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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.

Hankali da kuma ƙarshen wayewar kai

Charts Hulda

VUCA

M darajar da hulda coefficient

Rarraba al'ada, ta William Gubetes (Dalibi) r = 0.0727

Rarraba ba rarraba ba, da Spearman r = 0.003

Nuna kawai sakamako > r = 0.0727

Rarrabuwa	Da ba al'ada ba	Na al'ada	Da ba al'ada ba	Na al'ada	Na al'ada	Na al'ada	Na al'ada	Na al'ada
Duk Tambayoyi Duk Tambayoyi 1) Aminci (nawa kuka yarda ko ba da yarda ba?) 2) Gudanarwa (nawa ne ka yarda ko ba da yarda ba?)
1) Aminci (nawa kuka yarda ko ba da yarda ba?)
Answer 1	-	Rauni kyau 0.0666	Rauni kyau 0.0209	Rauni kyau 0.0941	Rauni korau -0.1169	Rauni korau -0.0089	Rauni korau -0.0469	Rauni kyau 0.0197
Answer 2	-	Rauni kyau 0.0169	Rauni korau -0.0027	Rauni kyau 0.0434	Rauni korau -0.0239	Rauni kyau 0.0400	Rauni korau -0.0073	Rauni korau -0.0555
Answer 3	-	Rauni korau -0.0228	Rauni korau -0.0256	Rauni kyau 0.0045	Rauni kyau 0.0553	Rauni korau -0.0232	Rauni korau -0.0105	Rauni kyau 0.0074
Answer 4	-	Rauni kyau 0.0329	Rauni korau -0.0047	Rauni kyau 0.0149	Rauni korau -0.0412	Rauni korau -0.0342	Rauni korau -0.0074	Rauni kyau 0.0449
Answer 5	-	Rauni korau -0.0094	Rauni korau -0.0261	Rauni korau -0.0230	Rauni kyau 0.0463	Rauni kyau 0.0346	Rauni kyau 0.0300	Rauni korau -0.0519
Answer 2	-	Rauni korau -0.0118	Rauni korau -0.0530	Rauni korau -0.0733	Rauni kyau 0.0702	Rauni korau -0.0155	Rauni kyau 0.0470	Rauni kyau 0.0129
Answer 6	-	Rauni korau -0.0643	Rauni kyau 0.0935	Rauni korau -0.0592	Rauni korau -0.0012	Rauni kyau 0.0092	Rauni korau -0.0034	Rauni kyau 0.0233
2) Gudanarwa (nawa ne ka yarda ko ba da yarda ba?)
Answer 7	-	Rauni kyau 0.0149	Rauni kyau 0.0058	Rauni kyau 0.0830	Rauni kyau 0.0595	Rauni korau -0.0326	Rauni korau -0.0791	Rauni korau -0.0463
Answer 8	-	Rauni kyau 0.0229	Rauni korau -0.0238	Rauni korau -0.0378	Rauni kyau 0.0297	Rauni kyau 0.0811	Rauni korau -0.0113	Rauni korau -0.0566
Answer 9	-	Rauni kyau 0.0132	Rauni korau -0.0401	Rauni korau -0.0533	Rauni korau -0.0207	Rauni kyau 0.0031	Rauni kyau 0.0593	Rauni kyau 0.0308
Answer 10	-	Rauni kyau 0.0224	Rauni kyau 0.0023	Rauni kyau 0.0128	Rauni korau -0.0592	Rauni korau -0.0166	Rauni korau -0.0136	Rauni kyau 0.0552
Answer 11	-	Rauni korau -0.0087	Rauni kyau 0.0333	Rauni kyau 0.0523	Rauni kyau 0.0403	Rauni korau -0.0660	Rauni kyau 0.0087	Rauni korau -0.0436
Answer 8	-	Rauni korau -0.0947	Rauni korau -0.0346	Rauni korau -0.0132	Rauni kyau 0.0111	Rauni kyau 0.0178	Rauni kyau 0.0731	Rauni kyau 0.0036
Answer 12	-	Rauni korau -0.0009	Rauni kyau 0.0838	Rauni korau -0.0344	Rauni korau -0.0762	Rauni korau -0.0236	Rauni korau -0.0102	Rauni kyau 0.0767

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Wannan aikin zai kasance a cikin zaben VUCA

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

2023.11.27

Valerii Kosenko

Mai Samfurin SaaS SDTEST®

Valerii ya cancanta a matsayin masanin ilimin zamantakewar jama'a-psychologist a 1993 kuma tun daga lokacin ya yi amfani da iliminsa a cikin gudanar da ayyukan.
Valerii ya sami digiri na biyu da kuma cancantar aikin da mai sarrafa shirye-shirye a cikin 2013. A lokacin shirinsa na Jagora, ya saba da Project Roadmap (GPM Deutsche Gesellschaft für Projektmanagement e. V.) da Spiral Dynamics.
Valerii shine marubucin binciken rashin tabbas na V.U.C.A. ra'ayi ta yin amfani da Ƙaƙwalwar Ƙarƙashin Ƙarƙashin Ƙarƙashin Ƙarƙashin Ƙarƙashin Ƙididdiga da Ƙididdiga na Lissafi a cikin ilimin halin dan Adam, da kuma 38 na kasa da kasa zabe.

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