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Artificial Intelligence in Education: State of the Practice -- Paths Toward the Future - Ilkka Tuomi #eden19

The document discusses the transformative potential of artificial intelligence (AI) in education, emphasizing that while AI will not replace teachers by 2030, it will drastically alter their roles. It highlights the importance of adapting educational practices, institutions, and policies in response to rapid technological changes. Additionally, the report calls for a collaborative approach involving educators in the development and implementation of AI tools in K-12 education.

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Related topics:
Distance Education• Distance Learning• Educational Management• Lifelong Learning•
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AI in Education State of Practice and Paths Toward the Future Ilkka Tuomi ilkka.tuomi@meaningprocessing.com
“AI will be the main driver of economic and productivity growth and will contribute to the sustainability and viability of the industrial base in Europe. Like the steam engine or electricity in the past, AI is transforming the world.” European Commission. December, 2018. “Coordinated Plan on Artificial Intelligence.”
“There is no doubt that AI will revolutionize the delivery and management of education and learning, ... And while we believe that teachers will not be replaced by machines by 2030, we still need a dynamic review of how AI will transform teachers’ roles.” UNESCO, April, 2019
Revolution? Education? Artificial intelligence? Learning? Transforming?
“civilizing” “developing” ● Inter-generational transfer ● Capability, growth, agency ● Reproduction of social and techno-economic structures The eternal tension
Capability and agency “Knowledge” What = “K” How = “S” When/Why = “E” Attitude Motivation Behavioral repertoire Social resources Material resources *
Framing the problem ● Human intelligence is socially and materially distributed – Ask not what’s inside your head, but what your head’s inside of. (Gibson) – It’s not what you know, it’s who you know. (Nardi) ● Technologies become real when they change social practices ● All innovation is social innovation ● “Electricity” did not revolutionize the world. We did, and used a lot of electricity, steam and coal in so doing.
Executive summary ”This policy foresight report suggests that in the next years AI will change learning, teaching, and education. The speed of technical change will be very fast, and it will create high pressure to transform educational practices, institutions, and policies. It is therefore important to understand the potential impact of AI on learning, teaching, and education, as well as on policy development.” (p.2)
To understand systemic change, we need to understand the current system ● Why the educational system is what it is today? ● Why did people have jobs in the past? ● Why did they need to learn skills and knowledge?
Why growth and jobs in the past? ● In the 20th century, innovation focused on areas where increased energy use was able to reduce human labor – We borrowed from the nature without intent to pay back ● Known as “non-renewable resources” and “fossil fuels” ● No labor costs, no pension payments, no social security for anaerobic bacteria that made all this possible ● This replacement of renewable energy with fossil fuels is know as “automation,” “mechanization of work” and, in economics, as “labor productivity growth,” and “technology”
The real source of growth in the Industrial Age The world production of cement was about 4.1 billion tons in 2017. This translates to 15,000 petajoules, or about 2,478,283,594 equivalent barrels of oil.
“Miracles You'll See In The Next Fify Years” Popular Mechanics, 1950 Because everything in her home is waterproof, the housewife of 2000 can do her daily cleaning with a hose.
Middle school teachers...”other things being equal” Task AI impact 1 Adapt teaching methods and instructional materials to meet students' varying needs and interests. High 2 Establish and enforce rules for behavior and procedures for maintaining order among students. ? 3 Confer with parents or guardians, other teachers, counselors, and administrators to resolve students' behavioral and academic problems. Low 4 Maintain accurate, complete, and correct student records as required by laws, district policies, and administrative regulations. High 5 Prepare, administer, and grade tests and assignments to evaluate students' progress. High 6 Prepare materials and classrooms for class activities. Medium 7 Instruct through lectures, discussions, and demonstrations in one or more subjects, such as English, mathematics, or social studies. Medium 8 Establish clear objectives for all lessons, units, and projects, and communicate these objectives to students. Medium 9 Assist students who need extra help, such as by tutoring and preparing and implementing remedial programs. High 10 Assign lessons and correct homework. High 11 Enforce all administration policies and rules governing students. Medium ... 15 Meet or correspond with parents or guardians to discuss children's progress and to determine priorities and resource needs. Medium source: O*NET and author’s estimates
Three types of AI ● Logic-based; algorithmic – 1955-1975: Computer is a universal logical machine; it is able to reason as well as humans ● Knowledge-based; representational – 1970-1990: Intelligent behavior depends on knowing the world; the question is about representing knowledge and its structures ● Data-driven – 1930-1965 --- 1985-1990 --- 2005-present – Machine learning algorithms that minimize prediction or clustering error – Known as artificial neural networks and machine learning
A new paradigm for programming ● Machine Learning is a diferent paradigm: instead of telling how to do things, you tell what the goal is. ● Requires examples (ofen millions of them). – The widely-used ImageNet database employed at its peak 48,940 people in 167 countries who sorted 14 million images – Google BERT Language model (2018): trained 340 million parameters four days with 5x1015 floating point operations per second ● 2x1021 computations to make a state-of-the-art machine learning model ● 3.3 billion words used for training ● Requires cheap parallel processing and new hardware architectures ● Requires data – It’s all about Internet connectivity ● Enabled by open source development tools and freely accessible”pre-trained” machine learning models (from Google et al.)
