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Related Concept Videos

Language01:16

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Language is a unique communication system that uses words and systematic rules to organize and transmit information. Unlike other forms of communication, which may involve postures, movements, odors, or vocalizations, language relies on symbols and grammar. This makes human communication distinct from that of other species, who also communicate but do not use language in the same way humans do.
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The different configurations of source-load connections include wye (star) and delta connections. The relationship between line and phase voltages and currents varies depending on the configuration. When the source is supplying power, it is transmitted through the wires to the load, and during this transmission, some power is absorbed by the wires, leading to line loss.
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Components of Language01:24

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Language, whether spoken, signed, or written, consists of specific components: lexicon and grammar. The lexicon is the vocabulary of a language, comprising its words. Grammar is the set of rules used to convey meaning through the lexicon. For example, English grammar adds “-ed” to most verbs to indicate past tense. Words are formed by combining phonemes, which are the basic sound units of a language. Different languages have different sets of phonemes (e.g., “ah” vs.
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Language Development01:22

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Children master language quickly and with relative ease, supported by both biological predisposition and reinforcement. B. F. Skinner (1957) proposed that language is learned through reinforcement, while Noam Chomsky (1965) argued that language acquisition mechanisms are biologically determined.
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Language serves as a bridge between ideas and communication, influencing how individuals perceive and interact with the world. Psychologists have long debated whether language shapes thought or vice versa. This discussion gained grip with Edward Sapir and Benjamin Lee Whorf in the 1940s, who proposed that language determines thought, a concept known as linguistic determinism. They suggested that the vocabulary and structure of a language influence how its speakers think and perceive reality.
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Comparing Large Language Model AI and Human-Generated Coaching Messages for Behavioral Weight Loss.

Zhuoran Huang1, Michael P Berry2, Christina Chwyl3

  • 1Khoury College of Computer Sciences, Northeastern University, 440 Huntington Ave, Boston, MA 02115 USA.

Journal of Technology in Behavioral Science
|January 22, 2026
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Summary
This summary is machine-generated.

Large language model (LLM) AI chatbots show promise for personalized weight control coaching. After revisions, AI messages matched human coaches in helpfulness and were often mistaken for human-written content.

Keywords:
Artificial intelligenceChatGPTLarge language modelMHealthWeight loss

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Area of Science:

  • Behavioral Science
  • Artificial Intelligence
  • Health Coaching

Background:

  • Automated weight control coaching messages can be repetitive and less effective than human coaching.
  • Large language model (LLM) AI chatbots offer potential for personalized and novel coaching messages.
  • The feasibility and acceptability of LLM-based weight management coaching remain understudied.

Purpose of the Study:

  • To assess the feasibility and acceptability of LLM AI (ChatGPT) for generating weight control coaching messages.
  • To compare the helpfulness of human-written versus AI-generated coaching messages.
  • To explore user perceptions of AI-generated messages in a weight-loss trial.

Main Methods:

  • Eighty-seven adults with a BMI ≥ 27 kg/m² participated in a weight-loss trial.
  • Participants rated the helpfulness of human-written and ChatGPT-generated coaching messages on a 5-point Likert scale.
  • Qualitative feedback and AI message identification were collected in two phases with message revisions.

Main Results:

  • Initial AI messages were rated less helpful, but revised AI messages matched human messages in helpfulness (82% rated ≥3).
  • 50% of AI messages were misidentified as human-written, indicating sophisticated mimicry.
  • Participants found AI messages empathetic and personalized but sometimes formulaic and data-focused.

Conclusions:

  • LLM AI shows preliminary feasibility for creating effective weight control coaching messages.
  • AI-generated messages can be perceived as helpful and personalized, rivaling human-written content after refinement.
  • Further enhancements are needed to address perceived formulaic and data-centric aspects of AI coaching.