.B. Kafai (Eds.), Makeology: Makerspaces as learning environments (pp. 64-79). New York, NY: Routledge
Ceschin, F. & Gaziulusoy, I. (2019). Design for Sustainability: A Multi-level Framework from Products to Socio-technical Systems. London: Routledge.
Denning, P. J. & Tedre, M. (2019). Computational Thinking. Cambridge, MA: MIT Press.
Enkenberg, J. (1993). Situation graphs as tools for ordering of students thinking and understanding of actual existing servo mechanisms.
Teoksessa B. Dennis, Editor, Control technology in elementary education (s.133g-150). NATO ASI Series.
Fields, D. Shaw.M., & Kafai, Y. (2018). Personal Learning Journeys: Reflective Portfolios as “Objects-to-Learn-With” in an E-textiles High School
Class. In V. Dagiene & E. Jastuė, Constructionism 2018: Constructionism, Computational Thinking and Educational Innovation: conference proceedings.
Retrieved Feb 28, 2019, from http://www. constructionism2018.fsf.vu.lt/proceedings.
Mariescu-Istodor, R., & Jormanainen, I. (2019). Machine Learning for High School Students. Proceedings of the 19th Koli Calling International
Conference on Computing Education Research, November 21–24, 2019, Koli, Finland. Accepted for publication.
Resnick M. (2017). Lifelong Kindergarten: Cultivate Creativity Through Projects, Passion, Peers, and Play. Cambridge, MA: MIT Press.
Also https://medium.com/@mres/creativity-and-learning-in-the-era-of-ai-57eea387249d
Kafai, Y. B. (2016). From computational thinking to computational participation in K–12 education. Communications of the ACM, 59(8):26–27.
Kafai, Y. B. Fields, D. A., & Searle, K. A. (2014). Electronic textiles as disruptive designs in schools: Supporting and challenging maker activities for
learning. Harvard Educational Review, 84(4), 532-556.
Papert, S. (1980). Mindstorms: Children, Computers, and Powerful Ideas. New York, NY, USA: Basic Books.
Pendleton-Jullian, A. & Brown, J. S. (2018). Design Unbound: Designing for Emergence in a White Water World. Cambridge, MA: The MIT Press.
Tedre, M., & P. J. Denning. (2016). The long quest for computational thinking. Proceedings of the 16th Koli Calling Conference on Computing Education
Research, November 24–27, 2016, Koli, Finland, pp. 120–129.
Valtonen, T., Tedre, M., Mäkitalo, K. & Vartiainen, H. (2019). Media Literacy Education in the Age of Machine Learning. Journal of Media Literacy
Education, 11(2), 20 -36.