1.
AI Tutors
Why we need them
and how they will work.
Anshul Bhagi
MIT ‘11 / ‘12
HBS ‘17
1

2.
4 takeaways for today
1) 1-on-1 tutors are great but don’t scale
2) Scalable + effecHve AI tutors are within reach
3) Basic approach to making machines ‘understand’:
vector representaHon of words / knowledge
4) Go-to-market strategy for AI tutors: narrow
domains, retrieval based, human-first
2 AI Tutors: tech-talk @ HBS. Anshul Bhagi

3.
Why AI for Ed?
1 on 1 tutor for everybody, any7me, anywhere
3
Source: Bloom (1984) research on impact of 1-on-1 tutoring
AI Tutors: tech-talk @ HBS. Anshul Bhagi

4.
Why now: AI tutors are within reach
Jill Watson
+
4 AI Tutors: tech-talk @ HBS. Anshul Bhagi

5.
What AI Tutors can do
Answer ques7ons based on past Q&A
“Read”, “learn”, and share knowledge
Generate ques7ons for text they see
Have long conversa7ons, be
personal assistants to students
Informa7on Retrieval
Neural Ques7on Genera7on
Machine Comprehension
Intent-classifica7on,
Reinforcement Learning,
Long-term memory, etc.
Retrieval vs. GeneraHve
Closed-Domain vs. Open-Domain
5 AI Tutors: tech-talk @ HBS. Anshul Bhagi

6.
old way:
based on text similarity
new way:
based on meanings
Informa7on Retrieval
finding answers to quesHons
6 AI Tutors: tech-talk @ HBS. Anshul Bhagi

7.
“Seman7c Space” and word vectors
• Turn words into mulH-dimensional vectors
• SemanHcally similar
words closer together
7 AI Tutors: tech-talk @ HBS. Anshul Bhagi

8.
Emoji vectors
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Source: Dango messaging app. Learned representaHons of Emojis in 2D space.
AI Tutors: tech-talk @ HBS. Anshul Bhagi

9.
From word vectors to sentence vectors
1) Use word counts (TF-IDF)
2) Take average of word vectors (pre-trained)
3) Create sentence vectors using neural
predicHon approach (Doc2Vec)
9 AI Tutors: tech-talk @ HBS. Anshul Bhagi

10.
Ques7ons as vectors
finding closest past quesHon -> answer
10 AI Tutors: tech-talk @ HBS. Anshul Bhagi

11.
SochoBot
Live Demo
11 AI Tutors: tech-talk @ HBS. Anshul Bhagi
hips://www.youtube.com/watch?v=azzLNPU17Go