The Turing test - ECNAIS€¦ · Deep learning Expert systems Logic programming Semantic networks...

Artificial Intelligence in Education

Prof. Juan Pavó[email protected]

Artificial Intelligence in Education, by Juan Pavón (UCM), is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License 1

Independent education in the digital worldMadrid, Spain | 24 - 26 November 2019

AI in EducationJuan Pavón (UCM) 2

Can machines think?

● 1950: The Turing Test (imitation game)[Alan M. Turing "Computing machinery and intelligence." Mind 59.236 (1950): 433]Available at http://aleph.calculemus.org/lect/07szt-intel/turpap.htm

De Bilby - Own work, public domain, https://commons.wikimedia.org/w/index.php?curid=3777581

http://aleph.calculemus.org/lect/07szt-intel/turpap.htm

AI in EducationJuan Pavón (UCM)

The Turing test

Q: Please write me a sonnet on the subject of the Forth Bridge.

A: Count me out on this one. I never could write poetry.

Q: Add 34957 to 70764

A: (Pause about 30 seconds and then give as answer) 105621.

Q: Do you play chess?

A: Yes.

Q: I have K at my K1, and no other pieces. You have only K at K6 and R at R1. It is your move. What do you play?

A: (After a pause of 15 seconds) R-R8 mate.

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Turing test competitions

Mitsuku: Loebner Prize winner (2013, 2016, 2017, 2018 , 2019)

● https://pandorabots.com/mitsuku/

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Computer game bot● Unreal Tournament bots

https://pandorabots.com/mitsuku/


Can machines think?

● Conscience argument: computers cannot have conscious experiences ⇒ John Searle (1980), Chinese room

https://www.youtube.com/watch?v=TryOC83PH1g https://www.youtube.com/watch?v=j_OPQgPIdKg (Consciousness & the Brain: John Searle at TEDxCERN)

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History of AI

● 1943 Warren McCulloch y Walter Pitts: first model of artificial neural networks

● 1956 Dartmouth Artificial Intelligence (AI) conference: birth of AI (McCarthy, Minsky, Newell, Simon, etc.) ○ Too optimistic expectations for the next ten years, that did not succeed

⇒ Abandonment of AI research during a decade

● 1982 Japan: Fifth Generation Computer Systems (FGCS)○ Prolog and Expert Systems

⇒ New interruption of the advance in AI

● 1990: Multi-agent systems,

○ The basis for AI distribution

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https://es.wikipedia.org/wiki/John_Searle

https://www.youtube.com/watch?v=TryOC83PH1g

https://www.youtube.com/watch?v=j_OPQgPIdKg


Intelligent Systems: Multi-Agent Systems (MAS)

AutonomyEnvironmentInteractionsOrganizationUsers

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Examples of intelligent agents

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http://www.youtube.com/watch?v=CpAeQ13Go9A&t=47


Intelligent systems

● Rationality principle (Allen Newell (1982). The knowledge level. Artificial Intelligence 18: 87-127):

If an agent has knowledge that one of its actions will lead to one of its goals, then the agent will select that action.

■ ... but this does not imply that it will take the best decision

⇒ The environment is dynamic, complex, uncertain, only partially known

● Reasoning○ An agent can decide:

■ which goal to follow or whether to react on an event■ what actions perform to attain a goal■ suspend or abandon a goal to follow another

● Learning○ An agent can progressively adapt to changes in dynamic

environments

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How do agents think?

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● BDI model (Bratman, 1987)

Beliefs

Intentions

Desires


Sociability

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Interaction

Dialogue

Delegation

Cooperation

Coordination

Negotiation


SMART everything...

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Artificial Intelligence today

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AI in 2010's

● 1997: IBM Deep Blue defeats G. Kasparov in chess

● 2011: IBM Watson wins the 'Jeopardy!', against the two best players in the history of this Q&A game

● 2014: the program Eugene (Russia) success with the Turing test as a 13 years old boy. University of Reading (UK).

● 2016: AlphaGo (Google DeepMind) defeats the “Go” world's champion

10120 10360

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Artificial Intelligence - Methods

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Artificial Intelligence

Machine learning (Data based AI)

Symbolic reasoning (Knowledge based AI)

Artificial Neural Networks

SVM Logistic Regression ...

