Using Artificial Intelligence in Legal Computer Assisted Instruction
R P Jones, Liverpool John Moores University
Legal Computer Assisted Instruction
As with television, the micro-computer, through the vehicle
of Computer Assisted Instruction (CAI), has been heralded as the means to
democratise education by delivering the same high- quality educational
information to all learners. Unlike television the computer can provide
individualised and self paced instruction. The earliest efforts to produce CAI
materials can be traced back to 1960, but it was in the early 1970's when the
main funded projects of PLATO and TICCIT began in the United States. These
projects were funded in order to evaluate the following claims which were being
made for CAI.
It provided the potential for self pacing, adaptive and
individual tuition.
Programs could provide instant feedback and correction.
During program development tutors carefully articulate
questions, answers and responses thereby enhancing their understanding of the
subject area.
It provided a means for equalising levels of achievement.
The program could provide links with other technological
developments: video, databases and expert systems.
Programs could rigidly control the sequence of topics made
available to the user.
Programs allow for lower educational costs in the long term.
Additionally CAI promised the following advantages over its
immediate predecessor, workbook programmed learning.
Sophisticated branching, which is impossible with workbooks.
Data collection; difficult with workbooks.
Easier control of cheating.
Random generation of problem situations thus making the
programs more interesting and varied for the user.
Easier routing of students according to their previous
responses and consequent adaptation to students' requirements.
By the late 1970's disenchantment with CAI had begun to
settle over many educators as well as the key funding agencies. It was realised
that the bulk of the educational software produced was only of minimal use and
some was probably detrimental. Critics were able to claim that CAI trivialises
and over simplifies a subject with its simplistic questioning and control
strategies and is in consequence an ineffective teaching method. A recent
report of the OECD (Lesgold, 1986) was able to state;
"... education stands at a crossroads. The reification
of ineffective teaching practices into computer artefacts will not improve
education, nor will handing potentially useful information tools to teachers
who have not been trained in their use ... a major effort is needed to refine
and demonstrate new educational science and technology and to train teachers to
use the tools."
Yet CAI is particularly appropriate to legal education for
it can give experience in expressing law-related propositions, not just in
recognising them. Within the limited legal vocabulary, free form responses can
more easily be anticipated by the CAI author thereby reducing his dependence
upon multiple choice techniques which are suggestive to the student and can
mislead. However the general difficulties encountered in CAI in the early
1970's did much to discourage the development of programs in law. One notable
exception was the EDUNET project in the USA which helped to encourage CAI
production in a range of disciplines, including law from as early as 1967
(Burris et al, 1979). This project led directly to the formation of the Centre
for Computer Assisted Legal Instruction (CCALI) based at the Universities of
Minnesota and Harvard (Burris, 1985).
One cause of the deficiencies within the then existing CAI
programs can be traced to the software tools used. CAI programs may be produced
using a wide range of software tools; the most popular being authoring systems
or languages. An authoring system is a complete interactive environment in
which the tutor may develop CAI materials. By their nature authoring systems
must attempt to cater for the complete education market providing a tool for
the teacher at primary, secondary and tertiary level. Facilities provided are
generally related to screen design, limited graphics and basic routing.
Unfortunately most systems encourage poor CAI method by leading authors into
premature program production without encouraging thought as to design or
function. Authoring systems often have inadequate facilities for response
matching and screen handling and, additionally, use of such systems means a
lack of standardisation between faculties with the consequent lack of portability
and a lack of co-operative effort. Authoring languages are computer languages
specifically oriented towards the development of CAI materials. They allow
greater flexibility within the program (but may lack functions standard to a
computer language such as Pascal). These languages require time to master and
suffer from similar problems of standards and portability as authoring systems.
Both authoring systems and languages have failed to evolve with the needs and
practices of CAI.
