1
00:00:00,280 --> 00:00:02,520
Speaker 1: This week on the Business of Tech powered by two

2
00:00:02,560 --> 00:00:06,600
Speaker 1: Degrees Business. Our education tech companies are going gangbusters, but

3
00:00:06,680 --> 00:00:10,520
Speaker 1: as artificial intelligence a massive disrupted coming for them, or

4
00:00:10,640 --> 00:00:14,120
Speaker 1: the technology that will make them even more successful and valuable.

5
00:00:14,440 --> 00:00:17,720
Speaker 2: We talked to CAMI co founder Henji Wang following a

6
00:00:17,720 --> 00:00:20,600
Speaker 2: major new investment in the company that values it at

7
00:00:20,680 --> 00:00:25,360
Speaker 2: three hundred million dollars, flush with cash and already forty

8
00:00:25,440 --> 00:00:29,400
Speaker 2: million students and teachers using its online tools. How will

9
00:00:29,440 --> 00:00:34,600
Speaker 2: AI factor into the future of education and Cami's business.

10
00:00:35,200 --> 00:00:38,720
Speaker 3: We are certainly at the tip of the iceberg, if

11
00:00:38,760 --> 00:00:42,839
Speaker 3: you will around what's possible in this technology. There's a

12
00:00:42,880 --> 00:00:45,440
Speaker 3: lot more that you can do to purposefully take AI,

13
00:00:45,600 --> 00:00:50,839
Speaker 3: take genitor of AI, make curriculum more accommodating to the students' needs.

14
00:00:51,360 --> 00:00:52,920
Speaker 3: I think that's the dream, is to be able to

15
00:00:52,960 --> 00:00:56,120
Speaker 3: personalize it for every single student. But doing our way

16
00:00:56,160 --> 00:00:57,000
Speaker 3: that is purposeful.

17
00:00:57,240 --> 00:00:59,319
Speaker 1: Now. This is the first in a series we are

18
00:00:59,320 --> 00:01:02,600
Speaker 1: doing on how AI is impacting various industries and the

19
00:01:02,600 --> 00:01:07,360
Speaker 1: opportunities for Kiwi businesses and entrepreneurs that AI represents. We'll

20
00:01:07,360 --> 00:01:11,520
Speaker 1: be looking at healthcare and manufacturing in upcoming episodes. But

21
00:01:11,920 --> 00:01:15,040
Speaker 1: given Ben's a former school teacher. We wanted to start

22
00:01:15,120 --> 00:01:16,319
Speaker 1: somewhere close to his heart.

23
00:01:16,600 --> 00:01:16,839
Speaker 4: Yeah.

24
00:01:16,880 --> 00:01:19,800
Speaker 2: I love education and I really enjoy talking to Henji

25
00:01:19,800 --> 00:01:22,560
Speaker 2: from CAMI about where it's going, and so my interview

26
00:01:22,600 --> 00:01:24,360
Speaker 2: with him will be coming up shortly.

27
00:01:24,560 --> 00:01:27,319
Speaker 1: First, let's take a look at the big dump of

28
00:01:27,440 --> 00:01:30,840
Speaker 1: product related news out of Apple this week, with the

29
00:01:30,920 --> 00:01:36,000
Speaker 1: launch of the iPhone sixteen lineup, a few interesting health

30
00:01:36,160 --> 00:01:39,280
Speaker 1: tech editions, and the debut, of course, of Apple Intelligence.

31
00:01:39,280 --> 00:01:41,759
Speaker 1: What did you make of it, Ben, Tuesday's big announcement.

32
00:01:42,520 --> 00:01:46,040
Speaker 2: Yeah, I think it was the definition of iterative in

33
00:01:46,080 --> 00:01:49,400
Speaker 2: many ways, but I think some of those iterations were

34
00:01:49,640 --> 00:01:52,680
Speaker 2: pretty cool. I know the online reaction that I saw

35
00:01:52,800 --> 00:01:56,040
Speaker 2: was pretty underwhelmed in general, but I thought there was

36
00:01:56,080 --> 00:01:59,720
Speaker 2: some interesting stuff in there. The little camera button that

37
00:02:00,040 --> 00:02:05,080
Speaker 2: turns the phone into a more of a focused camera tool.

38
00:02:05,400 --> 00:02:08,480
Speaker 2: I think like while most people might not find that

39
00:02:08,520 --> 00:02:10,480
Speaker 2: incredibly useful, there's going to be a good chunk of

40
00:02:10,480 --> 00:02:13,480
Speaker 2: people who do. Might open up more of the camera's

41
00:02:13,520 --> 00:02:18,200
Speaker 2: options to more people, And it really is working to

42
00:02:18,240 --> 00:02:21,919
Speaker 2: turn it into an industry tool, which I think is

43
00:02:22,000 --> 00:02:25,240
Speaker 2: super interesting. And if you look back earlier this year,

44
00:02:25,360 --> 00:02:29,600
Speaker 2: I went and jumped off the sky tower because Apple

45
00:02:29,800 --> 00:02:32,920
Speaker 2: had done a deal with aj Hacket Bungee to use

46
00:02:33,080 --> 00:02:37,120
Speaker 2: iPhone fifteen's in all of their replacing their cameras to

47
00:02:37,240 --> 00:02:41,639
Speaker 2: capture customer experiences. So people are starting to see the

48
00:02:41,760 --> 00:02:45,960
Speaker 2: value of using the iPhones in place of some of

49
00:02:46,000 --> 00:02:51,200
Speaker 2: these more expensive camera rigs, which I think is an

50
00:02:51,200 --> 00:02:54,400
Speaker 2: interesting trend that probably shouldn't be overlooked and isn't something

51
00:02:54,400 --> 00:02:57,160
Speaker 2: that I think most of the consumers maybe watching that

52
00:02:57,520 --> 00:03:00,280
Speaker 2: glow Time event would have noticed, but really stuck out me.

53
00:03:01,120 --> 00:03:04,960
Speaker 1: Yeah, I mean people are shooting entire movies on the

54
00:03:05,360 --> 00:03:09,959
Speaker 1: iPhone now, particularly the iPhone Pro and Pro Max or

55
00:03:10,000 --> 00:03:13,120
Speaker 1: whatever it's called, So the quality level is there, so

56
00:03:13,240 --> 00:03:16,160
Speaker 1: to have that tactile to be able to really control

57
00:03:16,200 --> 00:03:19,079
Speaker 1: that is really useful. But the key sort of takeaway

58
00:03:19,520 --> 00:03:23,320
Speaker 1: for me actually is the pricing's actually decreased for the

59
00:03:23,360 --> 00:03:25,919
Speaker 1: New Zealand market, and that's stayed the same in the US.

60
00:03:26,000 --> 00:03:29,600
Speaker 1: So the iPhone sixteen will go on sale here for

61
00:03:30,440 --> 00:03:34,480
Speaker 1: one five hundred and ninety nine dollars, and that is

62
00:03:34,520 --> 00:03:36,920
Speaker 1: down from one thousand, six hundred and forty nine dollars

63
00:03:37,000 --> 00:03:42,640
Speaker 1: for the iPhone fifteen, and that discount or lower price

64
00:03:43,000 --> 00:03:47,360
Speaker 1: is reflected across the range the sixteen pro, for instance,

65
00:03:47,600 --> 00:03:50,480
Speaker 1: is nineteen hundred and ninety nine dollars that used to

66
00:03:50,520 --> 00:03:52,680
Speaker 1: be two thousand and ninety nine. They're looking at the

67
00:03:52,720 --> 00:03:55,440
Speaker 1: New Zealand dollar compared to the US, going how much

68
00:03:55,480 --> 00:03:59,280
Speaker 1: can keiws take really in a recessionary environment. So that

69
00:03:59,440 --> 00:04:02,720
Speaker 1: is good for consumers, But for me, the other big

70
00:04:02,920 --> 00:04:05,600
Speaker 1: thing that caught my attention is really not on the iPhone.

71
00:04:05,640 --> 00:04:10,680
Speaker 1: It's on the Apple Watch and on the AirPod Pro.

72
00:04:11,560 --> 00:04:14,760
Speaker 1: These health features. I mean, it's super exciting turning the

73
00:04:15,080 --> 00:04:17,000
Speaker 1: AirPod pros and I've been talking about this for a

74
00:04:17,040 --> 00:04:19,920
Speaker 1: long time, but they finally nailed it, turning it basically

75
00:04:19,920 --> 00:04:23,560
Speaker 1: into what they're calling a clinical grade hearing aid, so

76
00:04:23,680 --> 00:04:27,960
Speaker 1: not necessarily replacing hearing aids that are usually very expensive

77
00:04:28,000 --> 00:04:30,400
Speaker 1: and finely tuned to the user. But if you have

78
00:04:30,560 --> 00:04:34,599
Speaker 1: sort of low or mid level hearing loss, being able

79
00:04:34,640 --> 00:04:38,880
Speaker 1: to use these to basically boost the volume, the clarity

80
00:04:39,320 --> 00:04:41,960
Speaker 1: and adapted very much to you. You can tune them

81
00:04:42,520 --> 00:04:43,760
Speaker 1: using the app on the iPhone.

82
00:04:43,839 --> 00:04:46,400
Speaker 2: It's pretty incredible, and you can do the hearing test

83
00:04:46,480 --> 00:04:48,520
Speaker 2: and the Apple Health app as well. So it's like

84
00:04:48,560 --> 00:04:51,920
Speaker 2: a beginning to end solution for people with like you say,

85
00:04:52,000 --> 00:04:55,480
Speaker 2: mild to moderate hearing loss like that is probably something

86
00:04:55,520 --> 00:04:57,279
Speaker 2: that I don't think many people would have had on

87
00:04:57,320 --> 00:04:59,960
Speaker 2: their Bengo cards, you know. But it really just goes

88
00:05:00,040 --> 00:05:04,599
Speaker 2: to show the potential for medtech in people's lives.

89
00:05:04,800 --> 00:05:06,800
Speaker 4: The only thing that does give me pause.

90
00:05:06,640 --> 00:05:10,920
Speaker 2: Is that the cost of these devices and the fact

91
00:05:10,920 --> 00:05:14,040
Speaker 2: that they are still very much in the high end premium,

92
00:05:14,240 --> 00:05:16,800
Speaker 2: does start to see a little bit of a diversion

93
00:05:17,720 --> 00:05:21,880
Speaker 2: or further diversion in healthcare for different ends of the

94
00:05:21,920 --> 00:05:25,080
Speaker 2: social spectrum. Absolutely, you know, it's not going to be

95
00:05:25,520 --> 00:05:27,679
Speaker 2: maybe the people that need it most who are getting

96
00:05:27,760 --> 00:05:31,200
Speaker 2: these things strapped to their wrist, perhaps, Yeah.

97
00:05:31,240 --> 00:05:34,680
Speaker 1: And if you need a device set runs iOS eighteen

98
00:05:35,560 --> 00:05:40,680
Speaker 1: for the hearing aids, you need the AirPod Pro two.

99
00:05:40,760 --> 00:05:43,200
Speaker 1: I think it is it's four hundred and seventy nine

100
00:05:43,240 --> 00:05:46,040
Speaker 1: dollars that they're going on, the mircrop in New Zealand,

101
00:05:46,600 --> 00:05:50,640
Speaker 1: a high end Apple Watch I think nine or ten series,

102
00:05:50,640 --> 00:05:53,920
Speaker 1: and the Ultra, so you're looking at five or six

103
00:05:54,040 --> 00:05:57,080
Speaker 1: hundred bucks for one of those. But they are the

104
00:05:57,200 --> 00:06:01,560
Speaker 1: not subscription based, which is good, so credit to them

105
00:06:01,600 --> 00:06:06,200
Speaker 1: for innovating these things. Neither of them have had FDA approval.

106
00:06:06,279 --> 00:06:09,800
Speaker 1: Or the right regulatory sign off. Yet they say that's

107
00:06:09,839 --> 00:06:13,120
Speaker 1: coming soon, so hopefully they get that over the line.

108
00:06:13,120 --> 00:06:16,680
Speaker 1: It's curious that they launched these things before they've actually

109
00:06:16,680 --> 00:06:18,960
Speaker 1: got it all signed and sealed, but.