Deep learning

Expert systems

Logic programming

Semantic networks


Machine Learning

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Training dataTraining

dataWorking data set

Inputdata

Prepare data

Learning algorithms create

Model

Trainingdata sets

Validation and Testdata sets

Evaluate SelectTest

Prepare data

ModelModel

BestModel

Predictionor

Classification

Results


Artificial Neural Networks

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f(s1, w1, s2, w2, …)

Combination Activation

s1

s2

s3

s4

w1

w2

w3

w4

o1

o2

o3


Perceptron

● Frank Rosenblatt (Cornell Aeronautical Laboratory, 1958)○ Mark I Perceptron machine

■ Image recognition■ Camera with 400 photocells,

randomly connected to the artificial neurons

■ Weights encoded in potentiometers, and weight updates during learning performed by electric motors

■ Not many classes of patterns

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Deep Learning

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Several layers of Artificial Neural Networks

Input layer(pixels)

Hidden layer 1(edges)

Hidden layer 2(parts)

Output layer(object class)

Dog

Cat

Horse

Bird

......


Image recognition

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Speech recognition

● Word Error Rate (%)○ Google Assistant < 5%○ Microsoft ~ 5%○ Human error 2-4%

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Deep learning - The case of the Microsoft bot

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What capacity of ethical judgment or reflection on knowledge and the context should have artificial agents?

Si Tay intentó hacerse "colega" de otros intervinientes en las redes sociales y supongamos por un momento que no hubo errores de diseño y Tay se limitó a seguir los comentarios más populares "de la manada". La pregunta es más incómoda: ¿Qué aprendió Tay de nosotros? ¿Qué tipo de opinión social - al menos la que se expresa en redes sociales - estamos construyendo? ¿Qué vemos en el espejo de Tay? [Javier Busquets, 2016]


Life 3.0

Artificial intelligence may enable us to launch Life 3.0 this century

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Life 3.0

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Max Tegmark (2017). Life 3.0: being human in the age of artificial intelligence


Life 3.0

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Illustration of Hans Moravec’s “landscape of human competence,” where elevation represents difficulty for computers, and the rising sea level represents what computers are able to doMax Tegmark (2017). Life 3.0: being human in the age of artificial intelligence


Artificial Intelligence levels

● Intelligence○ Ability to accomplish complex tasks○ General Intelligence: Ability to accomplish virtually any goal, including

learning

● Narrow AI○ Concrete objectives: play chess, drive an autonomous vehicle such as a

car or a drone, classify pictures, speech recognition, etc.

● Strong AI○ [Human-level] Artificial General Intelligence (AGI): perform any cognitive

task at least as well as humans

● Superintelligence○ General intelligence far beyond human level○ Singularity: Intelligence explosion (recursive self-improvement rapidly

leading to superintelligence)

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Super-intelligence

What will happen when intelligent systems become more intelligent than humans?

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Life 3.0

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Max Tegmark (2017). Life 3.0: being human in the age of artifcial intelligence


What can do AI for Education?

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Educators

Learners

Managers Families


Intelligent Tutoring Systems (ITS)

● Domain model (Cognitive model)

● Used in online learning○ Scalable○ Universal access

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Student model

Domain model

Student module

Tutor model

Tutor module

Domain module

Interface

UserUserUserUser



● Domain model (Cognitive model)

● Based on a theory of learning○ Concepts, facts and rules

■ Web-like mapping of the content to be learned■ Prerequisites and dependencies between the content elements

○ Problem-solving strategies○ Model tracing

● AI techniques○ Knowledge representation and reasoning

■ Rule-based expert systems■ Fuzzy logic■ Bayesian networks■ Case-based reasoning■ Ontologies

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Domain model

Domain module



● Different learning methodologies○ Different architectures○ But following, in general, a Instructional Design approach

■ There is a clear goal of instruction, which can be specified by a set of concrete outcomes■ It assumes a learner's profile (initial state)■ The process is driven by a set of actions towards the acquisition of specific concepts and

problem solving methods ⇒ Problem solving itinerary■ There are metrics for the achievement of the steps of the learning process ⇒ Monitor

the learner's progress from problem to problem

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Start Final test

Final topic

2nd topic

1st topic ...

... ...



● Student model○ Student's cognitive and affective states and their evolution through the

learning process ○ Trace of the student progress

■ Deviations from the domain model■ Identification of correct or right responses from the student■ Statistics on the progress (time, number of trials, errors, advances, etc.)

