AI in Education:AI in Education:what are we about?what are we about?

Jacobijn SandbergJacobijn Sandberg

University of AmsterdamUniversity of Amsterdam

Department of Social Science Department of Social Science InformaticsInformatics

OverviewOverview

• Problem: fragmentation of our field and the Problem: fragmentation of our field and the complex interrelationship between AI, complex interrelationship between AI, Education and New TechnologyEducation and New Technology

• Four topicsFour topics

– General demarcation of our fieldGeneral demarcation of our field

– The role of education in our researchThe role of education in our research

– The role of AI in our researchThe role of AI in our research

– The role of AI - recent developmentsThe role of AI - recent developments

AI in Education: general AI in Education: general demarcationdemarcation

• AI in current educational practiceAI in current educational practice

• AI developments illustrated by AI developments illustrated by ‘educational applications’‘educational applications’

• AI as a formalization technique to AI as a formalization technique to operationalize and validate theories on operationalize and validate theories on teaching and learningteaching and learning

• AI developments guided by new AI developments guided by new theoretical insights in education and theoretical insights in education and general technological developmentsgeneral technological developments

Our field at a glanceOur field at a glance

ARTIFICIALINTELLIGENCETHEORIESMODELSTECHNIQUES

EDUCATION:THEORIESMODELSTECHNIQUES

NEWTECHNOLOGIES:TECHNIQUES(INTERNET, XML,JAVA,MULTIMEDIA

The seven relationshipsThe seven relationships1. AI applied to an educational setting 1. AI applied to an educational setting

(Goldstein, 1982; Beck & Stern, 1999)(Goldstein, 1982; Beck & Stern, 1999)

2. Educational settings challenging AI in moving 2. Educational settings challenging AI in moving away from simple settings to complex, away from simple settings to complex, interactive, dynamic situations (Martial Vivet)interactive, dynamic situations (Martial Vivet)

3. AI and Education overlap in terms of goals: AI 3. AI and Education overlap in terms of goals: AI used as operationalisation of an educational used as operationalisation of an educational model (ITS: domain /knowledge representation, model (ITS: domain /knowledge representation, machine learning, natural language machine learning, natural language processing, planning)processing, planning)

The seven relationships The seven relationships continuedcontinued4. AI (persons) inspiring new technologies 4. AI (persons) inspiring new technologies

(Internet, Java)(Internet, Java)

5/6. New technologies welcomed by the 5/6. New technologies welcomed by the educational field (open and distance learning, educational field (open and distance learning, life long learning) challenging AI (e.g. ontologies life long learning) challenging AI (e.g. ontologies for indexing and retrieving instructional building for indexing and retrieving instructional building blocks)blocks)

7. Education asking for technology suited to its 7. Education asking for technology suited to its needs (adaptive environments: Hoppe, AIED, needs (adaptive environments: Hoppe, AIED, 1999)1999)

The three major playersThe three major players• Artificial IntelligenceArtificial Intelligence

– strong philosophical base (e.g. what is there know: strong philosophical base (e.g. what is there know: objective versus subjective)objective versus subjective)

• EducationEducation

– strong ideological base (basic skills versus meta-strong ideological base (basic skills versus meta-cognitive skils)cognitive skils)

• New TechnologiesNew Technologies

– strong economic/political base(e-commerce versus strong economic/political base(e-commerce versus shareware)shareware)

All seven relations are All seven relations are reflected in our literaturereflected in our literature

• My problem (and yours too, hopefully):My problem (and yours too, hopefully):

– the approach, background assumptions, the approach, background assumptions, expected outcomes, theoretical or practical expected outcomes, theoretical or practical relevance, remain implicitrelevance, remain implicit

• Solution: a general descriptive frameworkSolution: a general descriptive framework

– which facilitates understanding articles of which facilitates understanding articles of various naturevarious nature

– which guides the development of an which guides the development of an interesting research agendainteresting research agenda

Towards a general Towards a general frameworkframework• Requirements for a general frameworkRequirements for a general framework

– distinguish fundamentally different distinguish fundamentally different educational stanceseducational stances

– cater for new developments in education cater for new developments in education as well as in AIas well as in AI

– classify AI approaches or techniques in classify AI approaches or techniques in relation to the identified educational relation to the identified educational stancesstances

The role of education in The role of education in our researchour research

• There exist different models of There exist different models of teaching and learning, which reflect teaching and learning, which reflect fundamentally different theoretical fundamentally different theoretical stancesstances– on the nature of human cognition on the nature of human cognition

– on educational objectiveson educational objectives

• Changing the objectives (what) Changing the objectives (what) changes the means (how)changes the means (how)

Let’s look at one of the Let’s look at one of the oldest teaching methods: oldest teaching methods: lecturinglecturing• That is what I am doing right nowThat is what I am doing right now

• Are you supposed to learn anything?Are you supposed to learn anything?