110
00:06:19,360 --> 00:06:22,599
Speaker 4: Pretty confident about it. Yeah, exactly that they will.

111
00:06:22,760 --> 00:06:26,479
Speaker 1: I can't see it being too controversial. But I guess

112
00:06:26,080 --> 00:06:29,440
Speaker 1: the other thing, which is also not here yet the

113
00:06:29,440 --> 00:06:31,919
Speaker 1: phones have debuted, but Apple Intelligence.

114
00:06:32,040 --> 00:06:34,119
Speaker 4: Yeah, and that was my next point as well.

115
00:06:34,480 --> 00:06:36,479
Speaker 1: It's just like, why would you why would you do that?

116
00:06:36,480 --> 00:06:39,720
Speaker 1: You've got this big flagship event and it's like, oh,

117
00:06:39,760 --> 00:06:42,520
Speaker 1: that will be later this month. These features, these Apple

118
00:06:42,560 --> 00:06:48,040
Speaker 1: Intelligence AI driven text editing and in box summaries all

119
00:06:48,080 --> 00:06:50,560
Speaker 1: sounds great and we've been hearing about it for months.

120
00:06:51,400 --> 00:06:53,560
Speaker 1: Do it in a big bang when the actual phones

121
00:06:53,920 --> 00:06:56,000
Speaker 1: are unveiled at your biggest of end of the year.

122
00:06:56,800 --> 00:06:59,680
Speaker 4: Yeah, I mean it just I have to.

123
00:07:00,400 --> 00:07:04,200
Speaker 2: Like, it does get me really excited, the contextual personal

124
00:07:04,240 --> 00:07:07,479
Speaker 2: context stuff, being able to go through and have that

125
00:07:07,560 --> 00:07:10,640
Speaker 2: really smart assistant and all the upgrades to Siri. It

126
00:07:10,680 --> 00:07:12,800
Speaker 2: gets me really excited for that. But at the same time,

127
00:07:13,840 --> 00:07:16,280
Speaker 2: it's it's kind of like how many times can.

128
00:07:16,160 --> 00:07:17,840
Speaker 4: You can you get away with that?

129
00:07:18,040 --> 00:07:20,760
Speaker 2: Like I feel like I'm being I've been burned with

130
00:07:20,040 --> 00:07:25,280
Speaker 2: the Surface laptop and this like burned a little bit

131
00:07:25,280 --> 00:07:27,720
Speaker 2: with the originally with Samsung when they came out saying

132
00:07:27,720 --> 00:07:29,400
Speaker 2: they were going to have all these great AI features

133
00:07:29,440 --> 00:07:31,720
Speaker 2: and they were a bit milk toast and and so

134
00:07:31,960 --> 00:07:35,040
Speaker 2: come on, Apple, like, my hopes are up, so really

135
00:07:35,120 --> 00:07:36,840
Speaker 2: need you to deliver on this one.

136
00:07:37,360 --> 00:07:42,120
Speaker 1: Yeah, yeah, you're a business desk. A review of the

137
00:07:42,160 --> 00:07:45,960
Speaker 1: Microsoft Surface Laptop was was pretty pretty scathing, and really

138
00:07:45,960 --> 00:07:49,119
Speaker 1: that was down to the over promising on the AI function.

139
00:07:49,240 --> 00:07:51,160
Speaker 1: I was the same I reviewed it, and you know,

140
00:07:51,200 --> 00:07:54,600
Speaker 1: there's that button on keyboard which is your launch pad

141
00:07:54,600 --> 00:07:59,280
Speaker 1: into co pilot, and co Pilot's a good a good service,

142
00:07:59,280 --> 00:08:02,720
Speaker 1: but really that is it. All the other functions that

143
00:08:02,720 --> 00:08:06,240
Speaker 1: they were really touting, like the recall function wasn't available.

144
00:08:06,280 --> 00:08:09,480
Speaker 1: So I think Apple is in real danger of doing

145
00:08:09,480 --> 00:08:12,920
Speaker 1: the same thing. They obviously are making last minute tweaks.

146
00:08:12,960 --> 00:08:15,800
Speaker 1: It's very complicated to get these things working properly. But

147
00:08:15,840 --> 00:08:18,800
Speaker 1: if we don't see them having a really good impact

148
00:08:18,880 --> 00:08:21,000
Speaker 1: by the end of this month, I think they're going

149
00:08:21,000 --> 00:08:23,960
Speaker 1: to be in trouble and that may impact iPhone sixteen

150
00:08:24,080 --> 00:08:24,880
Speaker 1: sales ultimately.

151
00:08:25,360 --> 00:08:28,720
Speaker 2: Yeah, absolutely, unless the people, the early adopters get these

152
00:08:28,760 --> 00:08:31,400
Speaker 2: in their hands, and even the iPhone fifteen Pro people

153
00:08:31,600 --> 00:08:35,240
Speaker 2: get the Apple Intelligence in their hands and go this

154
00:08:35,320 --> 00:08:39,000
Speaker 2: is awesome and really generate that buzz online, it is

155
00:08:39,040 --> 00:08:41,520
Speaker 2: not going to have that pick up impact that they're

156
00:08:41,520 --> 00:08:43,800
Speaker 2: really hoping for for the iPhone sixteen because they're putting

157
00:08:43,800 --> 00:08:46,320
Speaker 2: a lot of eggs in this iPhone sixteen basket in

158
00:08:46,400 --> 00:08:50,640
Speaker 2: terms of their you know, dropping sales over recent years.

159
00:08:50,679 --> 00:08:55,400
Speaker 1: So yeah, and look, they're not really promising that much.

160
00:08:55,400 --> 00:08:59,320
Speaker 1: It's pretty basic, you know what you'll be able to

161
00:08:59,360 --> 00:09:02,080
Speaker 1: do in the initial run anyway. But what will be

162
00:09:02,120 --> 00:09:06,240
Speaker 1: interesting is all the developers who got access to Apple

163
00:09:06,280 --> 00:09:09,560
Speaker 1: Intelligence six months ago, what they will be coming up

164
00:09:09,559 --> 00:09:12,760
Speaker 1: with and what will appear on the App Store or

165
00:09:12,800 --> 00:09:16,640
Speaker 1: in their existing apps that draw on that neural processor

166
00:09:16,800 --> 00:09:20,560
Speaker 1: in the iPhone sixteen and which you can use on

167
00:09:20,600 --> 00:09:23,880
Speaker 1: the iPhone fifteen Pro as well. That is going to

168
00:09:23,920 --> 00:09:27,560
Speaker 1: supercharge the apps that we use every day. So that's

169
00:09:27,600 --> 00:09:29,920
Speaker 1: when the real value I think will come, both for

170
00:09:30,360 --> 00:09:35,440
Speaker 1: the surface and these co pilot PCs as well as

171
00:09:35,920 --> 00:09:38,880
Speaker 1: phones that have AI on them, when all the third

172
00:09:38,920 --> 00:09:41,960
Speaker 1: party app developers jump on board and make use of them.

173
00:09:42,160 --> 00:09:45,280
Speaker 1: Interesting that Apple had its big splash and literally the

174
00:09:45,320 --> 00:09:50,439
Speaker 1: next day on Wednesday, news from Europe that they've been

175
00:09:50,960 --> 00:09:55,360
Speaker 1: slapped with a twenty three billion dollar fee for back

176
00:09:55,440 --> 00:09:58,800
Speaker 1: taxes because of their arrangement with Ireland where they were

177
00:09:58,800 --> 00:10:02,440
Speaker 1: paying for a long time a very low effective tax rate.

178
00:10:02,640 --> 00:10:05,520
Speaker 1: And you have this weird situation where islands have got

179
00:10:05,600 --> 00:10:08,559
Speaker 1: rich off companies like Apple. We had a guest on

180
00:10:09,040 --> 00:10:11,920
Speaker 1: a few months back who was talking about the Celtic

181
00:10:11,960 --> 00:10:15,480
Speaker 1: Tiger and how rich Dublin is because of the tech sector,

182
00:10:16,320 --> 00:10:19,040
Speaker 1: and you had Ireland basically saying to the EU, leave

183
00:10:19,120 --> 00:10:21,400
Speaker 1: us alone. We've got this great deal going with Apple,

184
00:10:21,559 --> 00:10:23,880
Speaker 1: and the EU said, no, Ireland, you were part of

185
00:10:23,920 --> 00:10:27,600
Speaker 1: the EU. We will all have uniform tax policies. Apple's

186
00:10:27,640 --> 00:10:30,240
Speaker 1: going to have to pay. So they will have to pay.

187
00:10:30,280 --> 00:10:32,560
Speaker 1: They've said, I think in the fourth quarter they will

188
00:10:32,600 --> 00:10:37,040
Speaker 1: have a ten billion dollar US impact on their books

189
00:10:37,600 --> 00:10:40,360
Speaker 1: to settle that. So they'll have to sell a lot

190
00:10:40,360 --> 00:10:42,520
Speaker 1: more iPhone sixteen's to make up for it.

191
00:10:43,200 --> 00:10:46,720
Speaker 2: Absolutely, yeah, yeah, and I mean it goes. It does

192
00:10:47,200 --> 00:10:49,240
Speaker 2: speak volumes of the fact that they have capitulated to

193
00:10:49,280 --> 00:10:51,720
Speaker 2: a lot of the EU's demands in terms of USBC

194
00:10:52,040 --> 00:10:54,160
Speaker 2: in terms of opening the NMC chip and all these

195
00:10:54,200 --> 00:10:57,880
Speaker 2: other things. The Europe is clearly a very important market

196
00:10:57,960 --> 00:11:02,040
Speaker 2: to them. So yeah, I can't see them. Well, there's

197
00:11:02,040 --> 00:11:03,800
Speaker 2: probably a little bit of grumbling in the background. I

198
00:11:03,800 --> 00:11:06,520
Speaker 2: can't see them, you know, resisting too hard at paying

199
00:11:06,559 --> 00:11:08,440
Speaker 2: their their fair share over in the EU.

200
00:11:08,880 --> 00:11:12,320
Speaker 1: No, and very you know, suddenly the Irish government has

201
00:11:12,360 --> 00:11:15,840
Speaker 1: a twenty three billion dollar that's New Zealand dollar, sort

202
00:11:15,840 --> 00:11:18,280
Speaker 1: of thirteen billion euro windfall.

203
00:11:18,679 --> 00:11:19,280
Speaker 4: Imagine that.

204
00:11:19,480 --> 00:11:21,400
Speaker 1: Imagine what that would mean for the New Zealand government

205
00:11:21,520 --> 00:11:24,240
Speaker 1: to have that sort of money to put into infrastructure

206
00:11:24,280 --> 00:11:26,760
Speaker 1: or something. So you're trying to figure out what to

207
00:11:26,800 --> 00:11:29,600
Speaker 1: do with it. They want to set up a sovereign

208
00:11:29,640 --> 00:11:31,880
Speaker 1: fun there, one hundred billion dollars sovereign fun And that's

209
00:11:31,920 --> 00:11:34,320
Speaker 1: really because they're taking in so much corporate tax. Now,

210
00:11:34,679 --> 00:11:38,720
Speaker 1: they got all of those companies there at paying low tax,

211
00:11:38,800 --> 00:11:40,800
Speaker 1: and then the EU said no, they have to pay

212
00:11:40,840 --> 00:11:43,360
Speaker 1: a fair amount of tax. And the companies have invested

213
00:11:43,360 --> 00:11:44,720
Speaker 1: so much in Ireland are staying there.

214
00:11:44,800 --> 00:11:46,160
Speaker 4: So it's just worked.

215
00:11:46,240 --> 00:11:48,079
Speaker 1: It's just a masterful plan really.

216
00:11:48,160 --> 00:11:49,400
Speaker 4: By the I fantastic.

217
00:11:49,520 --> 00:11:52,000
Speaker 2: Yeah, it's a pity we's so far away that it

218
00:11:52,040 --> 00:11:53,360
Speaker 2: wouldn't quite work for us.

