# What the Evidence Says About the Dunning-Kruger Effect

The Dunning-Kruger effect is widely misunderstood as a phenomenon where rank beginners are wildly arrogant, but the evidence shows true beginners are actually quite cautious. While a brief surge of overconfidence can occur as people learn, modern statistical consensus suggests the classic effect is mostly a mathematical illusion driven by regression to the mean and general human optimism. Furthermore, cross-cultural studies prove that this bias is heavily influenced by Western self-enhancement culture rather than a universal deficit in human brain wiring.

## The Internet Myth Versus the Original Science

If you spend enough time reading online forums, watching educational YouTube videos, or scrolling through social media debates, you have likely seen someone accuse a loud, misinformed commenter of suffering from the Dunning-Kruger effect [cite: 1, 2, 3]. The phenomenon has entered the cultural lexicon as a catch-all insult for arrogant ignorance. In popular culture, the effect is almost always illustrated using a specific line graph that shows confidence skyrocketing to a massive peak at the very beginning of a person's learning journey. This peak is affectionately dubbed "Mount Stupid" [cite: 4]. 

According to this popular graph, an individual with low competence possesses an enormous amount of confidence. As this person actually learns more about a subject, their confidence plummets into a "Valley of Despair" before slowly climbing up a "Slope of Enlightenment" toward true mastery [cite: 5, 6]. The narrative suggests that the most ignorant people in the room are consistently the most self-assured. There is just one problem with this widespread belief: this graph has absolutely nothing to do with the actual Dunning-Kruger effect, and it never appeared in any peer-reviewed psychology paper [cite: 7].

### The True Origins of Mount Stupid

The "Mount Stupid" chart actually originated in 2011 from a webcomic called *Saturday Morning Breakfast Cereal* (SMBC), which drew a simplified, comedic interpretation of cognitive bias [cite: 4, 7]. Shortly after the comic was published, a blogger paired the comic's concept with the academic term "Dunning-Kruger," and the image spread virally across the internet [cite: 7]. From there, the chart found its way into corporate presentations, pop-psychology articles, and merchandise [cite: 6, 8, 9, 10]. 

The real Dunning-Kruger effect does not track a single person's learning curve over time, nor does it measure raw confidence [cite: 7, 11]. Furthermore, the original research never found that poor performers are more confident than experts. In fact, it found the exact opposite. People who perform poorly do not exhibit boundless arrogance; they simply overestimate their abilities by a wider margin than top performers do [cite: 7].

### What the 1999 Study Actually Found

In 1999, psychologists David Dunning and Justin Kruger published a seminal paper titled "Unskilled and Unaware of It" in the *Journal of Personality and Social Psychology* [cite: 12, 13, 14]. To study how well people could assess their own abilities, the researchers tested college students at Cornell University on various subjects, including humor, English grammar, and logical reasoning [cite: 15, 16, 17]. After completing the tests, the students were asked to estimate how well they performed compared to their peers.

When the researchers grouped the students into four tiers, or quartiles, based on their actual objective test scores, a distinct pattern emerged. The bottom twenty-five percent of test-takers drastically overestimated their performance. In the original 1999 study, participants in the bottom quartile estimated their scores to be around the 62nd percentile. While this is a massive overestimation relative to their actual 12th percentile performance, their absolute confidence was still lower than that of the top performers, who accurately estimated their scores in the 80th percentile [cite: 13, 17]. Conversely, the college students in the top twenty-five percent predicted their scores to be slightly lower than they actually were [cite: 15, 16, 17].

### The Double Burden Hypothesis

To explain this pattern, Dunning and Kruger proposed a metacognitive explanation that has since become known as the "double burden" or the "dual curse" [cite: 14, 18, 19]. They theorized that the exact same cognitive skills required to perform well in a domain are the skills required to evaluate one's performance accurately [cite: 11, 20, 21, 22]. 

If an individual is terrible at recognizing proper English grammar, they lack the foundational knowledge required to realize that their own grammar is terrible. Because they lack the expertise to spot their own mistakes, they assume they are doing just fine [cite: 17, 23]. According to this theory, incompetent people are deprived of the metacognitive insight necessary to recognize their incompetence, leading to inflated self-assessments [cite: 12, 17, 19]. Meanwhile, top performers suffer from a false-consensus effect: because a task comes easily to them, they incorrectly assume it must be easy for everyone else, leading them to underestimate their relative standing [cite: 15, 17, 22].

## The Beginner's Bubble and the Trajectory of Learning

If the classic "Mount Stupid" graph is a myth, does overconfidence ever behave the way the internet thinks it does? Modern research suggests it does, but only under very specific conditions that differ entirely from the original Dunning-Kruger framework. 

A major boundary condition of the Dunning-Kruger effect is that it does not apply to absolute, rank beginners [cite: 24, 25]. If you ask someone who has never played chess or flown a helicopter how good they are at those activities, they will not suffer from illusory superiority. They will accurately tell you they have zero skill, because they are not laboring under a false illusion of competence [cite: 11, 25, 26]. 

### How a Little Learning Becomes a Dangerous Thing

In 2018, David Dunning and Carmen Sanchez revisited the concept of overconfidence by tracking people continuously as they actively learned a new skill. Across multiple studies, they asked participants to complete multicue probabilistic learning tasks, such as attempting to diagnose fictional "zombie diseases" based on a complex set of physical symptoms [cite: 11, 19, 27]. 

The researchers found that true beginners are actually highly cautious, unconfident, and humble about their predictions [cite: 19, 24, 26]. However, it only takes a tiny amount of experience for overinflated confidence to strike. After just a few learning trials, participants formed quick, exuberant theories about how to approach the task. Because they experienced an increase in decision fluency—meaning they were making choices faster—they mistook this speed for accuracy [cite: 19, 27]. 

Researchers dubbed this rapid surge in unwarranted certainty the "beginner's bubble" [cite: 11, 19, 27]. Unlike the Dunning-Kruger effect, which evaluates people based on static competence levels at a single point in time, the beginner's bubble tracks the psychological journey of learning over time [cite: 7, 26]. 

| Phase of Learning | Actual Competence | Perceived Confidence | Psychological State |
| :--- | :--- | :--- | :--- |
| **Rank Beginner** | Zero | Very Low | Cautious, humble, and highly aware of their own ignorance [cite: 24, 25, 26]. |
| **Experienced Beginner** | Low | Very High | **The Beginner's Bubble**: High confidence driven by exuberant, error-filled theorizing and increased decision speed [cite: 11, 19, 27]. |
| **Correction Phase** | Moderate | Flattening or Dropping | Realization of task complexity; confidence dips or levels off while actual performance incrementally improves [cite: 24, 27, 28]. |
| **Proficient** | High | High | Confidence finally begins to align with actual, earned accuracy and a nuanced understanding of the domain [cite: 24, 28]. |

This bubble of overconfidence is particularly dangerous in fields like medicine and finance. Studies show that a medical resident fresh out of school is usually highly aware of what they do not know and will seek advice, avoiding the bubble. It is often the practitioner with just enough experience to feel comfortable who stops double-checking their work, falling prey to overconfident theorizing before they have reached true mastery [cite: 23, 25].

## Is the Dunning-Kruger Effect a Statistical Illusion?

While the psychological narrative of the "double burden" is compelling and intuitively satisfying, a growing movement of data scientists, mathematicians, and psychologists argue that the Dunning-Kruger effect is largely a statistical artifact [cite: 15, 16, 22, 29, 30, 31]. 

Between 2002 and 2024, rigorous re-examinations of the original data suggested that you do not need a psychological theory about metacognitive deficits to explain why the bottom quartile overestimates their scores. Advanced statistical models have repeatedly demonstrated that the effect can be explained almost entirely by basic mathematics, casting doubt on the idea that incompetent people possess uniquely broken self-awareness [cite: 22, 32, 33, 34, 35].

### The Problem of Autocorrelation

The most devastating critique of the Dunning-Kruger effect is that its famous data charting relies heavily on autocorrelation, which occurs when a variable is correlated with itself [cite: 29, 30, 36].

