Chatbots and New Audience Opportunities for Museums and Heritage Organisations

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Proceedings of Proceedings of EVA London 2018, UK
http://dx.doi.org/10.14236/ewic/EVA2018.33
164
Chatbots and New Audience Opportunities for
Museums and Heritage Organisations
Stefania Boiano
Ann Borda
Guiliano Gaia
Invisible Studio Ltd.
London
Health and Biomedical Informatics
The University of Melbourne
Invisible Studio Ltd.
London
UK
Australia
UK
stefania@invisiblestudio.net
aborda@unimelb.edu.au
giuliano@invisiblestudio.it
Stefania Rossi
Museo Poldi
Pezzoli
Milan, Italy
s.rossi@poldipezzoli.it
Pietro Cuomo
Art in the City
Milan
Italy
pietro@artinthecitymilano.com
This paper explores how chatbots can offer opportunities for museums and galleries in engaging
their audiences through recent developments, and through a case study approach focusing on the
design and implementation of an audience development pilot in Milan involving four historic house
museums (Case Museo di Milano). The pilot aimed to find new and interesting ways to engage
teenagers in visiting these museums through visualising narrative using a convergence of chatbot
technology and gamification.
Chatbots. Artificial intelligence. Museums. Gamification. Virtual museum guides. Teenagers.
1. AN INTRODUCTION TO CHATBOTS
Chatbots, also known as talkbots or chatterbots or
bots, are computer programs that mimic
conversation using auditory or textual methods.
More specifically the functionality of chatbots use
natural language processing (NLP) that has a
history rooted in artificial intelligence (AI) (Corti et
al. 2015, Dale 2017).
The concept of chatbots can be traced to Alan
Turing’s seminal publication Computing Machinery
and Intelligence that addresses an overarching
question: Can machines think? (Turing 1950,
Dennett 2004, Hill et al., 2015, Copeland et al.
2017). The parameters of this question would
shape the so-called Turing test. In its most simple
form, the test is carried out as an imitation game.
The test has a human interrogator speaking to a
number of computers and humans through an
interface. If the interrogator cannot distinguish
between the computers and the humans then the
Turing Test has been passed (Dennett 2004, Hill et
al. 2015, Warwick 2017, Copeland et al. 2017).
One of the earliest such natural language
applications was a chatbot called ELIZA developed
from 1964 to 1966 at the MIT Computer Science
and Artificial Intelligence Laboratory by computer
scientist Joseph Weizenbaum (Weizenbaum 1966).
ELIZA originally was created to use simple pattern
matching and a template-based response
(prewritten scripts) to emulate the conversational
style of a Rogerian psychotherapist.
Figure 1: Image of ELIZA terminal interface accessed
online (http://www.masswerk.at/elizabot/eliza.html).

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ELIZA generated global fascination in creating a
natural language bot that might pass the Turing
Test. The Loebner Prize launched in 1990 by Hugh
Loebner in conjunction with the Cambridge Center
for Behavioural Studies in Massachusetts (U.S.) is
an example of a competition expressly designed to
implement the Turing Test and builds on both the
progressive interest and advances in AI
developments (Loebner 2008).
Extending the pattern-matching techniques used in
ELIZA and advancing NLP capabilities, American
scientist Richard Wallace developed A.L.I.C.E.
(Artificial Linguistic Internet Computer Entity) in the
late 1990s. A.L.I.C.E., also known as Alicebot, is
acknowledged for its pioneering programming
using Artificial Intelligence Markup Language
(AIML), which is an XML schema for creating
natural language software agents. Wallace
released the first version of AIML in July 2001,
subsequently published the Pandora API on which
A.L.I.C.E is based (Wallace 2008). A.L.I.C.E.
became a three-time Loebner winner in 2000,
2001, and 2004.
Concurrent to A.L.I.C.E. developments,
‘Jabberwacky’ was being conceived by British
programmer, Rollo Carpenter. Jabberwacky was
intended to simulate “natural human chat in an
interesting, entertaining and humorous manner"
(Shah & Warwick 2017). The emergence of the
Internet provided Jabberwacky with a dynamic
database of thousands of online human
interactions from which to process responses.
Jabberwacky under the guise of ‘George’ and
‘Joan’ won the Loebner Prize in 2005 and 2006
respectively.
In 2008, Jabberwacky launched a new iteration
rebranded as ‘Cleverbot’. Like Jabberwacky,
Cleverbot is designed to learn from its
conversations with humans (more than 150 million
to date according to Wikipedia). It draws on past
interactions to determine future questions and
answers (Gehl 2014).