● AI techniques○ Knowledge representation and reasoning○ Rule-based expert systems○ Planning○ Machine learning

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Student model Student module



● Tutor model (Pedagogical model)

● Tutoring strategies and actions○ Guidance to the user during the learning process○ Uses information from domain and student models: what to do next

■ Compare what is needed to know (content model) with what is known (student model) to deliver the pedagogically appropriate unit of instruction to the student

■ Monitor the progress and deviations of the user in the learning process■ Provide hints when a student gets stuck on a problem■ Propose exercises to the student until a concept or rule has been learned

○ How to present the teaching material to the student○ How to evaluate student performance and provide feedback

● AI techniques○ Rule-based expert systems○ Planning○ Natural language processing: dialogue

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Student model

Domain model

Tutor model

Tutor module



● User Interface○ Dialogue with the user

■ Knowledge about patterns of interpretation (to understand a speaker) and action (to generate utterances) within dialogues

■ Domain knowledge needed for communicating content■ Knowledge needed for communicating intent

● AI techniques○ Natural language processing ○ Virtual reality○ Augmented reality○ Affective computing

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Interface UserUserUserUser



● 8 principles of ITS design and development

Principle 0: An intelligent tutor system should enable the student to work to the successful conclusion of problem solving.

1. Represent student competence as a production set.2. Communicate the goal structure underlying the problem solving.3. Provide instruction in the problem solving context.4. Promote an abstract understanding of the problem-solving knowledge.5. Minimize working memory load.6. Provide immediate feedback on errors.7. Adjust the grain size of instruction with learning.8. Facilitate successive approximations to the target skill.

Anderson, J., Boyle, C., Farrell, R., & Reiser, B. (1987). Cognitive principles in the design of computer tutors. In P. Morris (Ed.), Modeling cognition. NY: John Wiley.

Corbett A. T., Koedinger, K. R., & Anderson, J. R. (1997). Intelligent tutoring systems. In M. G. Helander, T. K. Landauer, & P. V. Prabhu (Eds.), Handbook of human-computer interaction (pp. 849–874). Amsterdam: Elsevier.

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● Benefits○ Multiply the size of classrooms○ Personalization to the learning styles and preferences of students

■ Adaptive learning■ Responsive teaching■ Asynchronous education■ Universal access

○ Facilitate trial-and-error learning■ Less intimidating, as not being in front of their peers or the teacher■ A relatively judgment-free environment, especially when AI tutors can offer solutions for

improvement

○ Reuse of learning objects○ Graduate-level professional education and lifelong learning

■ Need for face-to-face interaction is less important for working professional and career changers

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● Issues○ Expensive to design and implement

■ 1 hour of online instruction time = 300 hours of development time■ Authoring tools■ Cooperation multidisciplinary teams

○ Effectiveness: depends on design, implementation and educational focus■ Long term impact is unclear■ Evaluation of an ITS is costly and time consuming■ Lack of evaluation standards

○ The pedagogy of immediate feedback and hint sequences ⇒ Failure to develop deep learning in students ?

■ Some students immediately turn to the hints before attempting to solve the problem or complete the task)

■ Failure of the system to ask questions of the students to explain their actions

○ Many different models exist for representing knowledge, teaching styles and student knowledge

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ITS new trends

● Smart content creation○ New generation of authoring tools

■ Pedagogy oriented to apply authoring tactics on how to sequence the contents and learning activities

■ Content processing (e.g., Cram101, Netexlearning: Breakdown textbook content into digestible “smart” study guide: chapter summaries, true-false and multiple choice practice tests, flashcards, etc.)

● Virtual tutors○ Conversation in natural language, reasoning capabilities○ Emotion analysis ⇒ Affective computing

■ Flow of conversation■ Facial expression

● Serious games○ Engage learners to interact actively for longer periods of time○ Maximize motivation (and then learning)○ Usually, narrated visual stories○ DragonBox, Prodigy, BrainQuake, The MIT Education Arcade

■ https://education.mit.edu/project-type/games/

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Learning Analytics

● Massive data can be collected from online courses and other sources (surveys, results of tests and exams, etc.)⇒ Big Data processing ○ ML, Deep learning○ Natural language processing

● Many applications:○ Analyse student engagement, behaviour and outcomes○ Prevent dropouts: identify students at risk early○ Customization of Curriculum: adjust current learning strategies○ Identify places where courses need to improve○ Helpful feedback at different levels: students, courses, educational

institutions

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https://education.mit.edu/project-type/games/


Learning Analytics - Risks

● Costly and hard processes○ Education institutions still lack proper software to manage so much data○ Heterogeneity of the data sources and formats

● Data quality ○ Biased data○ Verifiable data

● Data and information governance○ Privacy, security, quality, and auditability

● Ethical issues○ Should students be able to opt-out of having their learning analytics data

collected? ○ What is the recourse for any individual who has had his or her data

misused or inappropriately shared? ○ Who owns the data mined from a learning process?