In the original study, researchers measured an individual's Actual Score and their Perceived Score. To demonstrate the Dunning-Kruger effect, they plotted the Actual Score on the horizontal axis, and the Error—calculated as the Perceived Score minus the Actual Score—on the vertical axis [cite: 30, 36, 37]. 

As data scientists like Blair Fix have pointed out, if you plot an equation where the Actual Score is subtracted from the vertical axis while also serving as the horizontal axis, you are mathematically guaranteeing a negative slope [cite: 30, 36]. As the actual score gets smaller, the overestimation value is forced by basic arithmetic to get larger. The finding that people with low scores overestimate their scores more than people with high scores is a mathematical inevitability of how the data is plotted [cite: 36].

To prove this, researchers have generated entirely random datasets where a hypothetical person's test score has absolutely zero connection to their perceived score. When this completely random noise is plotted using Dunning and Kruger's methodology, the exact same "unskilled and unaware" curve appears perfectly on the chart [cite: 29, 36, 38]. The signature shape of the Dunning-Kruger effect emerges not because of human psychology, but because of how the variables are constructed and subtracted from one another [cite: 16, 30].

### Regression to the Mean and Task Difficulty

Another statistical force at play is regression to the mean. No test is a perfect measure of true ability, as performance on any given day is influenced by random situational factors like fatigue, lucky guesses, or tricky wording [cite: 16, 22, 33, 38]. 

If someone scores in the absolute bottom twelfth percentile on a grammar test, part of that extreme score is due to a lack of skill, but part of it is due to statistical bad luck. Because their true, long-term ability is mathematically likely to be closer to the average than their single terrible test score suggests, any self-assessment they make will appear as an "overestimation" compared to that single extreme data point [cite: 16, 22, 32, 39]. 

Furthermore, researchers have found that the Dunning-Kruger pattern shifts entirely depending on how difficult a task is. On tasks that people generally perceive as easy, both poor and skilled performers tend to overestimate themselves, creating the classic Dunning-Kruger curve. However, on tasks that people perceive as exceptionally hard, the pattern often reverses, with both poor and skilled performers drastically underestimating their abilities [cite: 22]. The "double curse" theory cannot explain why task difficulty changes metacognitive awareness, but statistical modeling does.

### The Better-Than-Average Bias

When you combine statistical noise, autocorrelation, and a general human quirk known as the "Better-Than-Average" effect, the entire Dunning-Kruger phenomenon is explained without needing to diagnose the incompetent with a metacognitive deficit [cite: 14, 15, 22, 40]. 

Human beings are generally optimistic about their own traits. If you ask a room full of people how well they did on a standardized test, the vast majority of participants will guess they scored somewhere around the 60th to 70th percentile, regardless of their actual performance [cite: 15, 22]. Because everyone is drawn toward this optimistic middle ground, the math plays out predictably. If a terrible performer scores a 12 and guesses a 60, they have "overestimated" by 48 points. If a brilliant performer scores a 90 and guesses a 70, they have "underestimated" by 20 points.

By applying newly recommended, advanced statistical approaches—such as the Glejser test of heteroscedasticity and nonlinear quadratic regression—recent studies in 2020 and 2024 have shown that classical quartile-based analyses manufacture the Dunning-Kruger effect [cite: 32, 33, 34, 40]. The poorest performers do not have a unique, blinding psychological defect. They are simply subject to the exact same general optimism as everyone else, which mathematically results in a massive overestimation gap at the absolute bottom of the scale [cite: 15, 22].

## Cultural Differences: The East Asian Context

If the Dunning-Kruger effect and the general "Better-Than-Average" bias are fundamental flaws in human brain wiring, they should appear consistently across all human populations. However, cross-cultural psychology has proven that they do not. The cognitive biases that drive individuals to overestimate their abilities are heavily influenced by cultural environment.

The original Dunning-Kruger studies, like many psychological phenomena discovered in the twentieth century, were tested exclusively on Western university students [cite: 16, 35]. When researchers run the exact same self-assessment tests in East Asian countries, the results look drastically different. 

### The Missing Self-Enhancement Motive

In North America and Western Europe, self-enhancement—the motivation to view oneself positively and favorably—is widely considered a psychological universal. But in extensive meta-analyses spanning several decades, psychological researcher Steven J. Heine and his colleagues found that East Asian populations, particularly in Japan, frequently do not display these self-serving biases [cite: 41, 42, 43, 44, 45, 46]. 

Across ninety-one independent cross-cultural comparisons utilizing over thirty different testing methods, researchers found a pervasive and pronounced difference in how different cultures evaluate themselves [cite: 41, 47]. While Westerners consistently showed a clear self-serving bias, East Asians showed almost no self-enhancing bias, and in many testing methodologies, they displayed a distinct self-critical bias [cite: 41, 42, 47]. 

| Cultural Demographic | Prevailing Metacognitive Bias | Response to Failure | Psychological Orientation |
| :--- | :--- | :--- | :--- |
| **North Americans** | Strong Self-Enhancement | Discount negative feedback, attribute failure to external circumstances, persist less on tasks where they failed [cite: 48, 49]. | Independent self-construal, focused on uniqueness and personal self-esteem [cite: 48, 50]. |
| **Asian Americans** | Moderate Self-Enhancement | Mixed responses, heavily dependent on situational context and acculturation levels [cite: 41]. | Bicultural orientation, navigating both independent and interdependent norms. |
| **East Asians** | Self-Effacement / Self-Criticism | View failure as diagnostic of actual ability, attribute failure to self, work harder on follow-up tasks to improve [cite: 42, 48, 49]. | Interdependent self-construal, focused on social harmony, relational mobility, and maintaining face [cite: 43, 46, 48]. |

When Japanese students fail at a given task, they are significantly more likely to view the failure as accurate, attribute the shortcomings to their own lack of effort, and work significantly harder on follow-up tasks to correct their deficiencies [cite: 42, 48, 49]. North Americans, conversely, tend to discount negative feedback, bolster their self-assessments in unrelated domains to protect their ego, and persist less on tasks where they have recently failed [cite: 48, 49]. 

This cultural divergence effectively dismantles the idea that the "double curse" of incompetence is an unavoidable biological trait. In cultures that prioritize interdependence, mutual self-improvement, and relational mobility over individual self-esteem, the cognitive biases that drive people to wildly overestimate their abilities are heavily suppressed [cite: 42, 43, 46]. The Dunning-Kruger effect is not a universal rule of human nature; it is a phenomenon heavily mediated by Western cultural conditioning.

## The Reverse Dunning-Kruger Effect in the AI Era

While the classic Dunning-Kruger effect may be statistically shaky and culturally bound, modern technology is introducing entirely new ways for people to misjudge their competence. A fascinating body of research published in 2025 and 2026 highlights how Artificial Intelligence (AI) and Large Language Models (LLMs) are flipping the traditional competence paradigm on its head [cite: 51, 52, 53].

In traditional skill assessments, top performers and experts generally have the most accurate, realistic view of their own abilities. But when humans collaborate closely with AI, researchers are observing a troubling "Reverse Dunning-Kruger effect" [cite: 53]. 

### The LLM Fallacy and Attribution Errors

A comprehensive study published in the journal *Computers in Human Behavior* asked hundreds of participants to solve complex logical reasoning problems sourced from the Law School Admission Test (LSAT). Half of the participant group used AI tools like ChatGPT to assist them, while the other half did not use any technological assistance [cite: 51, 53]. 

The researchers discovered a highly counterintuitive trend: the users with the absolute highest level of AI literacy were the most likely to wildly overestimate their performance [cite: 51, 53]. 

This phenomenon occurs due to a cognitive attribution error recently termed the "LLM Fallacy" [cite: 52]. AI tools are specifically designed by developers to be frictionless, fluent, and highly opaque. When a highly skilled professional uses an AI to generate a report, write a block of code, or solve a logic problem, the interaction feels incredibly smooth and natural. Because the final output looks professional and sounds exactly like something the expert could have produced themselves, the human user unconsciously attributes the AI's competence to their own independent cognitive ability [cite: 52]. 

