Maria Knobelsdorf, University of Dortmund, Germany - Didaktik der ...

Sherlock Holmes). Students sort themselves in order of
intelligence, which can result in some illuminating and
amusing conversations (“Is a washing machine more or
less intelligent than a flea?”).
Once ordered, students indicate if they can do certain
things (e.g., be creative). This shows that certain characteristics
are more common at the intelligent end of
the line, triggering discussions on those features that
are necessary/sufficient to call an agent intelligent.
3. Chatbot comparison: We explore the idea of chatbots,
and what kinds of conversations might expose
the fact that they are programs rather than people.
Students write down three questions to ask a chatbot,
along with a sentence describing what the question is
testing (e.g. “Does it have a memory?”). The questions
are put to two chatbots and the answers recorded;
the chatbots are then compared in discussion.
4. TURI: SOFTWARE OVERVIEW
The Turi software provides an easy way for multiple instances
of the open source chatbot Program O [2] to be constructed
and allocated to individual students, with a simple
tabular interface for students to build their chatbot by
adding and editing AIML statements. It also enables students
to chat with their own bot and those created
Turi is web-based software, written using MySQL and
PHP within the CodeIgniter framework. Client side elements
of the software are written in HTML and JavaScript,
and have been tested on all common web browsers and platforms.
The server we currently use is a Virtual Private
Server with 1GB of RAM and a dual-core processor. We
have run side-by-side workshops with 30 children in each,
meaning that 60 separate chatbots were being edited at the
same time with no appreciable load visible in the server logs.
The student interface is deliberately simple. The fundamental
unit of the chatbot is the input-output pair, which
defines an input to the chatbot and the output the chatbot
will give upon receiving that specific input. There are
three views for the student chatbot creator: New Phrase,
Try Chatbot, and Chatbot Editor.
New Phrase permits chatbot input-output pairs to be added
or edited. Students type in input and the desired response –
examples of the four main kinds of call-response supported
are (see Table 1): simple call-response, *-response, *star
and call-random. This permits users to copy-andpaste
AIML templates for the common functions.
Try Chatbot allows the user to test out phrases that have
been entered, or pre-loaded. This provides a text box to
type a conversation into, and responses are shown above the
box scrolling upwards as the conversation continues.
Chatbot Editor is the view in which users get to see all of
the input-output sentence pairs entered thus far, with the
option to delete or to edit each one.
All three views are minimalist in layout, with clear links
and simple instructions. This simplicity is the result of
user testing early in the design phase, when we found that
younger users were happiest with clear tabular layout.
Administrative functions are hidden from the student view
completely. Student chatbots are created by setting a passphrase
for the session; students start Turi in a browser by
typing the day’s passphrase into the login screen. Each time
the passphrase is typed, Turi creates a new chatbot with a
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I/O pair Description
call-response Exact match of call generates response
*-response Wildcard matches *, for a given response.
*-star is replaced by text which
matched * in the input.
call-random Given the sentence which matches the input
call, one of a random list is selected as
response.
combination It is possible to combine these.
Table 1: The types of input-output pair covered by
Turi in our sessions
unique ID, and the student is ready to start. The instructor
has no need to determine in advance exactly how many chatbots
are required, and there is no need to give each student
a login. At the end of the session the instructor can either
drop the Turi instances for that class or can allow students
to create a name and a password for their chatbot. In the
second case students are able to continue working at home,
or over multiple sessions.
Turi has been written in English but this is easy to amend.
Chatbots created by students can be in any language with a
European alphabet – we have used chatbots successfully in
classes where some students have been creating call-response
pairs in French, German and Welsh.
5. EVALUATION, EXPERIENCE, IMPROVE-
MENTS
We have run the workshop with several hundred pupils
in the age range 12-19 (the number grows monthly). We
re-iterate that the aim is to introduce them to ideas about
AI and conversation, rather than to enable them to create
conversational agents.
The largest chatbot created during early engagements had
27 input-output pairs, and the average size of the created
chatbots had just under 10. All response types were evident
with the majority being simple. Later, we pre-seeded each
chatbot with a set of phrases showing the kinds of things it
is possible to do. This encouraged much greater creativity.
Feedback from teachers has been very positive as the material
is seen as novel and stimulating. We have found the
module to be one of the more popular workshops we run,
and disproportionately popular with girls. We have also deployed
the material at open access events for the general
public and provoked lengthy, involved interactions.
We continue to develop: in particular an inbuilt spell
checker has been found to be essential, and we will consider
employing a speech synthesiser.
6. REFERENCES
[1] H. Dee. Technocamps AI module.
http://users.aber.ac.uk/hmd1/ai.zip, 2012.
[2] E. Perreau. Program-O, 2010.
http://sourceforge.net/projects/program-o/.
[3] Rollo Carpenter. Cleverbot, 2012.
http://cleverbot.com/.
[4] Rollo Carpenter. Jabberwacky, 2012.
http://jabberwacky.com/.
[5] Technocamps. Creating the Next Generation of
Technologists. University of Swansea, 2012.
http://www.technocamps.com/.