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Related Concept Videos

Bullying02:04

Bullying

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A modern form of aggression is bullying. As you learn in your study of child development, socializing and playing with other children is beneficial for children’s psychological development. However, as you may have experienced as a child, not all play behavior has positive outcomes. Some children are aggressive and want to play roughly. Other children are selfish and do not want to share toys. One form of negative social interactions among children that has become a national concern is...
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Aggression01:47

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Humans engage in aggression when they seek to cause harm or pain to another person. Aggression takes two forms depending on one’s motives: hostile or instrumental. Hostile aggression is motivated by feelings of anger with intent to cause pain; a fight in a bar with a stranger is an example of hostile aggression. In contrast, instrumental aggression is motivated by achieving a goal and does not necessarily involve intent to cause pain (Berkowitz, 1993); a contract killer who murders for...
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Bystander Effect02:09

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The discussion of bullying highlights the problem of witnesses not intervening to help a victim. This is a common occurrence, as the following well-publicized event demonstrates. In 1964, in Queens, New York, a 19-year-old woman named Kitty Genovese was attacked by a person with a knife near the back entrance to her apartment building and again in the hallway inside her apartment building. When the attack occurred, she screamed for help numerous times and eventually died from her stab wounds.
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Automatic Processing and Automatic Social Behavior01:28

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Automatic processing refers to the cognitive operations that occur without conscious intent or awareness, playing a fundamental role in shaping social cognition and behavior. These processes enable individuals to navigate complex social environments efficiently by relying on mental shortcuts and pre-existing knowledge structures known as schemas. One of the most influential mechanisms underlying automatic processing is priming, which subtly activates mental representations through exposure to...
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Stereotype Content Model02:16

Stereotype Content Model

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The Stereotype Content Model (SCM) was first proposed by Susan Fiske and her colleagues (Fiske, Cuddy, Glick & Xu, 2002; see also Fiske, 2012 and Fiske, 2017). The SCM specifies that when someone encounters a new group, they will stereotype them based on two metrics: warmth—or that group’s perceived intent, and how likely they are to provide help or inflict harm—and competence—or their ability to carry out that objective. Depending on the warmth-competence...
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Empathy02:34

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Some researchers suggest that altruism operates on empathy. Empathy is the capacity to understand another person’s perspective, to feel what he or she feels. An empathetic person makes an emotional connection with others and feels compelled to help (Batson, 1991). Empathy can be expressed in several ways, including cognitive, affective, and motor. 
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Updated: Apr 15, 2026

Virtual Agent for Real-Time Motivational Interviewing by Integrating Adaptive Nonverbal Behavior and Language Models
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Designing Psychologically Grounded Artificial Intelligence for Supporting Bystander-Based Cyberaggression

Jinkyung Katie Park1, Pinxuan Alina Yu2, Vignesh Krishnan3

  • 1School of Computing, Clemson University, 105 Sikes Hall, Clemson, SC, United States, 1 6784466336.

JMIR Formative Research
|April 13, 2026
PubMed
Summary
This summary is machine-generated.

Generative AI shows promise in combating cyberaggression by providing bystanders with effective intervention messages. Baseline GPT models are most effective for medium-to-high incivility, while AllyGPT excels in low-incivility scenarios.

Keywords:
AIbystander interventioncyberaggressiondigital well-beinggenerative artificial intelligencelarge language modelmental healththeory-driven intervention

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Area of Science:

  • * Digital Behavior Change
  • * Human-Computer Interaction
  • * Cyberpsychology

Background:

  • * Cyberaggression significantly impacts mental health, causing distress and reduced self-esteem.
  • * Bystander intervention is crucial but often hindered by a lack of effective strategies and confidence.
  • * Generative artificial intelligence (AI) offers a new avenue to support bystanders in online interactions.

Purpose of the Study:

  • * To explore the feasibility of using generative AI for bystander intervention in social media cyberaggression.
  • * To assess AI's ability to generate effective, theory-aligned intervention messages.
  • * To evaluate the perceived potential of AI-generated responses for de-escalation and behavior change.

Main Methods:

  • * Collected 1000 cyberaggression examples from social media.
  • * Generated intervention responses using three prompt strategies: policy reminder, baseline GPT, and theory-driven GPT (AllyGPT).
  • * Evaluated responses through computational linguistic analysis and a mixed-methods approach with human coders and interviews.

Main Results:

  • * Baseline GPT responses were more emotionally positive and authentic; AllyGPT responses were more analytical and assertive.
  • * AllyGPT was rated highest for low-incivility, while baseline GPT performed best for medium- and high-incivility across all effectiveness dimensions.
  • * Human coders found baseline GPT responses natural and inclusive, whereas AllyGPT was sometimes seen as too direct; policy reminders lacked persuasive impact.

Conclusions:

  • * Effective AI-driven interventions require sensitivity to platform culture, social context, and user expectations.
  • * Combining psychological theory with adaptive design and feedback can enhance AI support for bystanders.
  • * This research lays the groundwork for human-centered AI systems promoting safer online environments and mental well-being.