Figure 2: Cleverbot interface from Cleverbot website
(http://www.cleverbot.com/).
In the endeavour to extend question-answering
(QA) capabilities posed in natural language, IBM
Watson was conceived in 2006 as a QA computing
system with the goal of outperforming human
contestants on the U.S. TV game show Jeopardy!
IBM Watson was developed as part of IBM's
DeepQA project (Ferrucci et al. 2010). Watson
became the first computer to defeat contestants on
the TV game show Jeopardy!, notably in a special
match between Watson and Jeopardy! champions,
Ken Jennings and Brad Rutter in 2011 (Best n.d.).
Chatbots in general are reaching milestones in
artificial intelligence capability, as well as their
pervasiveness in consumer facing products and
services. For example in 2014, a chatbot called
Eugene Goostman, portrayed as a 13-year-old
Ukrainian boy, won an AI contest marking the 60th
anniversary of Turing's death. In the Turing Test
2014 organised by the University of Reading, 33%
of the interrogators thought that Goostman was
human (You 2015).
Created from AIML technology by programmer,
Steve Worswick, ‘Mitsuku’ is a web-based chatbot
available on the freeware instant messaging mobile
app Kik Messenger and the Pandorabots website.
It is among a growing number of sophisticated bots
that can answer questions, play games, and is
capable of basic reasoning in QA (Corti et al. 2015,
Hill et al., 2015). Mitsuku is a three-time winner of
the Loebner prize in 2013, 2016, and recently in
2017.
Figure 3: Mitsuku avatar from Pandora website
(https://pandorabots.com/mitsuku/).

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Generally, the term ‘chatbot’ has referred to a
software application that engages in a dialogue
with a human using natural language. Most early
advances have been associated with written
language, but with advances in speech recognition,
there is a narrowing of these associations. An early
example is Dragon a simple speech understanding
system developed in 1975 by Dr James Baker from
Carnegie Mellon University. Other advances in the
speech recognition sector have been made
possible by VoiceXML, which has been published
in a series of standards since the first release in the
year 2000. The World Wide Web Consortium
(W3C) has recently established in 2017 a W3C
Community Group on Voice Interaction
(http://www.w3.org/community/voiceinteraction),
which aims to explore the future of system-initiated
directed dialogs of VoiceXML applications, such as
voice assistants.
In just a couple of years, there has been an
exponential rise of voice assistants such as Apple
Siri launched in 2010, Google Now in 2012,
Amazon’s Alexa and Microsoft’s Cortana in 2015,
and Google Assistant in 2016. Using NLP and
Internet of Things (IoT) platforms, these assistants
connect to web services to answer questions and
respond to user requests (Hoy 2018). Social media
platforms are similarly incorporating chatbot
functionality. Facebook opened up its Messenger
platform and API to developers in 2017, providing a
means to build a simple chatbot on Facebook.
Twitter opened its direct messaging channel to
chatbots in the previous year that began a hype
cycle in twitterbots (Alarifi et al. 2016), and other
messenger services are coming on board with
open APIs (Hoy 2018, Mool 2018).
2. MUSEUMS AND CHATBOTS
Museums have been piloting related technologies
of artificial intelligence and natural language
processing as demonstrators for more than a
decade (Boiano et al. 2003, Bickmore 2013,
Swartout 2010, Borda & Bowen 2017, Bordoni et
al. 2016). Emerging free chatbot-creating platforms,
e.g., Chatfuel, Chatterbot Eliza, among others, and
the availability of open APIs, for instance, can offer
both large and smaller museums the opportunity of
experimenting with chatbots with relatively low
effort while keeping costs and staff resources at a
low level (Bordoni et al. 2016, Boiano et al. 2003)
and potentially less impact on existing
infrastructure (Dale 2016).
The exponential growth in the use of chatbots by
marketers and online businesses in enhancing
customer experiences, often as messaging
applications that can personalise the interaction
(e.g., recommender systems), is providing further
comparable opportunities in the cultural sector
(Swartout 2010). There are in fact a growing
number of Museums currently going down this
route, and using bots as part of their audience
engagement programming.
For instance, the Heinz Nixdorf MuseumsForum in
Paderborn Germany (http://www.hnf.de) has an
early experience of using an avatar bot introduced
as MAX. Developed in 2004, MAX is a
conversational agent that directly engages with
visitors through a screen as a virtual museum guide
(Kopp et al. 2005).
Figure 4: Avatar Max, Heinz Nixdorf MuseumsForum
website, 2018 (https://tinyurl.com/yd3qd42q).
The Cooper-Hewitt Smithsonian Design Museum in
New York City has been a pioneer in chatbot
technologies with the creation of the Object Phone
in 2013 in which a visitor can text or call to ask for
more information about a museum object in the
collection. In 2016, Object Phone became a
subscription service so that a visitor can receive a
daily update (http://objectphone.cooperhewitt.org).
In the words of Micah Walter, Director of Digital &
Emerging Media:
I think institutions like museums have a great
opportunity in the chatbot space. If anything it
represents a new way to broaden our reach and
connect with people on the platforms they are
already using. What’s more interesting to me is
that chatbots themselves represent a way to
interact with people that is by its very nature, bi-
directional (Walter 2016).
The San Francisco Museum of Modern Art
(SFMOMA) has developed Send Me SFMOMA
(http://www.sfmoma.org/send-me-sfmoma), an
SMS service that provides an approachable
method of sharing the breadth of SFMOMA’s
collection with the public of which only 5% is seen
in the Galleries at any one time (Mollica 2017).
Using the service and texting the words “send me”
followed by a keyword, a colour, or an emoji, a
visitor will receive a related artwork image and
caption via text message.

Chatbots and New Audience Opportunities for Museums and Heritage Organisations
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Figure 5: Image from Send Me SFMOMA blog. SFMOM,
2017 (https://www.sfmoma.org/read/send-me-sfmoma/).
In March 2017, the Anne Frank House museum
(http://www.annefrank.org) in Amsterdam launched
a Facebook Messenger chatbot that allows users to
discover the personal history of Anne Frank and
practical visitor information. Not simply a collections
discovery bot, this application offers various
conversation paths, allowing users to follow
different paths in the Anne Frank story with concise
information and links to additional content, for
example, excerpts from her diary to the context of
World War II at the time.
In 2017 the Museum of Australian Democracy in
Canberra (http://www.moadoph.gov.au) marked the
50th anniversary of a landmark 1967 referendum in
which Australians voted overwhelmingly to amend
the Constitution to include Aboriginal people in the
census and to allow the Commonwealth
government to create laws for them. The Museum
launched a referendum chatbot that allows visitors
to learn about the historic and current impacts of
this vote through chatting with it on Facebook
Messenger. Directed towards children and
accessible to adults, it uses simple gamification
and responses, including emojis.
It acts like history in your pocket and is helping
MoAD spark a conversation about the
significance of the 1967 referendum. We’re
hoping it will be an effective way for people to
get the facts, hear Indigenous perspectives on
the referendum and reflect on its continuing
relevance today. — Marni Pilgrim, Digital
Engagement Manager, MOAD (O’Mallon 2017).
Not unlike the quick adoption of Facebook
Messenger among some of the Museum examples,
there is a rising trend in experimenting with Twitter
bots, such as the Museumbot (@museumbot) that
pulls open access images from a number of
archives such as the Metropolitan Museum of Art.
Other museum archive bots are steadily growing on
Twitter, including the Los Angeles County Museum
of Art LACMA bot (@LACMAbot), New York Public
Library NYPL postcards bot (@NYPLpostcards),
and the Museum of Modern Art MoMaR bot
(@MoMARobot).
Figure 6: Image from NYPL Postcard Bot on Twitter
(@NYPLpostcards) (retrieved 13 March 2018).
3. HOUSE MUSEUMS OF MILAN: CASE STUDY
The House Museums of Milan (Case Museo di
Milano) is a group of 4 historical homes in Milan:
Poldi Pezzoli Museum, Bagatti Valsecchi Museum,
Necchi Campiglio Villa and Boschi Di Stefano
House Museum (https://casemuseo.it). The House
Museums launched a strategic initiative in 2016

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that aimed to motivate people to visit the four
museums.
A London-based cultural innovation company,
InvisibleStudio (http://www.invisiblestudio.it), was
approached to introduce gamification into the
engagement process, specifically to attract teenage
audiences. The development of a chatbot-based
game aimed at teenagers was a direction chosen
by the House Museums curators and the
InvisibleStudio team due to several factors.
Teenagers are a notoriously difficult public to
engage in museums and often far more interested
in social interactions between themselves than in
cultural content – and this was proven to be even
more challenging if that content is associated with a
19th century historic house. In general, teenagers
visiting museums are identified with high levels of
distraction and highly adapted to the use of social
media (Kelly & Russo 2008, Endo 2016, Fors
2016). Recent research in the U.S., for example,
shows that online chatting is one of the highest
forms of social interaction of teenagers (Statista
2017).
The InvisibleStudio team had already experimented
with earlier chatbot technology in 2002, while
working at the Museum of Science and Technology
“Leonardo da Vinci” in Milan (Boiano et al. 2016,
Boiano & Gaiia 2017a). The process provided
important lessons in the development of a chatbot
application. This early chatbot was developed to
mimic a Leonardo da Vinci character with whom
the user would interact. This set high expectations
for the user experience, and led to frustrations
when the chatbot was not able to understand the
user beyond simple introductory chat (Hill et al.
2015). Consequently, user issues occurred quite
soon in the conversation (Boiano & Gaia 2017a).
With these lessons in hand, InvisibleStudio
changed their approach when creating a chatbot for
the House Museums project. The chatbot would
only be used as a tool or “virtual companion” to
help younger audiences solve a game set in the
real physical environment of the museum. By
engaging teenagers through gamification, it could
shift the user’s focus from the conversation with the
chatbot to the actual exploration of the Museum
collections (Boiano 2016, Cawston 2017).
The chatbot game for the House Museums was
developed using Facebook Messenger, and
intended mainly for young users and teenagers to
engage them in exploring the four homes. Aspects
of visualisation and storytelling were utilised with
the chatbot platform to simulate a teenaged girl
asking visitors to help her in defeating a mysterious
Renaissance magician (based on a real historical
figure) by solving mysteries in the galleries.
Exploration is encouraged by users in looking for
hidden clues that lead to a final discovery, which
provides a further incentive to engage with the
application.
Figure 7: Teenagers using the chatbot game in Milan.
Source: InvisibleStudio, 2017.
Before operational release of the chatbot, it was
piloted with teenagers aged 16-18. The pilot was
conducted by InvisibleStudio with 80 teenaged
students from local high schools in Milan. The pilot
resulted in the following findings:
 90% of students managed to complete the
game
 30% had connection problems
 34% were worried for their data traffic
 88% found the length of the game was right
 72% evaluated the game as highly
entertaining
 66% found it a useful learning tool,
especially if it was used with another
student or in a small group.
These results offered some clear directions for the
final development stages. Especially interesting for
the developers was the fact that students liked
using the chatbot in small groups, rather than on
their own, because the game triggered
collaboration within the team and created a friendly
competitive environment with other teams. (Boiano
& Gaia 2017b)
At the same time, the pace and quality of the
conversation emerged as a critical aspect for future
consideration, requiring a larger effort from the
development team to create engaging and realistic
non-linear narratives (Hill et al. 2015). For instance,
key features which required considerable tweaking
before publishing the application included: making
conversations more realistic by studying real chats
between users and guides, referencing objects
which the user can actually see “here and now” in
the galleries, and finding the perfect length for the
game (Boiano & Gaia 2017a, 2017b).

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Another key challenge was the necessity of
keeping open a continuous online connection
between users and the chatbot. This can prove
difficult in historical house museums, where the
older infrastructure is comprised of complex layouts
and thick walls, which can prevent wireless
connectivity or an even distribution of Wi-Fi
connections.
Other challenges still need to be addressed. These
are mainly related to the Facebook Messenger
platform itself, and changing preferences by
teenagers and audiences for different messaging
services and platforms which may not have open
APIs for chatbot development at this stage, such as
WhatsApp (Mool 2018). With the possibility of more
open APIs, museum chatbots could be developed
for a potentially larger audience (e.g., teenagers),
and potentially a larger uptake, which does not
depend on a subscription.
Despite the current limitations, the advanced
chatbot application in the House Museums has
become successfully operational through design
thinking, design iteration, and importantly,
teenagers’ participation in the development
process.
4. CONCLUSION
There is clear evidence of the potential of cultural
heritage organisations to play a significant role in
the development of chatbots. The sector already
has a strong provenance with decades long
experience in piloting emerging and embedded
technologies relevant to smart city services, and in
its understanding of user-centric interaction
characterised by context-awareness,
personalisation, and adaptation.
What emerged from this project is that the
convergence of chatbot and gamification has
proved to be a powerful tool in involving younger,
digital savvy generations in novel and interesting
ways to them (Cawston et al. 2017, Kelly & Russo
2008, Endo 2016, Fors 2016).
Our findings particularly suggest that users enjoy
interacting with a chatbot in a game context, and
that this engagement can provide a smarter way of
leading younger audiences to interact with objects
and historic environments with greater attention.
However with all the successes of this chatbot
launch, there also remain challenges that need
further consideration beyond the scope of this
paper. As mentioned above, the availability of a
wider range of chatbot platforms is one such
challenge. A more involved issue is the pace and
quality of the bot conversation which emerged as a
critical aspect of this project. The present chatbot
application required a bigger effort from the
developing team to create engaging and realistic
non-linear narrative lines, and this will be part of a
continuing iteration in future developments.
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