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Artificial Intelligence in Education focusing on the Skills3.0 project

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This presentation was given during the Elearning Fusion conference in Warsaw, Poland - April 2019. The presentation begins with a bit of algorithm, AI, machine learning history and background, provides some examples of AI in learning and finalizes with the Skills 3.0 project where InnoEnergy is working on.

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Artificial Intelligence in Education focusing on the Skills3.0 project

  1. 1. Artificial Intelligence in Education => Slideshare.net/Ignatia Inge de Waard @Ignatia 10 April ’19 ELF, Warsaw, Poland
  2. 2. 2www.innoenergy.com What to expect … a bit of AI & Ed 2
  3. 3. www.innoenergy.com The Skills 3.0 project @Ignatia 3 AI in HR: data mining emerging skills/competencies of the Sustainable Energy industry (e.g. roadmaps) AI in Education: matching the skills/competencies gap and address it using adaptive learning paths professional Skills gap Future-Proof learning
  4. 4. www.innoenergy.com Overview of the topics of this talk Evolution from teacher machine, over eLearning to deep learning AI the hype: what is it? Risks of AI, some ethical considerations InnoEnergy’s skills 3.0 project (using AI for building future-proof learning) What does this mean for us eLearning experts? @Ignatia 4
  5. 5. www.innoenergy.com Timeline from teaching machine to machine learning @Ignatia 5 Teaching Machine (Skinner) – Artificial Intelligence (1954) eLearning (1999) Big Data (2005) CCK08 MOOC (2008) Learning Analytics (LAK 2011) Deep Learning revolution (2012)
  6. 6. www.innoenergy.com Learning and machine learning coming together 6 Coming together Merging into new field eLearning Artificial Intelligence Business indicators Big Data Machine learning Artificial Intelligence in Education Monolithic development
  7. 7. www.innoenergy.com 7@Ignatia
  8. 8. www.innoenergy.com 8@Ignatia http://squirrelai.com/
  9. 9. 9www.innoenergy.com But what does it all mean? 9
  10. 10. www.innoenergy.com 10@Ignatia
  11. 11. www.innoenergy.com What is an algorithm? An algorithm is a set of rules to solve a problem. It is programmable It uses and can manipulate data. @Ignatia 11 Input (data) Algorithm Output (data)
  12. 12. www.innoenergy.com 12@Ignatia
  13. 13. www.innoenergy.com 13@Ignatia
  14. 14. 14www.innoenergy.com Some practical, ethical realities 14
  15. 15. www.innoenergy.com Some risks of algorithms programmed by the few resulting in complex deep learning Black boxes: the AI is seldom transparent, which means it becomes hard to follow-up in results. @Ignatia 15
  16. 16. www.innoenergy.com Filter bubbles can emerge: (un)professional hairstyle
  17. 17. www.innoenergy.com
  18. 18. www.innoenergy.com
  19. 19. 19www.innoenergy.com Let’s look at some examples of AI in Education 19
  20. 20. www.innoenergy.com 20 Enhancing eLearning : Cognii: moving eLearning expertise to AI in ED @Ignatia Integrated Chatbot (virtual assistant) Natural Language Processing (NLP) assessments
  21. 21. www.innoenergy.com Some really cool examples AI in ED: Magpie (provides learning options based on challenges). Try it out for free @Ignatia 21
  22. 22. www.innoenergy.com X5GON.org – fully automated creation of OER courses (Mitja Jerbol) @Ignatia 22
  23. 23. www.innoenergy.com The brilliance of teachers using AI innovatively (e.g. Google Translate app) For those not speaking Dutch or Polish: I just shared the BIG secret of life ;) @Ignatia 23
  24. 24. www.innoenergy.com 24@Ignatia Like @People1eFirst (Guy Ooms) Finland’s grand AI experiment Inside Finland’s plan to train 1% of its population in artificial intelligence. “We’ll never have so much money that we will be the leader of artificial intelligence. But how we use it — that’s something different" — Mika Lintilä, economy minister
  25. 25. 25www.innoenergy.com Sharing the skills 3.0 project 25
  26. 26. Possible solution to build a future-proof learning loop Introducing the Skills 3.0 project InnoEnergy @Ignatia
  27. 27. www.innoenergy.com 27 Digital Education Action Plan: Why? Stay Future-Proof InnoEnergy wants to play a pivotal learning role, and offer the best energy and innovation professional courses for business people/engineers 101 course Energy Introduction https://mini-course.ise.innoenergy.com/
  28. 28. www.innoenergy.com 28Wind Wind Solar Solar storage Storage Digital ization
  29. 29. 4 steps to Building a future-proof learning loop Find Emerging Industry Needs How: • AI screening industry reports, road maps • Workforce evaluation Pinpoint Skills gaps How: • Analyse between skills needed and profiles available Analyse profiles available How: • AI screening resumes from workforce (white & blue collar) • Employabil ity check Personalized training to address skills gap How: • AI to automate learning segments • Automated course creation (x5GON) • Hackathon Build a Future- Proof Learning Loop
  30. 30. Examples: employability after training & automated learning elements Employability check Wildfire Learning experiences • From WindEurope report • Key findings 1 http://bit.ly/windeurope1 • Key findings 2 http://bit.ly/windeurope2 • Electrification incentives http://bit.ly/windeurope3 • Policy recommendations http://bit.ly/windeurope4 • Wind turbine: http://bit.ly/techturbine • Inside nacelle http://bit.ly/technacelle
  31. 31. Digital Education Action Plan (DEAP) = Skills 3.0 project • @Ignatia 32 AI in HR: data mining emerging skills/competencies of the Sustainable Energy industry (e.g. roadmaps) AI in Education: matching the skills/competencies gap and address it using adaptive learning paths Skills gap
  32. 32. Digital Education Action Plan (DEAP) = Skills 3.0 project • @Ignatia 33 AI in HR: data mining emerging skills/competencies of the Sustainable Energy industry (e.g. roadmaps) AI in Education: matching the skills/competencies gap and address it using adaptive learning paths Skills gap Future-Proof Learning
  33. 33. 34www.innoenergy.com Education-related challenges being tackled 34 • Granularity (nuggets) & micro-credits • Courses available (production) • IP control content delivery partners IE challenges •GDPR (willingness to share CVs) •Revenue sharing conditions •Licenses and revenue (authors, universities, IE) Industry •Automating course production parts •Meaningful adaptive learning paths (reuse)AI •Pedagogical continuity for reusing material •Weighing skills & competenciesPedagogy
  34. 34. www.innoenergy.com What does this mean for us eLearning experts? @Ignatia 35
  35. 35. www.innoenergy.com InnoEnergy is supported by the EIT, a body of the European Union Inge de Waard Inge.deWaard@innoenergy.com @Ignatia Slideshare.net/Ignatia +32 479 78 98 37 Team effort, thank you: Yves Peirsman, Marloes Wichink Kruit, Anouk Gelan & Frank Gielen. Also thanking Donald Clark from Wildfire learning
  36. 36. www.innoenergy.com 37@Ignatia
  37. 37. www.innoenergy.com 38 We increase the value of our Learning Portfolio, fitting the demands of the Industry Increase (re)usability of course elements (cost reduction on course production) Offering adaptable learning paths for in-company training (with certification) It offers a Career Progression track with certification for learners (micro-credits) Energy companies can invite successful learners for interviews (real vacancies) The benefits of the DEAP project for InnoEnergy ?
  38. 38. www.innoenergy.com Tools for learning: Microsoft – Immersive Reader (mobile app) for inclusion @Ignatia 39

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