Use LLMs to generate initial course metadata labels, shifting human taggers to a confirmatory role¶

Strategy · All strategies
Evidence · no claims cited

Description¶

To lighten instructor load during data enrichment, CLICKSTREAM's early-stage AI features use large language models to automate tagging. The article reports that "Large language models (LLMs) have been shown to generate consistent metadata labels and descriptions for course materials by analyzing course syllabi, schedules, and resource naming conventions." Humans-in-the-loop move from generating labels to correcting LLM-created ones, which produces accurate metadata more quickly and supplies fine-tuning data for the classification model.

Design Implications¶

Context¶

Requirements¶

• Course syllabi, schedules, and consistent resource naming conventions that LLMs can analyze
• Human review of LLM-created labels

Constraints¶

• Described as early-stage development showing promise, not a validated production capability

Target Learners¶

• instructors and learning scientists contributing course data

Target Learning Goals¶

• efficient, accurate enrichment of learning interaction data to support instruction and research

Related Strategies¶

• Use structured multi-stage prompts with clearly defined constructs when eliciting LLM topic labels for qualitative coding
• Structured prompt framework for LLM-assisted inductive coding (role assignment, format specification, reasoning-based prompting)

Examples¶

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Key Sources¶

• Bernacki, M.L., and Plumley, R.D. (2026, February). CLICKSTREAM: Essential educational infrastructure to promote student learning and educational theory development. Digital Promise. https://doi.org/10.51388/20.500.12265/285