Over time, heavy AI users begin to lose track of where their actual skills end and the machine's capabilities begin. This leads to severe cognitive offloading. Instead of AI literacy improving a worker's self-monitoring, it fosters blind trust and overreliance. High-skill users assume that because they know how to prompt the machine effectively, they independently possess the knowledge the machine is outputting, leading experts to rapidly overestimate their independent, unassisted capabilities [cite: 51, 52, 53]. 

## Evidence-Based Strategies for Better Self-Assessment

Whether miscalibration stems from a genuine metacognitive deficit, a statistical quirk of regression, cultural conditioning, or the deceptive fluency of artificial intelligence, the practical consequence remains identical: people frequently make poor decisions because they do not know what they do not know. 

Fortunately, cognitive psychology and behavioral science have identified specific, evidence-backed strategies for "debiasing" self-assessments and improving the accuracy of our judgments [cite: 54].

### The "Consider the Opposite" Strategy

One of the most consistently validated debiasing techniques across medical, forensic, and corporate environments is the "consider the opposite" strategy [cite: 55, 56, 57, 58, 59, 60]. 

Human beings are heavily prone to confirmation bias, which naturally leads people to seek out, remember, and favor information that validates their initial assumptions while ignoring data that contradicts them [cite: 57, 61]. To counteract this deeply ingrained habit, the "consider the opposite" framework requires a decision-maker to explicitly pause and generate a handful of concrete reasons why their initial conclusion, working diagnosis, or self-assessment might be entirely wrong [cite: 56, 60, 61]. 

By forcing the brain to deliberately seek disconfirming evidence, this strategy disrupts the automatic pattern-matching of System 1 thinking and engages the slower, more analytical System 2 reasoning [cite: 57, 61, 62]. Meta-analyses have shown that this remarkably simple intervention can decrease decision-making bias by roughly twelve percent in professional environments, and significantly improves probability estimates [cite: 56, 60]. In medical settings, when anesthesiology residents were trained to explicitly consider opposite diagnoses, their communication and leadership self-assessment accuracy saw measurable improvements [cite: 55, 63].

### Slowing Down and Using External Anchors

Beyond considering the opposite, researchers recommend several other structural strategies to mitigate overconfidence and cognitive bias in the workplace:

**Forced Slowing Down:** Deliberately imposing time delays before finalizing high-stakes decisions prevents premature cognitive closure. Fast, intuitive decisions are highly susceptible to overconfidence. By systematically pausing to review diagnostic reasoning, professionals can catch errors before they solidify into absolute certainty [cite: 61, 62].

**Algorithmic Checklists and Decision Support:** Most proposed strategies for reducing bias rely on self-checking, which asks an imperfect cognitive system to police itself [cite: 62]. To bypass this flaw, researchers recommend external cognitive aids. Utilizing diagnostic checklists, algorithmic decision models, or computer decision support systems forces users to systematically evaluate alternative hypotheses based on objective criteria, rather than relying on their internal, subjective confidence [cite: 54, 62].

**Shifting to Learning Goals:** A person's mindset heavily dictates how they respond to corrective feedback. Adopting a "learning goal orientation" rather than a "performance goal orientation" is a powerful defense against overconfidence. Individuals focused purely on appearing competent to others will actively avoid situations where they might fail, shielding themselves from the exact feedback they need to improve. Conversely, individuals focused on genuinely mastering a skill view negative feedback as a vital tool for growth, making them significantly more likely to seek out, accept, and properly calibrate their confidence based on objective reality [cite: 23].

## Bottom line

The popular notion that incompetent people are too stupid to know they are stupid is a dramatic oversimplification of human psychology. True beginners are usually highly aware of their ignorance, and the famous "Mount Stupid" graph is a fabricated internet myth, not a scientific finding. While a brief surge of overconfidence can happen after acquiring a tiny amount of experience, modern statistical consensus suggests the classic Dunning-Kruger effect is largely a mathematical artifact created by regression to the mean and basic human optimism. 

