Intelligent Agents to Deliver Learning Materials Leen-Kiat Soh Computer Science & Engineering University of Nebraska Lincoln, NE Future Problem Solving Workshop— Hastings January 21, 2004

Challenges Customized education –e.g., Modularized courseware to meet specific requirements, deficiencies, and sequences Adapting education –e.g., Flexible courseware that adapts in real time to student behavior and aptitude Effective distance education –e.g., tools with anytime, anywhere capabilities –e.g., infrastructures to bring classroom experience to distance learners

Problem Corbett et al. (1999): –“the arsenal of sophisticated computational modules inherited from AI produce learning gains of approximately.3 to 1.0 standard deviation units compared with students learning the same content in a classroom.” Graesser et al. (2001): –“Human tutors produce impressive learning gains (between.4 and 2.3 standard deviation units over classroom teachers), even though the vast majority of tutors in a school’s system have modest domain knowledge, have no training in pedagogical techniques, and rarely use the sophisticated tutoring strategies of ITSs.” ITS = Intelligent Tutoring System

Problem 2 Woolf et al. (2002) also lists abilities that are needed or present in tutors: –Generative: Generates appropriate instructional material (problems, hints, help) based on student performance –Student modeling: Assesses the current state of a student’s knowledge and does something instructionally useful based on the assessment –Expert modeling: Models expert performance and does something instructionally useful based on domain knowledge

Problem 3 Woolf et al. (2002) also lists abilities that are needed or present in tutors, cont’d: –Instructional modeling: Changes pedagogical strategies based on the changing state of the student model, prescriptions of an expert model, or both –Mixed-initiative Human Computer Interaction (HCI) –Self-improving: Capacity to monitor, evaluate, and improve its own teaching performance as a function of experience

Problem 4 Graesser et al. (2001) criticize the current state of tutoring systems: –If students merely keep guessing until they find an action that gets positive feedback, they can learn to do the right thing for the wrong reasons – shallow learning –The tutor does not ask students to explain their actions – multiple choice questions –The user interface of tutoring systems requires students to display many of the details of their reasoning – no stepping back to see the “basic approach” –When students learn quantitative skills (e.g., algebra or physics problem solving), they are usually not encouraged to see their work from a qualitative, semantic perspective

Solution Ideas Examples –PACT (Koedinger et al. 1997): algebra, geometry, and computer languages –ANDES (Gertner and VanLehn 2000; VanLehn 1996): physics Used as an adjunct to college and high-school physics courses to help students do their homework problems Has an immediate feedback to enhance learning –SHERLOCK (Lesgold et al. 1992): electronics –ATLAS (VanLehn et al. 2000): Model-tracing Students scored significantly higher than the ANDES students on a conceptual post-test

Solution Ideas 2 Examples –AutoTutor (Graesser et al. 2001): introductory course in computer literacy Fundamentals of computer hardware, OS, Internet, etc. Was designed to be a good conversational partner that comprehends, speaks, points, and displays emotions, all in a coordinated fashion Simulates a multi-turn conversation to extract more information from the student and get the student to articulate pieces of the answer Pumps the student for what s/he knows before drilling down to specific pieces of the answer Uses Latent Semantic Analysis (LSA) to compute matches between the student’s speech acts to the expectations

Solution Ideas 3 Examples –AutoTutor (Graesser et al. 2001): introductory course in computer literacy, cont’d: Feeds back to the student at three levels: (a) backchannel feedback that acknowledges the learner’s input (b) evaluative pedagogical feedback on the learner’s previous turn based on the LSA values of the learner’s speech acts (negative, neutral negative, neutral positive, positive) (c) corrective feedback that repairs bugs and misconceptions that learners articulate

