Dialogue in Intelligent
Tutoring Systems
Dialogs on Dialogs Reading Group
CMU, November 2002
ITS?
• Goal: help a human learn to perform a task
• Task Model: models how an expert would
perform the goal task
• Student Model: models how the student
currently performs the task + prior
knowledge on how students usual perform
• Teacher Model: usually none…
AutoTutor (U. of Memphis)
• Textual conversation with an animated agent
tutor
• Originally for Computer Literacy, also for
Newtonian Physics and Research Methods
• Goal: get (long) answers to general, concrete
questions and elicit/correct student knowledge
e.g.: Suppose a runner is running in a straight line at constant
speed, and the runner throws a pumpkin straight up. Where
will the pumpkin land? Explain.
Autotutor: Dialogue
• Selects dialogue moves from:
–
–
–
–
Positive/negative feedback
Prompts
Hints
…
• Students can ask Wh- and Yes/No-questions
• Based on a “Dialogue Advancer Network”:
FSM that selects the next move according to
student’s last utterance
• Latent Semantic Analysis to match student
answers with expectations
Autotutor: Comments
• “super form filling” where the system
knows the value of the slots beforehand.
• Mostly system-initiative, no memory (if
the student asks a question, the system
forgets what it is doing)
• Global strategy fixed (by system
architecture)
ATLAS/ANDES (Pitt)
• ANDES: ITS for physics, no natural
language
• ATLAS: “add-on” to ANDES, provides
Knowledge Construction Dialogues for
hints (main task/evaluation is left to ANDES)
• KCD: recursive FSM
– Reactive planner to pick next KCD
– Can insert subdialogues (clarification,
rectification…) and go back to original topic
ATLAS/ANDES
BEETLE
(U. of Edinburgh)
• Fully plan-based tutorial dialogue:
– Top tier: global strategy/repair when failure
– Middle tier: handles specific tasks according to
the situation
– Bottom tier: performs primitive dialogue actions
• Teaches basic electricity and electronics
• Very dialogue-oriented but not completely
implemented yet
PACO: Pedagogical Agent for
Collagen (USC, Mitsubishi, MITRE)
• Simulation-based training
• Domain-independent: adapts to any
simulator (e.g. Gas Turbine Engine)
• Collaborative Discourse Theory-based:
– Rules describe interactions between three
agents: student, tutor, simulator
– Discourse acts: both utterances and domain
actions
Stanford’s CSLI System
• Also simulation-based (Shipboard
Damage Control)
• Complex dialogue management:
– tree based activity model (similar to CMU
Communicator) built dynamically
• Separation between dialogue
management and tutoring strategy:
– Tutoring Module constructs the activity tree
using recipes while the Dialogue Manager
uses the tree to conduct the dialogue
Simulation-based Systems
Comments
• Combine advanced dialogue architectures
with ITS
• Mixed-initiative dialogue management
• Really multimodal (click-based simulator,
speech-based tutorial dialogue)
CALL System (U. Le Mans)
• Actional approach to Language Learning:
– User must perform a task (cooking)
• Communicative approach:
– Interaction with a partner agent
• Tutor agent to give instructions/help
• Specificity of CALL: language is the
domain taught, not only a means of
teaching
CALL System
• Based on a theory of Human Computer
Dialogue
• Partner agent: usual dialogue system
• Tutor agent: must monitor language and
dialogue issues
• Evaluation in terms of efficacy/efficiency:
similar to evaluation of dialogue systems
(number of turns taken) but for the human!
What can dialogue bring to ITS?
• Human tutor-like instruction:
– Qualitative, natural (cf science)
– Helps the student construct knowledge
instead of just “telling” him/her  deeper
understanding (?)
• Realistic simulation of certain tasks when
teaching communicative skills
What characterizes tutorial
dialogue?
• Tutor has expectations about student’s
utterances
• Student must be able to:
– Exhibit knowledge
– Ask questions (although this does not happen
so often…)
• Open-ended: no specific goal (except
teaching)
• ?
Are standard dialogue systems
suitable for tutorial dialogues?
• Possibilities of state-of-the-art dialogue
systems underexploited?
– Strategies for repair/elicitation
– Confidence measures (integrated with student
model?)
– Real mixed-initiative: its meaning in the
context of tutoring
What can dialogue research learn
from ITS?
• Task modeling
• Student/User modeling
• Multi-level planning (long-term strategies,
mid-term tactics, short-term actions) more
necessary in ITS than anywhere else
(pedagogical goals)
Other issues…
• General lack of cooperation between
language technology and ITS researchers?
– Natural Language Understanding
– Dialogues
• Both dialogue systems and ITS require
heavy human work to create:
– how can we derive a task model automatically?
• Anything else?
Any other comment…