## Sources
1. [The psychology of being a beginner](https://www.researchgate.net/publication/358641729_The_psychology_of_being_a_beginner)
2. [overconfidence2017.pdf](https://people.uncw.edu/hakanr/documents/overconfidence2017.pdf)
3. [Confident or Cocky?](https://www.melissahughes.rocks/post/confident-or-arrogant)
4. [A little learning in the workplace is a dangerous thing](https://3starlearningexperiences.wordpress.com/2018/04/17/a-little-learning-in-the-workplace-is-a-dangerous-thing/)
5. [EurekAlert: Chess study reveals overconfidence effect](https://www.eurekalert.org/news-releases/1101176)
6. [How Does the Dunning-Kruger Effect Happen in Creativity](https://www.researchgate.net/publication/383801951_How_Does_the_Dunning-Kruger_Effect_Happen_in_Creativity_The_Creative_Self-Concept_Matters)
7. [No strong support for a Dunning-Kruger effect in creativity (PubMed)](https://pubmed.ncbi.nlm.nih.gov/38789493/)
8. [No strong support for a Dunning-Kruger effect in creativity (ResearchGate)](https://www.researchgate.net/publication/380853766_No_strong_support_for_a_Dunning-Kruger_effect_in_creativity_analyses_of_self-assessment_in_absolute_and_relative_terms)
9. [Is the Dunning-Kruger effect real?](https://www.clearerthinking.org/post/is-the-dunning-kruger-effect-real-or-are-unskilled-people-more-rational-than-it-seems)
10. [The Decision Lab: Dunning-Kruger Effect](https://thedecisionlab.com/biases/dunning-kruger-effect)
11. [Consider-the-Opposite Debiasing to Improve Self-Assessment](https://www.researchgate.net/publication/387901394_Consider-the-Opposite_Debiasing_to_Improve_Self-Assessment_Accuracy_in_Anesthesiology_Trainees_A_Prospective_Pretest-Posttest_Study)
12. [Cognitive bias mitigation in clinical reasoning](https://pmc.ncbi.nlm.nih.gov/articles/PMC3786644/)
13. [SHRM-SIOP Decision Making](https://siop.org/wp-content/uploads/2024/10/SHRM-SIOP_Decision_Making.pdf)
14. [Anchoring vs Confirmation Bias in Public Health](https://www.frontiersin.org/journals/public-health/articles/10.3389/fpubh.2026.1770198/full)
15. [Strategies to reduce bias in clinical diagnosis](https://pmc.ncbi.nlm.nih.gov/articles/PMC8520040/)
16. [Primary source of the mount stupid graphic](https://psychology.stackexchange.com/questions/17825/what-is-the-primary-source-of-the-mount-stupid-graphic)
17. [Training in the mitigation of anchoring bias](https://www.researchgate.net/publication/288219490_Training_in_the_mitigation_of_anchoring_bias_A_test_of_the_consider-the-opposite_strategy)
18. [Does the accuracy of expert judgment comply with...](https://www.emerald.com/md/article-split/47/3/454/287496/Does-the-accuracy-of-expert-judgment-comply-with)
19. [SHRM-SIOP Decision Making (Alternative Link)](https://www.siop.org/wp-content/uploads/2024/07/SHRM-SIOP_Decision_Making.pdf)
20. [Mount Stupid (Mind The Gap)](https://www.mindthegap.ai/post/mount-stupid)
21. [CMV: Popular educational YouTube channels like MinutePhysics...](https://www.reddit.com/r/changemyview/comments/2zycdx/cmv_popular_educational_youtube_channels_like/)
22. [In search of East Asian self-enhancement (PubMed)](https://pubmed.ncbi.nlm.nih.gov/18453453/)
23. [Self as cultural product: an examination of East Asian and North American selves](https://www2.psych.ubc.ca/~heine/docs/2001persist.pdf)
24. [In Search of East Asian Self-Enhancement (ResearchGate)](https://www.researchgate.net/publication/5400232_In_Search_of_East_Asian_Self-Enhancement)
25. [The Dunning-Kruger Effect is Autocorrelation (Reddit)](https://www.reddit.com/r/statistics/comments/18z8cvc/r_the_dunningkruger_effect_is_autocorrelation_if/)
26. [The Dunning-Kruger effect is (mostly) a statistical artefact](https://www.researchgate.net/publication/340361120_The_Dunning-Kruger_effect_is_mostly_a_statistical_artefact_Valid_approaches_to_testing_the_hypothesis_with_individual_differences_data)
27. [Overcoming Confirmation Bias in Psychological Assessment](https://psychology.town/psychodiagnostics/overcoming-confirmation-bias-psychological-assessment/)
28. [The Dunning-Kruger Effect is Autocorrelation (Economics from the Top Down)](https://economicsfromthetopdown.com/2022/04/08/the-dunning-kruger-effect-is-autocorrelation/)
29. [Gigazine: Dunning-Kruger effect autocorrelation](https://gigazine.net/gsc_news/en/20231128-dunning-kruger-effect-autocorrelation/)
30. [I can't let go of "The Dunning-Kruger Effect is Autocorrelation"](https://andersource.dev/2022/04/19/dk-autocorrelation.html)
31. [Statistical Explanation of the Dunning-Kruger Effect (PMC)](https://pmc.ncbi.nlm.nih.gov/articles/PMC8992690/)
32. [AI is changing the Dunning-Kruger Effect](https://realkm.com/2025/11/19/ai-is-changing-the-dunning-kruger-effect-with-higher-ai-literacy-correlating-with-overestimation-of-competence/)
33. [The Dunning-Kruger Effect: A Hidden Obstacle to Student Success](https://www.researchgate.net/publication/389660203_The_Dunning-Kruger_Effect_A_Hidden_Obstacle_to_Student_Success_A_Comprehensive_Study)
34. [The Dunning-Kruger effect: a review of significant findings](http://jogsc.com/pdf/2016/the_dunning.pdf)
35. [How The 'Dunning-Kruger' Trap Drains Your Potential](https://www.forbes.com/sites/traversmark/2025/04/24/how-the-dunning-kruger-trap-drains-your-potential-by-a-psychologist/)
36. [LiveScience: Dunning-Kruger effect](https://www.livescience.com/dunning-kruger-effect.html)
37. [Unmasking Dunning-Kruger Effect in Visual Reasoning Judgment](https://www.researchgate.net/publication/384086744_Unmasking_Dunning-Kruger_Effect_in_Visual_Reasoning_Judgment)
38. [The best option illusion in self and social assessment](https://www.researchgate.net/publication/324743785_The_best_option_illusion_in_self_and_social_assessment)
39. [Dunning-Kruger Effect Curve for PowerPoint](https://slidemodel.com/templates/dunning-kruger-effect-curve-for-powerpoint/)
40. [Science of People: Dunning-Kruger](https://www.scienceofpeople.com/dunning-kruger/)
41. [The LLM Fallacy (Newsletter)](https://www.newsletter.dyslexic.ai/p/newsletter-349-a-new-paper-just-named-the-problem-i-have-been-writing-about-for-three-years)
42. [AI is Triggering a Reverse Dunning-Kruger Effect](https://joyhealey.com/ai-is-triggering-a-reverse-dunning-kruger-effect-and-marketers-need-to-wake-up/)
43. [The Dunning-Kruger Effect and Social Media](https://www.wordonfire.org/articles/the-dunning-kruger-effect-and-social-media/)
44. [Replication Crisis: Dunning-Kruger Effect](https://atticusli.com/replication-crisis/dunning-kruger-effect/)
45. [Connecting the dots: Why success is not a character trait](https://medium.com/@borisgloger/connecting-the-dots-why-success-is-not-a-character-trait-7d550aa26614)
46. [Wikipedia: Dunning-Kruger effect](https://en.wikipedia.org/wiki/Dunning%E2%80%93Kruger_effect)
47. [Dunning-Kruger Effect among Managers (MDPI)](https://www.mdpi.com/2071-1050/17/13/5951)

