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  1. 1. AI in Education: what are we about? Jacobijn Sandberg University of Amsterdam Department of Social Science Informatics
  2. 2. Overview <ul><li>Problem: fragmentation of our field and the complex interrelationship between AI, Education and New Technology </li></ul><ul><li>Four topics </li></ul><ul><ul><li>General demarcation of our field </li></ul></ul><ul><ul><li>The role of education in our research </li></ul></ul><ul><ul><li>The role of AI in our research </li></ul></ul><ul><ul><li>The role of AI - recent developments </li></ul></ul>
  3. 3. AI in Education: general demarcation <ul><li>AI in current educational practice </li></ul><ul><li>AI developments illustrated by ‘educational applications’ </li></ul><ul><li>AI as a formalization technique to operationalize and validate theories on teaching and learning </li></ul><ul><li>AI developments guided by new theoretical insights in education and general technological developments </li></ul>
  4. 4. Our field at a glance
  5. 5. The seven relationships <ul><li>1. AI applied to an educational setting (Goldstein, 1982; Beck & Stern, 1999) </li></ul><ul><li>2. Educational settings challenging AI in moving away from simple settings to complex, interactive, dynamic situations (Martial Vivet) </li></ul><ul><li>3. AI and Education overlap in terms of goals: AI used as operationalisation of an educational model (ITS: domain /knowledge representation, machine learning, natural language processing, planning) </li></ul>
  6. 6. The seven relationships continued <ul><li>4. AI (persons) inspiring new technologies (Internet, Java) </li></ul><ul><li>5/6. New technologies welcomed by the educational field (open and distance learning, life long learning) challenging AI (e.g. ontologies for indexing and retrieving instructional building blocks) </li></ul><ul><li>7. Education asking for technology suited to its needs (adaptive environments: Hoppe, AIED, 1999) </li></ul>
  7. 7. The three major players <ul><li>Artificial Intelligence </li></ul><ul><ul><li>strong philosophical base (e.g. what is there know: objective versus subjective) </li></ul></ul><ul><li>Education </li></ul><ul><ul><li>strong ideological base (basic skills versus meta-cognitive skils) </li></ul></ul><ul><li>New Technologies </li></ul><ul><ul><li>strong economic/political base(e-commerce versus shareware) </li></ul></ul>
  8. 8. All seven relations are reflected in our literature <ul><li>My problem (and yours too, hopefully): </li></ul><ul><ul><li>the approach, background assumptions, expected outcomes, theoretical or practical relevance, remain implicit </li></ul></ul><ul><li>Solution: a general descriptive framework </li></ul><ul><ul><li>which facilitates understanding articles of various nature </li></ul></ul><ul><ul><li>which guides the development of an interesting research agenda </li></ul></ul>
  9. 9. Towards a general framework <ul><li>Requirements for a general framework </li></ul><ul><ul><li>distinguish fundamentally different educational stances </li></ul></ul><ul><ul><li>cater for new developments in education as well as in AI </li></ul></ul><ul><ul><li>classify AI approaches or techniques in relation to the identified educational stances </li></ul></ul>
  10. 10. The role of education in our research <ul><li>There exist different models of teaching and learning, which reflect fundamentally different theoretical stances </li></ul><ul><ul><li>on the nature of human cognition </li></ul></ul><ul><ul><li>on educational objectives </li></ul></ul><ul><li>Changing the objectives (what) changes the means (how) </li></ul>
  11. 11. Let’s look at one of the oldest teaching methods: lecturing <ul><li>That is what I am doing right now </li></ul><ul><li>Are you supposed to learn anything? </li></ul><ul><li>What, if you are? </li></ul><ul><li>What does a good lecturer do? </li></ul><ul><ul><li>Provoke and sustain interest </li></ul></ul><ul><ul><li>Clarify what is worthwhile to retain </li></ul></ul><ul><ul><li>Good lecturing is edutainment </li></ul></ul>
  12. 12. What is wrong with edutainment? <ul><li>It is just fun and no learning (Postman: we amuse ourselves to death) </li></ul><ul><li>Why do our kids love computer games and stick with those for hours on end? </li></ul><ul><li>Is there a lesson to be learned from the gaming industry? </li></ul>