These difficulties led CCALI to develop an alternative
authoring system. The Pascal Instructional Language (PIL) establishes
predefined operators to remove machine dependency, it also contains a number of
procedures that assist in basic areas of string manipulation and screen processing
(Burris, 1985). The adoption of a standard language and operating system meant
that the full facilities of the computer were available to the author, the
programs became portable between machines and costs of development of the
system were spread between all the users of Pascal. The system claimed
significant benefits including modularity of program design, the use of
structured program logic and the availability of data structures. The main
difficulty with PIL is the requirement for both an author and a competent
Pascal programmer, the system requires the establishment of an organisation
that can receive text files from authors and have them programmed. To meet this
deficiency CCALI have themselves produced a series of authoring systems that
enable the author to program his own CAI materials. This can encourage the
development of smaller and more specific programs for use by the individual
author 'in house'.
Even with the use of systems such as PIL the criticism of
legal CAI continues. Programs are claimed to merely replicate the same skills
as taught by traditional legal methods and to simplify and trivialise the law
into a series of yes/no or multiple choice scenarios.
Two initiatives in the UK were aimed specifically at this
problem of program quality. Jones (Jones, 1987) proposed a methodology to
provide a step by step guidance to authors in the preparation of legal CAI
materials. Authors were encouraged to consider the form and type of program
needed, justification and place in the curriculum. Leith (Leith, 1988) with the
aid of an IBM study contract developed the Lexical system as a purpose built
legal authoring system for use on IBM PC's and compatibles.
AI Techniques in Legal CAI
Although the standard of programs may improve as tutors begin
to gain experience, there is a limit to the sophistication one can attain using
the present software. In addition to the drawbacks of authoring systems
presented above, traditional CAI programs suffer from the problems all
traditional systems face, namely;
an inflexible algorithm - the knowledge about how to teach
is bound to the particular algorithm of the program.
data dependency - the algorithm manipulates data supplied by
the student rather than the knowledge of the student.
An attempt to overcome these deficiencies was generative
CAI. These systems had the capability to generate new problems from the
combination of different elements in a database (Uttal et al., 1969).
Unfortunately, this adaptivity was limited and often unrelated to the
individual student needs.
An alternative line of research is to consider whether the
techniques and tools used in Artificial Intelligence can be used to enhance CAI
programs. AI research has made available the programming languages LISP and PROLOG,
expert system shells and many insights into knowledge representation, inference
methods and the learning process.
The interface of Artificial Intelligence and CAI is referred
to as Intelligent Computer Assisted Instruction (ICAI) (Kearsley ed., 1987).
ICAI brings together work on artificial intelligence, cognitive psychology and
educational research. ICAI systems must look at user performance and evaluate
it with respect to expert performance, deciding what the user can already do
and what could lead the user closer to expert performance. This requires three
types of knowledge: knowledge of the domain expert, knowledge of how to
recognise the specific capabilities of the user (student model) and knowledge
of the course and mechanisms of learning (pedagogical expertise). With such
abilities the ICAI system can improve on the richness of feedback and the
degree of individualisation offered to the user.
The LEX Project
With a number of legal expert systems under development it
is tempting to develop an intelligent tutor out of an embryo expert system. The
LEX project provided such an opportunity. It was one of the European based
projects investigating legal expert systems from both a professional and
teaching perspective. It was a co-operative project between the University of
Tuebingen and IBM Germany. The author had the opportunity of working with the
LEX project in the development of an intelligent tutoring system (ITS).
It had always been the intention of the LEX research project
to investigate both the development of:
a tool for giving legal advice to lawyers and helping them
prepare a case (consultation system) and
an intelligent tutoring system for law students.
The consultation system supported a lawyer in analysing case
descriptions and advising a client. As far as possible the dialogue of the
lawyer with the system should take place in natural language. The lawyer should
be in a position to feed the description of a case and the request for a
solution into the computer in natural language. The system then attempted to
solve the case by means of applying its knowledge. In order to do so it
analysed the case description and the request and combined these with
statements from its knowledge base in order to form a series of arguments.
Wherever certain facts were lacking it was intended that the system would ask
for them.
The intelligent tutor should support a user in an
interactive learning environment where the system incorporated all knowledge
needed to teach the area of application.