**Sources:**
1. [reddit.com](https://vertexaisearch.cloud.google.com/grounding-api-redirect/AUZIYQHziHGMp_zhGhKDuXD_4AY4i1oKK1xP5M2wZ_IWgnELgSJemC2AJYhsc-X7vYgggCzZzwp8HcoKSJt5Mdzh_V7pseTaMuJiancmcJgOfz7gRB-O0ZHczMuTkXuBUBIYcche5QOQtiXbde4QvY_OOEdU4z0OyXFahapzCZbvXQet2AV1GYEgnNWouUjejZ4kFCXnhXPKbS-r1vBo)
2. [quora.com](https://vertexaisearch.cloud.google.com/grounding-api-redirect/AUZIYQGd4gMIRVvCMxtI9GfLohuhJURXfAf-B26ioQM3zMQ-P6aYmuQRC6IklXBVyP7A60Eg700kXPBQlE-Y_ZvnkgkyuLDH4zC1lBQWIuK7K-rEjB_2biPKsRVF97OOl0oZ9iN--43Lr6IUWnPVMJ8YR5RVyXZ3w6oSvNXEU5Xj4GtLqaiutVBLNTBhBZBv6Wak-Aq1UERBnDZQfDR7ncD4fdsMMK0pJFuzGKb006R8Zjuwx0KXSlUHddmD5exDangm36H-7OUgBsQ=)
3. [slatestarcodex.com](https://vertexaisearch.cloud.google.com/grounding-api-redirect/AUZIYQErniWcJABm1eDVbeuV-SWJOCUuQc8NsAMV8v1q-xoipVdQAUDcqZR-QXVtlwdplCM7KU3LUrcCFc-b5HTO45lKiWM6bi5zONGR9bJOw_7uDFHe5AODUv51LmJIFCWdh9Fgfs7g_826OB7TBtcRZE8acMgj0vC0rJmrqUuJjFfjLWqHT8k=)
4. [mindthegap.ai](https://vertexaisearch.cloud.google.com/grounding-api-redirect/AUZIYQG0OOqaqua1cfpFnaEzBr1BmxccnOPtqHxzzIDn0_yVZy_Dp9PpHumAhBXWf1WhIv4qeoe1Y9chhKRYJRxqo1OX0K64rNAx_YyLikMVLNu2hrtirLF1WwCxfF6qrkCCbOc3)
5. [sjsu.edu](https://vertexaisearch.cloud.google.com/grounding-api-redirect/AUZIYQGC5kUKkqBART1FLm255ROO8ApKnUc-xc9lYE9YFOoY1cQH7S4guDDlhO0Uo-o89qFJN4sL7dYGnF-Ff7QLfkP6cyQo0UF94IMPryHm5Rrecxyjwe1CGIb9fo0jwxlYkq4FYc_QY5q4G8kHlF42TbQO4QkpHd6ofSfgL7QUdpqCQ33DNV1qHr4=)
6. [slidemodel.com](https://vertexaisearch.cloud.google.com/grounding-api-redirect/AUZIYQEif_hsby6k2cVvZ2nrf2CbNW8P51ri6nQNRWIe78ZOVnzOL-MKsS2Fy68HaAJoypYqamEJ8ZyfEbtuqRubBbZyZbljkSL6532PFX8fINlXrgpbI82o1NgJ8YpxauYiC9yLCRm5fCkxLeOvgQ0Gv_c1X2c3807qPceBQsWCm6rAQwy7)
7. [stackexchange.com](https://vertexaisearch.cloud.google.com/grounding-api-redirect/AUZIYQFVZ8sIhQ902lkgizqLr7L28veQeF12xfPRpmq6Fr2nJgKobHTshb2dr3Y2ENrY9z7MHDql_zw81REiTegTfraGDfIF4dJHuGsIw8jMOnwjMhxRBz-24IT1FMmZJAje8zCEZTVs4gZ7ypX-4foSzddFu47FUwfkuDclkykGhBZ_ebi6LOunTkVQd5Ly0SVSQIcwcUHbzeA5iPcU9ThI0BDBEA==)
8. [zazzle.com.au](https://vertexaisearch.cloud.google.com/grounding-api-redirect/AUZIYQG7QdP0TmLsGF6U8JjkRQfQFNAqiMshFMZkf3KMQZY1PNHo3z9Avw8ilq0s0TdhUICAdaAmh2k4trzarYkfDIrPtLfGph4ePS0xtNa9Z9wvIL0sQFZrxb5fuU1h-v6sQYmQmt7GButA6EI_wPsIggelza0g3BSCfHaTuC67e1aAhmYoF2I=)
9. [zazzle.co.uk](https://vertexaisearch.cloud.google.com/grounding-api-redirect/AUZIYQHyRm-as9wF1XhtGtXgA75jfgas2bQ9W-3mi54vLvxEFVBqRZFV64VUoXSAaqEuiRzYwi_kHXHAkNmIxs8B_LOuWiq_3lyJQXOOKjV4cZsN98UY2u6WB0lqX_UmGpW3AOSIX_f1NjlWC469jv2jLx4jsdJcPSoNieHdTgS66ZMIJnfLmw==)
10. [journals.co.za](https://vertexaisearch.cloud.google.com/grounding-api-redirect/AUZIYQHpyEd7gmSEgzUEuMoq_k-_LnxS2O3E44d236C4f7DFFOPnwgOyOyKWs9-evKVqj0DoY0BisPnkZNshuHTaHlfV4JSvWaSm9fuC497vq5HpYJEiigRU583ZKvdFejmjMT-PaV57LrP7f6QmyXSRZx0=)
11. [livescience.com](https://vertexaisearch.cloud.google.com/grounding-api-redirect/AUZIYQG9MfKfFJ7pCSc47ez7SJIsuFPqRt21EyWxHnfFLS4u89UFXY6QWngYWgwpVkUgeIRPymH8oqeUanEHMfz1Zfed7ywBDTflJNbfTCI8Dft-yKQjlNYGcx4ujA4hDTFqbwpJprfccHPFntdiGLU=)
12. [bbwpublisher.com](https://vertexaisearch.cloud.google.com/grounding-api-redirect/AUZIYQHQco8II_8mbFhg-MfoY02JFVv4V4tvjwoHIuNGS5xnoBkLRlnZkUci4-vTYcl07shx0PEodx1TYPb1iosB3aP6EK_qJ6QXOW7pTe3MWYXYd7EvwG3RbdSbofqTEDYqKMek6L94dWaEqZvwlNZw9B98sXIl)
13. [wordonfire.org](https://vertexaisearch.cloud.google.com/grounding-api-redirect/AUZIYQFwBeezYkJ2zc3FAwlZGqnlnVSHuEfkJCc-asaRQxqQHZhHged0m8x6uVsmucLvqHvqZSHPKFTpuNgtS5DnwEIuUSGwHTqhUsD1dDL8-NUO7v0IHUHtpP-2IrMUtyFHgHbGIZGmRJxNBXYpnl5KEFGPTuIIIVWuB7YlaDkZH7HHHo6WirrB)
14. [wikipedia.org](https://vertexaisearch.cloud.google.com/grounding-api-redirect/AUZIYQHr8QendGQlkNPn2_NVSQs9s65qnH5lLyVOEZZZZzalobBnotPQEpoDnrDp8gRKYZTzUpVcuV8WN4-WiAk34kY2FBlGQ97JRPcIq9v3_1GsOu863sFQhURp3Lfk5G49_b42hkjiAV4eD2zLJCJTjAmB_g==)
15. [clearerthinking.org](https://vertexaisearch.cloud.google.com/grounding-api-redirect/AUZIYQHqdR8RZ4QWlsoeeJNaFuP6hBNwUQvv9fyZ_yKGoUISBgf5dvb3TG-mbW3cBQ7prnv6legWZIAthhXahmW1W1WdTNQKVwqNCuhDCRWwtolsm7zh2VhGNOjqORzL_OXde8mX-6Yb3TvLDGLL6Jf-jlr4HMVOQ9Zn709p_ngjerpyJ8kGCvel9sE8M8s79aORmkJ8dy73bYXmFVImMQ-5hYC7Bc0b9CGpC6gONxXr4GzaRA==)
16. [nih.gov](https://vertexaisearch.cloud.google.com/grounding-api-redirect/AUZIYQH3PCLIfzuxaJ47EM4MGOmxE1JXPZnOmpqMUAzSYu0M0eCewWIGm4XrPYAPOH7RqUHgYB5zFAZaiYarCD4heX8AI02Q06hZQv5fVnAruP-9Kkc3w0soO2f9HXYKbMxQ2VMmUeZH0Bc6)
