Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Non-equilibrium in the Cell01:16

Non-equilibrium in the Cell

5.3K
An important concept in studying metabolism and energy is that of chemical equilibrium. Most chemical reactions are reversible. They can proceed in both directions, releasing energy into their environment in one direction, and absorbing it from the environment in the other direction. The same is true for the chemical reactions involved in cell metabolism, such as the breaking down and building up of proteins into and from individual amino acids, respectively. Reactants within a closed system...
5.3K
Introduction to Cognitive Psychology01:20

Introduction to Cognitive Psychology

2.2K
Cognitive psychology is the field of psychology dedicated to examining how people think. It attempts to explain how and why we think the way we do by studying the interactions among human thinking, emotion, creativity, language, and problem-solving, as well as other cognitive processes. Cognitive psychology studies how information is processed and manipulated in remembering, thinking, and knowing.
This field emerged in the mid-20th century, following a period dominated by behaviorism, which...
2.2K
Purposive Learning01:22

Purposive Learning

423
E. C. Tolman emphasized the purposiveness of behavior — the idea that much of our behavior is goal-directed. For instance, employees who aim for a promotion work diligently to meet their targets. Tolman argued that when classical conditioning and operant conditioning occur, the organism acquires certain expectations. In classical conditioning, a child might fear a dog because they expect it to bite. In operant conditioning, a person might consistently work overtime because they expect a...
423
Observational Learning01:12

Observational Learning

795
Albert Bandura's observational learning, also known as imitation or modeling, occurs when a person observes and imitates another's behavior. It is a quicker process than operant conditioning. A well-known example is the Bobo doll study, where children who saw an adult acting aggressively towards the doll were more likely to act aggressively when left alone, compared to those who observed a nonaggressive adult. Many psychologists view observational learning as a form of latent learning...
795
Generalization, Discrimination, and Extinction01:24

Generalization, Discrimination, and Extinction

1.3K
Generalization, discrimination, and extinction are key concepts in operant conditioning that influence how behaviors are learned and maintained.
Generalization occurs when a behavior reinforced in one context is performed in similar situations. For instance, a student who studies diligently for calculus and receives excellent grades might apply the same study habits to psychology and history, expecting similar results. Generalization shows how learning in one setting can influence behavior in...
1.3K
Hindsight Biases01:12

Hindsight Biases

4.2K
Hindsight bias leads you to believe that the event you just experienced was predictable, even though it really wasn’t. In other words, you knew all along that things would turn out the way they did. Can you relate this to the phrase "Hindsight is 20/20" now? 
4.2K

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Technology-Supported Behavior Change-Applying Design Thinking to mHealth Application Development.

European journal of investigation in health, psychology and education·2024
See all related articles

Related Experiment Video

Updated: Jan 9, 2026

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
05:47

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Published on: June 13, 2025

1.3K

In AI We Trust? Exploring the Role of Explainable GenAI and Expertise in Education.

Camille Safarov1, Gregory Gadzinski1, Stephan Schlögl2

  • 1International University of Monaco - Omnes Education, Monaco.

Human Factors
|December 3, 2025
PubMed
Summary

AI trust miscalibration occurs when students over-trust AI, especially non-experts who favor polished explanations over accuracy. Experts benefit from detailed AI rationales but still under-rely on correct AI assistance.

Keywords:
(Mis)Calibrationdomain expertiseexplainable AIperformancetrust

Related Experiment Videos

Last Updated: Jan 9, 2026

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
05:47

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Published on: June 13, 2025

1.3K

Area of Science:

  • Human-Computer Interaction
  • Artificial Intelligence Ethics
  • Educational Psychology

Background:

  • The relationship between AI explainability and user trust is under-researched.
  • AI integration in education necessitates understanding student trust in AI outputs.
  • Human-AI relationships often form during educational phases.

Purpose of the Study:

  • To examine AI trust miscalibration among university students.
  • To assess how explanation length and student expertise influence AI recommendation alignment.
  • To understand factors shaping students' AI output assessments.

Main Methods:

  • Conducted in-class experiments with 248 university students.
  • Participants solved GMAT questions and received AI recommendations with varying explanation depths.
  • Measured trust by analyzing whether students aligned their final answers with AI recommendations.

Main Results:

  • Explanation complexity increases trust, but its impact varies with user expertise and AI accuracy.
  • Students with prior correct answers showed less deference, particularly to incorrect AI.
  • Prior agreement and AI consistency amplified trust, fostering critical engagement or uncritical acceptance.

Conclusions:

  • A heuristic of "AI knows better" affects non-experts, leading to uncritical acceptance of incorrect AI recommendations.
  • Experts benefit from detailed explanations when AI is accurate but may under-rely on correct AI.
  • Trust calibration depends on aligning student performance, AI reliability, and explanation design.