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Chatbots in learning & development What we can learn from chatbots?

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Page 1: in learning & development Chatbots · Deep(er) conversation Modeling a conversation tree in Dialogflow requires stitching together multiple intents via input and output context

Chatbots in learning & development

What we can learn from chatbots?

Page 2: in learning & development Chatbots · Deep(er) conversation Modeling a conversation tree in Dialogflow requires stitching together multiple intents via input and output context
Page 3: in learning & development Chatbots · Deep(er) conversation Modeling a conversation tree in Dialogflow requires stitching together multiple intents via input and output context
Page 4: in learning & development Chatbots · Deep(er) conversation Modeling a conversation tree in Dialogflow requires stitching together multiple intents via input and output context

Outline

Chatbots?

What are they?

How do they work?

Are they really smart?

Use cases

What are successful

business cases for bots?

What are the factors for

success?

What can I learn from chatbots?

Page 5: in learning & development Chatbots · Deep(er) conversation Modeling a conversation tree in Dialogflow requires stitching together multiple intents via input and output context

Chatbots

Page 6: in learning & development Chatbots · Deep(er) conversation Modeling a conversation tree in Dialogflow requires stitching together multiple intents via input and output context
Page 7: in learning & development Chatbots · Deep(er) conversation Modeling a conversation tree in Dialogflow requires stitching together multiple intents via input and output context
Page 9: in learning & development Chatbots · Deep(er) conversation Modeling a conversation tree in Dialogflow requires stitching together multiple intents via input and output context
Page 10: in learning & development Chatbots · Deep(er) conversation Modeling a conversation tree in Dialogflow requires stitching together multiple intents via input and output context

The Virtual Assistant race

Page 11: in learning & development Chatbots · Deep(er) conversation Modeling a conversation tree in Dialogflow requires stitching together multiple intents via input and output context
Page 12: in learning & development Chatbots · Deep(er) conversation Modeling a conversation tree in Dialogflow requires stitching together multiple intents via input and output context
Page 13: in learning & development Chatbots · Deep(er) conversation Modeling a conversation tree in Dialogflow requires stitching together multiple intents via input and output context

Arnon grunberg

Page 14: in learning & development Chatbots · Deep(er) conversation Modeling a conversation tree in Dialogflow requires stitching together multiple intents via input and output context
Page 15: in learning & development Chatbots · Deep(er) conversation Modeling a conversation tree in Dialogflow requires stitching together multiple intents via input and output context

How they work…or don’t

Page 16: in learning & development Chatbots · Deep(er) conversation Modeling a conversation tree in Dialogflow requires stitching together multiple intents via input and output context

Dialog Manager NLP engine(Intents + entities)Messaging

Responses

Actions Memory

System integration

Structured knowledge

Dialog design / copy Microsoft CortanaIBM WatsonAmazon Lex

Page 17: in learning & development Chatbots · Deep(er) conversation Modeling a conversation tree in Dialogflow requires stitching together multiple intents via input and output context

Who is the author of this article?

Page 18: in learning & development Chatbots · Deep(er) conversation Modeling a conversation tree in Dialogflow requires stitching together multiple intents via input and output context

Word2Vec

Page 19: in learning & development Chatbots · Deep(er) conversation Modeling a conversation tree in Dialogflow requires stitching together multiple intents via input and output context
Page 20: in learning & development Chatbots · Deep(er) conversation Modeling a conversation tree in Dialogflow requires stitching together multiple intents via input and output context
Page 21: in learning & development Chatbots · Deep(er) conversation Modeling a conversation tree in Dialogflow requires stitching together multiple intents via input and output context
Page 22: in learning & development Chatbots · Deep(er) conversation Modeling a conversation tree in Dialogflow requires stitching together multiple intents via input and output context

Shallow conversations

In Dialogflow state is managed via context

By default this state live 5 turns but it’s advised to

set the lifetime to 1 turn

This means the agent has little knowledge of

previous conversation

Leading to shallow conversations and user

frustration

Page 23: in learning & development Chatbots · Deep(er) conversation Modeling a conversation tree in Dialogflow requires stitching together multiple intents via input and output context

Deep(er) conversation

Modeling a conversation tree in Dialogflow

requires stitching together multiple intents via

input and output context.

Most companies end up building a bespoke dialog manager to manage Dialogflow context.

Page 24: in learning & development Chatbots · Deep(er) conversation Modeling a conversation tree in Dialogflow requires stitching together multiple intents via input and output context

Going off-topic

Users don’t follow the happy path you designed

for them and often go off topic.

Dealing with these dynamic conversations

requires you to keep track of the conversation

state

Goal

Off-topic

Page 25: in learning & development Chatbots · Deep(er) conversation Modeling a conversation tree in Dialogflow requires stitching together multiple intents via input and output context

Scope

Even with a limited scope

you will have to account

for a lot more than you

think

What peoplesay

What peopleexpect

What youmodeled

Sorry, I don’t understand

Page 26: in learning & development Chatbots · Deep(er) conversation Modeling a conversation tree in Dialogflow requires stitching together multiple intents via input and output context

Use cases

Page 27: in learning & development Chatbots · Deep(er) conversation Modeling a conversation tree in Dialogflow requires stitching together multiple intents via input and output context

Success of chat

✉ Email

hours/days

background

ceremony

spam

📞 Phone

instant*

disruptive

chatty

$5-15/call

💬 Chat

minutes

on notification

to the point

there

Page 28: in learning & development Chatbots · Deep(er) conversation Modeling a conversation tree in Dialogflow requires stitching together multiple intents via input and output context

