Home/Blog/AI Assistants Boost Beginners More Than Experts, Study Shows Correlation

AI Assistants Boost Beginners More Than Experts, Study Shows Correlation

There once was an AI named Chat who was really good at repeating back information it already knew. One day, Chat was given to some office workers [1] to help them with their jobs. Some of the workers were experts at their jobs, while others were still learning.

At first, Chat helped all the workers get more work done faster - even the experts! But soon, the experts noticed something funny. The workers who were still learning got way MORE help from Chat. The new workers improved a lot using Chat, doing their work faster and better than ever before!

The experts wondered why Chat didn't help them as much. That's when they realized - that Chat is an expert at repeating back facts but can't come up with brand new ideas. So, for workers who already knew those facts, Chat didn't offer them that much new help. But for newer workers still learning those basics, Chat was able to teach them so much more!

This shows a correlation - as in, two things that relate to each other and change together. The more expert a worker already was, the less helpful Chat was for them. But for newer workers, Chat could help them almost as much as the experts! It's because of their different starting points. Chat has a limit to how expert it can be. So, the closer a worker already was to Chat's expertise, the less new stuff Chat offered them.

The experts and newbies improved at different rates thanks to Chat. Their own expertise compared to Chat's matters for how much more they can learn. That connection in how much they improve is the correlation!

The SDTEST^® gives clues to someone's motivational values. However, additional polls can provide more pieces of the puzzle.

Imagine also giving an "A.I. and the end of civilization" poll. It asks people to rate at the agree or disagree level.

Now imagine 100 people who took both tests. You could match up each person's SDTEST^® colors with their rated answers about the danger of AI.

Comparing tests gives an expanded picture of values in action. More puzzle pieces make the whole image more apparent!

Multiple tests can work together, like colors blending on a palette. Other polls reveal what engages your values, like what is the perception of the danger of AI. Combined, they paint a richer picture of what motivates our thoughts and deeds.

Below you can read an abridged version of the results of our VUCA poll “A.I. and the end of civilization“. The full results of the poll are available for free in the FAQ section after login or registration.

Eòlas fuadain agus deireadh sìobhaltachd

Charts Co-dhàimh

VUCA

Tha e cudromach gu staitistigeach co-dhàimh

Sgaoileadh àbhaisteach, le Uilleam Sealy Howet (Oileanach) r = 8.74

Sgaoileadh neo-àbhaisteach, le spearman r = 0.39

Seall a-mhàin toraidhean a-mhàin > r = 8.74

Ceistean Poll

A h-uile ceist

1) Sàbhailteachd (dè an ìre a tha thu ag aontachadh no ag eas-aontachadh?)

2) Smachd (dè an ìre a tha thu ag aontachadh no ag eas-aontachadh?)

Sgaoileadh

Deimhinneach co-dhàimh,%

Negative co-dhàimh,%

Àbhaisteach

3.4 Answer 1 3.2 Answer 2 1.6 Answer 3 3.2 Answer 4 #1	-6 Answer 9 -1.5 Answer 10 -3.1 Answer 3 #1
3.1 Answer 5 3.2 Answer 6 1.9 Answer 7 7.6 Answer 8 #2	-7 Answer 11 -2.5 Answer 12 -4.3 Answer 7 #2

Àbhaisteach

6.1 Answer 3 4.9 Answer 3 0.6 Answer 4 #1	-5.4 Answer 1 -1.8 Answer 9 -1.6 Answer 10 -1.9 Answer 2 #1
2.6 Answer 5 0.1 Answer 6 4.8 Answer 7 8.8 Answer 7 3.3 Answer 8 #2	-11.5 Answer 11 -2.3 Answer 12 #2

Àbhaisteach

1 Answer 1 1.9 Answer 3 4 Answer 4 #1	-0.5 Answer 9 -1.3 Answer 10 -4.7 Answer 2 -0.2 Answer 3 #1
9.6 Answer 12 0.4 Answer 6 2.6 Answer 8 #2	-3.6 Answer 11 -3.1 Answer 5 -7.6 Answer 7 -1.1 Answer 7 #2

Àbhaisteach

8.2 Answer 10 1.3 Answer 3 8.2 Answer 3 #1	-13.1 Answer 1 -0.5 Answer 9 -2.4 Answer 2 -4.9 Answer 4 #1
6 Answer 11 3.3 Answer 12 4.4 Answer 7 3.3 Answer 7 #2	-2.8 Answer 5 -4.8 Answer 6 -11.3 Answer 8 #2

Àbhaisteach

12.4 Answer 1 3 Answer 9 0.6 Answer 10 2.9 Answer 2 #1	-1.5 Answer 3 -10.7 Answer 3 -5.1 Answer 4 #1
10.8 Answer 11 3.1 Answer 7 #2	-5.6 Answer 12 -0.9 Answer 5 -0.9 Answer 6 -2.3 Answer 7 -5.7 Answer 8 #2

Àbhaisteach

3.7 Answer 9 4.3 Answer 2 6.2 Answer 4 #1	-4 Answer 1 -3.3 Answer 10 -2.4 Answer 3 -5.8 Answer 3 #1
4.3 Answer 11 1 Answer 6 4.5 Answer 7 4.3 Answer 8 #2	-4.5 Answer 12 -3.8 Answer 5 -3.5 Answer 7 #2

Àbhaisteach

6.5 Answer 1 5 Answer 9 0.7 Answer 3 #1	-3.1 Answer 10 -0.7 Answer 2 -3.9 Answer 3 -3.6 Answer 4 #1
2.5 Answer 11 1.2 Answer 12 5.4 Answer 5 1.6 Answer 6 1.2 Answer 8 #2	-3.8 Answer 7 -12.3 Answer 7 #2

Export gu MS Excel

Bidh an gnìomh seo ri fhaighinn anns na cunntasan-bheachd VUCA agad fhèin

Ceart gu leòr

[1] https://www.ft.com/content/b2928076-5c52-43e9-8872-08fda2aa2fcf

2023.11.27

Valerii Kosenko

Pròiseact Pet WAS SAS SASTSTS®

Bha Valerii airidh air eòlaiche-inntinn oideachaidh oideachaidh sòisealta ann an 1993 agus tha e air an eòlas aige a chuir an gnìomh ann an riaghladh pròiseict.
Fhuair Valerii ceum Maighstir agus Teisteanas Pròiseact agus Manaidsear a 'Phrògraim ann an 2013. Aig a' phrògram a mhaighstir, thàinig e gu bhith eòlach air Mapa Rathaid Für Für Für Für. V.) agus daineamaigs snìomhach.
Ghabh Valerii diofar dhiofar dheuchainnean dinnamics snìomhach agus chleachd e an t-eòlas agus an t-eòlas aige gus an dreach làithreach de SDSTST atharrachadh.
Valerii an ùghdar air a bhith a 'sgrùdadh mì-chinnt an V.U.C.A. bun-bheachd a 'cleachdadh staitistig daineamaigs shnìomhanach agus matamataigeach ann an eòlas-inntinn, còrr air 20 cunntas-sluaigh eadar-nàiseanta.

Tha an dreuchd seo air 0 Beachdan

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Cuir dheth freagairt

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