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Collaborative Learning

● Improve communication and collaboration skills of students○ Group Formation

■ Learning analytics can facilitate the formation of well-balanced groups that can complete different projects, with different criteria

○ Evaluation of Collaborative Abilities■ Analyse data to determine each student’s degree of collaboration ■ Educators can suggest ways to help students in improving their collaborative abilities

○ Virtual Agents■ Virtual agents that can help students in group projects by answering student queries and

behave like a peer or someone who students can teach■ Virtual agents can test every student’s understanding of the subject matter by asking

questions

○ Moderation■ Monitor conversations of many groups using recording devices and alert educators in

case a group’s conversation goes off-topic

○ Integrate AR and VR to offer an interactive learning experience■ New ways and scenarios of interaction with others and the environment

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Other applications of AI in Education

● Automate repetitive tasks○ Grading homework and tests, faster and with greater precision

⇒ More time to interact with students, prepare for class, etc.

● Better follow up of students through different years and subjects○ Identify knowledge gaps to propose tasks to cover them○ Predict the expected behaviour of students for better planning of their

learning itinerary and personalized contents

● Planning and management in educational institutions○ Academic analytics (similar to Learning analytics)

● Chatbots○ Assistants with specific purposes

● Proctoring○ Sophisticated mechanisms to ensure the authenticity of the student to

take an exam and prevent from cheating

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Education with robots

● Let’s interact with the Nao Robot (Theater with NAO)○ Build one or more scenes in which the

Nao will interact with the students○ The scenes will be chosen and

designed together with the students

● Play: Let’s build a game with the Nao robot○ Programming a game with the NAO

robot

⇒ Looking for schools to collaborate○ Groups of ~14 years old girls with

disability○ 3-4 days activity

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Some conclusions

● Though quality education will always require active engagement by human teachers, AI promises to enhance education at all levels, especially by providing personalization at scale[Stanford University (2016). Artificial Intelligence and Life in 2030]

● Role of the teacher○ Evolution to a mentor/facilitator ?

■ Supplement AI lessons■ Assist students who are struggling■ Provide human interaction and hands-on experiences for students

● Better support to students with special needs

● Currently, many limitations○ Can teach fundamentals, but far for high-order thinking and creativity

● Shallow knowledge vs Deep knowledge○ Learning how to execute procedures or skills for more interdisciplinary

and collaborative problem solving

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References

● Peter Stone et al. "Artificial Intelligence and Life in 2030." One Hundred Year Study on Artificial Intelligence: Report of the 2015-2016 Study Panel, Stanford University, Stanford, CA, September 2016. Doc: http://ai100.stanford.edu/2016-report. Accessed: September 6, 2019.

● Max Tegmark (2017). Life 3.0: Being Human in the Age of Artificial Intelligence. KNOPF.

● Barcelona Declaration for the proper development and usage of Artificial Intelligence in Europe: http://www.iiia.csic.es/barcelonadeclaration/

● Ali Alkhatlan, Jugal Kalita (2018). Intelligent Tutoring Systems: A Comprehensive Historical Survey with Recent Developments. https://arxiv.org/abs/1812.09628v1

● Kathy Pretz (2018). Why Schools Are Getting More Serious About Teaching Engineering Students About Ethics:http://theinstitute.ieee.org/ieee-roundup/blogs/blog/why-schools-are-getting-more-serious-about-teaching-engineering-students-about-ethics

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http://ai100.stanford.edu/2016-report

http://www.iiia.csic.es/barcelonadeclaration/

https://arxiv.org/abs/1812.09628v1

http://theinstitute.ieee.org/ieee-roundup/blogs/blog/why-schools-are-getting-more-serious-about-teaching-engineering-students-about-ethics

http://theinstitute.ieee.org/ieee-roundup/blogs/blog/why-schools-are-getting-more-serious-about-teaching-engineering-students-about-ethics

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