Use cases

Customer Support

24/7/365

60% simple Questions

Actually solve issue

@ scale

Marketing & Sales

Where customers are

Permissive marketing

“In the moment”

Support is sales

Assistants

Take the bot out of the

human

onboarding

Proactive

Page 29: in learning & development Chatbots · Deep(er) conversation Modeling a conversation tree in Dialogflow requires stitching together multiple intents via input and output context

Billie van bol.com doet 45% klantcontact

Billie was in zijn eentje goed voor het werk van

225 FTE op de klantenservice

https://www.emerce.nl/nieuws/billie-van-bol-com-doet-45-klantcontact

Page 30: in learning & development Chatbots · Deep(er) conversation Modeling a conversation tree in Dialogflow requires stitching together multiple intents via input and output context
Page 31: in learning & development Chatbots · Deep(er) conversation Modeling a conversation tree in Dialogflow requires stitching together multiple intents via input and output context

“ Chatbots Will Make The Future of Customer Care ‘Extremely Proactive’

https://aibusiness.com/chatbots-klm-interview/

Page 32: in learning & development Chatbots · Deep(er) conversation Modeling a conversation tree in Dialogflow requires stitching together multiple intents via input and output context

Case: Horecava

Page 33: in learning & development Chatbots · Deep(er) conversation Modeling a conversation tree in Dialogflow requires stitching together multiple intents via input and output context

Roos | virtual event hostess

“Roos acts as a virtual hostess to Horecava: providing hospitality and driving engagement through a personal conversational interface”

Goals:

- Provide a more convenient registration experience (especially on mobile)

- Give events a more personal (inter)face- Help with event onboarding: increase

engagement and reduce no shows- Answer common questions 24x7 quickly

Page 34: in learning & development Chatbots · Deep(er) conversation Modeling a conversation tree in Dialogflow requires stitching together multiple intents via input and output context
Page 35: in learning & development Chatbots · Deep(er) conversation Modeling a conversation tree in Dialogflow requires stitching together multiple intents via input and output context

Hi, NielsKan ik je helpen?

Progressive Web App

https://horecava.app

Page 36: in learning & development Chatbots · Deep(er) conversation Modeling a conversation tree in Dialogflow requires stitching together multiple intents via input and output context

Questions Planning NewsRegistration

Page 37: in learning & development Chatbots · Deep(er) conversation Modeling a conversation tree in Dialogflow requires stitching together multiple intents via input and output context

Tapas

Timed, bite-size-content

Event

Practical tips, ticket

Inspiration, planning

Campaign Pre-registration Registration Onboarding

5% Open rate

50% Engagement

Page 38: in learning & development Chatbots · Deep(er) conversation Modeling a conversation tree in Dialogflow requires stitching together multiple intents via input and output context

21.308 Visitors

16.932 Active users

2.095.984 Messages

make it easy for customers to get their problem solved painlessly and quickly

Page 39: in learning & development Chatbots · Deep(er) conversation Modeling a conversation tree in Dialogflow requires stitching together multiple intents via input and output context

L&D cases?

Page 40: in learning & development Chatbots · Deep(er) conversation Modeling a conversation tree in Dialogflow requires stitching together multiple intents via input and output context

1:1 tutoring @ Scale

Holy grail for learning; providing a 1:1 tutoring

personalized training @ scale

✅ Chatbots are patient / repeat ∞

✅ No shame, pressure

❌ High investment

❌ Deep conversations

Page 41: in learning & development Chatbots · Deep(er) conversation Modeling a conversation tree in Dialogflow requires stitching together multiple intents via input and output context

Learning on the job

Integrate bots into the daily work, good

examples is a customer service agent that gets

suggestions from a chatbot on what to reply

✅ Suggestion instead of auto-reply

✅ In the flow of work, efficiency

✅ Learning works both ways

❌ Don’t get in the way (Microsoft Clippy)

Page 42: in learning & development Chatbots · Deep(er) conversation Modeling a conversation tree in Dialogflow requires stitching together multiple intents via input and output context

Teaching Assistant

Everything around learning: Course

information, enrollment, advice on courses,

reminders, notification

✅ Chatbots are good at keeping schedule

✅ Personal enrollment, human augmentation

✅ Notifications, reminders

❌ Too strict / Ethics

Page 43: in learning & development Chatbots · Deep(er) conversation Modeling a conversation tree in Dialogflow requires stitching together multiple intents via input and output context

Habit forming

Chatbot to provide coaching / repeat material

in short-form (Tapas) The best way to learn

something is to repeat it for at least a month

(Spaced interval learning)

✅ Keep people motivated

✅ Micro learning

❌ Spam

Page 44: in learning & development Chatbots · Deep(er) conversation Modeling a conversation tree in Dialogflow requires stitching together multiple intents via input and output context

Role playing

Best way to learn is by actually “doing it” playing

out a scenario

✅ Playful way to apply learnings

✅ Chatbots are patient / repeat ∞

❌ Deep conversations

Duolingo chatbots

Page 45: in learning & development Chatbots · Deep(er) conversation Modeling a conversation tree in Dialogflow requires stitching together multiple intents via input and output context

Arnon grunberg

Page 46: in learning & development Chatbots · Deep(er) conversation Modeling a conversation tree in Dialogflow requires stitching together multiple intents via input and output context

How can I use a chatbot in my L&D practice?

Page 47: in learning & development Chatbots · Deep(er) conversation Modeling a conversation tree in Dialogflow requires stitching together multiple intents via input and output context

https://anne.bsqd.me

[email protected]