Variables for Clustering Analysis (H1)_
| POSNEGAI | How excited or concerned would you be if artificial intelligence computer programs could do each of the following? |
| a | Know people’s thoughts and behaviors |
| b | Perform household chores |
| c | Make important life decisions for people |
| d | Diagnose medical problems |
| e | Perform repetitive workplace tasks |
| f | Handle customer service calls |
| POSNEGHE | How excited or concerned would you be about potential new techniques that could change human abilities in the following ways? |
| a | Slow the aging process to allow the average person to live decades longer |
| b | Allow some people to far more quickly and accurately process information |
| c | Prevent some people from getting serious diseases or health conditions |
| d | Allow some people greatly increased strength for lifting heavy objects |
| e | Allow some people to see shapes and patterns in crowded spaces far beyond what the typical person can see today |
| f | Allow some people to hear sounds far beyond what the typical person can hear today |
ANOVA Results for General Attitudes toward Technology, Science and AI (H2)_
| Variable | Cluster | M | SD | n | η2 | Tukey results |
|---|---|---|---|---|---|---|
| CNCEXC_W99 | Skeptics | 2.65 | 0.52 | 2951 | .29 | Skeptics > Cautious > Optimists |
| Cautious | 2.21 | 0.63 | 4336 | |||
| Optimists | 1.62 | 0.66 | 2639 | |||
| ALGFAIR_W99 | Skeptics | 2.35 | 0.69 | 2928 | .14 | Skeptics > Cautious > Optimists |
| Cautious | 2.06 | 0.73 | 4309 | |||
| Optimists | 1.58 | 0.72 | 2621 | |||
| TECH1_W99 | Skeptics | 1.87 | 0.68 | 1541 | .11 | Skeptics > Cautious > Optimists |
| Cautious | 1.55 | 0.62 | 2157 | |||
| Optimists | 1.29 | 0.52 | 1289 | |||
| SC1_W99 | Skeptics | 1.57 | 0.67 | 1403 | .08 | Skeptics > Cautious > Optimists |
| Cautious | 1.29 | 0.53 | 2175 | |||
| Optimists | 1.15 | 0.43 | 1348 |
The 4 Variables about General Attitudes toward Technology, Science and AI (H2)_
| CNCEXC | Artificial intelligence computer programs are designed to learn tasks that humans typically do, for instance recognizing speech or pictures. Overall, would you say the increased use of artificial intelligence computer programs in daily life makes you feel… |
| 1 More excited than concerned | |
| ALGFAIR | Do you think it is possible or not possible for people to design artificial intelligence computer programs that can consistently make fair decisions in complex situations? |
| 1 Possible | |
| TECH1 | Overall, would you say technology has had a mostly positive effect on our society or a mostly negative effect on our society? |
| SC1 | Overall, would you say science has had a mostly positive effect on our society or a mostly negative effect on our society? |
| 1 Mostly positive |
ANOVA Results for AI Technologies in Various Domains (H3)_
| Variable | Cluster | M | SD | n | η2 | Tukey results |
|---|---|---|---|---|---|---|
| SMALG2_W99 | Skeptics | 2.20 | 0.81 | 1538 | .06 | Skeptics > Cautious > Optimists |
| Cautious | 1.90 | 0.84 | 2156 | |||
| Optimists | 1.66 | 0.84 | 1288 | |||
| FACEREC2_W99 | Skeptics | 1.92 | 0.83 | 1537 | .02 | Skeptics > Cautious > Optimists |
| Cautious | 1.81 | 0.83 | 2156 | |||
| Optimists | 1.64 | 0.83 | 1287 | |||
| DCARS2_W99 | Skeptics | 2.62 | 0.62 | 1540 | .19 | Skeptics > Cautious > Optimists |
| Cautious | 2.14 | 0.81 | 2153 | |||
| Optimists | 1.64 | 0.80 | 1287 | |||
| BCHIP2_W99 | Skeptics | 2.76 | 0.48 | 1405 | .16 | Skeptics > Cautious > Optimists |
| Cautious | 2.51 | 0.63 | 2162 | |||
| Optimists | 2.03 | 0.80 | 1346 | |||
| GENEV2_W99 | Skeptics | 2.33 | 0.72 | 1403 | .13 | Skeptics > Cautious > Optimists |
| Cautious | 1.97 | 0.76 | 2170 | |||
| Optimists | 1.56 | 0.73 | 1347 | |||
| EXOV2_W99 | Skeptics | 2.28 | 0.67 | 1406 | .17 | Skeptics > Cautious > Optimists |
| Cautious | 1.83 | 0.69 | 2173 | |||
| Optimists | 1.47 | 0.66 | 1345 |
Comparison between Applied AI (as in this study) and GenAI_
| Aspect | Applied AI (as in this study) | Generative AI (GenAI) |
|---|---|---|
| Core Function | Perception, prediction, and autonomous decision in real-world contexts | Creates new and complex outputs such as text, images, music, or code |
| Typical Examples | Facial recognition, driverless cars, brain–computer interfaces, misinformation detection | ChatGPT, Copilot, DeepSeek, Grok, Gemini |
| Learning and Data | Typically multimodal and task-specific | Trained on massive datasets to learn patterns of human expression |
| User Interaction | Often indirect (embedded in devices and platforms); outputs are felt via actions or decisions | Direct interaction via prompts; outputs are visible artifacts (text, images, code) |
| Risks | Physical harm, systemic bias, privacy, liability; rare-event risks with high consequences | Misinformation, bias, privacy, automation of creative or knowledge work |
| Primary Concerns | Safety, accountability, data privacy, bias in decision outcomes, etc. | Authenticity, transparency, accuracy, intellectual property, ethical use, etc. |
The 6 Variables about AI Technologies in Various Domains (H3)_
| Cluster | Size | POSNEGAI (Mean ± SD) | POSNEGHE (Mean ± SD) |
|---|---|---|---|
| 1 Skeptics | 2951 | 4.28 ± 0.37 | 3.38 ± 0.96 |
| 2 Cautious | 4336 | 3.26 ± 0.28 | 2.56 ± 0.78 |
| 3 Optimists | 2639 | 2.18 ± 0.45 | 1.80 ± 0.70 |