The approach of developing an intelligent tutor from an
existing system has a number of problems; it imposes the existing expert system
methodology on the learner, the student is viewed as a deficient system, it
assumes that the result is more important than the means (this is totally
inappropriate in law where the result is of importance only to the litigants)
and finally the existing expert system fails to provide adequate explanations
to enable the user to determine why a particular rule is corrrect or what is
the strategy behind the goal structure. This approach necessitated the
development in LEX of a number of additional knowledge bases to deal with
pedagogical and assessment issues. A similar experience is found in the
discipline of medicine where the GUIDON and NEOMYCIN projects have attempted to
develop an intelligent tutor out of the MYCIN expert system (Clancey, 1983).
LEX has its area of application in a small number of
practically significant offences of German traffic law as the first field of
application. At their head is a regulation (paragraph 142, German Penal Code)
according to which whoever is involved in a traffic accident and subsequently
fails to fulfil certain obligations, for example to remain at the scene of the
accident, is punishable. The reasons for this choice were, on the one hand, the
great practical significance of this area of the law and, on the other, the
complicated structure of the norms which also make generous use of general
clauses and indefinite legal terminology. For this reason it was believed that
many of the problems that can arise in the formalisation of rules are to be
encountered paradigmatically in this field.
Since the LEX system had been designed as a consultant for a
lawyer, the task of conversion aimed to make the LEX system become much more
active in its actions with the user. LEX uses its expertise to access the
knowledge base (faces inwards); the tutoring system must use its expertise to
assess the student (face outwards), allowing the student to become the most important
part of the system.
Design Decisions
Experience in developing legal CAI programs in the USA
(Burris, 1985), UK and at the University of Tuebingen led the project group to
isolate the following design decisions in relation to the tutoring program;
the system should be based on a natural language dialogue
between user and system and not be restricted (as present legal CAI programs
are) to YES/NO, multiple choice answers or keyword matching. LEX offers a
limited natural language capacity that would certainly enhance the ITS program.
the ITS should have a student model so that it could adapt
its teaching to the individual user's needs.
this student model would enable the system to choose not
only appropriate teaching material but also the most appropriate discourse
method and CAI technique.
this model would be easily accessible for tutors to evaluate
both the system and the user's performance.
the system should have the capability of presenting the same
legal material in differing CAI formats.
user understanding would be compared with that of the system
but firm conclusions as to user knowledge would not be solely dependent upon
this comparison (Clancey, 1983).
problem solving methodology would be judged rather than the
result, this was made possible by being able to compare the system's inference
process with that of the user.
the system could act as an intelligent front end to a legal
database (JURIS) and as a research tool, users should be able to switch between
the tutor and consultancy mode.
Implementation
Implementation required the development of a number of
additional rule-bases, designed to assess and route the user and components to
hold the additional teaching materials. The system was able to pose case facts
to users, compare their response to its own and ask further responses based
upon that analysis (Haft et al., 1987).
ICAI in the Academic Environment
The author's experience with the LEX Project provided an
opportunity to consider the development of similar systems with Leicester
Polytechnic. Computing facilities and expertise dictated that development work
should be based in the PROLOG language. Components could therefore be developed
individually and alternative reasoning strategies could be used, neither would
have been possible if development had been attempted in an expert system shell.
System Architecture
Three different Intelligent Tutorial System (ITS)
architectures were studied. These were the ACT principle pioneered by John
Anderson and his colleagues at Carnegie-Mellon University, the four component
model suggested by Hartley and Sleeman and the five ring model presented by
O'Shea et al.
Spectrum to show the three Intelligent Tutorial System
architectures studied
exploratory learning environments
Anderson's ACT
principle
Hartley &
Sleeman's proposal
O'Shea
et al
proposal
traditional
CAL
John Anderson's Advance Computer Tutoring (ACT) Principle
(Anderson, 1985)
John Anderson, a psychologist from Carnegie-Mellon
University, researched in adaptive control of thought. His work has resulted in
an ITS architecture called the (ACT) principle. The principle can support ITSs
in such diverse applications as Geometry tutoring and LISP tutoring. It was the
ACT principle which was behind a tutoring system for the LISP program language.
The ACT's ITS architecture consists of four main components:
Domain expert: this solves problems in the domain which the
student is currently trying to learn. These solutions can then be compared with
the student's solutions. The domain expert is also referred to as the 'ideal
student' model.
Bug catalogue: this is a library of common mistakes and
misconceptions that a student is likely to make.
Tutorial knowledge: this contains the strategies used to
teach domain knowledge.
User interface: this controls the interactions between the
student and the tutor.
The ACT theory is intended to be used in systems where it
can take a monitoring role. For example, in the LISP Tutor a student is
required to write a piece of LISP code using a normal editor. Whilst the
student is doing this the ACT ITS is in the background monitoring his progress
and will only come into action when it recognises a coding or planning error.
In comparison to other ITSs, the ACT tutor incorporates a
dogmatic and authoritarian approach. Its main concern is deviation of the
student from the ideal student model. With a secondary concern of looking out
for common known mistakes that a novice is likely to make. After this it will
then try to provide corrective information to steer the student back on to the
ideal path.
Anderson's ACT principle puts little emphasis on the student
model. There is no representation of the student model as a separate component
in the architecture but rather the student modelling is embodied within the
overall tutoring philosophy. That is the strategy to assess the student via the
ideal student model and the bug catalogue of common misconceptions.
The Hartley-Sleeman Four Component Model
Hartley and Sleeman suggested that an ITS should contain
four distinct knowledge bases:
Expert rule-bases - Knowledge of the task domain.
A student model/history of the student's behaviour.
Teaching data - possible teaching operations.
Means-ends-guidance-rules (MEGRs) which uses knowledge held
in the student model to provide the student with the next appropriate teaching
step.
This method differs from Anderson's ACT principle in that it
does not give misconceptions in the domain (the bug catalogue) primary
importance, but instead it uses the student model as the primary component.
This student model is a model of the student's performance throughout the
tutorial and possibly in other tutorials.
Another difference with this proposal and Anderson's is the
style of tuition delivered. Whilst the ACT principle stays in the background
analysing the student's behaviour and correcting it when needed, Hartley and
Sleeman's proposal is more tutoring oriented. The interaction provided by the
user interface is controlled by the ITS. The MEGRs use information held about
the student in the student model to choose which of the possible teaching
operations the ITS should present the student with next.
The Hartley-Sleeman model has been around for over a decade
and a half and has stood the test of time. It has been adopted as the general
structure for many ITSs and is suitable for many diverse applications, with
perhaps some design variations depending on the functional requirements for the
system.
This method is perhaps unsuitable when there is a
requirement to support the handling of a range of strategies within a given
tutorial system.
O'Shea et al Five Ring Model (O'Shea, 1985)
This method is called the five ring model as it has five
components in its architecture. These are:
Student history
Student model
Teaching strategy
Teaching generator
Teaching administrator
This method has some similarity to the proposed method by
Hartley and Sleeman. Like Hartley and Sleeman's it gives primary importance to
the student model. It also gives much importance to the teaching strategy and
in that way it is radically different to Anderson's ACT principle. The uses of
an ideal student model and bug catalogue, which the ACT principle makes primary
use of, are undermined in favour of emphasis on teaching strategy.
This method has more in common with traditional Computer
Aided Instruction (CAI) methods in the way it places more emphasis on the
teaching strategy. It is also possible to buy a tool kit for building ITSs that
use the five ring model in much the same way that you can use authoring systems
to develop traditional CAI.
These methods can be viewed as being points on a spectrum
(shown above). At one end of the spectrum is exploratory learning environments.
These require the student to move around knowledge bases of the knowledge
domain and are best suited for teaching abstract and general concepts such as
use of analogies and model building.
At the other end of the spectrum is traditional CAI which
puts emphasis on teaching strategy rather than representation of the knowledge
domain. Between the two extremes are the three methods discussed.
The ACT principle is suited for teaching problem solving in
a specific domain where the ITS can just sit in the background and detect and
correct the student's understanding when required.
In the middle of the spectrum the Hartley and Sleeman method
is more suitable for tuition that has to be tutor driven rather than student
driven. But with that aside, it still proves suitable for most ITSs as this is
the most desired form of interaction between tutor system and student. Also
within its general architecture are all the components required for a truly
intelligent tutorial system.
The O'Shea et al five ring model is better for teaching
concepts which are more concrete and specific. This also makes it easy to
construct a domain independent tool kit for an ITS using this principle and, as
I have already mentioned, one is already commercially available.
It was concluded that the Hartley-Sleeman four component
model provides suitable general guide lines for the preparation of legal ITS
materials and indeed previous work on other related projects (LEX) had already
followed these guidelines.
Components required were therefore; expert rule-bases on the
area of law to be taught and on the pedagogical expertise on how to teach such
areas of law, components to store, assess and to give decisions on progression
of a particular student - collectively referred to as the student model.
Rule-Bases
The project began by developing a number of expert legal rule-
bases. Three main legal domains were chosen, the United Kingdom Data Protection
Act 1984, Unfair Contract Terms 1977 and the Housing Act 1985. These domains
were chosen because the School already had traditional CAI programs in these
domains.
The development of the rule-bases followed work on
formalisation of statute law conducted at Imperial College (Sergot et al.,
1986) and at Leicester Polytechnic (Routon, 1989). They represent an attempt to
build what are referred to as "Shallow" models of the law.
More recently effort has gone into the hierarchical
formalisation of statutory material. Traditional representations treat the
rules contained in the statute as if they stand on par with each other, but
typically a statute is organised hierarchically. There may be parts, within
parts will be sections and within sections sub-sections. Further sections may
not contain rules about the legal domain but rules about rules. The proposal is
to raise these rules to a higher (level) from whence they may operate more
realistically. For example in the Data Protection Act 1984;
A data user shall be treated as being registered under
Section 5 if Section 7 (6) (a) and (b) but subject to the exception in Section
7 (8) a or Section 7 (8) b.
A traditional representation of Section 7(6) would be in
logical form;
registered if a and b and (not S7(8)a or S7(8)b).
How much clearer to raise the exception in Section 7 (8) to
a meta level and have a representation
registered if a and b and ss8.
ss8 if S7(8)a or S7(8)b.
The rule in ss8 now represents the exception and now is
given the status of a meta level rule. It reflects more accurately the
hierarchical nature of the Act and allows easier updating should amendment be
made to the exception (Routon, 1989).
Apart from faithful representations of the Act, attempts
have been made to add additional rule-bases that contain the lawyer's heuristic
knowledge of the Act. For example in the Data Protection Act 1984 much of the
Act revolves around the definition of Data, (Section 1). In PROLOG the code of
the section would be;
"Data" means information recorded in a form in
which it can be processed by equipment operating automatically in response to
instructions given for that purpose.
data(X):-
information(X),
recorded_in_processable_form(X),
automatically_processed(X).
The rule has been enhanced with heuristic knowledge by the
use of so called 'statutory predictions' (Susskind, 1987) to allow for expected
interpretation of the above section. The heuristic knowledge related to the
requirement that data must be recorded in a form capable of being processed
could be that data held on a disk is so held. This is represented in an
additional knowledge base as
recorded_in_processable_form(DATA):
held_on_floppy_disk(DATA).
It was then possible to further add probability factors to
give
recorded_in_processable_form( Data),
[held_on_floppy_disk( Data),prob(0.9)
It is essential to maintain these predictions separate from
the main rule-base as they do not represent the formal representation of the
statute. These additional rule-bases do provide an interesting experimental
area for students to work in.
Within the tutorial system a static rule-base would find
little use, it being much too limited and restrictive to be useful. Ideally the
rule-base should have an adaptive representation of the law and should be able
to return to the assessment component not only the goal (decision) but also its
reasoning path. The technique of using meta predicates is again being used
here. Meta interpreters are interpreters for the language written in the
language itself and in consequence can modify the reasoning strategy of the
PROLOG system thereby providing an adaptive representation of the knowledge.
Further they can look down and maintain a trail of the reasoning process in the
rule-base. This trail can then be returned to the assessment component and
compared with the student's reasoning trail.
Development of the Pedagogical Rule-base
This is an attempt to represent how to teach the particular
subject. Unfortunately there is no correct teaching strategy so the system
merely attempts to represent an overall hierarchy of teachable legal
skills/topics and their inter-related dependencies based upon the formalisation
by Ashley and Rissland (Ashley and Rissland, 1985). At present the rule-base is
in linear form with alternate pathways for student progress. In an individual
teaching module an and/or graph is devised using a flow pattern through the
subject matter. For example, if the student is presented with a new set of case
facts and is requested to isolate the main legal issues, the student error may
be either a failure to isolate the relevant facts or a misunderstanding of the
legal concept. At present the pedagogical rule-base can deal with the first
type of error and provides a mechanism for the student to be directed to
remedial facilities.
Student Model
The model comprises comparison, storage and assessment
elements. Firstly the student response is compared with the response from the
rule-base. This information then forms part of the information stored in the
user profile. The information may be on both explicit, i.e. use of function
keys, teaching modules completed and specific responses given, or implicit,
such as the degree of congruence between the students response and that of the
rule-base. This information is used to assess the student understanding. There
are two main assessment techniques used within the project. The first involves
a comparison of the user with a perfect model devised by the tutor; deviations
from the perfect model are "bugs" that need to be eliminated (Brown
and Burton, 1978). The second is an attempt to build comprehensive profiles of
student's understanding (Sleeman, 1982).
In the comparison technique the program makes comparisons at
an individual question level. These are then extended to compare a group of
responses. The group of responses and subsequent routing represent a pathway
through the pedagogical rule-base which are then mapped on to the ideal tutor's
path. For example, a tutor may devise a series of questions on the particular
topic. For each question there may be several variants, hard, default easy,
etc., as well as remedials and help facilities. The system will route the user
though the system depending upon responses given so that a student showing an
error on question 1 could be routed to the easier variant of question 2
following a remedial on question 1. The student easily answers the question 2
and so is routed to question 7. The path from question 1 to remedial to
question 2 easy and question 7 so on represents that student's path.
To provide additional assessment information the system
attempts to build a comprehensive model of the individual student. The system
reviews the information retained on the user, essentially the results of
mapping the student's progress on the pedagogical rule-base. Tutors are then encouraged
to develop rules about understanding and misunderstanding based upon the
particular student path, domain knowledge, difficulty levels and student's
assumed knowledge. Therefore a tutor is encouraged to articulate his view of
the understanding of our student struggling with question 1 but answering the
easy variant of question 2 without difficulty.
The results of the assessment techniques are then passed to
a set of rules referred to as means-ends-guidance-rules (MEGR). These rules
make decisions on routing i.e. the content and form of the next teaching
module. The rules are an attempt to embody the tutor's heuristic knowledge of
how to judge the user's progress and how to decide what instructional material
the system should present and how and when it should be presented. A simple
algorithm is then used to take this information and search the pedagogical
rule-base for the next appropriate topic. It is possible to enhance the
algorithm by adding values to the links in the pathways specifying important
and unimportant links. The algorithm could then choose only the important links
for a student who is finding the subject difficult, but making the more able
student consider all the links.
Implementations and Further Work
The project team concentrated upon the development of a
comprehensive architecture and development of a number of usable rule-bases.
Simplified student models were formulated allowing routes through a small
pedagogical rule-base. The rule-bases have already found important teaching uses
with students being encouraged to use the rule-bases as an exploratory learning
environment and as additions to the existing traditional CAI programs.
Originally the rule-bases were developed using IBMPC compatibles but now
development is being concentrated onto work stations (Apollo) that have been
found to have far more useful implementations of PROLOG.
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Published in the Law Technology Journal: Vol 2, No 1