219
00:11:53,240 --> 00:11:59,160
Speaker 1: But no, right now, onto our featured interview with Henji Wang,

220
00:11:59,360 --> 00:12:02,520
Speaker 1: co founder of CAMI. Last month, The Herald reported that

221
00:12:02,600 --> 00:12:06,679
Speaker 1: BV Investment Partners had taken a controlling stake in CAMI.

222
00:12:06,760 --> 00:12:09,440
Speaker 1: This is a Boston based private equity company that's been

223
00:12:09,480 --> 00:12:13,240
Speaker 1: around for decades and invested in a lot of ed

224
00:12:13,320 --> 00:12:17,040
Speaker 1: tech companies. That deal valued the company at three hundred

225
00:12:17,200 --> 00:12:18,080
Speaker 1: million dollars.

226
00:12:18,280 --> 00:12:18,600
Speaker 4: Now.

227
00:12:18,840 --> 00:12:22,960
Speaker 1: It follows another US private equity company taking a big

228
00:12:23,000 --> 00:12:26,640
Speaker 1: stake in Dunedin's Education Perfect back in twenty twenty one

229
00:12:27,240 --> 00:12:30,160
Speaker 1: that valued the company at four hundred and fifty five

230
00:12:30,240 --> 00:12:34,400
Speaker 1: million dollars. We've also got Crimson Education, which raised ten

231
00:12:34,440 --> 00:12:37,200
Speaker 1: million US last year and a funding round that valued

232
00:12:37,200 --> 00:12:41,679
Speaker 1: it it supposedly US four hundred and sixty million dollars.

233
00:12:41,920 --> 00:12:45,840
Speaker 2: Those are incredible valuations for education related startups and they're

234
00:12:45,840 --> 00:12:47,720
Speaker 2: doing very well overseas.

235
00:12:47,960 --> 00:12:49,120
Speaker 4: That's just three of them.

236
00:12:49,320 --> 00:12:51,960
Speaker 2: There are several other fast growing ones, and it's that

237
00:12:51,960 --> 00:12:54,120
Speaker 2: that that doesn't really get the same level of bars

238
00:12:54,160 --> 00:12:57,480
Speaker 2: as like fintech or agritech, but which New Zealand seems

239
00:12:57,480 --> 00:12:58,200
Speaker 2: to do very well in.

240
00:12:58,840 --> 00:13:01,880
Speaker 1: I'll tell you who has done very well from Boston

241
00:13:01,960 --> 00:13:06,199
Speaker 1: Ventures buying CAMI. The New Zealand Growth Capital Partners, the

242
00:13:06,800 --> 00:13:11,280
Speaker 1: government's venture investment vehicle. It's a spire fund will get

243
00:13:11,320 --> 00:13:14,640
Speaker 1: thirty seven million dollars back from the sale of CAMI.

244
00:13:15,120 --> 00:13:20,000
Speaker 1: That represents a seventyfold increase on its investment. But Ben

245
00:13:20,040 --> 00:13:22,959
Speaker 1: tell us a bit more about exactly what CAMI does

246
00:13:23,080 --> 00:13:24,440
Speaker 1: in the education tech space.

247
00:13:25,559 --> 00:13:29,720
Speaker 2: Basically, at its core, CAMI is a document sharing platform,

248
00:13:29,920 --> 00:13:34,679
Speaker 2: so teachers can easily share PDFs with students and they

249
00:13:34,679 --> 00:13:39,520
Speaker 2: can be annotated and they can be easily marked if

250
00:13:39,559 --> 00:13:42,480
Speaker 2: they know the kids write their answers on the sheets

251
00:13:42,520 --> 00:13:43,240
Speaker 2: and things like that.

252
00:13:43,280 --> 00:13:44,560
Speaker 4: I mean, that's that Its very core.

253
00:13:44,800 --> 00:13:50,160
Speaker 2: But it has evolved now into this really comprehensive assessment

254
00:13:50,240 --> 00:13:53,480
Speaker 2: and learning product. So you can take a pack of

255
00:13:55,000 --> 00:13:58,720
Speaker 2: resources and you can give them to students and you

256
00:13:58,760 --> 00:14:02,360
Speaker 2: can create assessment it's based on them, and you know,

257
00:14:02,400 --> 00:14:06,600
Speaker 2: it's got remote learning aspects to it. And it's basically

258
00:14:06,640 --> 00:14:12,320
Speaker 2: like a document management tool on steroids specifically for education

259
00:14:12,480 --> 00:14:16,360
Speaker 2: for teachers and students. And obviously in the digital age,

260
00:14:16,720 --> 00:14:20,360
Speaker 2: when all of your resources are online on your computer

261
00:14:20,800 --> 00:14:23,960
Speaker 2: and they're going to different types of computers to chromebooks,

262
00:14:24,000 --> 00:14:27,920
Speaker 2: to PCs, iPads. Being able to have something that can

263
00:14:27,960 --> 00:14:31,240
Speaker 2: work across all of those platforms is really useful for

264
00:14:31,760 --> 00:14:33,600
Speaker 2: education worldwide.

265
00:14:34,440 --> 00:14:37,200
Speaker 1: Yeah, and I've had a huge cut through in one

266
00:14:37,280 --> 00:14:39,680
Speaker 1: hundred and eighty countries, big in the US. It's been

267
00:14:39,880 --> 00:14:43,760
Speaker 1: the focus of them. Founded by students so very much,

268
00:14:43,800 --> 00:14:47,800
Speaker 1: people who are feeling the pain points of learning in

269
00:14:47,840 --> 00:14:52,080
Speaker 1: the digital age and decided to go about and fix that.

270
00:14:52,320 --> 00:14:56,440
Speaker 1: So here's been talking to CAMI co Founderhnji Wang about

271
00:14:56,440 --> 00:14:59,480
Speaker 1: a startup journey and the role AI is likely to

272
00:14:59,480 --> 00:15:04,040
Speaker 1: play and the type of educational platforms Cammi is developing.

273
00:15:10,440 --> 00:15:13,160
Speaker 2: Hi, Henji, Welcome to the Business of Tech podcast. Thanks

274
00:15:13,160 --> 00:15:15,880
Speaker 2: so much for joining us. Thanks for having me, Ben So,

275
00:15:16,080 --> 00:15:19,280
Speaker 2: you are one of the co founders of a New

276
00:15:19,360 --> 00:15:22,400
Speaker 2: Zealand's success story in the tech scene and especially the

277
00:15:22,520 --> 00:15:27,200
Speaker 2: ed tech scene known as Cammi, and Cammy's been around

278
00:15:27,240 --> 00:15:29,640
Speaker 2: for just over a decade now. I think you just

279
00:15:29,680 --> 00:15:34,080
Speaker 2: said to me basically selling a really powerful and useful

280
00:15:34,080 --> 00:15:37,360
Speaker 2: piece of software into schools. Do you want to give

281
00:15:37,440 --> 00:15:40,720
Speaker 2: us just a very brief background about how Cami came

282
00:15:40,760 --> 00:15:40,960
Speaker 2: to be.

283
00:15:41,480 --> 00:15:45,840
Speaker 3: Absolutely so. Cammy is this wonderful platform app that's used

284
00:15:45,880 --> 00:15:48,440
Speaker 3: by over forty million teachers and students around the world.

285
00:15:48,720 --> 00:15:52,000
Speaker 3: But before we got there, we started this business as

286
00:15:52,240 --> 00:15:56,760
Speaker 3: just an app that we were as best friends at university.

287
00:15:57,240 --> 00:15:59,880
Speaker 3: We wanted to solve a problem that we had ourselves,

288
00:16:00,080 --> 00:16:04,560
Speaker 3: which was how do you effectively take study notes in class?

289
00:16:05,120 --> 00:16:09,560
Speaker 3: And this is over decadedgos So when devices were just

290
00:16:09,680 --> 00:16:13,200
Speaker 3: starting to show up in every classroom, when technologists just

291
00:16:13,200 --> 00:16:16,560
Speaker 3: started to show up more privalently in the class, prominently

292
00:16:16,600 --> 00:16:21,080
Speaker 3: in the class brother and we knew that we had

293
00:16:21,120 --> 00:16:23,440
Speaker 3: a look and there wasn't great software and we needed

294
00:16:23,480 --> 00:16:26,400
Speaker 3: to do something about it. So that's the solution that

295
00:16:26,440 --> 00:16:30,400
Speaker 3: we came up with. And then of course this trend

296
00:16:30,400 --> 00:16:36,520
Speaker 3: around teacher time and resource availability. Teachers are increasingly cling

297
00:16:36,640 --> 00:16:40,600
Speaker 3: more and how do you have a way to still

298
00:16:40,600 --> 00:16:44,040
Speaker 3: be able to personalize and make learning accessible and make

299
00:16:44,080 --> 00:16:48,640
Speaker 3: it exciting, but do it in a way that uses

300
00:16:48,760 --> 00:16:54,440
Speaker 3: technology in the most purposeful way possible and embracing that

301
00:16:54,560 --> 00:16:57,720
Speaker 3: I think has always been the biggest challenge in education.

302
00:16:58,160 --> 00:17:00,160
Speaker 2: I think that kind of leads us quite naturally to

303
00:17:00,720 --> 00:17:03,360
Speaker 2: the main topic of the conversation that we are here

304
00:17:03,360 --> 00:17:07,200
Speaker 2: to have, which is, of course artificial intelligence and how

305
00:17:07,280 --> 00:17:11,240
Speaker 2: AI is increasingly playing a role in the classroom, and

306
00:17:11,280 --> 00:17:13,320
Speaker 2: I think probably the first thing we should do is

307
00:17:13,359 --> 00:17:18,320
Speaker 2: split out AI as we're thinking about it, into what

308
00:17:18,359 --> 00:17:21,200
Speaker 2: we might call now classic AI. And then we've got

309
00:17:21,200 --> 00:17:25,000
Speaker 2: this other public facing side of AI, the new wave

310
00:17:26,280 --> 00:17:29,320
Speaker 2: generative AI, LM based AI, whatever you'd like to call it,

311
00:17:29,400 --> 00:17:35,600
Speaker 2: that involves interactivity between dynamic language based interactivity between kind

312
00:17:35,600 --> 00:17:38,680
Speaker 2: of the user and the software. So, yeah, let's go

313
00:17:38,760 --> 00:17:42,200
Speaker 2: back to the early days of how Chemi was using AI.

314
00:17:42,280 --> 00:17:45,760
Speaker 2: I'm assuming you've had some kind of algorithmic presence in

315
00:17:45,800 --> 00:17:47,359
Speaker 2: your product for several years.

316
00:17:47,760 --> 00:17:49,639
Speaker 3: Yeah, So if we were to zoom back a little

317
00:17:49,640 --> 00:17:53,119
Speaker 3: bit and just think about that implementation of technology in

318
00:17:53,160 --> 00:17:57,400
Speaker 3: the classroom, Cammy, one of our proof points is we

319
00:17:57,560 --> 00:18:00,960
Speaker 3: save teachers. This is teachers telling us we save teachers

320
00:18:01,000 --> 00:18:04,199
Speaker 3: in the average of eight hours a week. Wow, So

321
00:18:04,280 --> 00:18:07,760
Speaker 3: that is huge. That's over an hour each day that

322
00:18:07,800 --> 00:18:11,159
Speaker 3: we're giving back to the teacher to enable them to

323
00:18:11,320 --> 00:18:15,440
Speaker 3: focus back on their students, their learning outcomes, making things

324
00:18:15,480 --> 00:18:18,600
Speaker 3: more accessible. And if you look at how we arrived

325
00:18:18,640 --> 00:18:21,160
Speaker 3: at that point, we did it in a way that

326
00:18:21,720 --> 00:18:25,639
Speaker 3: takes technology that already exists today and put it together

327
00:18:25,680 --> 00:18:29,639
Speaker 3: in a way that really drives activity in the classroom.