  13. 13. What is right with edutainment <ul><li>Captivating </li></ul><ul><li>Challenging </li></ul><ul><li>Gratifying </li></ul><ul><li>Our challenge: to keep all this, without creating superficial learning (AI in the gaming industry: virtual reality) </li></ul>
  14. 14. Fun and learning: becoming an expert or even just proficient <ul><li>Can never be just fun (mastery of a music instrument or any sports) </li></ul><ul><li>Calls upon: </li></ul><ul><ul><li>prolonged effort </li></ul></ul><ul><ul><li>frustration tolerance </li></ul></ul><ul><ul><li>reflection (away from the experiential mode: Norman, 1993) </li></ul></ul><ul><li>Brings: Long-term Satisfaction (instead of mere instant gratification) </li></ul>
  15. 15. Between fashion and science <ul><li>AI in Education: rolling on the waves of time </li></ul><ul><li>As education changes - our field changes with it </li></ul><ul><li>ITS as the answer to the need for individualized education: simulating the ideal teacher </li></ul><ul><li>CSCLE as the answer to the need for team work </li></ul>
  16. 16. Background assumptions of fashionable ideas <ul><li>Knowledge is dynamic (subjective, value-determined, ever changing, never true): life long learning </li></ul><ul><li>If that were true, human kind would have no chance of surviving whatsoever </li></ul><ul><li>There are constants in the world which people learn to identify to shape their world as a relatively stable, reliable and predictable place </li></ul>
  17. 17. The old fashioned way <ul><li>Knowledge as relatively stable </li></ul><ul><li>Have a look at the use of words (natural categories versus expert jargon) </li></ul><ul><li>Who determines the meaning of words? </li></ul><ul><li>Knowledge as transferable </li></ul>
  18. 18. Three basic teaching/learning scenarios (Andriessen & Sandberg, 1999) <ul><li>The transmission scenario: the empty vessel metaphor (old-fashioned; prevailing classroom teaching, lecturing) </li></ul><ul><li>The studio scenario: the constructive agent metaphor (current; study-house) </li></ul><ul><li>The negotiation scenario: the situated/distributed cognition metaphor (post-modern) </li></ul>
  19. 19. Transmission scenario: Main characteristics <ul><li>Closed domain </li></ul><ul><li>Well-defined learning goal </li></ul><ul><li>Fixed learning route </li></ul><ul><li>Instruction & practice </li></ul><ul><li>Diagnosis of errors and remediation </li></ul><ul><li>Outcome: Domain knowledge and skills </li></ul><ul><li>EMMA: Quigley, AIED 1989 </li></ul>
  20. 20. Studio scenario: Main characteristics <ul><li>Open or closed domain </li></ul><ul><li>Well-defined learning goal </li></ul><ul><li>Flexible learning route </li></ul><ul><li>Project-based learning </li></ul><ul><li>Interaction with different agents (human or otherwise) </li></ul><ul><li>Outcome: domain knowledge as well as social and practical skills </li></ul><ul><li>Barnard & Sandberg, 1997; Adorni et al., AIED 1999 </li></ul>
  21. 21. Negotiation scenario <ul><li>Open domain </li></ul><ul><li>Ill-defined learning goal </li></ul><ul><li>Open learning route </li></ul><ul><li>Argumentation/negotiation </li></ul><ul><li>Reflection </li></ul><ul><li>Outcome: conceptual change (comparison Socratic Dialogue: WHY; Stevens, Collins, and Goldin, 1982) </li></ul><ul><li>Baker et al., AIED 1999 </li></ul>
  22. 22. Past educational practice <ul><li>Prevailing transmission scenario, reflected in: </li></ul><ul><ul><li>classroom teaching: lecturing </li></ul></ul><ul><ul><li>drill and practice </li></ul></ul><ul><ul><li>little room for discussion/reflection </li></ul></ul><ul><ul><li>little room for complex problem solving </li></ul></ul>
  23. 23. Current educational practice <ul><li>Studio scenario, as reflected in: </li></ul><ul><ul><li>more emphasis on complex problem solving </li></ul></ul><ul><ul><li>more emphasis on student initiative and responsibility </li></ul></ul><ul><ul><li>more emphasis on problem analysis and solving method selection </li></ul></ul><ul><ul><li>more emphasis on open tasks (writing an essay, conducting a debate, giving a talk) </li></ul></ul>
  24. 24. Tomorrow’s educational practice? <ul><li>Negotiation scenario, reflected in: </li></ul><ul><ul><li>student directed learning </li></ul></ul><ul><ul><li>student defined problems and solutions </li></ul></ul><ul><ul><li>student sharing of knowledge and evolving ideas </li></ul></ul>