17. [thedecisionlab.com](https://vertexaisearch.cloud.google.com/grounding-api-redirect/AUZIYQG5IpLsnVTmOIUcuSXqaom4Cmez0AdYsrgmvl5BBp5lCwKc1SlIZYOSPI-gQgXhVEqIeNiOyQIgVrGGRETYpavLow9d_OBZ6U8NBbZF1Re1-SbAV5PXq4EjTG95_2axnDBQdLiVOrYHTDIqB8zs)
18. [researchgate.net](https://vertexaisearch.cloud.google.com/grounding-api-redirect/AUZIYQG1rR42EuPVuZnP5Vk3sd6AI5oFeQzedvHWNmNn_lzql_xfeY43ZjwQTfE6VKw7Fxr7vqT-GNCXdTAk-3ks43EmbrJUpOZEsYPnZ0X1qHABuiSXmqoJl7dSUTHrXx5lfhZXV_Lzzp8-0P7zraBiWNXXSGa8QGfWtJ1wLJ1kVcLw-4i7fXmoSoxnKLWRCDthdvY3fTH8GgrvPW_UUon4ZJI4IijFbGw=)
19. [researchgate.net](https://vertexaisearch.cloud.google.com/grounding-api-redirect/AUZIYQEnmvZ8PR5V_jz8gvMMZtqQuWaJi3do1E29uFzE4tA8wXEGial_YkED9Jtn_HymzsIhA657Uu9p-v7payHlxsTgQXC-Vx06a1jx84PQWTwkiLu6dcEm4_60wTt_MgKHS4TwwoPL9UinJMuntK6IE8sUqL131kFxx3z_BFPFWnX_DTFBMoFUqM4EQfVKj6KWfWvA77SLRUDQpZKrJaGfThTMX5zJFSJc43tTkuCRQLjMi_UC41GnTgxbhD_75X4eEc2U6TvVE1-IMrSbKYOzZDOJDA95jH22pjhpt9beJcChHRhA9PArwA==)
20. [melissahughes.rocks](https://vertexaisearch.cloud.google.com/grounding-api-redirect/AUZIYQGzUZF6F8HYT5kZGzm_MIKP4co0xnZpqBmKWwamr6T-C0vFyODuLy5g4i1FnFr_kiHEL2ba5aIpaMCtRCU_BU5-Q0sbKagtl79hQilyCzSeAAdI3nNqL8KSO1znyMxLyHLaMaKt7wW88_xgk9x7LiQx)
21. [jogsc.com](https://vertexaisearch.cloud.google.com/grounding-api-redirect/AUZIYQGf1YkQh5YIJljQSVofPx3hfniaBQ_7dGs7hWiNkjsH2JtAx0Evm0J3Ncqxw327_CNior7zZKAixxJ5nK6wS2U6-VWXhlg4rM68n-M1dEjsE6wqEjNWLiP_FplC8XZ7WA==)
22. [atticusli.com](https://vertexaisearch.cloud.google.com/grounding-api-redirect/AUZIYQE1Wk30j1TTa2k5GEISw7Odtsr7vM9UcGIPwfKTlai1hUNm2Cu3EecIUyVyuV2G3yRqbGSTRthakDAG6Ajv5wB3PHcNVMAJPmXZ3qPJxTca6Jrj1vOXl9ncZTQYq661Fl-jcY48BF_EIEPj3zJ-bcov3S8veS8=)
23. [forbes.com](https://vertexaisearch.cloud.google.com/grounding-api-redirect/AUZIYQFslqeNalIPUI6ogZSdwyaVhHOPu5_ewvFqzfRyBE3VJ3FOrBuZTUn3vieTNXAPg4nIHXA9903u9gbB3evKESFgiRNsFFUEdGUDjqpTEDdo-b2nuh_It2rckrSxkSGw57S2q-yzz2Rcrzu-cx9QN9iTzWlf3TcBiS0s1aPbwcT02hDEFyvaZpK61OYaE0wvbRbewQ5u6NMkcCwJWubQkmWvw_wOfXVSyyy8yguFXTk=)
24. [wordpress.com](https://vertexaisearch.cloud.google.com/grounding-api-redirect/AUZIYQFWzj-Ov-Vyadsp4ExTixEUeSmgxZpQzFoPzQdHQHF4M5nE03XvtIBZOXkV2su2M6wk8qLkJCstfxT654yy5V6Rznlw03IxpIeg9243lU7nw0DhK2pjY3Jz_4qS5NevLBT-Y_8gW0n6byl4G9DOgpcRfu7Ruk1Mfhrkb8IXL3cZGihFBvQFsK9pNnM5NzPabQkLjfpvwn2vlqgHFXLfV2B4NBSiUGaUJoo=)
25. [eurekalert.org](https://vertexaisearch.cloud.google.com/grounding-api-redirect/AUZIYQHAkzV0RCvEd78H8t8PL9oDSZgr2V6iACqjKuejicwk7FsGWTLi8j9MCUcuxBLoIZKT0R5RuKou0eUk-1aUuM737l2OLiNbsVOhATAWcgBVseoAZ4dG6ixz76VhPOKj3Yk8iCgwGzA=)
26. [researchgate.net](https://vertexaisearch.cloud.google.com/grounding-api-redirect/AUZIYQFZE5W5vlD9wG1uJl52XHTXPgGPsWNb1QQRc5HRRfjU6shOaMDP4c5uGrg-4-77imnwPeiSkc2jwtWA7CFSGOTTttJ2ye5IlDFYn4vBzdABmko56Rb-d4fzvxcawfHKjPbbzCbq5ypGOd2SLtvJpb-QEEmCts84Bd3OG0bN9kQnOKF8EOSaMubaSdzL)
27. [uncw.edu](https://vertexaisearch.cloud.google.com/grounding-api-redirect/AUZIYQGYQ-OK1FLI9zd8u5NZew8nOS7h_1qLTQKgpxrf72HlANQ6fpkG8KTqVpk3gGzYX82YAGhRut9sMFg7lqNRHEtdyQE2sa3UApbLH_WwRy7jk99lWGAdwRC0kJshbNxKBXtoLmSU-eu74-WqTM0TJRcr3gCMUEw=)
28. [researchgate.net](https://vertexaisearch.cloud.google.com/grounding-api-redirect/AUZIYQHKILvmqNkxG1MW81vlt8TrSDz1ypftYQVDPsv1ZnQyjnnOR6TjtcpuMv6-i-bbooFwuYkhVXr3hPA8uDTufiGmBsd9nEmdPiv53BkqbuZSYUa4JpSWwqV2Cm2rPnzClKzDz6MebNWzcgBuWEzLHow6s-AGZBKFCg6PdYyUyHJsNV2Q7JAnprD92IKewQmXL4jT2R6rF5_kR4336LPk1b8=)
29. [reddit.com](https://vertexaisearch.cloud.google.com/grounding-api-redirect/AUZIYQF4pIDjhqiu3CC0WVgt9lJayAOHdMNV0UmURbfXTgd96PTX13UV9nFrTZDQZ7r_qw021oy_wEY1zC0VKLz_liWMi4-Y9rvgrj7N0o3N0WRi-F2se0FJmVd4fUMEt_wKhpAwonWd0PrdK7VJ4TumXYEFGfVOslytisehss-7LdyEkCaLz3kx8vmnhCY3D1L4c7URu1H1EYJZADJkK0I=)
30. [economicsfromthetopdown.com](https://vertexaisearch.cloud.google.com/grounding-api-redirect/AUZIYQEjEGk7S8QIEEKmCm9dzSrXII5s-H8AKyZIxEV9t7TU3JEDK32gB6_fE_mMBWtfPWWAjxFS7oGU9UqDhBbrmBRx7dgNO8vFhEvuctLZHWXHini_wchVg8NPNsNGYn749ybe8QGz1UwHt53XD7eX2Rbn0R_UwTYiY5nQmZzaDFdLeg9g7PDFhSt59uUiqfOobcHzqA==)
31. [mdpi.com](https://vertexaisearch.cloud.google.com/grounding-api-redirect/AUZIYQGJHhxCfolVMrFkYsuLcdqDGETrkjAYzmnYptCB4jybRKma33pG1463EfK1Rzs-vjjl7g5B_-FWWxwG1gvDyh_kGf8fo_48ao4BAJGP4WGUc4Hm8tQAK3ZCrNyWFoKcVA==)
32. [nih.gov](https://vertexaisearch.cloud.google.com/grounding-api-redirect/AUZIYQFjaibQTxTu_kp4CZuzrJhVKVlGCyJobem8EM2yUs1BdvRBsjOglXq78AS-T54ObZ17DaKJKN4F7KBJihOHBChtykzuXs2P4fo0LF9qqVo4yzBj8JSF96OJ5AeIXVeZ7Q==)