328
00:18:29,920 --> 00:18:34,320
Speaker 3: We do it by, for instance, saving a clique for

329
00:18:34,480 --> 00:18:37,439
Speaker 3: the teacher or saving a clique for the students so

330
00:18:37,520 --> 00:18:40,040
Speaker 3: that they don't have to think about it and worry

331
00:18:40,040 --> 00:18:43,119
Speaker 3: about it. So anything that we can do to simplify

332
00:18:43,280 --> 00:18:46,399
Speaker 3: technology in the classroom is actually a huge gain for

333
00:18:46,480 --> 00:18:49,160
Speaker 3: the teacher. Just as an example, when we did all

334
00:18:49,200 --> 00:18:52,919
Speaker 3: of this, we achieved this eight hour a week on

335
00:18:53,040 --> 00:18:56,880
Speaker 3: average saving for the teacher outcome before we had any

336
00:18:56,960 --> 00:19:00,960
Speaker 3: of the new generation of LAMAI implemented an product. So

337
00:19:01,040 --> 00:19:04,120
Speaker 3: that tells you something about how we as a company

338
00:19:04,240 --> 00:19:08,560
Speaker 3: know how to implement technology in the classroom, and we've

339
00:19:08,560 --> 00:19:12,760
Speaker 3: done it using your traditional AI. There is a lot

340
00:19:12,760 --> 00:19:16,840
Speaker 3: of One example would be how we take an image

341
00:19:17,320 --> 00:19:21,159
Speaker 3: that teachers have and digitizing that image. So take a

342
00:19:21,280 --> 00:19:24,520
Speaker 3: scan of a document and we make it as seamless

343
00:19:24,520 --> 00:19:27,480
Speaker 3: as possible to digitize it into a format that you

344
00:19:27,480 --> 00:19:31,120
Speaker 3: can start using. You can start interacting as a student.

345
00:19:31,600 --> 00:19:34,639
Speaker 3: So I, as a teacher, take a scan of a

346
00:19:34,640 --> 00:19:37,240
Speaker 3: piece of curriculum, I send it off to a student

347
00:19:37,320 --> 00:19:40,880
Speaker 3: that might have special accommodations or special needs, and they

348
00:19:40,880 --> 00:19:44,840
Speaker 3: can start immediately using it with their speech to text.

349
00:19:45,680 --> 00:19:48,439
Speaker 3: So it allows it to read out loud the text

350
00:19:48,720 --> 00:19:50,960
Speaker 3: or sorry rather text a speech not speech to text.

351
00:19:51,400 --> 00:19:54,480
Speaker 3: On the opposite end, Cammie also has tools that allow

352
00:19:54,640 --> 00:19:57,840
Speaker 3: the student to speak their answer back to the to

353
00:19:57,920 --> 00:20:01,560
Speaker 3: their curriculum and record all whether that's a video, whether

354
00:20:01,640 --> 00:20:06,800
Speaker 3: that's sort of a text written dictated down. And that's

355
00:20:07,200 --> 00:20:11,880
Speaker 3: perhaps what you would consider your more traditional AI implementation

356
00:20:11,960 --> 00:20:13,679
Speaker 3: of CAME, but we do in a way that is

357
00:20:14,160 --> 00:20:17,960
Speaker 3: as little friction as possible. And so yeah, that would

358
00:20:17,960 --> 00:20:21,000
Speaker 3: probably be a few examples of how we've gone on

359
00:20:21,119 --> 00:20:24,760
Speaker 3: to purposely implement this in a way. And for the

360
00:20:24,800 --> 00:20:27,480
Speaker 3: more tech savvy readers out there, it's just a very

361
00:20:27,920 --> 00:20:33,080
Speaker 3: seamless integration of what is known as the OCI technology solution.

362
00:20:33,160 --> 00:20:37,520
Speaker 2: Right, So it's just kind of any time that you

363
00:20:37,920 --> 00:20:42,200
Speaker 2: see that there's a way to improve a process basically

364
00:20:42,320 --> 00:20:46,480
Speaker 2: to make it less manual using these the implementation of

365
00:20:46,560 --> 00:20:49,439
Speaker 2: these algorithms, you can kind of do that because you

366
00:20:49,480 --> 00:20:53,879
Speaker 2: are a software product, and speech to text must have

367
00:20:53,960 --> 00:20:57,520
Speaker 2: been kind of revolutionary for a lot of people when

368
00:20:57,600 --> 00:21:00,920
Speaker 2: that was first able to be rolled out on mass

369
00:21:01,280 --> 00:21:03,440
Speaker 2: Having that same kind of experience with the new generation

370
00:21:03,840 --> 00:21:04,920
Speaker 2: the LM based AI.

371
00:21:05,359 --> 00:21:08,760
Speaker 3: In short, yes, I think one important thing I should

372
00:21:08,800 --> 00:21:11,919
Speaker 3: note before we get into that is whenever we develop product,

373
00:21:11,920 --> 00:21:15,919
Speaker 3: our philosophy is to speak to the customer directly. And

374
00:21:16,000 --> 00:21:19,800
Speaker 3: if you look at every single feature, every single change

375
00:21:19,800 --> 00:21:22,440
Speaker 3: that we've rolled out in our product, we can tie

376
00:21:22,480 --> 00:21:25,720
Speaker 3: that to a conversation we've had with a feature with

377
00:21:25,840 --> 00:21:29,480
Speaker 3: a parent, with an administrator, perhaps sometimes with a student,

378
00:21:29,920 --> 00:21:34,240
Speaker 3: and so that is extremely important to us being able

379
00:21:34,240 --> 00:21:37,400
Speaker 3: to link it back to this will help this person

380
00:21:37,840 --> 00:21:40,800
Speaker 3: and why. And when you look at how we've started

381
00:21:40,800 --> 00:21:44,560
Speaker 3: to implement LLMS, we've done it in a way that

382
00:21:44,720 --> 00:21:49,560
Speaker 3: is extremely purposeful. So our first enhancement using LLMS and

383
00:21:49,640 --> 00:21:52,840
Speaker 3: Generator of AI is to take an existing piece of

384
00:21:52,880 --> 00:21:57,600
Speaker 3: curriculum and you enhance it by bringing out or teasing

385
00:21:57,640 --> 00:22:01,680
Speaker 3: out all the sort of quizzes and questions that typically

386
00:22:01,680 --> 00:22:05,399
Speaker 3: a teacher would spend time typing manually out for the

387
00:22:05,480 --> 00:22:09,720
Speaker 3: students to answer. We now pull that out. If there's

388
00:22:09,760 --> 00:22:13,040
Speaker 3: an answer key, we'll use take advantage of that answer key,

389
00:22:13,520 --> 00:22:16,480
Speaker 3: and it pulls it all out so that the teacher

390
00:22:16,520 --> 00:22:20,120
Speaker 3: can then organize it on their document, on their template,

391
00:22:20,200 --> 00:22:23,080
Speaker 3: rather before they send it out to the students to answer.

392
00:22:23,440 --> 00:22:25,600
Speaker 3: And one of the things that we've done is we

393
00:22:25,720 --> 00:22:29,240
Speaker 3: can also select using l and MS the right answer,

394
00:22:29,359 --> 00:22:31,960
Speaker 3: so it saves teachers time. They don't have to say

395
00:22:32,520 --> 00:22:34,840
Speaker 3: they don't have to grade it manually anymore. They can

396
00:22:34,880 --> 00:22:39,600
Speaker 3: simply let it auto grade because at the time that

397
00:22:39,640 --> 00:22:43,800
Speaker 3: we generate these quizzes or these questions, we've already got

398
00:22:43,840 --> 00:22:46,600
Speaker 3: an idea of what the answer will be, and we

399
00:22:46,760 --> 00:22:49,120
Speaker 3: have the teacher check all of this before they send

400
00:22:49,160 --> 00:22:50,960
Speaker 3: it off to the students, which is an important bit

401
00:22:51,320 --> 00:22:54,040
Speaker 3: around the limitations of these LMS.

402
00:22:54,600 --> 00:22:59,600
Speaker 2: So you're automating. So when you say a curriculum, that's

403
00:22:59,800 --> 00:23:02,680
Speaker 2: what talking about is like a stack of resources, right,

404
00:23:02,840 --> 00:23:07,840
Speaker 2: So a bunch of PDFs or word documents or that

405
00:23:08,040 --> 00:23:12,000
Speaker 2: teachers gather over years to create essentially a pack of

406
00:23:12,040 --> 00:23:15,000
Speaker 2: resources that they use to teach a particular unit. So

407
00:23:15,640 --> 00:23:18,480
Speaker 2: for example, if it's a history class, there might be

408
00:23:18,680 --> 00:23:23,879
Speaker 2: a French Revolution unit, and so they'll have a whole

409
00:23:23,880 --> 00:23:29,080
Speaker 2: bunch of documents, scanned pages from books, website resources they

410
00:23:29,080 --> 00:23:32,399
Speaker 2: printed out, and then stuff they've made themselves, quizzes, like

411
00:23:32,440 --> 00:23:36,639
Speaker 2: you said, all these kinds of things, and with the

412
00:23:36,760 --> 00:23:40,280
Speaker 2: LLM tools, because of that contextual understanding, you're able to

413
00:23:40,359 --> 00:23:44,960
Speaker 2: go in and take out just the quizzes and say, okay,

414
00:23:45,000 --> 00:23:47,920
Speaker 2: here you go. Here's the ten different quizzes that you've

415
00:23:47,920 --> 00:23:50,479
Speaker 2: got in this curriculum. You can kind of use them

416
00:23:50,960 --> 00:23:52,880
Speaker 2: how you want to now, and here's the answer key.

417
00:23:53,240 --> 00:23:54,639
Speaker 2: And then on top of that, you said there was

418
00:23:54,680 --> 00:23:57,160
Speaker 2: a kind of automatic marking aspect to it as well.

419
00:23:57,520 --> 00:24:01,840
Speaker 3: Yeah, so as it calls out these questions from the resource,

420
00:24:02,320 --> 00:24:07,280
Speaker 3: it can also use its knowledge base in the LLM

421
00:24:07,640 --> 00:24:10,439
Speaker 3: to select what is the right answer for you. But

422
00:24:10,520 --> 00:24:12,760
Speaker 3: nine times out of ten that resource already has an

423
00:24:12,800 --> 00:24:16,520
Speaker 3: answer key be able to leverage that or the answer

424
00:24:16,560 --> 00:24:20,160
Speaker 3: to that is already from the curriculum that you're teaching.

425
00:24:20,280 --> 00:24:23,400
Speaker 3: So you can simply go and we'll tease that out

426
00:24:23,720 --> 00:24:26,520
Speaker 3: and auto allow you as a teacher to auto grape,

427
00:24:26,560 --> 00:24:28,320
Speaker 3: so that when you send it off to the students,

428
00:24:28,600 --> 00:24:31,560
Speaker 3: you have the option to say, great, it automatically for me,

429
00:24:31,680 --> 00:24:34,639
Speaker 3: But don't show the student the answer or show the

430
00:24:34,680 --> 00:24:37,080
Speaker 3: student the answer so that they can learn from that

431
00:24:37,280 --> 00:24:41,720
Speaker 3: and perhaps learn from why they got the answer wrong,

432
00:24:42,040 --> 00:24:45,439
Speaker 3: because there will be a hint field inside a camy

433
00:24:45,520 --> 00:24:47,800
Speaker 3: that allows the student to be able to see that

434
00:24:47,880 --> 00:24:50,000
Speaker 3: hint and understand, oh, I got it wrong because of

435
00:24:50,040 --> 00:24:53,440
Speaker 3: this or this is the hint towards the right answer

436
00:24:53,440 --> 00:24:57,720
Speaker 3: that I can try and reread my resource. So it's

437
00:24:57,840 --> 00:25:04,000
Speaker 3: very flexible, and it's designed specifically because checking for understanding

438
00:25:04,320 --> 00:25:07,520
Speaker 3: as a teacher is so important that we want to

439
00:25:07,560 --> 00:25:09,800
Speaker 3: be able to provide the teacher with all the tools

440
00:25:09,960 --> 00:25:12,919
Speaker 3: that need to check that sort of that what they're

441
00:25:12,960 --> 00:25:16,159
Speaker 3: trying to teach is actually being comprehended by the student.