Solution Ideas 4 Examples –CIRCISM (Freedman and Evens 1996) –BEE (Basic Electricity and Electronics) tutor (Rosé et al. 1999) –EVELYN Reading Coach and EMILY Reading Coach (Mostow and Aist 2002): Project LISTEN Help students read by listening to children read aloud –BELVEDERE (Suthers et al. 2002): Supports students in collaboratively solving ill-structured problems in science and other areas (such as public policy) as they develop critical inquiry skills

Solution Ideas 5 Teachable Agents (Biswas et al. 2002) –let students teach the agents to do things; through teaching, the students learn Articulate Software (Forbus 2002); properties are: –It should be fluent (some understanding of the subject being taught) –It should be supportive (scaffolding) –It should be generative (pose new questions) –It should be customizable (manually)

Solution Ideas: Intelligent Agents What is an agent? –An agent is an entity that takes sensory input from its environment, makes autonomous decisions, and carries out actions that affect the environment –A thermostat is an agent –A calculator is not an agent Environment sensory input output actions Agent think!

Solution Ideas: Intelligent Agents 2 What is an intelligent agent? –An intelligent agent is one that is capable of flexible autonomous actions in order to meet its design objectives, where flexibility means: Reactivity: agents are able to perceive their environment, and respond in a timely fashion to changes that occur in order to satisfy their design objectives Pro-activeness: agents are able to exhibit goal-directed behavior by taking the initiative in order to satisfy their design objectives Social ability: agents are capable of interacting with other agents (and possibly humans) in order to satisfy their design objectives (Wooldridge and Jennings 1995)

Solution Ideas: Intelligent Agents 3 Machine Learning in AI says Agents that learn are intelligent Not all agents are intelligent! The acquisition of new knowledge and motor and cognitive skills and the incorporation of the acquired knowledge and skills in future system activities, provided that this acquisition and incorporation is conducted by the system itself and leads to an improvement in its performance.

Solution Ideas: Agent Environment Inaccessible vs. accessible – Incomplete vs. complete data Deterministic vs. non-deterministic –Certainty vs. uncertainty Episodic vs. non-episodic –Each episode is independent or not Static vs. dynamic –Remain unchanged except by the performance of actions by the agent? Discrete vs. continuous –“Chess game” vs. “taxi driving”

Solution Ideas: Why Agents? If the system-to-be-built has, during the execution of the system –Incomplete data –Uncertainty in the assessment/interaction of its environment –Inter-dependent episodes of events –No full control over the events in the environment –An “open world”, instead of a “closed world” In other words, agents are used when you need to build a system that is adaptive to an uncertain, dynamic, and at times unexpected environment –So you can make full use of the autonomous property of an agent Why does a person hire an agent?

Evaluation Criteria Pre-Design –Necessity Is the agent solution necessary? Or an overkill? –Feasibility Does the agent solution make sense? Is it impossible to implement? Do we have the resources? Will it work? During Design –Modularity Are there different modules and components? Is the “data” separated from the “brain”? –Extensibility What if we want to apply it to another problem? –Scalability What if we want to apply it to the same but bigger problem? Some examples, not exhaustive

Evaluation Criteria 2 Post-Design –Correctness Are the algorithms correctly designed? Are the solutions correct? –Usefulness Is the solution useful? Does it actually address the problem? Does it help the user? –Reliability Does the solution work for all possible problems? Will the performance deteriorate after a while? –Adaptiveness/Learning Can the solution evolve by itself to solve new problems or to become better at solving old problems? Some examples, not exhaustive AI

ILMDA Intelligent Learning Materials Delivery Agent

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Latent Semantic Analysis Latent Semantic Analysis (LSA) is a theory and method for extracting and representing the contextual-usage meaning of words by statistical computations applied to a large corpus of text (Landauer and Dumais, 1997). The underlying idea is that the aggregate of all the word contexts in which a given word does and does not appear provides a set of mutual constraints that largely determines the similarity of meaning of words and sets of words to each other. –Based on Landauer, T. K., P. W., and D. Laham (1998) Introduction to Latent Semantic Analysis, Discourse Processes, 25: