Correcting Object-Related Misconceptions: 
How Should The System Respond? t 
Kathleen F. McCoy 
Department of Computer & Inft~rmation Science 
University of Pennsylvania 
Philadelphia, PA 19104 
Abstract 
Tills paper describes a computational method for correcting 
users' miseonceptioas concerning the objects modelled by a 
compute," s.ystem. The method involves classifying object-related 
misc,mce|,tions according to the knowledge-base feature involved 
in the incorrect information. For each resulting class sub-types are 
identified, :.:cording to the structure of the knowledge base, which 
indicate wh:LI i.formativn may be supporting the misconception 
and therefore what information to include in the response. Such a 
characteriza*i,,n, along with a model of what the user knows, 
enables the syst.cm to reas,m in a domain-independent way about 
how best to c~rrv,'t \[he user. 
1. Introduction 
A meier ar,.a of Al research has been the development of 
"expert sys.tcms" - systems which are able to answer user's 
que:~titms concerning a particular domain. Studies identifying 
desirabl,, iutora,'tive capabilities for such systems \[Pollack et al. 82\] 
have ft,und that. it is not sufficient simply to allow the user to ,~k 
a question and Itavo the system answ~.r it. Users often want to 
question the system's rea-~oning,to make sure certain constraints 
have been taken into consideration, anti so on. Thus we must 
strive to provide expert systems with the ability to interact with 
the user in the kind of cooperative di:LIogues that we see between 
two bullish ctmversational partners. 
Allowing .,uch interactions between the system and a user 
raises difficulties for a Natural-Language system. Since the user is 
interacting with a system a.s s/he would with a human export, s/he 
will nizam likely exp-ct the system to b(have as a human expert. 
Among other things, the n:.er will expect the systenl to be adhering 
to the cooperative principles of conversation \[Grice 7,5, .loshi 821. 
If these principte~ are not followed by the system, the user is bkeiy 
to become confu~ed. 
In this paper I.focus on one a,;pect of the cooperative 
behavior found between two conversat, ional partners: responding to 
recognized differences in the beliefs of the two participants. Often 
when two people interact, ouc reveals-a belief or assumption that 
is incompatible with the b~*liefs held by the other. Failure to 
correct this disparity may not only implicitly confirm the disparate 
bcli,'f, but may even make it impos~;ibie to complete tile ongoing 
task. Imagine the following excilange: 
U. Give ll|e the ItUI.L NO of all Destroyers whose 
MAST_IIEIGIIT is above 190. 
E. All Destrt,yers that I know al)out |lave a 
MAbT_HEIGllT between 85 and 90. Were you 
thinking of the Aircraft-Carriers? 
in this example, the user (U) ha.s apparently ctmfused a Destroyer 
with an Aircraft-Carrier. This confusion has caused her to 
attribute a property value to Destroyers that they do not have. In 
this case a correct a/tswer by the expert (E} of *none" is likely to 
confuse U'. In order to continue the conver.-ation with a minimal 
amount of eoafu.~ion, the user's incorrect belief must first be 
addressed. 
My primary interest is in what an expert system, aspiring to 
human expert performance, should include in such responses. In 
particular, \[ am concerned with system responses to te~'ognized 
disparate beliefs/assumptions about cbflct.~. In the past this 
problem has been h, ft to the tutoring or CAI systems \[Stevens et 
aL 79, Steven~ & ('ollins 80, Brown g:: Burton 78, Sleeman 82\], 
which attetupt to correct student's misconceptions concerning a 
particular domain. For the most part, their approach ha.~ been to 
list a priori :dl mi.-conceptions in a given domain. Tile futility t,f 
this appr,~ach is empha'.,ized in \[gleeman ,~2\]. In contrast,the 
approach taken hvre i~ to ,-la:,~iry. in a dolttnin independent way, 
obj,'ct-related di.-pariti,~s ;u:c,~rding to the l'~n.wh'dge ~:tse (l(.I~) 
feature involved. A nund)er of respon:~e strategies :ire associated 
with each resulting cla,~. Deciding which strategy to use for a 
given miseoncepti,m will be determined by analyzing a user model 
and the discourse situation. 
2. What Goes Into a Correction? 
In this work I am making thc btllowing assunlptions: 
• \]:or th*, purposes .f the initial correct.ion attempt, the 
system is a~umed to have complet,, attd corr~'ct 
knowledge of the domain. Th:tt is. the system will 
initiMly perceive a disparity as a mise.neel,tion on the 
part of the u~er It willthus attempt to bring tile 
user's beli~,fs into line with its own. 
• The system's KB i~tclude-: the following fo:t~trce: an 
object taxonomy, knowledge of object attributes and 
their possible values, and intornlation about I)O.~ible 
relationships between ol)jects. 
• Tile user's KB contains similar features, llowev,'r, 
mneh of the information (content} in the system's !'(B 
may he mb-.~ing from the u~or '~ b~ll \[e.g., the us+,r's l'(\[~ 
may I)e ~parser ot coarser than the system's I(B, c,r 
various attributes (,~f c~:nccpts ma~ t;e missi:~g frets the 
u~,'r's I'(P,}. In additi~m. ~.me inf,~rmation ia the u.,er's 
KB may be wrong, in tiffs work, to say that the user's 
KB is u'rong means that it is i.,:'m.:i.~terJ with the 
,~g.,t,m) KB (e.g., things may be c!a.'~ified differently, 
properties attributed differently, and ~'o on). 
IThiz v, ork is p~rtiMb" supported by the NSF gr~nt #MC~81-07200. 
444 
• Whiw the system t~\]ay n,,t km,w e:;actly what is 
c(m~ained in the user's l,~b', information about the user 
¢-:tt~ b, ~ d(,riv,'d hum two smtrcrs. First, the .~ystem can 
have ,q tm.h,I of a canoni,:at u,mr. (Of course this 
m.,h,\[ m:ty turn o.t t,, differ from any given user's 
model.) ~.~,,,'~,ndl)', it ,'an deriw" knowledge about what 
• the user kn.ws fr,nt the ongoing dise.urse. This l:tt,.r 
type of km)~h'dge eop,~titutes what the' system discer~s 
to bt, tits, mutual h(.liv.:s of the system attd user as 
defin.d iu \[.h,.hi 82\]. "\['he.-,e t~s,~ s,)ur(',~,s .f informati,m 
together r'.n~t it ul c the s)stem's model of the user's 
KB. ThN h,,,:t.I itself may be incompi,,te arid/or 
ine,,rrect witlt respect tt, t\]te system's KB. 
A tt,-'r'~; utterance refh.cls .ither the state of his/her 
KIL -r ~,,m,) re:~s..i~,g s/he ha~ just done t() fill in 
some mi.,sing p:;rt of ~.h:,t K,q, or both. 
(;lynn Ilu,~e a~suinptit,ns, we earl consider what shouhl I)e 
htch~d,:d in a rcsp.nse to an object-r,'htt,'d disparity. If a person 
exhiltit~ wh.at hi-/her conv~ r.-.ationa\] partn~,r perceives as a 
Inisconcellti,,n, I IH' vory least one w~mld expect from that partner 
is to deny t|.. fal.e inf.rmation ~ - for example - 
U. I th.ugh| a whale wa~ a fish. 
g. It's n.t. 
'l'ranscript~ of "u:d ura\[ly ~wcurring" expert systems show that 
experts often include more informati,m in their response than a 
siHIpl,' d,'nial. Tit(. ,'xp~,rt Inn)' provide all alterhative true 
st:~tem~.nt (e.g., "\Vha;,.~ :,re marnnt:d';'). S/he may offer 
ju~.t ifb'at ion andb,r supp.rt for the rt~rr,wtion (e.g., °VChales are 
nt:~mln:~l~ J)r,('au~*" t il%V hen:/the through hmgs and h'ed their young 
with milk.'}. S/he nmy als. refute the faulty reasoning s/he 
tho~tght the ns~r had d.ne tt, ~,rrive at the misconception (e.g., 
"llaving fins and li~ ing in the water is not enough to make a whale 
a fish.'}. This behavior can be characterized a.s confirming the 
corr4.et inh,rmation which mc\]y have h'd the user to the wrong 
conclusion, but indi(:ating w.hy the false conclusion does no! follow 
by bringing in a:lditional, overriding information, s 
The ltroblem f,,r a computer sy,-tem is to decide what kind ~¢ 
ihformu!itm re:C,' I,e supporting a given misconception. What 
things m::y he relevant? What faulty reasoning may have been 
done? 
1 char:~cterize -bject-relatcd misconeeptious in terms of the 
Kll fl,tturt inwJved. Misclgssifying an object, °1 thought a whale 
was a fish', i.wAw.s the SUlwrordinate KB feature. Giving an 
object a pr-p.rty it doe~ not have, "~Vhat is the interest rate on 
this st,,ck?', lovely,.: the, attriltu:e KB feature. This 
chatact¢~ri~:di-n i. helpful in d,-termining, in terms of the structure 
of a K\[L what htform;\]tion may be supporting a particular 
mis,'onr,'ption. Thus, it is helpful in determining what to include 
in the r-..'ponso. 
2Throtlghout this work I am as.-~tmlng that tht miseone*ption if impttrt~nt 
to the tlk~k at hand and should therefore be corrected. The re.q,~ases I am 
intcrest(,d in £eneraVing at( the "full blown" resl, Ot;~es. if • mlsecneeption is 
det~,c\]rd which N n,al ilnl,or\].t.!~t to the task at hand. it is conceivable that 
eith,:r th,. lillSc')ll,'olltiOB tl~ ignored or a It, rlrtlllled I vPr¢~on of o/\]e t;\[' those 
r,,~l,Oll..,.$ |In givPii. 
5'l'h~. :~r~l, ~;b' exhH.ih,.I hy *i~, '..:,r;.,u ..xp,tt~ is v,,cy Anfilar to the "grain 
of truth" rorr~.,'tion f,~.nd ic tu~erit~g si\]uations a~ i,t, I,';fied in tWo.If & 
Mcl),*.ald ~3 I. "FhN .'trat,'gy first id,.nGSes th,, grai~; t,( truth i\[~ a student's 
answ~.r xlld lip-it go~.'< Oil to give tit- eorr¢,t I ;,n,~or. 
In the foil.wing sections l will discuss the two classes of 
object trii~.conreptions just mentioned: superordinate 
misconceptions and attribute misconceptions. Examples of these 
classes :d.ng with correction strategies will be given. In addition, 
indications of how a system might choose a particular strategy will 
be investigated. 
3. Superordinate Misconceptions 
Si.,.e the information ttmt human experts include in their 
respon~l. Co a gal.,r.rdinate misc.ncepti,m seems to hinge on the 
exl..rl's l,ere~.ption <,f ~ tiw misconception occurred or what 
informati(,n may h:tve bt.cn supporting the misconception, I have 
sub-cat,'g,,rized s,qwrordinate misconct, ptions according to the kind 
of support they hate. F.r each type (~ub-category) of 
sup,,r(udinat(, mis,.(,m:,,iJtion, 1 have identified information thal. 
would I." relevant u, the correction. 
In this analysis t,f supf.rordinate misconceptieus, I am 
assulning that the user's knowledge al)out the snperordinate 
concept is correct. The user therefore arrives at the misconception 
because of his/her incomplete understanding of the object. 1 am 
also, for I he moment, ignoring misconceptions that occur because 
two objects have similar names. 
Given these restrictions, 1 found three major correction 
strategies used by human experts. These correspond to three 
reasons why u user might misclassify an object: 
TYPE ONE - Object Shares Many Properties with Posited 
Supe~ordinate - This may cause the user wrongly to c.nclude that 
these shared attributes are inherited from the superordinate. This 
type of misconc,.ption is illustrated by an example involving a 
student and a teacher: 4 
U. \] thoughl a whale w.~s a fish. 
E. No, it's a mammal. Ahhou~h it has fins and li~e~ in the 
water, it's a mamntal s~nce it is warm blooded and 
feeds its young with milk. 
Nc, tice the expert not only specifi~ the correct s0perordinate, but 
also gives additional inf.rn=ati,~n tt, justify the c~)rre, :i,~n. She 
do~.s this by acknowledging that there are some pr6per~ies that 
whales .d/are with fish which m:O' lead the student to conclude th8% 
a whah: is a fish. At the same time she indicates that these 
pc.pectins are not sufficient, h,r inclusion in the cla.~s of fish. The 
whale, in fact, lia.s other properties which define it to be a 
mamm:d. 
Thus, the strategy the expert uses when s/he perceives the 
misc,,J,ct,ption tu be of TYPE ONE may be characterized as: (I) 
l)e,y the posited superordinate and iudk:ate the correct one, (2) 
State at tributes (prol>,'rties) that the obj+ct has in common with 
the posited super<~rdin:tte, (at State defining attributes of the real 
super-r<thmte, thus giviug evidence/justification for the correct 
ch,~+:ifi,'~ti.n. The sy,lem may hdlow this strategy when the user 
mod~l indicates that the itser thinks the p++sited suFerordinate and 
the .hi\]el are simih\]r bee:ruse they share man)' common properties 
{n,,t held by the real SUl~.rordinate). 
TYPE TWO - Objt,ct Shares Properties with Auother Object 
which is a Member of Pos:ited Superordinate - In this c:rse the 
lAhho,Jgh the analysis given hero wa~ d~:rived through ,t~,lying xr~uLI 
human interactions, the exarapDs given ire simply illustrative and have not 
been extrs,,-t~d frorn a real interaetiJn. 
445 
misclassified object and the "other object" are similar because they 
have some other common superordinate. The properties that they 
share arc no_..~t those inherited from the posited superordinate; but 
those inherited from this other common superordhlate. Figure 
3-1 shows a representation of this situation. OBJECT and 
OTIIEIi-LIBJEC'E have many common properties because they 
slt:.t.re a CtHltllton superordinate (COMMON-St !I'E|2OI2DINATE). 
Hence. if the user knows that OTIIEI1-OBJECT is a tnember of 
the POSrFED SUPEROllDINATE, ~/J|e inay wr~mgly conclude 
that OBJECT is also a member of POSITED :SUI>ERORD1NATE. 
Figure 3-1: TYPE TWO Superordinate Misconeeptio. 
For example, imagine the following exchange taking place i't 
a junior high sch.-I bioh,gy ela_,~s (here U is a st,d,.nt, E a 
teacher): 
U. I thought a tomato was a vegetable. 
E. No it's a fruit. You may think it's a vegetable since 
you grow tomatoes in your vegetal',\]e garden :?h)ug 
with the lettuce and green beans. However. it's a fruit 
because it's really the ripened ovary of a seed plant. 
Here it is intportant for the student to understand about plants. 
Thus, the teacher denies the posited superordinate, vegetable, and 
gives the corr,-ct one, fruit. She backs this up by refuting evidence 
that the student may I)e using to support the misconception. In 
this ca...e, the stl.h nt may wrongly believe that tomatoes are 
vegetables becau~.e lh~'y are like some other objects which are 
vegetables, lettuce and green beans, in that all three share the 
common super.rdln:tte: I,l:mts grown in vegetable garden. The 
teacher acknowledges this similarity but refutes the conclusion that 
tomatoes are vegetables by giving the property of tomatoes which 
define them to be fruits. 
The correction strategy used in this case was: (I) Deny the 
chk, csification posited by the user attd indicate the correct 
ela:.,.ifieation. (2) Cite the -tiler memb~.rs of the posited 
sup*,rordinale that the user may be either confusing with the 
object being discu.'.sed (Dr makhtg a b:td an:dogy from. (,3) Give the 
features which disling~Jl.h the correct and p~sited superordinates 
thus justifying the classlfi(':ttion. A system may f.llow lt.;s 
strategy if a structure like that ht figure ;3-1 is f(~und in the user 
model. 
TYPE THREE - Wrong Information - The user either has 
been told wrying informali.n and h.'~ not done any rea;tming to 
justify it, or has tttisclassified the object in response to some 
cotnpl*.x rea.soniug process that the system can't duplicate. In this 
kind of situation, the system, just like a human expert, can only 
c.rtect the wrong information, give the corresponding true 
information, at.t possibly give some defining features 
distinguishing the posited and actual superordiuates. ;f this 
cnrrection does not satisfy the user. it is up to him/her to continue 
the interaction until the underlying misconception is ch.ared up 
(see \[.J'eff~rson 72\]). 
The iuformation included in this kind of response is similar 
to that which McKeown's TEXT system, which answers questions 
about database structure \[McKeown 82 l, would include if the user 
had asked about the diff~.rence between two entities. In her case, 
the information included would depend on how' similar the two 
objects were according to the system KB, not on a model of what 
the user knows or why the user might be asking the question. 5 
U. Is a debenture a secured bond? 
S. No it's an unsecured bond - it has nothing backing it 
should the issuing company default. 
AND 
U. Is the whiskey a missile? 
S. No. it's a submarine which is an underwater vehicle 
(not a destructive device). 
The strategy folh;wed in these ca..,es can be characterized as: 
(1} Deny posited supev,rdinate and give correct one. (2) Give 
additional iuformathm as lleeded. Tills .xtra inform:ttion may 
include defining features of the correct, superordinate or 
information ab.ut the highest superordinate that distinguishes the 
object from the posited superordinate. This strategy may be 
followed by the system when there is insufficient evidence in the 
user Ioodel for concI.Jding that either a TYPE ONE or a TYPE 
TWO mlsconcepti(m has occurred. 
4. Attribute Misconceptions 
A second class of nlisconception occurs when a person 
wrongly attributes a properly to an object. There are at least 
three reasons wl v thi~, kind of ntisc~mception :nay occur. 
TYPE ()NE - Wren!.; Object - The user is either confusing 
the obj,ct being discussed with :Hmther object that has the 
specified property, or s/he is making a b~.t analogy using a similar 
object. In either c.'~e the second object should be included in the 
correfti.:lu SO the problem does not f:,~ulinu¢*. 
\[u the foll,)wing example the ,'xpert assume.,~ the user is 
confusiug the object with asimilar object. 
U. I have my money in a money market certificate so I 
can get to it right away. 
E. But you can't! Your money is tied up in a eertit'icate 
- do you mean a money market fund? 
The strategy followed in this situation can be characterized 
~.s: (l)Deny the wrong information. (2) (;ire the corresp.mling 
correct information. (3) Mention the object of confusion or possible 
analogical reas.ning. This s rategy can I)e followed by a .sy~tenl 
v.'hPit there is at}other obj,'ct which is "cio~e in con, eel = to Ihe 
object being discussed and zhi,:h ha.- the property involved in the 
inisconceptiou. Or course, the perception of h(,w "cl(.:~e in 
cant'clot = two objects are chan'~.es with conte.\t. This may be 
because some attributes are highlighted in SOlile contexts and 
hidden in others. };'or this reason it is anticipated that a el':sette'~s 
5McKeown do~* indl.-:~te that this kind of inf'~rm:,tlon wou\],i improve her 
re-ponsos. Th- niaior Ihru:~t of her work was ,~n t,,:.i ..trlicture; the tie# of i 
user model could hP eL.aily hltegrilil.d into her t'ri, m.w,-,rk. 
446 
measure such as that described in \[Tversky 77\], which takes into 
account the salience of various attributes, will be useful. 
TYPE TWO - Wrong Attribute - The user has confused the 
attribute being discussed with another attribute. In this case the 
correct attribute should be included in the response along with 
additional information concerning the confused attributes (e.g., 
their similarities and differences). In the following example the 
similarity of the two attributes, in this case a common function, is 
mentioned in the response: 
U. Where are the gills on the whale? 
S. Whales don't have gills, they breathe through lungs. 
The strategy followed was: (1) Deny attribute given, (2) Give 
correct attrihutc, (3) Bring in similarities/differences of the 
attributes which may have led to the confusion. A system may 
follow this strategy when a similar attribute can be found. 
There may be some difficulty in distinguishing between a 
TYPE ONE and a TYPE TWO attribute misconception. In some 
situations the user model alone will not be enough to distinguish 
the two cases. The use of past immediate focus (see \[Sidner 83\]) 
looks to be promising in this case. Heuristics are currently being 
worked out for determining the most likely misconception type 
based on what kinds of things {e.g., sets of attributes or objects) 
have been focused on in the recent past. 
TYPE THREE - The user w~s simply given bad information 
or has done some complicated reasoning which can not be 
duplicated by the system. Just as in the TYPE TI~IREE 
superordinate misconception, the system can only respond in a 
limited way. 
U. 1 am not working now and my husband has opened a 
spousal IRA for us. 1 understand that if 1 start 
working again, and want to contribute to my own IRA, 
that we will have to pay a penalty on anything that 
had been in our spousal account. 
E. No - There is no penalty. You can split that spousal 
one any way you wish• You can have 2000 in each. 
Here the strategy is: (1) Deny attribute given, (2) Give correct 
attribute. This strategy can be followed by the system when there 
is not enough evidence in the user model to conclude that either a 
TYPE ONE or a TYPE TWO attribute misconception has 
occurred. 
5. Conclusions 
• In this paper I have argued that any Natural-Language 
system that allows the user to engage in extended dialogues must 
be prepared to handle misconceptions. Through studying various 
transcripts of how people correct misconceptions, I found that they 
not only correct the wrong information, but often include 
additional information to convince the user of the correction 
and/or refute the reasoning that may have led to the 
misconception. This paper describes a framework for allowing a 
computer system to mimic this behavior. 
The approach taken here is first to classify object-related 
misconceptions according to the KB feature involved. For each 
resulting class, sub-types are identified in terms of the structure of 
a KB rather than its content. The sub-types characterize the kind 
of information that may support the misconception. A correction 
strategy is associated with each sub-type that indicates what kind 
of information to include in the response. Finally, algorithms are 
being developed for identifying the type of a particular 
misconception based on a user model and a model of the discourse 
situation. 
6. Acknowledgements 
I would like to thank Julia tlirschberg, Aravind Joshi, 
Martha Poll.~ck, and Bonnie Webber for their many helpful 
comments concerning this work. 
7. References 
\[Brown & Burton 78\] 
Brown, J.S. and Burton, R.R. Diagnostic Models 
for Procedural Bugs in B~ic Mathematical Skills. Cognitive 
Science 2(2):155-192, 1978. 
\[Grice 75\] Grice, H. P. Logic and Conversation. In P. Cole 
and J. L. Morgan (editor), Syntax and Semantics 111: Speech 
Acts, pages 41-58. Academic Press, N.Y., 1975. 
\[Jefferson 721 Jefferson, G. Side Sequences. In David Sudnow 
(editor), Studies in. Social Interaction, . Macmillan, New York, 
1972. 
\[Joshi 82\] Joshi, A. K. Mutual Beliefs in Question-Answer 
Systems. in N. Smith \[editor), Mutual Beliefs, . Academic Press, 
N.Y., 1982. 
\[McKeown 82\] McKeown, K.. Generating Natural Language 
Text in Response to Questions About Database Structure. PhD 
thesis, University of Pennsylvania, May, 1982. 
\[Pollack et al. 82\] 
Pollack, M., Hirschberg, J., & Webber, B. User 
Participation in the Reasoning Processes of Expert Systems. In 
t'¥oceedings of the 198e National Conference on Artificial 
Intelligence. AAAI, Pittsburgh, Pa., August, 1982. 
\[Sidner 83\] Sidner, C. L. Focusing in the Comprehension of 
Definite Anaphora. In Michael Brady and Robert Berwick (editor), 
Computational lt4odcl8 of Discourac, pages 267-330. MIT Press, 
Cambridge, Ma, 1983. 
\[Sleeman 82\] Sleeman, D. Inferring (Mal) Rules From Pupil's 
Protocols. In Proceedings of ECAI-8~, pages 160-164. ECAI-82, 
Orsay, France, 1982. 
\[Stevens & Collins 80\] 
Stevens, A.L. and Collins, A. Multiple Conceptual 
Models of a Complex System. In Richard E. Snow, Pat-Anthony 
Fedcrico and William E. Montague (editor), Aptitude, Learning, 
and Instruction, pages 177-197. Erlbaum, Hillsdale, N.J., 1980. 
\[Stevens et al. 79\] 
Stevens, A., Collins, A. and Goldin, S.E. 
Misconceptions in Student's Understanding. Intl. J. Alan-Machine 
Studic,s 11:145-156, 1979. 
\[Tversky 77\] Tversky, A. Features of Similarity. Psychological 
Review 84:327-352, 1977. 
\[Woolf & McDonald 83J 
Woolf, B. and McDonald, D. Human-Computer 
Discourse in the Design of a PASCAL Tutor. In Ann Janda 
leditor}, CItI'88 Conference Proceedings - Human Factors in 
Computing Systems, pages 230-234. ACM SIGCHI/HFS, Boston, 
Ma., December, 1983. 
447 