• What, if you are?What, if you are?

• What does a good lecturer do?What does a good lecturer do?– Provoke and sustain interestProvoke and sustain interest

– Clarify what is worthwhile to retainClarify what is worthwhile to retain

– Good lecturing is edutainmentGood lecturing is edutainment

What is wrong with What is wrong with edutainment?edutainment?

• It is just fun and no learning It is just fun and no learning (Postman: we amuse ourselves to (Postman: we amuse ourselves to death)death)

• Why do our kids love computer Why do our kids love computer games and stick with those for hours games and stick with those for hours on end?on end?

• Is there a lesson to be learned from Is there a lesson to be learned from the gaming industry? the gaming industry?

What is right with What is right with edutainmentedutainment• CaptivatingCaptivating

• ChallengingChallenging

• GratifyingGratifying

• Our challenge: to keep all this, without Our challenge: to keep all this, without creating superficial learning (AI in the creating superficial learning (AI in the gaming industry: virtual reality)gaming industry: virtual reality)

Fun and learning: Fun and learning: becoming an expert or becoming an expert or even just proficienteven just proficient

• Can never be just fun (mastery of a music Can never be just fun (mastery of a music instrument or any sports)instrument or any sports)

• Calls upon: Calls upon:

– prolonged effortprolonged effort

– frustration tolerancefrustration tolerance

– reflection (away from the experiential mode: reflection (away from the experiential mode: Norman, 1993)Norman, 1993)

• Brings: Long-term Satisfaction (instead of Brings: Long-term Satisfaction (instead of mere instant gratification)mere instant gratification)

Between fashion and Between fashion and sciencescience• AI in Education: rolling on the waves of timeAI in Education: rolling on the waves of time

• As education changes - our field changes As education changes - our field changes with itwith it

• ITS as the answer to the need for ITS as the answer to the need for individualized education: simulating the individualized education: simulating the ideal teacherideal teacher

• CSCLE as the answer to the need for team CSCLE as the answer to the need for team workwork

Background assumptions Background assumptions of fashionable ideasof fashionable ideas• Knowledge is dynamic (subjective, value-Knowledge is dynamic (subjective, value-

determined, ever changing, never true): life determined, ever changing, never true): life long learninglong learning

• If that were true, human kind would have no If that were true, human kind would have no chance of surviving whatsoeverchance of surviving whatsoever

• There are constants in the world which people There are constants in the world which people learn to identify to shape their world as a learn to identify to shape their world as a relatively stable, reliable and predictable placerelatively stable, reliable and predictable place

The old fashioned wayThe old fashioned way

• Knowledge as relatively stableKnowledge as relatively stable

• Have a look at the use of words (natural Have a look at the use of words (natural categories versus expert jargon)categories versus expert jargon)

• Who determines the meaning of words?Who determines the meaning of words?

• Knowledge as transferableKnowledge as transferable

Three basic Three basic teaching/learning teaching/learning scenarios (Andriessen & scenarios (Andriessen & Sandberg, 1999)Sandberg, 1999)• The transmission scenario: the empty vessel The transmission scenario: the empty vessel

metaphor (old-fashioned; prevailing metaphor (old-fashioned; prevailing classroom teaching, lecturing)classroom teaching, lecturing)

• The studio scenario: the constructive agent The studio scenario: the constructive agent metaphor (current; study-house)metaphor (current; study-house)

• The negotiation scenario: the The negotiation scenario: the situated/distributed cognition metaphor situated/distributed cognition metaphor (post-modern)(post-modern)

Transmission scenario:Transmission scenario:Main characteristicsMain characteristics

• Closed domainClosed domain

• Well-defined learning goalWell-defined learning goal

• Fixed learning routeFixed learning route

• Instruction & practiceInstruction & practice

• Diagnosis of errors and remediationDiagnosis of errors and remediation

• Outcome: Domain knowledge and skillsOutcome: Domain knowledge and skills

• EMMA: Quigley, AIED 1989EMMA: Quigley, AIED 1989

Studio scenario: Main Studio scenario: Main characteristicscharacteristics• Open or closed domainOpen or closed domain• Well-defined learning goalWell-defined learning goal

