Can generative AI and ChatGPT outperform humans on cognitive-demanding
problem-solving tasks in science?
- URL: http://arxiv.org/abs/2401.15081v1
- Date: Sun, 7 Jan 2024 12:36:31 GMT
- Title: Can generative AI and ChatGPT outperform humans on cognitive-demanding
problem-solving tasks in science?
- Authors: Xiaoming Zhai, Matthew Nyaaba, and Wenchao Ma
- Abstract summary: This study compared the performance of ChatGPT and GPT-4 on 2019 NAEP science assessments with students by cognitive demands of the items.
Results showed that both ChatGPT and GPT-4 consistently outperformed most students who answered the NAEP science assessments.
- Score: 1.1172147007388977
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: This study aimed to examine an assumption that generative artificial
intelligence (GAI) tools can overcome the cognitive intensity that humans
suffer when solving problems. We compared the performance of ChatGPT and GPT-4
on 2019 NAEP science assessments with students by cognitive demands of the
items. Fifty-four tasks were coded by experts using a two-dimensional cognitive
load framework, including task cognitive complexity and dimensionality. ChatGPT
and GPT-4 responses were scored using the scoring keys of NAEP. The analysis of
the available data was based on the average student ability scores for students
who answered each item correctly and the percentage of students who responded
to individual items. Results showed that both ChatGPT and GPT-4 consistently
outperformed most students who answered the NAEP science assessments. As the
cognitive demand for NAEP tasks increases, statistically higher average student
ability scores are required to correctly address the questions. This pattern
was observed for students in grades 4, 8, and 12, respectively. However,
ChatGPT and GPT-4 were not statistically sensitive to the increase in cognitive
demands of the tasks, except for Grade 4. As the first study focusing on
comparing GAI and K-12 students in problem-solving in science, this finding
implies the need for changes to educational objectives to prepare students with
competence to work with GAI tools in the future. Education ought to emphasize
the cultivation of advanced cognitive skills rather than depending solely on
tasks that demand cognitive intensity. This approach would foster critical
thinking, analytical skills, and the application of knowledge in novel
contexts. Findings also suggest the need for innovative assessment practices by
moving away from cognitive intensity tasks toward creativity and analytical
skills to avoid the negative effects of GAI on testing more efficiently.
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