33. [researchgate.net](https://vertexaisearch.cloud.google.com/grounding-api-redirect/AUZIYQGX4c20iR0dTcwMNNpLuxtChkNiS361OU7xYpGnOL1hXGM4apR3epub9e71MF_CtouV9iIX_UuEvEI5GeW4vFHRp-3kZ2txrHJ8XFNTSfLPwJ_bOCXJ3gNQHy-UJ2pbjg_CHtW65sdE2qqwpKjos8FwAdWV1bataTdSkAKKiGT4sjZPybmgn9CX8ANu1CWK6Nb3gafUDf3YM1rFJ7eaSYfzoqqnpIfX5KToG921MUrAXjUz2i73RSYWcc6-_QKOSu88EufBiSG7F1dUGuwDe1h0A5v-UjQ9uQ6ZMZstVw2DsSn9unUPFpwVzUJzIYs4b4Q=)
34. [researchgate.net](https://vertexaisearch.cloud.google.com/grounding-api-redirect/AUZIYQHFN9G30tADaXReLd8dLJVsKbVurQMPpg2G__RKatPU-7kXsEWWfz_ZTI00lp4eC9j_XDGarQ9Xs9-tojXio4X9nsNAVTLkYq9JWnyj92xws_Krm4gKsQyRkK2AJCeJXvl4DmtVBfW_Pc8spWwBg7smppXbXoqO9kOpWmZGd0M4MhCjWazt-x89m0ziCFaP6U6ROqnlT7543mD9G6cevEUjqSLW_EE6MNPEXaPTOcYERq3NDEO5njH8VJpioyCT14g2V3KIFQB2WKwPCnBE1PsYIyrY4Osf)
35. [medium.com](https://vertexaisearch.cloud.google.com/grounding-api-redirect/AUZIYQHCp7a_m3I8tL9XmZpJBqMsQkg_4jFBSukriKPIhH02rVBtPAtoIjIn4SC057QnQf-m6_oBnVUKcZdcuRMAXfVeecf43MHd3OWdUFYB56I0F0d5lpLwCf8P1JDvHabjI3jX4T08lPFug-Cxa_OTuVCZ1q8YtdLSvkF4Uo4iWzLBXgeFyL-_ZcTaKyBAXVXbIQHMORocVWh-StDzhQ==)
36. [gigazine.net](https://vertexaisearch.cloud.google.com/grounding-api-redirect/AUZIYQG2GeXhOkhPrWFWoBuSvtPJU1iOHN4ifHZu2t3tURbW7WbbF1v19GS_cY9rwPmxLFL_Y5LlkqAhRVCPX7NpOm10JA3wHu-xQQvwtCMdG8VYApLAhLiI8uxKtsxcFKGqENTucPjNZaN5hJYrByfWEGLpMrNfIS7epIiUJsTjbD7_3VIMx3-JpA==)
37. [andersource.dev](https://vertexaisearch.cloud.google.com/grounding-api-redirect/AUZIYQHEQZ9HATdHQiLK0hYmO6uKDkeGn7Z8J3yGj-sSpTbWhlWnvvQLd78_F8hIRPjgbHXeHN9lA3gN7Ok0RHgvjqj6So8JU4wZcmqNICUpaKqhylXILPZwUjiuHO4xo6nqTzGO-Rrj1OkuEJYo2xdbHMvT)
38. [replicationindex.com](https://vertexaisearch.cloud.google.com/grounding-api-redirect/AUZIYQHvbGqwOzAtDZDeEL4oZpEHuu5bfZ7hu-4usGJHk7t1lETx_KIROXG2j_PuV5KB4wgIyZCM6tew-c4psJpHgQ02KoqfmZRVEK7VLcec2b1ztwHDIZTw5S2PxdOauzEMKdq6ubyAuf-upBsDYHnO9nDjasBZ2cO7PI3VjnkzHvJG8ZAm)
39. [nih.gov](https://vertexaisearch.cloud.google.com/grounding-api-redirect/AUZIYQEQ7PKvFTISxrsTyNW8MGumqcCZN9igqe5ddwsoG8UJN4FvQdBRi9h0T10_1d4ctu8LBQQClbTQZXGHTFxblVToA7POKDTnOEXqPpAQGdrUi4BoBHaHhjZRQudUmqtOl5Dqja8s5Vxv)
40. [researchgate.net](https://vertexaisearch.cloud.google.com/grounding-api-redirect/AUZIYQHa4SZn5KqP4RxMlkEDpTQHobPbxeU4PjuGWPMnbUwt9DJxZp6qOd_wh3B8RzvYpS_lICy_9ScLFje6lt9vbpVU8aThzNAAWgFkQJLBQhSZIUIdDf-woS39KJPI2uskmOcNcMlNSQD_r_ftVN2fUDgkrJovYcI8-RsFD4HJKg_g-vxXj08GuFONAujAG7C6IpLxda8v7kPWc_qcgOjPrbBDHFiAOoUAHE5600Bty6Kg-VGaXe10oC37X1C4flKIiBLLW4XC9ao_YqY2nUh7ym0N7cJ_MA1xV_JniiL5burLBA==)
41. [nih.gov](https://vertexaisearch.cloud.google.com/grounding-api-redirect/AUZIYQFvNaVaR98zlDAmNZu6h6xbfrVg8uGkXaV0VklFHyXmOUuN1MhXYAFrT5eUdNicjqjS8ItFiTt24dl1MNER72R3T-49elvFA7gEB69vuMApAlZCHE1bINE180LF7CpCnA==)
42. [researchgate.net](https://vertexaisearch.cloud.google.com/grounding-api-redirect/AUZIYQGGT7BwGsqIqJR36ZvX8OeW2Fj9dH0KS3B8uNvjAHLXAxFv5VqXj_pazEsvjxaPVQK128Wqh0uDDnKRWGddbKtiOKfGXZkFlgaotTWGJRDre7DYZwFh2fQr1cJJFzQ8IgiW43NPTpJCCOG3r7BI8SIyukrRKRUzHyzLIi1ASAEgAbZBZ8dEXFfxxkNbaW7kdw==)
43. [scispace.com](https://vertexaisearch.cloud.google.com/grounding-api-redirect/AUZIYQE3scjExMlVlcy5Mvgkv0bh3aNS1v5mfnzV-RP8vvw_hU4bKXMnqS_gZVugctL8-fvXrAw7XHFvDiOkuE9VIz4uGiUWm0IiM9Qte2FdoZrWsFFhMzAGuDGXizakbMkrrOUXd3x5FXLueig-1I2H7HqHlni8tp2J5DVtS6WfyTQdmwRZlj4ygA==)
44. [nih.gov](https://vertexaisearch.cloud.google.com/grounding-api-redirect/AUZIYQEG-LpbsB2-gdK0treZVqx-2B2E_zdWE4z6Rx3oL7ih4yOM4qpqcgUwpxY3Gvk9_ucZFlK7u6K4XpG7ldmWsP_tpEn-nBL1T6xH16MeubBp6ztbfG8mlpoOpj2V61Gy8g==)
45. [ubc.ca](https://vertexaisearch.cloud.google.com/grounding-api-redirect/AUZIYQF7o9LNRi9B7E28nJv1I_Pw0CkmMYCV5W53-7fYq-B2ZWvPapH98El6jKvegpVO-tabyJsu9pzCgC7tMjJ4bac08dEfrXXBsq1SqB6VHTR1IMCdiRNksE0UryIxF9EUgpJEwQmR52XJJ_x4TiarR0F7j6ROhA==)
46. [ubc.ca](https://vertexaisearch.cloud.google.com/grounding-api-redirect/AUZIYQGqbzX2UGNmPdgB8S5HrCsDA9zuLGaPd5jRpXJiJ4dvlTjHmyvV2EpzRj44zYMcvZPqU_lYPTMQ-Cwfp4dQlA0064-nhb74relmC8TYjYfncfoZr91HLcSQRWO1cPF4zRU0DjtcmBBBvF_i2f_KBraeoq3kuvc8BSI=)
47. [ubc.ca](https://vertexaisearch.cloud.google.com/grounding-api-redirect/AUZIYQFoN7y-3FollI6an4zQBV0w-KkpkisD930WRrbHGlSrAymQF3gMXsz5vzZN6kn1cKs2djmveU2VhujB87lS-jAOgzDpyziMux73wOq3Mj-c2PAAUOdvVcIUoIZlunaOgukAsqE9pDAMRFHWfRkq6j0_jvlsCg==)
48. [ubc.ca](https://vertexaisearch.cloud.google.com/grounding-api-redirect/AUZIYQELVK7d0wagVNyWihBGFwly1QMCDjNutmHSGVPJGZS3RkHVX_pKO-wz6ZaTsqpUyTu2OLHA7H5SqTt-2Jke0m807kLs-qe6InE6UuRoYMzgG0EPDZxeeuT7UpipDsw57WOLPti_jn0rhQvWeA==)