442
00:25:16,600 --> 00:25:18,960
Speaker 2: That's that's really interesting. I guess that's the one of

443
00:25:18,960 --> 00:25:21,679
Speaker 2: the strengths of these generative AI models is that they

444
00:25:21,680 --> 00:25:26,439
Speaker 2: are really good at taking unstructured data and unearthing specific

445
00:25:26,480 --> 00:25:28,439
Speaker 2: things that you want from it, and you know you

446
00:25:28,440 --> 00:25:32,960
Speaker 2: can make these requests. So what is what are the

447
00:25:33,160 --> 00:25:37,760
Speaker 2: what are the possibilities you mentioned character based AI? So

448
00:25:38,240 --> 00:25:41,200
Speaker 2: like a Marie Antoinette comes on and as a as

449
00:25:41,200 --> 00:25:44,160
Speaker 2: an AI and perhaps the students can talk to her.

450
00:25:44,280 --> 00:25:46,840
Speaker 2: That seems like an interesting use case.

451
00:25:46,920 --> 00:25:47,600
Speaker 4: Is that is that.

452
00:25:47,520 --> 00:25:49,399
Speaker 2: Something that you've had chats with about your with your

453
00:25:49,440 --> 00:25:50,399
Speaker 2: customers about.

454
00:25:50,480 --> 00:25:53,000
Speaker 3: Out there in the wild. We have seen a lot

455
00:25:53,359 --> 00:25:57,199
Speaker 3: a lot of different platforms that do that. We have

456
00:25:57,320 --> 00:26:00,360
Speaker 3: seen some success in the classroom where a teacher might

457
00:26:00,720 --> 00:26:06,800
Speaker 3: take a particular resource and essentially generate artifacts of different

458
00:26:06,840 --> 00:26:11,480
Speaker 3: reason side resources supplemental resources from that core curriculum resource.

459
00:26:11,840 --> 00:26:14,919
Speaker 3: These generated resources are done in a way that is

460
00:26:15,480 --> 00:26:20,320
Speaker 3: more exciting and engaging for the students. So an example

461
00:26:20,359 --> 00:26:24,880
Speaker 3: would be hate this particular piece of core curriculum and

462
00:26:25,480 --> 00:26:29,760
Speaker 3: generator in the voice of Lightning the Queen the movie

463
00:26:29,840 --> 00:26:33,919
Speaker 3: Cars and catch out exactly, and you can have that

464
00:26:33,920 --> 00:26:37,679
Speaker 3: sort of personality sort of explain the content back to you.

465
00:26:38,359 --> 00:26:40,679
Speaker 2: It reminds me of that meme of a parent who

466
00:26:40,760 --> 00:26:43,800
Speaker 2: said that the kids wouldn't eat frozen vegetables, so they

467
00:26:43,840 --> 00:26:45,679
Speaker 2: stuck some poor patrol stickers on and said they were

468
00:26:45,720 --> 00:26:49,639
Speaker 2: poor patrol frozen vegetables. Like, is that, you know, figuring

469
00:26:49,680 --> 00:26:52,320
Speaker 2: out a way to meet kids where they where.

470
00:26:52,160 --> 00:26:55,080
Speaker 3: They are, where they're interested exactly. And I think if

471
00:26:55,119 --> 00:26:57,480
Speaker 3: you can do that, you can you've If you can

472
00:26:57,480 --> 00:27:00,439
Speaker 3: get them interested in the curriculum and the content and

473
00:27:00,520 --> 00:27:02,880
Speaker 3: the way that you know you're able to meet them

474
00:27:02,920 --> 00:27:08,359
Speaker 3: in the middle, that's transformative. And I think ultimately those

475
00:27:08,359 --> 00:27:11,200
Speaker 3: are some of the great successes that we don't often

476
00:27:11,240 --> 00:27:13,560
Speaker 3: hear about in the class out of the classroom rather,

477
00:27:14,760 --> 00:27:19,320
Speaker 3: but that's something that is you know, really helping the

478
00:27:19,560 --> 00:27:23,600
Speaker 3: sort of engagement and interactivity of that material in the classroom. Yeah,

479
00:27:23,880 --> 00:27:24,080
Speaker 3: it is.

480
00:27:24,200 --> 00:27:26,919
Speaker 2: It is interesting, like we have this sense that for

481
00:27:27,440 --> 00:27:30,919
Speaker 2: education to be valuable, it has to be kind of

482
00:27:32,200 --> 00:27:35,919
Speaker 2: formal or it has to be dry, right, But you know,

483
00:27:36,200 --> 00:27:38,920
Speaker 2: the reality is if you go to the best teachers classrooms,

484
00:27:38,920 --> 00:27:42,359
Speaker 2: they're always covered in colors and posters on the wall,

485
00:27:42,560 --> 00:27:45,560
Speaker 2: and you know, trying to try to walk that line

486
00:27:45,600 --> 00:27:48,280
Speaker 2: between meeting kids where they are with interest and not

487
00:27:48,359 --> 00:27:51,240
Speaker 2: being cringed, which is always a tough one to walk.

488
00:27:51,480 --> 00:27:54,119
Speaker 2: But the idea that you can use these kind of

489
00:27:54,560 --> 00:27:58,320
Speaker 2: artificial intelligence tools to enhance that because not every teacher

490
00:27:58,400 --> 00:28:00,800
Speaker 2: is going to be hit with the kids. So if

491
00:28:00,840 --> 00:28:03,800
Speaker 2: you can kind of say, like, I want to take

492
00:28:03,840 --> 00:28:06,440
Speaker 2: this resource and what are some ways that I could

493
00:28:06,920 --> 00:28:10,200
Speaker 2: make it relevant for an audience of eight to nine

494
00:28:10,280 --> 00:28:12,840
Speaker 2: year olds, what are some of the things they're interested in,

495
00:28:12,880 --> 00:28:15,800
Speaker 2: and how can I bring these together?

496
00:28:15,880 --> 00:28:18,560
Speaker 4: That seems like a really powerful use case.

497
00:28:18,960 --> 00:28:22,120
Speaker 3: Absolutely, and I think this is why long term, the

498
00:28:22,160 --> 00:28:25,440
Speaker 3: teacher's role in the classroom remains the same, which is

499
00:28:25,520 --> 00:28:29,000
Speaker 3: to guide the students and help them along the learning journey.

500
00:28:29,280 --> 00:28:35,880
Speaker 3: Learning journey, and AI is simply here to facilitate, to support,

501
00:28:36,400 --> 00:28:40,160
Speaker 3: but never to replace. You always need a teacher to

502
00:28:40,200 --> 00:28:43,760
Speaker 3: be that initial seed of I think creativity in the

503
00:28:43,760 --> 00:28:46,200
Speaker 3: classroom of how do you do this but in a

504
00:28:46,280 --> 00:28:50,480
Speaker 3: way that is intentional and purposeful around all the learning

505
00:28:50,560 --> 00:28:53,600
Speaker 3: theory that is out there, all the scientific evidence that

506
00:28:53,800 --> 00:28:56,800
Speaker 3: is out there, around how do you best have good

507
00:28:57,040 --> 00:29:02,400
Speaker 3: useful curriculum and the implementation of that curriculum. So I

508
00:29:02,400 --> 00:29:04,040
Speaker 3: don't think the role of the teacher is going in

509
00:29:04,080 --> 00:29:06,600
Speaker 3: a way in times certain AI is simply there to

510
00:29:06,720 --> 00:29:11,280
Speaker 3: help facilitate and really give drive meaningful productivity gains in

511
00:29:11,400 --> 00:29:13,040
Speaker 3: the teacher workflow.

512
00:29:13,520 --> 00:29:17,080
Speaker 2: Are you sure that we will not have robot teachers

513
00:29:17,400 --> 00:29:21,560
Speaker 2: coming in the future where kids all put on their

514
00:29:21,640 --> 00:29:25,440
Speaker 2: VR headsets and they go to the virtual classroom. I mean,

515
00:29:25,480 --> 00:29:28,040
Speaker 2: that's obviously an exaggeration, But what about the possibility of

516
00:29:28,360 --> 00:29:33,480
Speaker 2: virtual tas right teachers assistants who can provide that kind

517
00:29:33,520 --> 00:29:38,040
Speaker 2: of tier one support for kids where the teacher. Like

518
00:29:38,080 --> 00:29:41,280
Speaker 2: you said, class numbers are growing, Teacher time is really hard.

519
00:29:41,640 --> 00:29:45,240
Speaker 2: Quite often a teacher in a classroom of thirty maybe

520
00:29:45,360 --> 00:29:48,600
Speaker 2: five students will take up the majority of their time.

521
00:29:49,080 --> 00:29:53,920
Speaker 2: So is there a way of providing an AI support

522
00:29:54,120 --> 00:29:57,360
Speaker 2: for teachers to be able to actually help students with

523
00:29:57,520 --> 00:30:00,680
Speaker 2: those first level questions before they go to the teacher.

524
00:30:01,120 --> 00:30:05,920
Speaker 2: Is that desirable or is that kind of using technology

525
00:30:05,960 --> 00:30:10,120
Speaker 2: to justify having worse teacher to student ratios? You know,

526
00:30:10,480 --> 00:30:12,800
Speaker 2: that's a big question to ask, But I'm just thinking about, like,

527
00:30:12,840 --> 00:30:15,200
Speaker 2: as we move into the future of education where it

528
00:30:15,280 --> 00:30:17,680
Speaker 2: doesn't look like we're going to be exploding numbers of

529
00:30:17,720 --> 00:30:20,720
Speaker 2: teachers while kid numbers are going to go up, like,

530
00:30:20,760 --> 00:30:22,560
Speaker 2: how do we is there a way that we can

531
00:30:22,720 --> 00:30:24,000
Speaker 2: help to mitigate that?

532
00:30:24,000 --> 00:30:27,280
Speaker 3: That's an interesting idea. I haven't given it too much thought,

533
00:30:27,440 --> 00:30:31,560
Speaker 3: but I think as a general thumb, if we're able

534
00:30:31,600 --> 00:30:34,320
Speaker 3: to help the teacher, you know, do their job job

535
00:30:34,360 --> 00:30:38,160
Speaker 3: more effectively, AKAA have a tutor that is able to

536
00:30:38,440 --> 00:30:44,160
Speaker 3: advide let's say, first line support to any sort of

537
00:30:44,240 --> 00:30:47,480
Speaker 3: questions about the correculum, then yeah, that could be a

538
00:30:48,720 --> 00:30:51,840
Speaker 3: useful feature of a platform, Like do you do.

539
00:30:51,840 --> 00:30:54,680
Speaker 2: You see there being a space for And I'm not

540
00:30:54,680 --> 00:30:56,280
Speaker 2: saying that Cammie's going to do it right now, but

541
00:30:56,360 --> 00:30:58,280
Speaker 2: I'm just kind of curious whether you think that that

542
00:30:58,440 --> 00:31:01,520
Speaker 2: is a future that we're headed towards, or is that

543
00:31:01,520 --> 00:31:03,440
Speaker 2: that's just so far in the future it's not even

544
00:31:03,480 --> 00:31:08,080
Speaker 2: worth thinking about having actual chatbots that can help students

545
00:31:08,120 --> 00:31:08,920
Speaker 2: in the classroom.

546
00:31:09,280 --> 00:31:11,200
Speaker 3: I think there are already companies that are trying to

547
00:31:11,240 --> 00:31:15,800
Speaker 3: do that today. I think there haven't hit mainstream yet.

548
00:31:16,120 --> 00:31:17,960
Speaker 3: You know, I've cluely seen quite a few of them.