• Flexible learning routeFlexible learning route

• Project-based learningProject-based learning

• Interaction with different agents (human or Interaction with different agents (human or otherwise)otherwise)

• Outcome: domain knowledge as well as Outcome: domain knowledge as well as social and practical skillssocial and practical skills

• Barnard & Sandberg, 1997; Adorni et al., Barnard & Sandberg, 1997; Adorni et al., AIED 1999AIED 1999

Negotiation scenarioNegotiation scenario• Open domainOpen domain

• Ill-defined learning goalIll-defined learning goal

• Open learning routeOpen learning route

• Argumentation/negotiationArgumentation/negotiation

• ReflectionReflection

• Outcome: conceptual change (comparison Outcome: conceptual change (comparison Socratic Dialogue: WHY; Stevens, Collins, and Socratic Dialogue: WHY; Stevens, Collins, and Goldin, 1982)Goldin, 1982)

• Baker et al., AIED 1999Baker et al., AIED 1999

Past educational practicePast educational practice

• Prevailing transmission scenario, Prevailing transmission scenario, reflected in:reflected in:– classroom teaching: lecturingclassroom teaching: lecturing

– drill and practicedrill and practice

– little room for discussion/reflectionlittle room for discussion/reflection

– little room for complex problem solvinglittle room for complex problem solving

Current educational Current educational practicepractice• Studio scenario, as reflected in:Studio scenario, as reflected in:

– more emphasis on complex problem solvingmore emphasis on complex problem solving

– more emphasis on student initiative and more emphasis on student initiative and responsibilityresponsibility

– more emphasis on problem analysis and more emphasis on problem analysis and solving method selectionsolving method selection

– more emphasis on open tasks (writing an more emphasis on open tasks (writing an essay, conducting a debate, giving a talk)essay, conducting a debate, giving a talk)

Tomorrow’s educational Tomorrow’s educational practice?practice?• Negotiation scenario, reflected in:Negotiation scenario, reflected in:

– student directed learningstudent directed learning

– student defined problems and solutionsstudent defined problems and solutions

– student sharing of knowledge and student sharing of knowledge and evolving ideasevolving ideas

Negotiation scenario in Negotiation scenario in real lifereal life• ‘‘The negotiation household’ (modern life)The negotiation household’ (modern life)

– if you’ll do the dishes, I will put the children if you’ll do the dishes, I will put the children to bedto bed

• Children pick it up quite easilyChildren pick it up quite easily– if I eat my vegetables, you read me a storyif I eat my vegetables, you read me a story

• Driving parents crazy - all conversation Driving parents crazy - all conversation limited to if-then statementslimited to if-then statements

Evolution of the scenariosEvolution of the scenarios• The scenarios build on one another and The scenarios build on one another and

form a partial hierarchyform a partial hierarchy

• Transmission at the lowest level Transmission at the lowest level (novices), studio at the intermediate (novices), studio at the intermediate level (professionals), negotiation at the level (professionals), negotiation at the highest level (experts: they determine highest level (experts: they determine what the words mean)what the words mean)

• When moving from beginner to expert When moving from beginner to expert one moves through various cycles in one moves through various cycles in which the three scenarios have their partwhich the three scenarios have their part

Take AI in education as an Take AI in education as an exampleexample• Transmission: read the books and Transmission: read the books and

learn the facts (e.g. Intelligent Tutoring learn the facts (e.g. Intelligent Tutoring Systems, 1982, from Sophie to Mycin)Systems, 1982, from Sophie to Mycin)

• Studio: work as a researcher in the AI Studio: work as a researcher in the AI and Education fieldand Education field

• Negotiation: be at the forefront of the Negotiation: be at the forefront of the field and co-determine its directionfield and co-determine its direction

HypothesisHypothesis

• To teach and learn domain facts and To teach and learn domain facts and rules: transmissionrules: transmission

• To teach and learn procedures and To teach and learn procedures and problem solving strategies: studioproblem solving strategies: studio

• To teach and learn meta-cognitive skills To teach and learn meta-cognitive skills to create new knowledge and to reflect to create new knowledge and to reflect on one’s understanding: negotiationon one’s understanding: negotiation

What do we win?What do we win?• Firstly, we can map the different scenarios Firstly, we can map the different scenarios

to different levels of expertise and to different levels of expertise and therefore to different learning objectivestherefore to different learning objectives

• Secondly, we can investigate what part AI Secondly, we can investigate what part AI can play in the various scenarioscan play in the various scenarios

• Thirdly, we can be more explicit in our Thirdly, we can be more explicit in our writings about our background writings about our background assumptions, stances, and choicesassumptions, stances, and choices

Hey, but were is AI?Hey, but were is AI?• What do we (or I) mean by AI?What do we (or I) mean by AI?