  25. 25. Negotiation scenario in real life <ul><li>‘The negotiation household’ (modern life) </li></ul><ul><ul><li>if you’ll do the dishes, I will put the children to bed </li></ul></ul><ul><li>Children pick it up quite easily </li></ul><ul><ul><li>if I eat my vegetables, you read me a story </li></ul></ul><ul><li>Driving parents crazy - all conversation limited to if-then statements </li></ul>
  26. 26. Evolution of the scenarios <ul><li>The scenarios build on one another and form a partial hierarchy </li></ul><ul><li>Transmission at the lowest level (novices), studio at the intermediate level (professionals), negotiation at the highest level (experts: they determine what the words mean) </li></ul><ul><li>When moving from beginner to expert one moves through various cycles in which the three scenarios have their part </li></ul>
  27. 27. Take AI in education as an example <ul><li>Transmission: read the books and learn the facts (e.g. Intelligent Tutoring Systems, 1982, from Sophie to Mycin) </li></ul><ul><li>Studio: work as a researcher in the AI and Education field </li></ul><ul><li>Negotiation: be at the forefront of the field and co-determine its direction </li></ul>
  28. 28. Hypothesis <ul><li>To teach and learn domain facts and rules: transmission </li></ul><ul><li>To teach and learn procedures and problem solving strategies: studio </li></ul><ul><li>To teach and learn meta-cognitive skills to create new knowledge and to reflect on one’s understanding: negotiation </li></ul>
  29. 29. What do we win? <ul><li>Firstly, we can map the different scenarios to different levels of expertise and therefore to different learning objectives </li></ul><ul><li>Secondly, we can investigate what part AI can play in the various scenarios </li></ul><ul><li>Thirdly, we can be more explicit in our writings about our background assumptions, stances, and choices </li></ul>
  30. 30. Hey, but were is AI? <ul><li>What do we (or I) mean by AI? </li></ul><ul><ul><li>The creation of systems that exhibit human-like intelligence realised in a way that not necessarily reflects the way human intelligence is organised (Suthers, AIED 1999: strong AI, minimalist AI and ‘background’ AI) </li></ul></ul><ul><ul><li>The ability to derive new conclusions from given data, the ability to solve problems, to learn (adaptivity) </li></ul></ul>
  31. 31. What does AI do? <ul><li>AI as a modelling science, creating computational, executable models of intelligent behaviour </li></ul><ul><li>That is exactly what ITS research did and still does - constructing and validating computational models of teaching and learning processes </li></ul>
  32. 32. So What? <ul><li>So, there is nothing wrong with ITS research! </li></ul><ul><li>But, how does it relate to education in practice? </li></ul><ul><li>It doesn’t, but does it have to? </li></ul><ul><li>Not all research necessarily bears on today’s or even tomorrow’s educational practice (informed education: basic skills) </li></ul>
  33. 33. AI in relation to the three scenarios <ul><li>Focus on AI as a modelling science </li></ul><ul><li>Major distinction between knowledge models and process models </li></ul><ul><li>The AI models focus on different aspects and have a different grain-size </li></ul><ul><li>For example: fine-grained student modelling (transmission) versus moderately grain-sized interaction modelling (studio) </li></ul>
  34. 34. Knowledge models in Transmission <ul><li>domain models (closed domains) </li></ul><ul><li>task models (learning environment) </li></ul><ul><li>cognitive state models (interaction) </li></ul><ul><li>conceptual indexing (information) </li></ul>
  35. 35. Process models in transmission <ul><li>Model tracing (diagnosis) </li></ul><ul><li>Expert reasoning (criterion task) </li></ul><ul><li>Student - tutor interaction </li></ul><ul><li>Monitoring (information) </li></ul>
  36. 36. Knowledge models in studio <ul><li>Multiple task models (flexible route) </li></ul><ul><li>Agent models (learning environment) </li></ul><ul><li>Multiple agents (interaction) </li></ul><ul><li>Multiple sources (information) </li></ul>
  37. 37. Process models in studio <ul><li>Agent interaction (flexible route) </li></ul><ul><li>Agents and tools (learning environment) </li></ul><ul><li>Procedural facilitation (collaboration) </li></ul><ul><li>Reflection (information) </li></ul>
