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Introduction: Recent AI advances, particularly the introduction of large language models (LLMs), have expanded the capacity to automate various tasks, including the analysis of text. This capability may be especially helpful in education research, where lack of resources often hampers the ability to perform various kinds of analyses, particularly those requiring a high level of expertise in a domain and/or a large set of textual data. For instance, we recently coded approximately 10,000 state K-12 computer science standards, requiring over 200 hours of work by subject matter experts. If LLMs are capable of completing a task such as this, the savings in human resources would be immense. Research Questions: This study explores two research questions: (1) How do LLMs compare to humans in the performance of an education research task? and (2) What do errors in LLM performance on this task suggest about current LLM capabilities and limitations? Methodology: We used a random sample of state K-12 computer science standards. We compared the output of three LLMs – ChatGPT, Llama, and Claude – to the work of human subject matter experts in coding the relationship between each state standard and a set of national K-12 standards. Specifically, the LLMs and the humans determined whether each state standard was identical to, similar to, based on, or different from the national standards and (if it was not different) which national standard it resembled. Results: Each of the LLMs identified a different national standard than the subject matter expert in about half of instances. When the LLM identified the same standard, it usually categorized the type of relationship (i.e., identical to, similar to, based on) in the same way as the human expert. However, the LLMs sometimes misidentified ‘identical’ standards. Discussion: Our results suggest that LLMs are not currently capable of matching human performance on the task of classifying learning standards. The mis-identification of some state standards as identical to national standards – when they clearly were not – is an interesting error, given that traditional computing technologies can easily identify identical text. Similarly, some of the mismatches between the LLM and human performance indicate clear errors on the part of the LLMs. However, some of the mismatches are difficult to assess, given the ambiguity inherent in this task and the potential for human error. We conclude the paper with recommendations for the use of LLMs in education research based on these findings.
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This content will become publicly available on June 1, 2026
Can AI Develop Curriculum? Integrated Computer Science As a Test Case (Research to Practice)
Introduction: Because developing integrated computer science (CS) curriculum is a resource-intensive process, there is interest in leveraging the capabilities of AI tools, including large language models (LLMs), to streamline this task. However, given the novelty of LLMs, little is known about their ability to generate appropriate curriculum content. Research Question: How do current LLMs perform on the task of creating appropriate learning activities for integrated computer science education? Methods: We tested two LLMs (Claude 3.5 Sonnet and ChatGPT 4-o) by providing them with a subset of national learning standards for both CS and language arts and asking them to generate a high-level description of learning activities that met standards for both disciplines. Four humans rated the LLM output – using an aggregate rating approach – in terms of (1) whether it met the CS learning standard, (2) whether it met the language arts learning standard, (3) whether it was equitable, and (4) its overall quality. Results: For Claude AI, 52% of the activities met language arts standards, 64% met CS standards, and the average quality rating was middling. For ChatGPT, 75% of the activities met language arts standards, 63% met CS standards, and the average quality rating was low. Virtually all activities from both LLMs were rated as neither actively promoting nor inhibiting equitable instruction. Discussion: Our results suggest that LLMs are not (yet) able to create appropriate learning activities from learning standards. The activities were generally not usable by classroom teachers without further elaboration and/or modification. There were also grammatical errors in the output, something not common with LLM-produced text. Further, standards in one or both disciplines were often not addressed, and the quality of the activities was often low. We conclude with recommendations for the use of LLMs in curriculum development in light of these findings.
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- Award ID(s):
- 2311746
- PAR ID:
- 10643576
- Publisher / Repository:
- ASEE Conferences
- Date Published:
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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