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Comparing and assessing four AI chatbots' competence in economics.

Patrik T Hultberg1, David Santandreu Calonge2, Firuz Kamalov3

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Area of Science:

  • Economics
  • Artificial Intelligence
  • Educational Technology

Background:

  • AI chatbots offer academic support but have variable accuracy.
  • Differences in response accuracy and explanation quality exist among AI models.
  • Understanding AI chatbot capabilities is crucial for academic integrity and learning.

Purpose of the Study:

  • To evaluate the accuracy and explanation quality of four AI chatbots (GPT-3.5, GPT-4, Bard, LLaMA 2) in university-level economics.
  • To compare chatbot performance across different levels of cognitive complexity using Bloom's taxonomy.
  • To determine if AI chatbot performance varies significantly with increasing economics prompt complexity.

Main Methods:

  • Utilized a standard economics test and advanced problems for university-level assessment.
  • Applied Bloom's taxonomy to structure prompts and evaluate cognitive complexity.
  • Compared the accuracy of conclusions and quality of explanations from four distinct AI chatbots.

Main Results:

  • The null hypothesis of equal performance was rejected, indicating significant differences among chatbots.
  • AI chatbot performance varied notably in accuracy and explanation quality for economics.
  • Performance disparities widened as the complexity of economics prompts increased.

Conclusions:

  • AI chatbots exhibit significant performance variations in economics education.
  • Prompt complexity is a key factor influencing AI chatbot effectiveness.
  • Findings inform students in selecting appropriate AI tools and educators in curriculum design.