549
00:31:18,720 --> 00:31:22,000
Speaker 3: It doesn't take a lot to implement something like this,

550
00:31:22,640 --> 00:31:25,000
Speaker 3: But the real question is like, how do you do

551
00:31:25,080 --> 00:31:29,080
Speaker 3: this with the right amount of controls and visibility and

552
00:31:29,120 --> 00:31:34,800
Speaker 3: transparency and safety so that it's actually easy for your

553
00:31:35,000 --> 00:31:38,720
Speaker 3: non icky teacher that doesn't have a lot of time

554
00:31:38,760 --> 00:31:41,640
Speaker 3: to go out and train about you know, all the

555
00:31:41,680 --> 00:31:44,920
Speaker 3: biases and limitations of any I and understand that fully

556
00:31:45,000 --> 00:31:46,920
Speaker 3: before they roll it out in the classroom. How do

557
00:31:46,960 --> 00:31:49,480
Speaker 3: you do how do you do that well? I haven't

558
00:31:49,480 --> 00:31:52,640
Speaker 3: seen that done yet, but I can see a future

559
00:31:52,680 --> 00:31:56,280
Speaker 3: of five teen years from now where that's a possibility,

560
00:31:56,560 --> 00:32:00,000
Speaker 3: and I can see how that's going to be super beneficial,

561
00:32:00,160 --> 00:32:02,680
Speaker 3: I think for the classroom. But you've got to do

562
00:32:02,720 --> 00:32:06,000
Speaker 3: it right, and I think that's the bet that I

563
00:32:06,000 --> 00:32:07,480
Speaker 3: haven't seen done well yet.

564
00:32:07,760 --> 00:32:12,280
Speaker 2: You mentioned personalization as well, using artificial intelligence to be

565
00:32:12,360 --> 00:32:16,360
Speaker 2: able to contextually understand the way that a student answer

566
00:32:16,760 --> 00:32:20,040
Speaker 2: different prompts, you know, using prompt might be the wrong word,

567
00:32:20,280 --> 00:32:22,920
Speaker 2: but be able to answer different questions in class like

568
00:32:23,480 --> 00:32:27,680
Speaker 2: can look at their writing, how they answer exam questions,

569
00:32:27,680 --> 00:32:32,800
Speaker 2: how they answer short answer, how they approach solving math questions,

570
00:32:33,320 --> 00:32:37,480
Speaker 2: and learn about the best way to help to guide them.

571
00:32:38,040 --> 00:32:41,360
Speaker 2: Can be fed information about their learning style, about their

572
00:32:42,600 --> 00:32:45,600
Speaker 2: you know, any learning support they might need, and can

573
00:32:45,840 --> 00:32:49,720
Speaker 2: help to amend curricula in a way that will be

574
00:32:49,880 --> 00:32:52,360
Speaker 2: supportive of students. We talked about it in terms of

575
00:32:52,800 --> 00:32:55,160
Speaker 2: you know, lightning McQueen voices and things like that, but

576
00:32:55,600 --> 00:32:58,160
Speaker 2: is there is there potential for it to actually more

577
00:32:58,200 --> 00:33:02,440
Speaker 2: specifically be able to address personal needs or help teachers

578
00:33:02,480 --> 00:33:06,080
Speaker 2: to address students' personal learning needs throughout the development of

579
00:33:06,080 --> 00:33:08,440
Speaker 2: a curriculum plan for a classroom.

580
00:33:08,720 --> 00:33:11,719
Speaker 3: Yeah, there's a lot there. I think there is a

581
00:33:11,720 --> 00:33:15,160
Speaker 3: lot of opportunity to realize with AI that we haven't

582
00:33:15,200 --> 00:33:18,760
Speaker 3: realized yet. And a lot of it comes down to

583
00:33:19,160 --> 00:33:22,880
Speaker 3: how do you personalize this curriculum to the interests of

584
00:33:22,920 --> 00:33:26,280
Speaker 3: the child, to what they've mentioned or said in the

585
00:33:26,360 --> 00:33:29,480
Speaker 3: past that might be of interest to them, and then

586
00:33:29,560 --> 00:33:31,760
Speaker 3: how do you do it in a way that still

587
00:33:31,800 --> 00:33:36,480
Speaker 3: provides an instructural value based on all the learning sciences.

588
00:33:36,560 --> 00:33:40,080
Speaker 3: So you know, we are certainly at this sort of

589
00:33:41,400 --> 00:33:44,760
Speaker 3: tip of the iceberg, if you will, around just scratching

590
00:33:44,760 --> 00:33:49,040
Speaker 3: the surface of what's possible in this technology. There's a

591
00:33:49,080 --> 00:33:51,520
Speaker 3: lot more that you can do to you know, purposefully,

592
00:33:51,560 --> 00:33:54,360
Speaker 3: take AI, take genet of AI, do it in a

593
00:33:54,440 --> 00:34:02,440
Speaker 3: way that can make curriculum more interacting, interact of engaging, accessible,

594
00:34:03,040 --> 00:34:06,560
Speaker 3: more accommodating to the students' needs, and do it in

595
00:34:06,560 --> 00:34:09,920
Speaker 3: a way that means that you don't necessarily need a

596
00:34:10,000 --> 00:34:14,480
Speaker 3: special needs accommodation or special needs sort of special needs

597
00:34:14,520 --> 00:34:18,040
Speaker 3: flag in this student system. To be able to have

598
00:34:18,120 --> 00:34:20,520
Speaker 3: that support offered to you, I think that's a dream,

599
00:34:20,640 --> 00:34:23,240
Speaker 3: is to be able to personalize it for every single student.

600
00:34:23,600 --> 00:34:27,200
Speaker 3: But doing in a way that is purposeful. I you know,

601
00:34:27,440 --> 00:34:30,160
Speaker 3: I con certain I see all those sort of opportunities

602
00:34:30,160 --> 00:34:33,080
Speaker 3: out there, and I think that's what's really exciting. Over

603
00:34:33,080 --> 00:34:36,320
Speaker 3: the next five ten years of just working in this space,

604
00:34:36,520 --> 00:34:39,480
Speaker 3: you know, we're able to realize a lot of things

605
00:34:39,520 --> 00:34:42,040
Speaker 3: that we've been thinking about and dreaming about for such

606
00:34:42,080 --> 00:34:42,680
Speaker 3: a long time.

607
00:34:44,600 --> 00:34:47,799
Speaker 2: What is the things that you're hearing most concern I

608
00:34:47,800 --> 00:34:50,120
Speaker 2: mean when chat GPT first came out, you know, it

609
00:34:50,160 --> 00:34:53,880
Speaker 2: was all about plagiarism, kids just asking chat GPT to

610
00:34:53,880 --> 00:34:56,360
Speaker 2: write essays for them, and then there's the battle of

611
00:34:56,400 --> 00:34:59,520
Speaker 2: the AI detectors and is it written by AI.

612
00:34:59,320 --> 00:35:00,640
Speaker 4: Or YadA YadA.

613
00:35:00,800 --> 00:35:03,239
Speaker 2: Is that still the biggest concern that teachers are having

614
00:35:03,640 --> 00:35:06,239
Speaker 2: or is there something else that's coming along in the

615
00:35:06,400 --> 00:35:09,040
Speaker 2: in the realm of AI and education that's starting to

616
00:35:09,320 --> 00:35:09,920
Speaker 2: displace that.

617
00:35:10,440 --> 00:35:13,560
Speaker 3: Yeah, that's a great question. I think there are certainly

618
00:35:13,920 --> 00:35:17,160
Speaker 3: there's certainly a huge market of AI detectors, but if

619
00:35:17,160 --> 00:35:20,320
Speaker 3: you actually if you look at the efficacy of those detectors,

620
00:35:20,400 --> 00:35:23,040
Speaker 3: it's actually very low. I think one of the dangerous

621
00:35:23,120 --> 00:35:26,640
Speaker 3: things to do in education is to say to a child,

622
00:35:26,880 --> 00:35:29,920
Speaker 3: we're forty three percent sure that you've laid rised as text,

623
00:35:30,280 --> 00:35:33,120
Speaker 3: and even if it gets to eighty or ninety percent,

624
00:35:33,480 --> 00:35:36,040
Speaker 3: you never want to have ten percent of doubt in

625
00:35:36,120 --> 00:35:40,319
Speaker 3: there to then go on to you know, go through

626
00:35:40,520 --> 00:35:43,360
Speaker 3: a disimilary process for the student. So unless you're one

627
00:35:43,440 --> 00:35:47,120
Speaker 3: hundred percent certain you I don't think I personally don't

628
00:35:47,120 --> 00:35:49,600
Speaker 3: think you should go through a process like that. So

629
00:35:50,239 --> 00:35:53,680
Speaker 3: what's important to educate as in my opinion is and

630
00:35:53,719 --> 00:35:58,720
Speaker 3: we've implemented this throughout throughout CAEMI, is giving the teacher

631
00:35:58,719 --> 00:36:02,640
Speaker 3: of visibility of how this work was evolved to where

632
00:36:02,640 --> 00:36:06,239
Speaker 3: it is today. And if we can show the teacher, hey,

633
00:36:06,280 --> 00:36:09,359
Speaker 3: the student spent this amount of time on CAMI, and

634
00:36:09,840 --> 00:36:12,920
Speaker 3: they edited this piece of work a few different times

635
00:36:12,920 --> 00:36:16,359
Speaker 3: and we went through a few different iterations of answering this,

636
00:36:16,800 --> 00:36:19,439
Speaker 3: then you can see that the proof of work is there.

637
00:36:19,800 --> 00:36:22,400
Speaker 3: And I think that's the bit that's important, that visibility

638
00:36:22,440 --> 00:36:25,600
Speaker 3: of how the student came about to this answer, And

639
00:36:26,280 --> 00:36:29,960
Speaker 3: ultimately my hope is that everyone's able to get to

640
00:36:30,000 --> 00:36:34,400
Speaker 3: that point throughout every single product. But yeah, this is

641
00:36:34,440 --> 00:36:37,279
Speaker 3: why I think it's so important to you know, when

642
00:36:37,320 --> 00:36:41,280
Speaker 3: you're implementing technology, you're not just jumping at the next

643
00:36:41,560 --> 00:36:45,279
Speaker 3: API that's available by chat reput but actually do it

644
00:36:45,320 --> 00:36:47,920
Speaker 3: in a way where you're thinking about how does what

645
00:36:48,080 --> 00:36:50,040
Speaker 3: is the impact of this in the classroom, and how

646
00:36:50,040 --> 00:36:53,280
Speaker 3: do I actually help the teacher. You know that famous

647
00:36:53,360 --> 00:36:55,560
Speaker 3: quote people don't know what they want until you show

648
00:36:55,600 --> 00:36:58,640
Speaker 3: it to them, right by Steve Jobs. And I think

649
00:36:58,640 --> 00:37:03,200
Speaker 3: that's the thesis that we've always come tried to try

650
00:37:03,239 --> 00:37:04,720
Speaker 3: to have at the core of CAMI.

651
00:37:04,960 --> 00:37:08,920
Speaker 2: You can see that working really well. You know in primary,

652
00:37:08,960 --> 00:37:12,640
Speaker 2: intermediate high school where you can get kids to use

653
00:37:12,920 --> 00:37:16,680
Speaker 2: a specific software where you can kind of hover over

654
00:37:16,719 --> 00:37:18,600
Speaker 2: them as they do it by pen and paper if.

655
00:37:18,480 --> 00:37:19,239
Speaker 4: You really need to.

656
00:37:20,160 --> 00:37:23,600
Speaker 2: When you get to university, the conversation changes though, because

657
00:37:24,280 --> 00:37:26,440
Speaker 2: you're sending kids away to go and do work. And

658
00:37:26,440 --> 00:37:28,600
Speaker 2: then back in my day you're printed it out and

659
00:37:28,640 --> 00:37:30,600
Speaker 2: you're stuck a cover paper and put it in a slot.

660
00:37:30,640 --> 00:37:33,600
Speaker 2: But these days it's a bit more advanced. You hand

661
00:37:33,640 --> 00:37:37,000
Speaker 2: in a word document. Are you expecting that we'll in

662
00:37:37,040 --> 00:37:40,440
Speaker 2: that realm start to see that you might need to

663
00:37:40,480 --> 00:37:43,480
Speaker 2: submit something that will give you a record of changes

664
00:37:43,520 --> 00:37:46,880
Speaker 2: throughout and then can you use like a souped up

665
00:37:46,920 --> 00:37:50,400
Speaker 2: air detection to look back at those changes and say, oh, okay,

666
00:37:50,440 --> 00:37:53,560
Speaker 2: actually we can recognize that this work has been done

667
00:37:54,040 --> 00:37:57,960
Speaker 2: legitimately and give it a green TECH or a red

668
00:37:58,040 --> 00:38:00,759
Speaker 2: cross saying it was just copy and pasted from Wikipedia

669
00:38:00,960 --> 00:38:01,640
Speaker 2: or a chat GPT.