Rosenblatt’s Perceptron, 1958 “Fire together, wire together”
GoogLeNet, 2014
8 million web pages, 1.5 billion trainable parameters: OpenAIGPT-2,February2019
Algorithmic computing ● ‘You insist that there is something a machine cannot do. If you will tell me precisely what it is that a machine cannot do, I can always make a machine which will do just that’. – Von Neumann, 1963, ”The general and logical theory of automata.”
Data-driven computing ● ‘If a typical person can do a mental task with less than one second of thought, we can probably automate it using AI either now or in the near future.’ – Andrew Ng, HBR, 2016
What, exactly, is machine learning? Top: Cultural Middle: Cognitive Bottom: Behavioral socialphysiological Perception,Attention,Emotion Practice Institution Value Sign Concept Word Habit Reflex Routine Piaget Vygotsky Freire How culture makes us How we make the culture Skinner Anything here can be done with less than one second of thinking.
What, exactly, is machine learning? Top: Cultural Middle: Cognitive Bottom: Behavioral socialphysiological Perception,Attention,Emotion Anything here can be done with less than one second of thinking. Symbol-processing AI Deep learning Innovation and knowledge creation
Yes, AI can make magic ● But all its “intelligence” comes from humans ● Most current machine learning systems are based on human- labeled data examples (e.g. face recognition, object detection, question-answering systems...) ● ...or statistical regularities in human generated text, speech, movement, art ● ...or closed rule-based worlds where machines can generate training data (chess, go…) ● ...or combination of these (e.g. deepfake videos...)
It sees what it has learned Lev Vygotsky, ~1928
Google Inception trained with ImageNet data
AI at School Student teaching (instructivist) Student supporting (constructivist) Teacher supporting System supporting ● Intelligent tutoring systems (including automatic question generators and brain- based teaching) ● Dialogue-based tutoring systems ● Language learning applications (including pronounciation detection) ● Exploratory learning environments ● Formative writing evaluation ● Learning network orchestrators ● Language learning applications ● AI Collaborative learning ● AI Continuous assessment ● AI Learning companions ● Course recommendation ● Self-reflection support (learning analytics, meta- cognitive dashboards) ● Learning by teaching chatbots ● ITS+learning diagnostics ● Summative writing evaluation ● Student forum monitoring ● AI teaching assistants ● Automatic test generation ● Automatic test scoring ● OER content recommendation ● Plagiarism detection ● Student attention and emotion detection ● Educational data mining for resource allocation ● Diagnosing learning difficulties (e.g. dyslexia) ● Synthetic teachers ● AI as a learning research tool Adapted from Holmes, W. et al. (2019)
Future is about change in the present Education is a solution to social and economic problems. To understand the impact of AI, we need to understand the Industrial Age; what made the 20th Century so special? ● Why jobs and “economic growth”? ● Why need for skills and knowledge? ● Why schools and teaching? How the digital, global, and AI-enhanced world will be diferent?
JRC “AI for and with Teachers” ● Co-creating future-oriented uses of AI in K-12 by integrating expertise in teaching, educational innovation, policy, and AI technology ● Exploratory work, with potential for a EU-wide initiative ● Aims for an orchestrated network of forward-looking actors and stakeholders, and produces an “AI Handbook for and with Teachers” ● AI is high on the political agenda. We don’t want that teachers will be electrocuted with it. Teachers need to be involved in inventing AI. ● Policy-makers will soon realize that the core of national and international AI strategies will be education. First they think it’s about teaching AI. Then they will realize its about a shif in balance from teaching for employment to the development of agency capabilities in the age of the AI.
Thank you! ilkka.tuomi@meaningprocessing.com