49. [researchgate.net](https://vertexaisearch.cloud.google.com/grounding-api-redirect/AUZIYQE07Le_rgPwn1qea0B7xeN0R2Es0nJODx6qmzixGjyTiJbUm6GJkQ8CKLD8Lu3LUH-YYWUrsQLEb4M0YElTL7ErGkvo6bCatMsx2geOy_kifhMMq_6jNMSHIneV7iivTHOrm1nExyFLax0Y17fJrlDxuHiNoUZYdiEGg1MFgcBsBspVv1IFkHJLRWK7iATGXdWt4P38T1YMAnZ2jB04IPJcrD6AGXhKIDZSAUA-1iIRBG9h5UEqIUdvODCoptfMiN4LWv1PVtfuAPQ3Iw==)
50. [researchgate.net](https://vertexaisearch.cloud.google.com/grounding-api-redirect/AUZIYQHMMjzk9lysFD5fr3Zy2ovM8P05pgZwlAYNcIlZX2f_Fplp2w6kLYhPDW6cdDiHwO6eg3U3BlpYdmrQ2ycvw9x14DZdeHylqsNmSIE36RANGWcCOCuwra3-aU1ekFtzBvGIG67ELa5LlTgdQAJ6VigKtI_APTDFeo_gV4g0AhD765XaQw1AU4m3GdNB2qUfml0ASo1bofcQd2Spkm57KqShjH7dtSvK_E1Nya_eF_aBdiJrveOFTa4-)
51. [realkm.com](https://vertexaisearch.cloud.google.com/grounding-api-redirect/AUZIYQEN91LV8TgC8pMGIPdpjJIaJ1Q6U2LkZ_GetcDwviVFbgyMlDB_m9C1Qd6sSgXqEbgAuD6Ww9owJ9HrwcLk_XaA2dVJsd_6jUL47FTFQMHnrDJAb4-RaCKx-T6HB_b0g6X56G2oCSDBCVGLrRVMILrL5mRkgNGE4b45YHuWn9pT-d1erUSqdFGpZJ3dFqGDk7d4xYWy4FAKGmFdYKfCgeJXcjaIkpqr0p5rg1J5qaQwftj2uGc9UI6Uv5ABO5UDsy33ij1a)
52. [dyslexic.ai](https://vertexaisearch.cloud.google.com/grounding-api-redirect/AUZIYQHJyGoJ01mkdwoemr2qX1jnXyYJlwGLzYfEzv9Ocp7ca7SpjFKMVYymM8RHPb27yte7mZH_OnzdFbTR_pJOwoXWZCcXkjT4MwQO7jtENoz7WMzW7raSWiliTOizEf7SWqWDDMIwMLcf_W-gUCVRwTybOMixYLu7StwUTMudlwTw4xXEd5sz0iAWAO13iH498QhzXBXNKQwHaE4uo5pvZJuLGwFtonrMx8z-XORnwTU-41RA-sv8rTM=)
53. [joyhealey.com](https://vertexaisearch.cloud.google.com/grounding-api-redirect/AUZIYQHtJTsbpl5T8-rtsofoPi-k5c1hTqSTv1ltd1HnFevcsT93K7tTnm59emuHXP62uBmJu2Xdq_WfWdB-F_XxbKWmz8YNxYlmeXRIOEIV1ooA_SKuXb0WhSAn1KJtRXjpRwxSDzpfwYfK6TIpzcSGyQoJl7zJfnHhQ5hepAwruEUMxxWtG3-NI_5YYHlnw3LPOOCvZeHezeUnq0jDpQ==)
54. [nih.gov](https://vertexaisearch.cloud.google.com/grounding-api-redirect/AUZIYQG6EN9VPA4xTBUC9KWndZ6RuNpV8kvKhAPrH8rrutVej7wnffozC9QbeWulHMSpz1m4Mhv6xXho3ABqTuMMJpG5Ljg546SZ81DYvBx4MOrorcoe1aCe9CsAshVwD-Io2mqoSZUuc_1M)
55. [researchgate.net](https://vertexaisearch.cloud.google.com/grounding-api-redirect/AUZIYQHo812rH9RPaXqRG6i3ny60o_0InfJu7scRMkPPPjSekqv455d4EeftEJsDDpAKgMUWv6yFjPx1B-Tbt11Bcs9PHZ1yDcCTtqKkQxtGBMry-Hqvgi2JwbEAKCSNdLwBKZq1to7v4EsHwP-RfshcCzL2sGlBg9MQS-IVzMKHIybD50eR-3KxBRk6GnswjsRgenNvju7bFGuXUXwjMKKoGBxxyEVH6ePA6mbnuTb1COyelmZWPCzfdVGcLvmAli2CH-4osmFJPu9M4Bhn32MLxHzYWhkSxNiQsge0ZW0462rBwtSiNCUm9YAAbNTRH5I=)
56. [siop.org](https://vertexaisearch.cloud.google.com/grounding-api-redirect/AUZIYQE_MYDHuMCJdDz3iVRQGGRpdY0MadFLnfG3ROGcSb2ZhY09ltuDn_erdhSCWlu_QIXf-sBmaNeyIB52gwiY5TnI-LnvElVg7gORKFSKfhPYrLubnNjQFfDOLTSPpzhlyBmgknAAqcOWAqwP2_Y48IM2-QN3-G5KNLRUOsNfcWfW)
57. [frontiersin.org](https://vertexaisearch.cloud.google.com/grounding-api-redirect/AUZIYQF9faEfzpkZ0hNOvGqaUVGmRExwwtaGW5mQcZJqFWnJaLwhBSBPkt0ymxtfDExa_RYVGXNBHj5lQLRJxNTelVTBTw1RM-W_1reGio16wXxGZiVWUSseDwM2Hk9jWi9ohTDnI0vH5CUX-4mpwrBIn0F-iEGfub_t0BAzTZUorSPIhlGuiDEWiCztmIBnvACM_kcX)
58. [researchgate.net](https://vertexaisearch.cloud.google.com/grounding-api-redirect/AUZIYQF9c5qcGapWV3dAE8C-6j2MK54OG5gdC8fwXbGMrCNHvZejvaPTDteJxGdsUjzeloHZEyfBVzxEBcM4G2hQ6XGza7gPmrChBoma9wX3-lO6W4Csp4Svj55WM-iPgi__q838MDgTs9f8_Xmt9GNq09DdqSSFlkl33UEyNriuuCpW0hDq4RSPe2Wo946Gkt_Bt8Fowg9O2h9s1cyIn5SEdStTChlRioIlcGS3teW2tAZTbcKtpP1nrEXrfM92cw0wfCa3X4Q=)
59. [emerald.com](https://vertexaisearch.cloud.google.com/grounding-api-redirect/AUZIYQGlLij7NqVbirlvcUOJ_QSt_DgTMUl1mZsMC95QHPgmtzVveOyQd_RHY96uuchqLZQfWF4jzf7-uZ5Z5G78b4dh9WyW5RCgiYzYbCzpNAXlY7RjFZ6qkSWxuQzA2ua-8Dh4VX8QCitfOHLpoVHicQD-2cCOfg48Sv8tJQQ_RdwLRiheinxsNIofyihE2p_k84JQEZXEx6WrLlODYbmuuHE=)
60. [siop.org](https://vertexaisearch.cloud.google.com/grounding-api-redirect/AUZIYQGCsMfJGMOJo7gSDVk06_4AUMkfCNS3EktarxfyDzZ-j5MA1cR5NEJqoMhF4y6K36ZqfWNQfBz7yoC9Qk0DOc_1lgrRZziBjfRNdlSRP2OHPTIitWHyyv4MzaFgcraxVkBSEhSQFR8InOiyqo3_9hTEgz6-NDWfjYPl0KgJnfouqR6d-w==)
61. [psychology.town](https://vertexaisearch.cloud.google.com/grounding-api-redirect/AUZIYQEApK8l2dgYG0_ZPChkXiANFFC8jbi4QwTomQWMTha4lZzAgQCzPNugtb4o1_YIdfrvTGrL9bUd2KtcoLoI6GTTv6QC5Jz-DLCmsjxwZhe8am53OXvObPK2N2OQdWoxFLJg_LKRHra2kY4ZLX-OLOAU0RtjpdNFgOKeGEl-aCEmZnMD-b6QQyhWg1q30oEwOh9KmB2JAlM=)
62. [nih.gov](https://vertexaisearch.cloud.google.com/grounding-api-redirect/AUZIYQFPQossJr2oh_yyVDaHrd5R5kbhASWgyJ-FxqbNymk6KI50tJh4-5Zh9IAiUd504T6py1BQ54QX_NEHQnHW37Jb0tON9R7LP8jmBE0vUQES4k2_U7Kg2b9fUHTRqvmpJ13cIh-Il4x9)
63. [Current time information in Orlando, FL, US.](https://vertexaisearch.cloud.google.com/grounding-api-redirect/AUZIYQHH5xK5XEV4flNFCYYzfCq5cww3aLeN3EmQyIz8koeSKYaJ60LLJaiI37AcVMBQ2k60RUSi-TUnRNXoStmxaqiZMEtMHbxi2t3ZT29rUfPZzPZgZasd6Q9WmLw9HNE-Qk1UfWvxpZ7FOVWchLed)