670
00:38:02,120 --> 00:38:04,840
Speaker 3: Yeah, I think you're onto something. I think it's important

671
00:38:04,880 --> 00:38:08,680
Speaker 3: to have some sort of way to have the teacher

672
00:38:08,760 --> 00:38:12,920
Speaker 3: review how you came about, you know, arriving at this answer,

673
00:38:12,960 --> 00:38:16,600
Speaker 3: and if it means you know, being able to have

674
00:38:16,680 --> 00:38:21,359
Speaker 3: some sort of supplementary material that comes along it alongside

675
00:38:21,360 --> 00:38:24,600
Speaker 3: it to say Hey, I visited these ten websites and

676
00:38:24,640 --> 00:38:27,160
Speaker 3: this is how I arrived at this answer, And those

677
00:38:27,160 --> 00:38:31,680
Speaker 3: are my references, digital fingerprints, if you will, that's attached

678
00:38:31,680 --> 00:38:34,959
Speaker 3: to my work. I think that's the sort of thing

679
00:38:35,080 --> 00:38:38,360
Speaker 3: that ultimately, I think everyone should go towards, you know,

680
00:38:38,400 --> 00:38:40,759
Speaker 3: and if you take a step back and think about, well,

681
00:38:41,080 --> 00:38:44,640
Speaker 3: what is that in twenty years ago? Yeah, it was

682
00:38:44,760 --> 00:38:50,160
Speaker 3: a videography, was us putting references next to our to

683
00:38:50,200 --> 00:38:56,240
Speaker 3: support our answers. That was showing that you've done that research.

684
00:38:56,960 --> 00:39:02,719
Speaker 3: And ultimately, fundamentally that hasn't changed. And today's world, Yeah.

685
00:39:02,800 --> 00:39:03,960
Speaker 4: It's very interesting, isn't it.

686
00:39:04,000 --> 00:39:07,919
Speaker 2: Because you can also go to a product like Perplexity

687
00:39:08,280 --> 00:39:11,400
Speaker 2: and you can say, give me a quote about the

688
00:39:11,400 --> 00:39:14,359
Speaker 2: French revolutions or you know, the impact of the French

689
00:39:14,400 --> 00:39:19,239
Speaker 2: Revolution on the European economy and attribute, you know, find

690
00:39:19,280 --> 00:39:22,560
Speaker 2: a good source for it. And yeah, I mean it's

691
00:39:22,560 --> 00:39:24,480
Speaker 2: still a bit of a way away from just being

692
00:39:24,480 --> 00:39:26,960
Speaker 2: able to then say and make a whole.

693
00:39:27,360 --> 00:39:28,120
Speaker 4: Essay out of it.

694
00:39:28,239 --> 00:39:33,280
Speaker 2: So that augmentative side of AI seems to becoming more

695
00:39:33,840 --> 00:39:39,879
Speaker 2: prevalent in all all areas rather than the replacement side

696
00:39:39,880 --> 00:39:42,560
Speaker 2: of AI. Everyone was so concerned about is that a

697
00:39:42,600 --> 00:39:45,040
Speaker 2: trend that you're seeing as well, that you kind of

698
00:39:45,520 --> 00:39:48,440
Speaker 2: talked about it a little bit, but that teachers are

699
00:39:48,800 --> 00:39:54,640
Speaker 2: starting to say, oh, I see how AI can augment

700
00:39:54,760 --> 00:39:58,840
Speaker 2: students to do research, How AI can augment students to

701
00:39:59,280 --> 00:40:01,560
Speaker 2: give them a base line of writing that they can

702
00:40:01,600 --> 00:40:04,560
Speaker 2: then you know, edit and evolve into something that is

703
00:40:05,360 --> 00:40:09,200
Speaker 2: that is their own work, rather than the fear of well,

704
00:40:09,280 --> 00:40:10,960
Speaker 2: it's just gonna kids are going to lose all their

705
00:40:11,000 --> 00:40:12,600
Speaker 2: skills of being able to do anything.

706
00:40:13,040 --> 00:40:15,760
Speaker 3: Yeah, I certainly empathize with that point of view. I think,

707
00:40:16,280 --> 00:40:19,080
Speaker 3: you know, some people might disagree with this statement, but

708
00:40:19,600 --> 00:40:23,320
Speaker 3: AI is sort of having its calculator calculator moment inside

709
00:40:23,320 --> 00:40:26,640
Speaker 3: of education where once upon a time, and I think

710
00:40:26,680 --> 00:40:30,920
Speaker 3: we were all required to know how to do basic arithmetic.

711
00:40:31,440 --> 00:40:34,880
Speaker 3: And you can sider of see in some education systems

712
00:40:34,920 --> 00:40:38,640
Speaker 3: that requirements go on simply because everyone can pull out

713
00:40:38,640 --> 00:40:42,160
Speaker 3: their phones or a calculator and do that arithmetic. So

714
00:40:42,400 --> 00:40:45,680
Speaker 3: they've moved on their curriculum to perhaps things that are

715
00:40:45,719 --> 00:40:51,400
Speaker 3: more creative or interesting about math and stats, where it

716
00:40:51,640 --> 00:40:55,920
Speaker 3: frees you from having to learn arithmetic to you know,

717
00:40:55,920 --> 00:40:58,200
Speaker 3: you still have to have an understanding of math, right

718
00:40:58,239 --> 00:41:01,239
Speaker 3: and numbers and have a sick feel of how these

719
00:41:01,320 --> 00:41:05,600
Speaker 3: numbers play together. But to actually get the right precise

720
00:41:05,840 --> 00:41:09,200
Speaker 3: measurement or calculation, you don't necessarily need to know that

721
00:41:09,239 --> 00:41:11,840
Speaker 3: you can punch it into a calculator. And I suspect,

722
00:41:12,200 --> 00:41:15,520
Speaker 3: you know, if you think for five ten years, if

723
00:41:15,520 --> 00:41:18,279
Speaker 3: we can give everyone that calculator but for writing, and

724
00:41:18,360 --> 00:41:21,640
Speaker 3: everyone can write great. But it's then moved on to

725
00:41:22,280 --> 00:41:26,399
Speaker 3: how do you formulate ideas and integrate it into an

726
00:41:26,520 --> 00:41:30,680
Speaker 3: argument about something about your thoughts or perhaps a point

727
00:41:30,680 --> 00:41:33,160
Speaker 3: that you're trying to make. Then that could be really interesting,

728
00:41:33,440 --> 00:41:37,000
Speaker 3: right because it frees you up again from writer's block,

729
00:41:37,480 --> 00:41:41,480
Speaker 3: from all those things that you know are spelling and

730
00:41:41,520 --> 00:41:44,880
Speaker 3: perhaps you can I'm just sort of spitballing here, but

731
00:41:45,360 --> 00:41:47,920
Speaker 3: then you can really focus on the things that the

732
00:41:48,040 --> 00:41:51,239
Speaker 3: crux of you know, what it makes writing great, and

733
00:41:51,320 --> 00:41:56,000
Speaker 3: perhaps we can generate a whole generation of great writers.

734
00:41:56,480 --> 00:41:59,520
Speaker 3: And you know, I don't know. I think there is

735
00:42:00,360 --> 00:42:02,960
Speaker 3: the world's are oyster here and we can do a

736
00:42:03,000 --> 00:42:06,640
Speaker 3: lot with this. But I think to take that baby step,

737
00:42:06,719 --> 00:42:08,439
Speaker 3: you have to really think about how do you bear

738
00:42:08,520 --> 00:42:10,680
Speaker 3: some poment ai in the classroom and do it in

739
00:42:10,719 --> 00:42:15,239
Speaker 3: a way that's purposeful and try not to experiment with

740
00:42:15,480 --> 00:42:21,040
Speaker 3: these ideas because you ultimately it's these kids future at risk, right,

741
00:42:21,200 --> 00:42:24,400
Speaker 3: It's that's what's at stake here. So you can't just

742
00:42:24,480 --> 00:42:27,520
Speaker 3: play fast sinelos like what we've done with AI and

743
00:42:27,600 --> 00:42:30,040
Speaker 3: the corporate world. I think you have to be really

744
00:42:30,160 --> 00:42:32,880
Speaker 3: careful about you know, what are the safety guard rails,

745
00:42:33,440 --> 00:42:36,600
Speaker 3: back to my original points and doing them the right way.

746
00:42:36,760 --> 00:42:39,839
Speaker 3: So I think there's a lot of opportunity in front

747
00:42:39,880 --> 00:42:42,160
Speaker 3: of us. We just have to spend the time to

748
00:42:42,200 --> 00:42:44,839
Speaker 3: do it right and realize all the all the sort

749
00:42:44,880 --> 00:42:45,720
Speaker 3: of possibilities.

750
00:42:53,400 --> 00:42:55,239
Speaker 2: What do you think, Peter, do you think we're going

751
00:42:55,320 --> 00:42:58,880
Speaker 2: to be revolutionizing education with AI in the coming years.

752
00:43:00,040 --> 00:43:02,879
Speaker 1: Well, Henji's very cautious there, and we've seen this a lot.

753
00:43:03,000 --> 00:43:06,319
Speaker 1: You know, education when Generative AI really came out of

754
00:43:06,360 --> 00:43:10,160
Speaker 1: the starting gate, that was the one industry where everyone

755
00:43:10,280 --> 00:43:12,040
Speaker 1: had a lot of angst about and it was all

756
00:43:12,080 --> 00:43:16,759
Speaker 1: about plagiarism. And it's interesting his comments there around you know,

757
00:43:16,800 --> 00:43:20,520
Speaker 1: to what extent do you sort of start persecuting students

758
00:43:20,800 --> 00:43:25,720
Speaker 1: around plagiarism? Are they forty percent plagiaristic or one hundred percent?

759
00:43:25,920 --> 00:43:31,600
Speaker 1: You know, there's consequences for starting to make those claims

760
00:43:32,120 --> 00:43:36,000
Speaker 1: against the students, and these are not perfect, these plagiarism checkers.

761
00:43:36,040 --> 00:43:38,719
Speaker 1: So that's where it started, and that seems to have

762
00:43:38,840 --> 00:43:41,320
Speaker 1: died a death really over the last six months. I

763
00:43:41,400 --> 00:43:44,759
Speaker 1: think there are genuine concerns about plagiarism, but it's sort

764
00:43:44,760 --> 00:43:46,880
Speaker 1: of flipped around now where I think a lot of

765
00:43:47,880 --> 00:43:53,439
Speaker 1: administrators in tertiary and secondary education in particular are looking

766
00:43:53,480 --> 00:43:58,160
Speaker 1: at it and going, actually, we're stretched for resources. Teachers

767
00:43:58,440 --> 00:44:02,560
Speaker 1: have high teacher to student ratios when it comes to

768
00:44:03,239 --> 00:44:04,960
Speaker 1: the amount of time they can give a student. If

769
00:44:04,960 --> 00:44:07,879
Speaker 1: these tools can actually help us and shave time off,

770
00:44:08,000 --> 00:44:10,960
Speaker 1: like he was saying, even the traditional AI stuff was

771
00:44:11,000 --> 00:44:13,520
Speaker 1: saving in some cases up to eight hours a week

772
00:44:13,560 --> 00:44:16,799
Speaker 1: of admin for teachers. So if the next step of

773
00:44:16,840 --> 00:44:21,240
Speaker 1: that is reducing that admin burden and improving the experience

774
00:44:21,239 --> 00:44:25,800
Speaker 1: for teachers, fantastic. It is about augmentation of the teacher's

775
00:44:25,920 --> 00:44:28,960
Speaker 1: role rather than replacing aspects of what they do.