– The creation of systems that exhibit human-The creation of systems that exhibit human-like intelligence realised in a way that not like intelligence realised in a way that not necessarily reflects the way human necessarily reflects the way human intelligence is organised (Suthers, AIED 1999: intelligence is organised (Suthers, AIED 1999: strong AI, minimalist AI and ‘background’ AI)strong AI, minimalist AI and ‘background’ AI)

– The ability to derive new conclusions from The ability to derive new conclusions from given data, the ability to solve problems, to given data, the ability to solve problems, to learn (adaptivity)learn (adaptivity)

What does AI do? What does AI do?

• AI as a modelling science, creating AI as a modelling science, creating computational, executable models computational, executable models of intelligent behaviourof intelligent behaviour

• That is exactly what ITS research That is exactly what ITS research did and still does - constructing and did and still does - constructing and validating computational models of validating computational models of teaching and learning processesteaching and learning processes

So What?So What?• So, there is nothing wrong with ITS So, there is nothing wrong with ITS

research!research!

• But, how does it relate to education in But, how does it relate to education in practice?practice?

• It doesn’t, but does it have to?It doesn’t, but does it have to?

• Not all research necessarily bears on Not all research necessarily bears on today’s or even tomorrow’s educational today’s or even tomorrow’s educational practice (informed education: basic skills)practice (informed education: basic skills)

AI in relation to the three AI in relation to the three scenariosscenarios• Focus on AI as a modelling scienceFocus on AI as a modelling science

• Major distinction between knowledge Major distinction between knowledge models and process modelsmodels and process models

• The AI models focus on different aspects The AI models focus on different aspects and have a different grain-sizeand have a different grain-size

• For example: fine-grained student For example: fine-grained student modelling (transmission) versus moderately modelling (transmission) versus moderately grain-sized interaction modelling (studio)grain-sized interaction modelling (studio)

Knowledge models in Knowledge models in Transmission Transmission • domain models (closed domains)domain models (closed domains)

• task models (learning environment)task models (learning environment)

• cognitive state models (interaction)cognitive state models (interaction)

• conceptual indexing (information)conceptual indexing (information)

Process models in Process models in transmissiontransmission• Model tracing (diagnosis)Model tracing (diagnosis)

• Expert reasoning (criterion task)Expert reasoning (criterion task)

• Student - tutor interactionStudent - tutor interaction

• Monitoring (information)Monitoring (information)

Knowledge models in Knowledge models in studiostudio• Multiple task models (flexible route)Multiple task models (flexible route)

• Agent models (learning Agent models (learning environment)environment)

• Multiple agents (interaction)Multiple agents (interaction)

• Multiple sources (information)Multiple sources (information)

Process models in studioProcess models in studio

• Agent interaction (flexible route)Agent interaction (flexible route)

• Agents and tools (learning Agents and tools (learning environment)environment)

• Procedural facilitation Procedural facilitation (collaboration)(collaboration)

• Reflection (information)Reflection (information)

Knowledge models in Knowledge models in negotiationnegotiation• Multiple user models (team work)Multiple user models (team work)

• Agent models (learning Agent models (learning environment)environment)

• Interaction Models (collaboration)Interaction Models (collaboration)

• Knowledge Infrastructure Models Knowledge Infrastructure Models (knowledge management)(knowledge management)

Process models in Process models in NegotiationNegotiation• Issue tracing (argumentation)Issue tracing (argumentation)

• Student/tutor/partner interaction Student/tutor/partner interaction (learning environment)(learning environment)

• Interaction; negotiation (collaboration)Interaction; negotiation (collaboration)

• Reflection, knowledge management Reflection, knowledge management (information)(information)

Function of the frameworkFunction of the framework• Meta-level description of our fieldMeta-level description of our field

• Providing a standardised vocabulary Providing a standardised vocabulary (glossary of terms)(glossary of terms)

• Meant to be used as a vehicle for Meant to be used as a vehicle for reflectionreflection

• Limitation: just one possible perspective, Limitation: just one possible perspective, neglect of new technology as a separate neglect of new technology as a separate factorfactor

AI and other technologiesAI and other technologies

• At the start of AI, there was no Internet or At the start of AI, there was no Internet or JAVA or XML or …JAVA or XML or …

• Now we see hybrid applications combining Now we see hybrid applications combining AI and other emerging technologiesAI and other emerging technologies

• AI as supporting emerging technologies, to AI as supporting emerging technologies, to provide ‘smartness’, flexibility, and provide ‘smartness’, flexibility, and human-centeredness to our designs human-centeredness to our designs (Norman, Self - AI systems are systems (Norman, Self - AI systems are systems that care!)that care!)

