Pedagocical Agents


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Pedagocical Agents

  1. 1. Pedagogical agents T he experience of Consorzio FOR.COM. Mikail Feituri ICT manager Consorzio FOR.COM. Rome, 23 October 2008
  2. 2. <ul><li>software elements being responsible for carrying out given tasks by means of artificial intelligence techniques </li></ul><ul><li>Conceptually, the agents implement a metaphor being common to the typical way of operating in the market: </li></ul><ul><ul><li>visiting a place, </li></ul></ul><ul><ul><li>using a service (possibly following a negotiation) </li></ul></ul><ul><ul><li>then moving elsewhere. </li></ul></ul><ul><ul><li>After the agent has gathered the results wished, it goes back to the user. </li></ul></ul>Intelligent agent
  3. 3. <ul><li>Definition: </li></ul><ul><li>particular type of intelligent agent </li></ul><ul><li>actual virtual tutor accompanying the student of the educational system during the learning process </li></ul><ul><li>Features: </li></ul><ul><li>always visible to the user within the educational milieu </li></ul><ul><li>human (or humanoid) forms </li></ul><ul><li>Interacts with the user both verbally and non verbally </li></ul><ul><li>It moves and interacts directly with the learning milieu and within the milieu itself. </li></ul>Pedagogical agent
  4. 4. Parmenide project <ul><li>Goals: </li></ul><ul><li>Two pilot applications for training of operators employed in the transport sector in the anti firing security field. </li></ul><ul><li>Features of the applications: </li></ul><ul><li>Innovative assessment tool </li></ul><ul><li>Extremely stimulating scenarios for the students </li></ul><ul><li>The virtual tutor simulates a teacher who submits an exam to a teacher </li></ul>Platform
  5. 5. BEHIND THE PILOT APPLICATION <ul><li>The pilot application starts on choosing randomly an important question among those available. </li></ul><ul><li>Our expert in anti firing security has defined which questions have to be considered as important. </li></ul><ul><li>Another parameter, which has been considered, is the difficulty of the question. </li></ul>
  6. 6. <ul><li>The system works with 3 Fuzzy Logic inference system (FIS). </li></ul><ul><li>Fuzzy Logic, with its linguistic rules, simulates human behaviour. In fact, it translates human behaviour based on natural language syntax in an artificial language suitable for computers. </li></ul>FUZZY LOGIC
  7. 7. First Fuzzy inference engine FIS 1 Importance Difficulty Fastness Correct / Incorrect Knowledge depth It defines a learning path for the student
  8. 8. <ul><li>The knowledge depth is the degree of user knowledge about the topic </li></ul><ul><li>Depending on the quality of the user answer, the system provides again another important question or any other question. </li></ul><ul><li>The system behaves like a normal teacher </li></ul><ul><li>In the pilot application the minimum question numbers is 3 and the maximum is 5 </li></ul>Knowledge depth
  9. 9. It provides the score carried out by a user when he / she answered a question. Second Fuzzy inference engine FIS 2 Importance Difficulty Fastness Correct / Incorrect Score
  10. 10. It defines the verbal and non verbal tutor behaviour FIS 3 Cumulative score Knowledge Depth (if answer is right) Verbal and non verbal behaviour (facial expressions) Score (if answer is wrong) Third Fuzzy inference engine
  11. 11. <ul><li>More than 100 verbal feedback are stored in the database. </li></ul><ul><li>This messages are classified from very negative to very positive. </li></ul><ul><li>The tutor decides which one to supply from the third fuzzy engine output. </li></ul>Verbal behaviour
  12. 12. <ul><li>The tutor is able to provide 11 different facial expressions </li></ul><ul><li>The tutor puts on a neutral expression when he reads the questions and she provides the didactic pills. </li></ul><ul><li>The tutor decides which one to supply from the third fuzzy engine output. </li></ul>Non Verbal behaviour
  13. 13. <ul><li>We tried to avoid virtual tutor behaviours which can be classified as unstable. </li></ul><ul><li>For this aim, we considered the user performance carried out in all the questions and not just in the last question answered. </li></ul><ul><li>On doing this, we tried to simulate the behaviour of a normal teacher who submits an exam to a student. </li></ul>Non Hysterical behaviour
  14. 14. Remarks and improvements <ul><li>The number of questions is very limited because this is a pilot application for testing new didactic methods. </li></ul><ul><li>Only multiple choice questionnaire for each scenario has been used because of the particularity of the didactic topic </li></ul><ul><li>Among other sectors, more complex and various scenarios could be used. </li></ul>
  15. 15. Looking ahead: T 2 project <ul><li>T 2 adapts and transfers the pedagogical and didactic model developed in PARMENIDE in the field of microfinance </li></ul><ul><li>The aim is to apply the “PARMENIDE model” to a comprehensive and already produced E-course </li></ul>
  16. 16. Looking ahead: COACH BOT project <ul><li>COACH BOT is a pilot project that aims essentially to develop an intelligent tutor </li></ul><ul><li>Like a real tutor, the pedagogical agent will provide help, suggestions on the lessons, in-depth information, ... </li></ul><ul><li>For this, the development should be focus on the agent’s dialogue capacity with the student </li></ul><ul><li>The artificial intelligent techniques to be used will be probably rather different from the ones developed for PARMENIDE. </li></ul>
  17. 17. <ul><li>Thank you! </li></ul><ul><ul><li>Mikail Feituri </li></ul></ul><ul><ul><li>FOR.COM. Interuniversity Consortium </li></ul></ul><ul><ul><li>Rome – Italy </li></ul></ul><ul><ul><li>[email_address] </li></ul></ul><ul><ul><li>+39 06 37725542 </li></ul></ul>