776
00:44:29,840 --> 00:44:32,920
Speaker 2: Yeah, it's amazing, like as a former teacher to think that,

777
00:44:33,320 --> 00:44:36,680
Speaker 2: you know, having eight hours a week back would have

778
00:44:36,719 --> 00:44:40,120
Speaker 2: been just incredible. And I think there is still a

779
00:44:40,200 --> 00:44:42,880
Speaker 2: huge amount of potential for AI in other areas as well,

780
00:44:42,920 --> 00:44:46,960
Speaker 2: like for example, report writing, Like I'm sure parents all

781
00:44:47,000 --> 00:44:50,680
Speaker 2: love to think of their teachers sitting down and spending

782
00:44:50,800 --> 00:44:54,400
Speaker 2: twenty minutes contemplating every student in their class and crafting

783
00:44:54,440 --> 00:44:58,200
Speaker 2: the perfect report, but realistically, we just didn't have time

784
00:44:58,360 --> 00:45:00,840
Speaker 2: for that kind of stuff. It was very much a

785
00:45:00,960 --> 00:45:05,000
Speaker 2: cut and based exercise where you consider based on results,

786
00:45:05,000 --> 00:45:07,680
Speaker 2: based on strengths, based on weaknesses, and then you just

787
00:45:07,760 --> 00:45:11,000
Speaker 2: kind of put together as fast as you can, but

788
00:45:11,680 --> 00:45:13,160
Speaker 2: in a way that and then you add like a

789
00:45:13,160 --> 00:45:15,680
Speaker 2: personal comment, you know, for each one, and if you

790
00:45:15,680 --> 00:45:19,880
Speaker 2: can use AI to kind of look at the student's

791
00:45:21,000 --> 00:45:25,360
Speaker 2: history and then prepopulate something, and then add you know,

792
00:45:25,400 --> 00:45:27,480
Speaker 2: a personal comment on top of that, which is basically

793
00:45:27,480 --> 00:45:30,920
Speaker 2: what was being done already. That's a huge amount of

794
00:45:30,920 --> 00:45:34,480
Speaker 2: administrative work. I know that's not really exactly in Cami's wheelhouse,

795
00:45:34,520 --> 00:45:36,359
Speaker 2: but those are the kinds of areas that I think,

796
00:45:36,520 --> 00:45:38,480
Speaker 2: you know, could really have a lot of impact.

797
00:45:39,080 --> 00:45:41,280
Speaker 1: You were talking to him about the generative AI stuff,

798
00:45:41,320 --> 00:45:45,320
Speaker 1: in particular automated grading, which which is great as long

799
00:45:45,440 --> 00:45:49,000
Speaker 1: as you said that you still have those personal comments.

800
00:45:49,040 --> 00:45:51,880
Speaker 1: You know what I sort of feared but sort of

801
00:45:51,920 --> 00:45:54,720
Speaker 1: loved as well. When I was a kid at school.

802
00:45:54,920 --> 00:45:59,799
Speaker 1: Was the comments in the margins from my teachers, all

803
00:45:59,800 --> 00:46:02,680
Speaker 1: the red lines and the underscoring and the crossing out

804
00:46:02,719 --> 00:46:05,600
Speaker 1: and the exclamation marks, that sort of stuff that showed

805
00:46:05,600 --> 00:46:09,840
Speaker 1: that a teacher had read my essay and was passionate

806
00:46:09,920 --> 00:46:13,160
Speaker 1: about me understanding how it could be improved. So making

807
00:46:13,200 --> 00:46:17,000
Speaker 1: sure that that human touch is still there. Even if

808
00:46:17,320 --> 00:46:20,160
Speaker 1: a lot of the basic grading is automated.

809
00:46:21,040 --> 00:46:23,880
Speaker 2: Yeah, it'll be much harder on like long form essays,

810
00:46:24,880 --> 00:46:27,920
Speaker 2: but even then you could get some basic stuff to

811
00:46:27,960 --> 00:46:30,080
Speaker 2: be like, well, this looks like it's probably going to

812
00:46:30,120 --> 00:46:33,280
Speaker 2: be in this range. Here are some areas, some sentences

813
00:46:33,320 --> 00:46:35,760
Speaker 2: you might want to look at. Here are some issues

814
00:46:35,800 --> 00:46:37,960
Speaker 2: that you might want to address with the student around

815
00:46:37,960 --> 00:46:41,720
Speaker 2: spelling or grammar or sentence construction or word use choice,

816
00:46:41,760 --> 00:46:44,920
Speaker 2: and those kinds of things can be automated. The general

817
00:46:45,800 --> 00:46:49,840
Speaker 2: feel of an argument is always going to have to

818
00:46:49,880 --> 00:46:53,960
Speaker 2: be a teacher, I think, because although you know AI

819
00:46:54,080 --> 00:46:57,480
Speaker 2: could potentially take some of that load, you probably don't

820
00:46:57,520 --> 00:47:01,480
Speaker 2: want it to because the margin of even if it's

821
00:47:01,480 --> 00:47:03,960
Speaker 2: a two percent margin of error, that's still too big

822
00:47:04,000 --> 00:47:07,200
Speaker 2: when you're dealing with education. I also really like this

823
00:47:07,280 --> 00:47:10,640
Speaker 2: idea of having like a little personal assistant, a little

824
00:47:10,719 --> 00:47:13,840
Speaker 2: you know, AI assistant that knows a lot about your

825
00:47:13,880 --> 00:47:19,800
Speaker 2: topic that students can access to ask first tier questions

826
00:47:19,840 --> 00:47:22,720
Speaker 2: like that, first tier support using chatbots for that.

827
00:47:22,640 --> 00:47:25,920
Speaker 1: Like, yeah, I could see the barriers to entry for

828
00:47:25,960 --> 00:47:27,800
Speaker 1: that to be really really low. I mean, if you

829
00:47:28,480 --> 00:47:32,320
Speaker 1: feed in a database of all your curriculum notes and that,

830
00:47:32,480 --> 00:47:35,000
Speaker 1: I think, you know, a co pilot could do quite

831
00:47:35,000 --> 00:47:37,640
Speaker 1: a good job of that now in terms of just

832
00:47:38,480 --> 00:47:43,200
Speaker 1: giving you answers to history questions or geography or whatever.

833
00:47:43,280 --> 00:47:46,000
Speaker 1: But I think that the real breakthrough, and no one

834
00:47:46,080 --> 00:47:49,160
Speaker 1: really is talking too deeply in education about this year

835
00:47:49,160 --> 00:47:52,120
Speaker 1: because it is difficult to do well, is a genuine

836
00:47:52,160 --> 00:47:55,480
Speaker 1: co pilot for the student that goes on them throughout

837
00:47:55,520 --> 00:48:00,880
Speaker 1: their entire learning process, potentially over a decade or longer.

838
00:48:01,480 --> 00:48:03,799
Speaker 1: Where for instance, I was terrible at maths at school.

839
00:48:03,840 --> 00:48:06,640
Speaker 1: I really struggled, and I got coaching in maths. I

840
00:48:06,719 --> 00:48:10,600
Speaker 1: hated going after school to do another hour of maths.

841
00:48:11,000 --> 00:48:13,400
Speaker 1: But if I had a copilot that was looking at

842
00:48:13,400 --> 00:48:16,719
Speaker 1: my test results, looking where I was struggling, giving me

843
00:48:16,800 --> 00:48:19,600
Speaker 1: suggestions and tuition that I could do in a nice,

844
00:48:19,840 --> 00:48:23,719
Speaker 1: easy digital format, that would be really useful and so

845
00:48:23,800 --> 00:48:27,319
Speaker 1: that involves probably a little bit more complexity. There's all

846
00:48:27,320 --> 00:48:32,120
Speaker 1: the curriculum information, but also analyzing my performance and giving

847
00:48:32,160 --> 00:48:35,560
Speaker 1: me suggestions to improve it. There's a whole pedagogy that

848
00:48:35,640 --> 00:48:41,040
Speaker 1: needs to be developed in the AI driven world to

849
00:48:41,120 --> 00:48:42,759
Speaker 1: support all of that. But I think that's going to

850
00:48:42,800 --> 00:48:44,280
Speaker 1: be the big game change of for students.

851
00:48:44,680 --> 00:48:49,280
Speaker 2: Yeah, and you know, kind of getting used to using

852
00:48:49,360 --> 00:48:52,560
Speaker 2: these tools in a way that is supportive of how

853
00:48:52,760 --> 00:48:54,880
Speaker 2: you do your work. I think that's going to be

854
00:48:54,880 --> 00:48:58,239
Speaker 2: really important as these students move into the workplace. So

855
00:48:58,520 --> 00:49:01,160
Speaker 2: these may not happen in primary school or even intermediate,

856
00:49:01,160 --> 00:49:03,640
Speaker 2: but as they start to get into high school, having

857
00:49:04,200 --> 00:49:07,760
Speaker 2: the ability to know when to ask a chatbot something

858
00:49:08,040 --> 00:49:11,239
Speaker 2: and how to ask, and what the limitations are and

859
00:49:11,719 --> 00:49:16,600
Speaker 2: you know what their strengths are. Learning those through education

860
00:49:16,760 --> 00:49:19,200
Speaker 2: I think is going to be super valuable as you

861
00:49:19,440 --> 00:49:22,080
Speaker 2: then emerge into the working world and may have to

862
00:49:22,120 --> 00:49:25,600
Speaker 2: rely on one to actually do your job. If you're

863
00:49:25,640 --> 00:49:28,359
Speaker 2: not familiar with it, it may actually, you know, hold

864
00:49:28,400 --> 00:49:29,800
Speaker 2: you back a little bit in the long run.

865
00:49:30,040 --> 00:49:34,840
Speaker 1: Yeah, So well done to Hinji Wang and team staying

866
00:49:34,880 --> 00:49:37,680
Speaker 1: on at CAMI at least for the time being, as

867
00:49:37,719 --> 00:49:41,319
Speaker 1: this big US company takes a controlling stake in it,

868
00:49:41,480 --> 00:49:45,080
Speaker 1: so they seem to seem to be poised really for

869
00:49:46,040 --> 00:49:49,040
Speaker 1: excellent growth, particularly in the US, which this Boston based

870
00:49:50,160 --> 00:49:52,880
Speaker 1: investment company I think is particularly interested in the US market.

871
00:49:52,960 --> 00:49:55,480
Speaker 1: So it seems like it's onwards and upwards for them,

872
00:49:55,520 --> 00:49:58,080
Speaker 1: with a dose of AI thrown in.

873
00:49:58,560 --> 00:50:03,000
Speaker 2: Absolutely, congratulations came and all the founders there. That's our

874
00:50:03,040 --> 00:50:05,600
Speaker 2: show for another week. Thank you very much to henji

875
00:50:05,640 --> 00:50:08,280
Speaker 2: Wan for taking the time to talk about CAMI, AI

876
00:50:08,360 --> 00:50:09,360
Speaker 2: and education.

877
00:50:09,880 --> 00:50:12,960
Speaker 1: Show notes on Cammi's big payday. Some background reading on

878
00:50:13,120 --> 00:50:16,319
Speaker 1: AI and education and coverage of those Apple announcements are

879
00:50:16,320 --> 00:50:18,520
Speaker 1: in the show notes. You'll find them in the Tech

880
00:50:18,560 --> 00:50:20,759
Speaker 1: section of the Business Desk website, and.

881
00:50:20,800 --> 00:50:23,480
Speaker 2: The Business of Tech is of course on iHeartRadio and

882
00:50:23,640 --> 00:50:26,799
Speaker 2: all major podcast platforms. Leave us a rating in your

883
00:50:26,840 --> 00:50:29,480
Speaker 2: favorite app to help boost our exposure and get in

884
00:50:29,520 --> 00:50:32,879
Speaker 2: touch with me with your feedback, ideas, topics, and guest suggestions.

885
00:50:32,920 --> 00:50:33,920
Speaker 4: Email me Ben.

886
00:50:33,760 --> 00:50:36,799
Speaker 2: At Businessdesk dot co, dot z or find us both

887
00:50:36,880 --> 00:50:38,759
Speaker 2: on LinkedIn and sometimes x.

888
00:50:39,040 --> 00:50:41,759
Speaker 1: Another dose of the Business of Tech coming your way

889
00:50:41,960 --> 00:50:42,840
Speaker 1: next Thursday.

890
00:50:43,120 --> 00:50:43,640
Speaker 4: Catch you then,