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Related Concept Videos

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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...
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Associative learning is a fundamental concept in behavioral psychology, wherein a connection is established between two stimuli or events, leading to a learned response. This process is critical in understanding how behaviors are acquired and modified. Conditioning, the mechanism through which associations are formed, can be divided into two main types: classical conditioning and operant conditioning, each elucidating different aspects of associative learning.
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Albert Bandura's theory of observational learning identifies four critical processes: attention, retention, motor reproduction, and reinforcement or motivation.
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Learning disabilities are cognitive disorders caused by neurological impairments that affect cognitive functions like language and reading, without indicating overall intellectual or developmental challenges. These disabilities differ from global intellectual or developmental disabilities as they are limited to distinct cognitive functions. Common learning disabilities include dysgraphia, dyslexia, and dyscalculia, each of which impacts unique aspects of learning.
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Attributional patterns toward students with and without learning disabilities: Artificial intelligence models vs.

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Artificial intelligence (AI) Large Language Models (LLMs) show less frustration and more sympathy towards students with learning disabilities (LD) than teachers. LLMs also express greater optimism about academic success for all students.

Keywords:
AttributionCultural differencesExpectationsGenerative artificial intelligenceLearning disabilitiesTrainee teachers

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Area of Science:

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

Background:

  • Large Language Models (LLMs) are increasingly used in education, necessitating an understanding of their attributional patterns.
  • Student attributions (feedback, frustration, sympathy, future failure expectations) are crucial for learning and inclusion.
  • Differences in teacher and AI responses to students with and without learning disabilities (LD) require investigation.

Purpose of the Study:

  • To compare the attributional patterns of four advanced LLMs (ChatGPT3.5, ChatGPT4, Claude, Gemini) with those of Australian and Chinese trainee teachers.
  • To examine how LLMs and teachers respond to vignettes depicting students with and without learning disabilities (LD).
  • To assess AI's potential role in fostering inclusive educational outlooks.

Main Methods:

  • Eight vignettes describing students with varying abilities and efforts were presented to LLMs and trainee teachers.
  • LLMs evaluated each vignette ten times, generating 320 evaluations for comparative analysis.
  • Trainee teachers provided comparable ratings, allowing for direct comparison of attributional patterns.

Main Results:

  • LLMs exhibited lower frustration and higher sympathy for LD students compared to trainee teachers.
  • LLMs expressed lower expectations of future academic failure for both LD and non-LD students than trainee teachers.
  • ChatGPT3.5 and Gemini were rated for more negative feedback, while ChatGPT4 showed more positive feedback, aligning with some teacher groups.

Conclusions:

  • LLMs may promote a more positive and inclusive outlook for students with LD due to lower judgmental tendencies and higher optimism.
  • AI tools need recalibration to align with cultural and emotional nuances, especially concerning feedback delivery.
  • The study highlights the potential of AI in education while underscoring the need for careful implementation and ongoing evaluation.