A hybrid approach example: A hybrid approach example: CREDIT prototype, Anjo CREDIT prototype, Anjo Anjewierden, UoAAnjewierden, UoA• Dynamically generating HTML-pages on Dynamically generating HTML-pages on

the basis of:the basis of:

– underlying model of assessment and underlying model of assessment and accreditation (CML2)accreditation (CML2)

– underlying prolog code (selecting the underlying prolog code (selecting the appropriate elements from the model)appropriate elements from the model)

– together generating HTML pages together generating HTML pages (dynamically; depending on the user’s input; (dynamically; depending on the user’s input; user data and selection of perspective)user data and selection of perspective)

Example CREDITExample CREDIT

• Supports either a transmission scenario or Supports either a transmission scenario or a studio scenarioa studio scenario

• Models: Underlying domain (qualification Models: Underlying domain (qualification structure); generic procedure (allowing structure); generic procedure (allowing variations in APL); interaction possibilities variations in APL); interaction possibilities (realised in the Interface)(realised in the Interface)

• No detailed user modelling (just three No detailed user modelling (just three broad categories of users distinguished)broad categories of users distinguished)

A further exampleA further example

• Metadata or ontologies for indexing and Metadata or ontologies for indexing and retrieving what is thereretrieving what is there

• Educational metadata (Japan (Mizoguchi), Educational metadata (Japan (Mizoguchi), Europe (ARIADNE, IMAT), USA (IMS))Europe (ARIADNE, IMAT), USA (IMS))

• Outcome: easily retrievable material, re-Outcome: easily retrievable material, re-use of existing components, oganisatonal use of existing components, oganisatonal memorymemory

IMAT (Integrating Manuals IMAT (Integrating Manuals and Training: Kabel, et al., and Training: Kabel, et al., 1999)1999)

• Electronic Technical DocumentationElectronic Technical Documentation

• Separate Training ManualsSeparate Training Manuals

• Problems:Problems:

– duplication/maintaining duplication/maintaining updates/selecting the right parts for updates/selecting the right parts for trainingtraining

The IMAT solutionThe IMAT solution

• Develop ontologies to index Develop ontologies to index documents for instructional documents for instructional authoringauthoring

• Segmentation into fragmentsSegmentation into fragments

• Description of fragments:Description of fragments:– 4 indexing categories4 indexing categories

Indexing categoriesIndexing categories

1. Syntactical properties (e.g. table; header; 1. Syntactical properties (e.g. table; header; footnote)footnote)

2. Type of description (e.g. structural; 2. Type of description (e.g. structural; behavioral)behavioral)

3. Domain-specific vocabulary (radar scan; 3. Domain-specific vocabulary (radar scan; converter)converter)

4. Instructional role (e.g. explanation; 4. Instructional role (e.g. explanation; exercise)exercise)

­­Radar­image­memory­(on­the­RSC­SB). This memory contains the radar picture, composed on the basis of the available

MTI, linear and/or IFF video signals. ­­Synthetic­image­memory­(on­the­GEN). This memory contains the synthetic picture elements, composed on the basis of the data sent by the SMRMU TD unit via the interface unit.

explanationillustration

Instructional role Instructional role ontology: an exampleontology: an example

Example IMATExample IMAT

• Supports all three scenariosSupports all three scenarios

• Models: Fine-grained model of Models: Fine-grained model of instructional role elementsinstructional role elements

Examples and the Examples and the frameworkframework• The framework should guide what is of The framework should guide what is of

interest to model in relation to a research interest to model in relation to a research questionquestion

• The examples show how the type of The examples show how the type of model(s) differ in content and scopemodel(s) differ in content and scope

• The framework allows interpretation of The framework allows interpretation of on-going research in terms of aspects on-going research in terms of aspects relevant for AI and Educationrelevant for AI and Education

ConclusionConclusion• The role of AI is changing from sole The role of AI is changing from sole

technology to complementing technology technology to complementing technology (hybrid systems)(hybrid systems)

• No need to change the nameNo need to change the name

• We should state our assumptions (on AI, We should state our assumptions (on AI, Education and Technology) more explicitlyEducation and Technology) more explicitly

• Future work: Refinement of the frameworkFuture work: Refinement of the framework