  38. 38. Knowledge models in negotiation <ul><li>Multiple user models (team work) </li></ul><ul><li>Agent models (learning environment) </li></ul><ul><li>Interaction Models (collaboration) </li></ul><ul><li>Knowledge Infrastructure Models (knowledge management) </li></ul>
  39. 39. Process models in Negotiation <ul><li>Issue tracing (argumentation) </li></ul><ul><li>Student/tutor/partner interaction (learning environment) </li></ul><ul><li>Interaction; negotiation (collaboration) </li></ul><ul><li>Reflection, knowledge management (information) </li></ul>
  40. 40. Function of the framework <ul><li>Meta-level description of our field </li></ul><ul><li>Providing a standardised vocabulary (glossary of terms) </li></ul><ul><li>Meant to be used as a vehicle for reflection </li></ul><ul><li>Limitation: just one possible perspective, neglect of new technology as a separate factor </li></ul>
  41. 41. AI and other technologies <ul><li>At the start of AI, there was no Internet or JAVA or XML or … </li></ul><ul><li>Now we see hybrid applications combining AI and other emerging technologies </li></ul><ul><li>AI as supporting emerging technologies, to provide ‘smartness’, flexibility, and human-centeredness to our designs (Norman, Self - AI systems are systems that care!) </li></ul>
  42. 42. A hybrid approach example: CREDIT prototype, Anjo Anjewierden, UoA <ul><li>Dynamically generating HTML-pages on the basis of: </li></ul><ul><ul><li>underlying model of assessment and accreditation (CML2) </li></ul></ul><ul><ul><li>underlying prolog code (selecting the appropriate elements from the model) </li></ul></ul><ul><ul><li>together generating HTML pages (dynamically; depending on the user’s input; user data and selection of perspective) </li></ul></ul>
  43. 43. Example CREDIT <ul><li>Supports either a transmission scenario or a studio scenario </li></ul><ul><li>Models: Underlying domain (qualification structure); generic procedure (allowing variations in APL); interaction possibilities (realised in the Interface) </li></ul><ul><li>No detailed user modelling (just three broad categories of users distinguished) </li></ul>
  44. 44. A further example <ul><li>Metadata or ontologies for indexing and retrieving what is there </li></ul><ul><li>Educational metadata (Japan (Mizoguchi), Europe (ARIADNE, IMAT), USA (IMS)) </li></ul><ul><li>Outcome: easily retrievable material, re-use of existing components, oganisatonal memory </li></ul>
  45. 45. IMAT (Integrating Manuals and Training: Kabel, et al., 1999) <ul><li>Electronic Technical Documentation </li></ul><ul><li>Separate Training Manuals </li></ul><ul><li>Problems: </li></ul><ul><ul><li>duplication/maintaining updates/selecting the right parts for training </li></ul></ul>
  46. 46. The IMAT solution <ul><li>Develop ontologies to index documents for instructional authoring </li></ul><ul><li>Segmentation into fragments </li></ul><ul><li>Description of fragments: </li></ul><ul><ul><li>4 indexing categories </li></ul></ul>
  47. 47. Indexing categories <ul><li>1. Syntactical properties (e.g. table; header; footnote) </li></ul><ul><li>2. Type of description (e.g. structural; behavioral) </li></ul><ul><li>3. Domain-specific vocabulary (radar scan; converter) </li></ul><ul><li>4. Instructional role (e.g. explanation; exercise) </li></ul>
  48. 48. Instructional role ontology: an example ­ Radar image memory (on the RSC­SB). This memory contains the radar picture, composed on the basis of the available MTI, linear and/or IFF video signals. ­ Synthetic image memory (on the GEN). This memory contains the synthetic picture elements, composed on the basis of the data sent by the SMRMU­TD unit via the interface unit. explanation illustration
  49. 49. Example IMAT <ul><li>Supports all three scenarios </li></ul><ul><li>Models: Fine-grained model of instructional role elements </li></ul>
  50. 50. Examples and the framework <ul><li>The framework should guide what is of interest to model in relation to a research question </li></ul><ul><li>The examples show how the type of model(s) differ in content and scope </li></ul><ul><li>The framework allows interpretation of on-going research in terms of aspects relevant for AI and Education </li></ul>
  51. 51. Conclusion <ul><li>The role of AI is changing from sole technology to complementing technology (hybrid systems) </li></ul><ul><li>No need to change the name </li></ul><ul><li>We should state our assumptions (on AI, Education and Technology) more explicitly </li></ul><ul><li>Future work: Refinement of the framework </li></ul>